Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors

The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Michail E. Klontzas [verfasserIn] Emmanouil Koltsakis [verfasserIn] Georgios Kalarakis [verfasserIn] Kiril Trpkov [verfasserIn] Thomas Papathomas [verfasserIn] Apostolos H. Karantanas [verfasserIn] Antonios Tzortzakakis [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	artificial intelligence machine learning radiomics renal cell carcinoma renal oncocytoma

Übergeordnetes Werk:	In: Cancers - MDPI AG, 2010, 15(2023), 14, p 3553
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:14, p 3553

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/cancers15143553

Katalog-ID:	DOAJ093931026

Internformat


LEADER	01000naa a22002652 4500
001	DOAJ093931026
003	DE-627
005	20240413022933.0
007	cr uuu---uuuuu
008	240413s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.3390/cancers15143553 \|2 doi
035			\|a (DE-627)DOAJ093931026
035			\|a (DE-599)DOAJ06c65198288243edadccc7ef61d2dbf9
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a RC254-282
100	0		\|a Michail E. Klontzas \|e verfasserin \|4 aut
245	1	0	\|a Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.
650		4	\|a <sup<99m</sup<Tc Sestamibi SPECT/CT
650		4	\|a artificial intelligence
650		4	\|a machine learning
650		4	\|a radiomics
650		4	\|a renal cell carcinoma
650		4	\|a renal oncocytoma
653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
700	0		\|a Emmanouil Koltsakis \|e verfasserin \|4 aut
700	0		\|a Georgios Kalarakis \|e verfasserin \|4 aut
700	0		\|a Kiril Trpkov \|e verfasserin \|4 aut
700	0		\|a Thomas Papathomas \|e verfasserin \|4 aut
700	0		\|a Apostolos H. Karantanas \|e verfasserin \|4 aut
700	0		\|a Antonios Tzortzakakis \|e verfasserin \|4 aut
773	0	8	\|i In \|t Cancers \|d MDPI AG, 2010 \|g 15(2023), 14, p 3553 \|w (DE-627)614095670 \|w (DE-600)2527080-1 \|x 20726694 \|7 nnns
773	1	8	\|g volume:15 \|g year:2023 \|g number:14, p 3553
856	4	0	\|u https://doi.org/10.3390/cancers15143553 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/06c65198288243edadccc7ef61d2dbf9 \|z kostenfrei
856	4	0	\|u https://www.mdpi.com/2072-6694/15/14/3553 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2072-6694 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 15 \|j 2023 \|e 14, p 3553

Indexfelder

author_variant	m e k mek e k ek g k gk k t kt t p tp a h k ahk a t at
matchkey_str	article:20726694:2023----::ahnlannitgaigu9mutssaiipccadaimcdtaheeotmlhrc
hierarchy_sort_str	2023
callnumber-subject-code	RC
publishDate	2023
allfields	10.3390/cancers15143553 doi (DE-627)DOAJ093931026 (DE-599)DOAJ06c65198288243edadccc7ef61d2dbf9 DE-627 ger DE-627 rakwb eng RC254-282 Michail E. Klontzas verfasserin aut Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results. <sup<99m</sup<Tc Sestamibi SPECT/CT artificial intelligence machine learning radiomics renal cell carcinoma renal oncocytoma Neoplasms. Tumors. Oncology. Including cancer and carcinogens Emmanouil Koltsakis verfasserin aut Georgios Kalarakis verfasserin aut Kiril Trpkov verfasserin aut Thomas Papathomas verfasserin aut Apostolos H. Karantanas verfasserin aut Antonios Tzortzakakis verfasserin aut In Cancers MDPI AG, 2010 15(2023), 14, p 3553 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:15 year:2023 number:14, p 3553 https://doi.org/10.3390/cancers15143553 kostenfrei https://doaj.org/article/06c65198288243edadccc7ef61d2dbf9 kostenfrei https://www.mdpi.com/2072-6694/15/14/3553 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 14, p 3553
spelling	10.3390/cancers15143553 doi (DE-627)DOAJ093931026 (DE-599)DOAJ06c65198288243edadccc7ef61d2dbf9 DE-627 ger DE-627 rakwb eng RC254-282 Michail E. Klontzas verfasserin aut Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results. <sup<99m</sup<Tc Sestamibi SPECT/CT artificial intelligence machine learning radiomics renal cell carcinoma renal oncocytoma Neoplasms. Tumors. Oncology. Including cancer and carcinogens Emmanouil Koltsakis verfasserin aut Georgios Kalarakis verfasserin aut Kiril Trpkov verfasserin aut Thomas Papathomas verfasserin aut Apostolos H. Karantanas verfasserin aut Antonios Tzortzakakis verfasserin aut In Cancers MDPI AG, 2010 15(2023), 14, p 3553 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:15 year:2023 number:14, p 3553 https://doi.org/10.3390/cancers15143553 kostenfrei https://doaj.org/article/06c65198288243edadccc7ef61d2dbf9 kostenfrei https://www.mdpi.com/2072-6694/15/14/3553 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 14, p 3553
allfields_unstemmed	10.3390/cancers15143553 doi (DE-627)DOAJ093931026 (DE-599)DOAJ06c65198288243edadccc7ef61d2dbf9 DE-627 ger DE-627 rakwb eng RC254-282 Michail E. Klontzas verfasserin aut Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results. <sup<99m</sup<Tc Sestamibi SPECT/CT artificial intelligence machine learning radiomics renal cell carcinoma renal oncocytoma Neoplasms. Tumors. Oncology. Including cancer and carcinogens Emmanouil Koltsakis verfasserin aut Georgios Kalarakis verfasserin aut Kiril Trpkov verfasserin aut Thomas Papathomas verfasserin aut Apostolos H. Karantanas verfasserin aut Antonios Tzortzakakis verfasserin aut In Cancers MDPI AG, 2010 15(2023), 14, p 3553 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:15 year:2023 number:14, p 3553 https://doi.org/10.3390/cancers15143553 kostenfrei https://doaj.org/article/06c65198288243edadccc7ef61d2dbf9 kostenfrei https://www.mdpi.com/2072-6694/15/14/3553 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 14, p 3553
allfieldsGer	10.3390/cancers15143553 doi (DE-627)DOAJ093931026 (DE-599)DOAJ06c65198288243edadccc7ef61d2dbf9 DE-627 ger DE-627 rakwb eng RC254-282 Michail E. Klontzas verfasserin aut Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results. <sup<99m</sup<Tc Sestamibi SPECT/CT artificial intelligence machine learning radiomics renal cell carcinoma renal oncocytoma Neoplasms. Tumors. Oncology. Including cancer and carcinogens Emmanouil Koltsakis verfasserin aut Georgios Kalarakis verfasserin aut Kiril Trpkov verfasserin aut Thomas Papathomas verfasserin aut Apostolos H. Karantanas verfasserin aut Antonios Tzortzakakis verfasserin aut In Cancers MDPI AG, 2010 15(2023), 14, p 3553 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:15 year:2023 number:14, p 3553 https://doi.org/10.3390/cancers15143553 kostenfrei https://doaj.org/article/06c65198288243edadccc7ef61d2dbf9 kostenfrei https://www.mdpi.com/2072-6694/15/14/3553 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 14, p 3553
allfieldsSound	10.3390/cancers15143553 doi (DE-627)DOAJ093931026 (DE-599)DOAJ06c65198288243edadccc7ef61d2dbf9 DE-627 ger DE-627 rakwb eng RC254-282 Michail E. Klontzas verfasserin aut Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results. <sup<99m</sup<Tc Sestamibi SPECT/CT artificial intelligence machine learning radiomics renal cell carcinoma renal oncocytoma Neoplasms. Tumors. Oncology. Including cancer and carcinogens Emmanouil Koltsakis verfasserin aut Georgios Kalarakis verfasserin aut Kiril Trpkov verfasserin aut Thomas Papathomas verfasserin aut Apostolos H. Karantanas verfasserin aut Antonios Tzortzakakis verfasserin aut In Cancers MDPI AG, 2010 15(2023), 14, p 3553 (DE-627)614095670 (DE-600)2527080-1 20726694 nnns volume:15 year:2023 number:14, p 3553 https://doi.org/10.3390/cancers15143553 kostenfrei https://doaj.org/article/06c65198288243edadccc7ef61d2dbf9 kostenfrei https://www.mdpi.com/2072-6694/15/14/3553 kostenfrei https://doaj.org/toc/2072-6694 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 14, p 3553
language	English
source	In Cancers 15(2023), 14, p 3553 volume:15 year:2023 number:14, p 3553
sourceStr	In Cancers 15(2023), 14, p 3553 volume:15 year:2023 number:14, p 3553
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	<sup<99m</sup<Tc Sestamibi SPECT/CT artificial intelligence machine learning radiomics renal cell carcinoma renal oncocytoma Neoplasms. Tumors. Oncology. Including cancer and carcinogens
isfreeaccess_bool	true
container_title	Cancers
authorswithroles_txt_mv	Michail E. Klontzas @@aut@@ Emmanouil Koltsakis @@aut@@ Georgios Kalarakis @@aut@@ Kiril Trpkov @@aut@@ Thomas Papathomas @@aut@@ Apostolos H. Karantanas @@aut@@ Antonios Tzortzakakis @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	614095670
id	DOAJ093931026
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ093931026</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413022933.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/cancers15143553</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ093931026</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ06c65198288243edadccc7ef61d2dbf9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC254-282</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Michail E. Klontzas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a"><sup<99m</sup<Tc Sestamibi SPECT/CT</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">radiomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">renal cell carcinoma</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">renal oncocytoma</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. Tumors. Oncology. Including cancer and carcinogens</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Emmanouil Koltsakis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Georgios Kalarakis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kiril Trpkov</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Thomas Papathomas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Apostolos H. Karantanas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Antonios Tzortzakakis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Cancers</subfield><subfield code="d">MDPI AG, 2010</subfield><subfield code="g">15(2023), 14, p 3553</subfield><subfield code="w">(DE-627)614095670</subfield><subfield code="w">(DE-600)2527080-1</subfield><subfield code="x">20726694</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:14, p 3553</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/cancers15143553</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/06c65198288243edadccc7ef61d2dbf9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2072-6694/15/14/3553</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2072-6694</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2023</subfield><subfield code="e">14, p 3553</subfield></datafield></record></collection>
callnumber-first	R - Medicine
author	Michail E. Klontzas
spellingShingle	Michail E. Klontzas misc RC254-282 misc <sup<99m</sup<Tc Sestamibi SPECT/CT misc artificial intelligence misc machine learning misc radiomics misc renal cell carcinoma misc renal oncocytoma misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors
authorStr	Michail E. Klontzas
ppnlink_with_tag_str_mv	@@773@@(DE-627)614095670
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	RC254-282
illustrated	Not Illustrated
issn	20726694
topic_title	RC254-282 Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors <sup<99m</sup<Tc Sestamibi SPECT/CT artificial intelligence machine learning radiomics renal cell carcinoma renal oncocytoma
topic	misc RC254-282 misc <sup<99m</sup<Tc Sestamibi SPECT/CT misc artificial intelligence misc machine learning misc radiomics misc renal cell carcinoma misc renal oncocytoma misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc <sup<99m</sup<Tc Sestamibi SPECT/CT misc artificial intelligence misc machine learning misc radiomics misc renal cell carcinoma misc renal oncocytoma misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc <sup<99m</sup<Tc Sestamibi SPECT/CT misc artificial intelligence misc machine learning misc radiomics misc renal cell carcinoma misc renal oncocytoma misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Cancers
hierarchy_parent_id	614095670
hierarchy_top_title	Cancers
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)614095670 (DE-600)2527080-1
title	Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors
ctrlnum	(DE-627)DOAJ093931026 (DE-599)DOAJ06c65198288243edadccc7ef61d2dbf9
title_full	Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors
author_sort	Michail E. Klontzas
journal	Cancers
journalStr	Cancers
callnumber-first-code	R
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
author_browse	Michail E. Klontzas Emmanouil Koltsakis Georgios Kalarakis Kiril Trpkov Thomas Papathomas Apostolos H. Karantanas Antonios Tzortzakakis
container_volume	15
class	RC254-282
format_se	Elektronische Aufsätze
author-letter	Michail E. Klontzas
doi_str_mv	10.3390/cancers15143553
author2-role	verfasserin
title_sort	machine learning integrating <sup<99m</sup<tc sestamibi spect/ct and radiomics data achieves optimal characterization of renal oncocytic tumors
callnumber	RC254-282
title_auth	Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors
abstract	The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.
abstractGer	The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.
abstract_unstemmed	The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	14, p 3553
title_short	Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors
url	https://doi.org/10.3390/cancers15143553 https://doaj.org/article/06c65198288243edadccc7ef61d2dbf9 https://www.mdpi.com/2072-6694/15/14/3553 https://doaj.org/toc/2072-6694
remote_bool	true
author2	Emmanouil Koltsakis Georgios Kalarakis Kiril Trpkov Thomas Papathomas Apostolos H. Karantanas Antonios Tzortzakakis
author2Str	Emmanouil Koltsakis Georgios Kalarakis Kiril Trpkov Thomas Papathomas Apostolos H. Karantanas Antonios Tzortzakakis
ppnlink	614095670
callnumber-subject	RC - Internal Medicine
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.3390/cancers15143553
callnumber-a	RC254-282
up_date	2024-07-03T20:15:03.267Z
_version_	1803590254746140672
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ093931026</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413022933.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/cancers15143553</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ093931026</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ06c65198288243edadccc7ef61d2dbf9</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC254-282</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Michail E. Klontzas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The increasing evidence of oncocytic renal tumors positive in <sup<99m</sup<Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of <sup<99m</sup<Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7–100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7–100%) and visual evaluation of <sup<99m</sup<Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5–99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and <sup<99m</sup<Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that <sup<99m</sup<Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with <sup<99m</sup<Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of <sup<99m</sup<Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of <sup<99m</sup<Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a"><sup<99m</sup<Tc Sestamibi SPECT/CT</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">radiomics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">renal cell carcinoma</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">renal oncocytoma</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. Tumors. Oncology. Including cancer and carcinogens</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Emmanouil Koltsakis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Georgios Kalarakis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kiril Trpkov</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Thomas Papathomas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Apostolos H. Karantanas</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Antonios Tzortzakakis</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Cancers</subfield><subfield code="d">MDPI AG, 2010</subfield><subfield code="g">15(2023), 14, p 3553</subfield><subfield code="w">(DE-627)614095670</subfield><subfield code="w">(DE-600)2527080-1</subfield><subfield code="x">20726694</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:14, p 3553</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.3390/cancers15143553</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/06c65198288243edadccc7ef61d2dbf9</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.mdpi.com/2072-6694/15/14/3553</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2072-6694</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2023</subfield><subfield code="e">14, p 3553</subfield></datafield></record></collection>
score	7.4010506

Nicht das Richtige dabei?

Schreiben Sie uns!

Machine Learning Integrating <sup<99m</sup<Tc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?