Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma
Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment stra...
Ausführliche Beschreibung
Autor*in: |
Fang Yan [verfasserIn] Qian Da [verfasserIn] Hongmei Yi [verfasserIn] Shijie Deng [verfasserIn] Lifeng Zhu [verfasserIn] Mu Zhou [verfasserIn] Yingting Liu [verfasserIn] Ming Feng [verfasserIn] Jing Wang [verfasserIn] Xuan Wang [verfasserIn] Yuxiu Zhang [verfasserIn] Wenjing Zhang [verfasserIn] Xiaofan Zhang [verfasserIn] Jingsheng Lin [verfasserIn] Shaoting Zhang [verfasserIn] Chaofu Wang [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2024 |
---|
Übergeordnetes Werk: |
In: npj Precision Oncology - Nature Portfolio, 2017, 8(2024), 1, Seite 12 |
---|---|
Übergeordnetes Werk: |
volume:8 ; year:2024 ; number:1 ; pages:12 |
Links: |
---|
DOI / URN: |
10.1038/s41698-024-00577-y |
---|
Katalog-ID: |
DOAJ097280119 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | DOAJ097280119 | ||
003 | DE-627 | ||
005 | 20240413180605.0 | ||
007 | cr uuu---uuuuu | ||
008 | 240413s2024 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1038/s41698-024-00577-y |2 doi | |
035 | |a (DE-627)DOAJ097280119 | ||
035 | |a (DE-599)DOAJ1ce1530f669a4a3ba193648ec48199e2 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
050 | 0 | |a RC254-282 | |
100 | 0 | |a Fang Yan |e verfasserin |4 aut | |
245 | 1 | 0 | |a Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
264 | 1 | |c 2024 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients. | ||
653 | 0 | |a Neoplasms. Tumors. Oncology. Including cancer and carcinogens | |
700 | 0 | |a Qian Da |e verfasserin |4 aut | |
700 | 0 | |a Hongmei Yi |e verfasserin |4 aut | |
700 | 0 | |a Shijie Deng |e verfasserin |4 aut | |
700 | 0 | |a Lifeng Zhu |e verfasserin |4 aut | |
700 | 0 | |a Mu Zhou |e verfasserin |4 aut | |
700 | 0 | |a Yingting Liu |e verfasserin |4 aut | |
700 | 0 | |a Ming Feng |e verfasserin |4 aut | |
700 | 0 | |a Jing Wang |e verfasserin |4 aut | |
700 | 0 | |a Xuan Wang |e verfasserin |4 aut | |
700 | 0 | |a Yuxiu Zhang |e verfasserin |4 aut | |
700 | 0 | |a Wenjing Zhang |e verfasserin |4 aut | |
700 | 0 | |a Xiaofan Zhang |e verfasserin |4 aut | |
700 | 0 | |a Jingsheng Lin |e verfasserin |4 aut | |
700 | 0 | |a Shaoting Zhang |e verfasserin |4 aut | |
700 | 0 | |a Chaofu Wang |e verfasserin |4 aut | |
773 | 0 | 8 | |i In |t npj Precision Oncology |d Nature Portfolio, 2017 |g 8(2024), 1, Seite 12 |w (DE-627)884384454 |w (DE-600)2891458-2 |x 2397768X |7 nnns |
773 | 1 | 8 | |g volume:8 |g year:2024 |g number:1 |g pages:12 |
856 | 4 | 0 | |u https://doi.org/10.1038/s41698-024-00577-y |z kostenfrei |
856 | 4 | 0 | |u https://doaj.org/article/1ce1530f669a4a3ba193648ec48199e2 |z kostenfrei |
856 | 4 | 0 | |u https://doi.org/10.1038/s41698-024-00577-y |z kostenfrei |
856 | 4 | 2 | |u https://doaj.org/toc/2397-768X |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_2014 | ||
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 8 |j 2024 |e 1 |h 12 |
author_variant |
f y fy q d qd h y hy s d sd l z lz m z mz y l yl m f mf j w jw x w xw y z yz w z wz x z xz j l jl s z sz c w cw |
---|---|
matchkey_str |
article:2397768X:2024----::riiilnelgneaeassmnoplepesoidfu |
hierarchy_sort_str |
2024 |
callnumber-subject-code |
RC |
publishDate |
2024 |
allfields |
10.1038/s41698-024-00577-y doi (DE-627)DOAJ097280119 (DE-599)DOAJ1ce1530f669a4a3ba193648ec48199e2 DE-627 ger DE-627 rakwb eng RC254-282 Fang Yan verfasserin aut Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients. Neoplasms. Tumors. Oncology. Including cancer and carcinogens Qian Da verfasserin aut Hongmei Yi verfasserin aut Shijie Deng verfasserin aut Lifeng Zhu verfasserin aut Mu Zhou verfasserin aut Yingting Liu verfasserin aut Ming Feng verfasserin aut Jing Wang verfasserin aut Xuan Wang verfasserin aut Yuxiu Zhang verfasserin aut Wenjing Zhang verfasserin aut Xiaofan Zhang verfasserin aut Jingsheng Lin verfasserin aut Shaoting Zhang verfasserin aut Chaofu Wang verfasserin aut In npj Precision Oncology Nature Portfolio, 2017 8(2024), 1, Seite 12 (DE-627)884384454 (DE-600)2891458-2 2397768X nnns volume:8 year:2024 number:1 pages:12 https://doi.org/10.1038/s41698-024-00577-y kostenfrei https://doaj.org/article/1ce1530f669a4a3ba193648ec48199e2 kostenfrei https://doi.org/10.1038/s41698-024-00577-y kostenfrei https://doaj.org/toc/2397-768X 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_2014 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 8 2024 1 12 |
spelling |
10.1038/s41698-024-00577-y doi (DE-627)DOAJ097280119 (DE-599)DOAJ1ce1530f669a4a3ba193648ec48199e2 DE-627 ger DE-627 rakwb eng RC254-282 Fang Yan verfasserin aut Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients. Neoplasms. Tumors. Oncology. Including cancer and carcinogens Qian Da verfasserin aut Hongmei Yi verfasserin aut Shijie Deng verfasserin aut Lifeng Zhu verfasserin aut Mu Zhou verfasserin aut Yingting Liu verfasserin aut Ming Feng verfasserin aut Jing Wang verfasserin aut Xuan Wang verfasserin aut Yuxiu Zhang verfasserin aut Wenjing Zhang verfasserin aut Xiaofan Zhang verfasserin aut Jingsheng Lin verfasserin aut Shaoting Zhang verfasserin aut Chaofu Wang verfasserin aut In npj Precision Oncology Nature Portfolio, 2017 8(2024), 1, Seite 12 (DE-627)884384454 (DE-600)2891458-2 2397768X nnns volume:8 year:2024 number:1 pages:12 https://doi.org/10.1038/s41698-024-00577-y kostenfrei https://doaj.org/article/1ce1530f669a4a3ba193648ec48199e2 kostenfrei https://doi.org/10.1038/s41698-024-00577-y kostenfrei https://doaj.org/toc/2397-768X 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_2014 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 8 2024 1 12 |
allfields_unstemmed |
10.1038/s41698-024-00577-y doi (DE-627)DOAJ097280119 (DE-599)DOAJ1ce1530f669a4a3ba193648ec48199e2 DE-627 ger DE-627 rakwb eng RC254-282 Fang Yan verfasserin aut Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients. Neoplasms. Tumors. Oncology. Including cancer and carcinogens Qian Da verfasserin aut Hongmei Yi verfasserin aut Shijie Deng verfasserin aut Lifeng Zhu verfasserin aut Mu Zhou verfasserin aut Yingting Liu verfasserin aut Ming Feng verfasserin aut Jing Wang verfasserin aut Xuan Wang verfasserin aut Yuxiu Zhang verfasserin aut Wenjing Zhang verfasserin aut Xiaofan Zhang verfasserin aut Jingsheng Lin verfasserin aut Shaoting Zhang verfasserin aut Chaofu Wang verfasserin aut In npj Precision Oncology Nature Portfolio, 2017 8(2024), 1, Seite 12 (DE-627)884384454 (DE-600)2891458-2 2397768X nnns volume:8 year:2024 number:1 pages:12 https://doi.org/10.1038/s41698-024-00577-y kostenfrei https://doaj.org/article/1ce1530f669a4a3ba193648ec48199e2 kostenfrei https://doi.org/10.1038/s41698-024-00577-y kostenfrei https://doaj.org/toc/2397-768X 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_2014 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 8 2024 1 12 |
allfieldsGer |
10.1038/s41698-024-00577-y doi (DE-627)DOAJ097280119 (DE-599)DOAJ1ce1530f669a4a3ba193648ec48199e2 DE-627 ger DE-627 rakwb eng RC254-282 Fang Yan verfasserin aut Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients. Neoplasms. Tumors. Oncology. Including cancer and carcinogens Qian Da verfasserin aut Hongmei Yi verfasserin aut Shijie Deng verfasserin aut Lifeng Zhu verfasserin aut Mu Zhou verfasserin aut Yingting Liu verfasserin aut Ming Feng verfasserin aut Jing Wang verfasserin aut Xuan Wang verfasserin aut Yuxiu Zhang verfasserin aut Wenjing Zhang verfasserin aut Xiaofan Zhang verfasserin aut Jingsheng Lin verfasserin aut Shaoting Zhang verfasserin aut Chaofu Wang verfasserin aut In npj Precision Oncology Nature Portfolio, 2017 8(2024), 1, Seite 12 (DE-627)884384454 (DE-600)2891458-2 2397768X nnns volume:8 year:2024 number:1 pages:12 https://doi.org/10.1038/s41698-024-00577-y kostenfrei https://doaj.org/article/1ce1530f669a4a3ba193648ec48199e2 kostenfrei https://doi.org/10.1038/s41698-024-00577-y kostenfrei https://doaj.org/toc/2397-768X 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_2014 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 8 2024 1 12 |
allfieldsSound |
10.1038/s41698-024-00577-y doi (DE-627)DOAJ097280119 (DE-599)DOAJ1ce1530f669a4a3ba193648ec48199e2 DE-627 ger DE-627 rakwb eng RC254-282 Fang Yan verfasserin aut Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients. Neoplasms. Tumors. Oncology. Including cancer and carcinogens Qian Da verfasserin aut Hongmei Yi verfasserin aut Shijie Deng verfasserin aut Lifeng Zhu verfasserin aut Mu Zhou verfasserin aut Yingting Liu verfasserin aut Ming Feng verfasserin aut Jing Wang verfasserin aut Xuan Wang verfasserin aut Yuxiu Zhang verfasserin aut Wenjing Zhang verfasserin aut Xiaofan Zhang verfasserin aut Jingsheng Lin verfasserin aut Shaoting Zhang verfasserin aut Chaofu Wang verfasserin aut In npj Precision Oncology Nature Portfolio, 2017 8(2024), 1, Seite 12 (DE-627)884384454 (DE-600)2891458-2 2397768X nnns volume:8 year:2024 number:1 pages:12 https://doi.org/10.1038/s41698-024-00577-y kostenfrei https://doaj.org/article/1ce1530f669a4a3ba193648ec48199e2 kostenfrei https://doi.org/10.1038/s41698-024-00577-y kostenfrei https://doaj.org/toc/2397-768X 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_2014 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 8 2024 1 12 |
language |
English |
source |
In npj Precision Oncology 8(2024), 1, Seite 12 volume:8 year:2024 number:1 pages:12 |
sourceStr |
In npj Precision Oncology 8(2024), 1, Seite 12 volume:8 year:2024 number:1 pages:12 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
isfreeaccess_bool |
true |
container_title |
npj Precision Oncology |
authorswithroles_txt_mv |
Fang Yan @@aut@@ Qian Da @@aut@@ Hongmei Yi @@aut@@ Shijie Deng @@aut@@ Lifeng Zhu @@aut@@ Mu Zhou @@aut@@ Yingting Liu @@aut@@ Ming Feng @@aut@@ Jing Wang @@aut@@ Xuan Wang @@aut@@ Yuxiu Zhang @@aut@@ Wenjing Zhang @@aut@@ Xiaofan Zhang @@aut@@ Jingsheng Lin @@aut@@ Shaoting Zhang @@aut@@ Chaofu Wang @@aut@@ |
publishDateDaySort_date |
2024-01-01T00:00:00Z |
hierarchy_top_id |
884384454 |
id |
DOAJ097280119 |
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">DOAJ097280119</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413180605.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1038/s41698-024-00577-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ097280119</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ1ce1530f669a4a3ba193648ec48199e2</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">Fang Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</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">Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients.</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">Qian Da</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hongmei Yi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shijie Deng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lifeng Zhu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mu Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yingting Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ming Feng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jing Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xuan Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yuxiu Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wenjing Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiaofan Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jingsheng Lin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shaoting Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Chaofu Wang</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">npj Precision Oncology</subfield><subfield code="d">Nature Portfolio, 2017</subfield><subfield code="g">8(2024), 1, Seite 12</subfield><subfield code="w">(DE-627)884384454</subfield><subfield code="w">(DE-600)2891458-2</subfield><subfield code="x">2397768X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1038/s41698-024-00577-y</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/1ce1530f669a4a3ba193648ec48199e2</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1038/s41698-024-00577-y</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2397-768X</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_2014</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">8</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield><subfield code="h">12</subfield></datafield></record></collection>
|
callnumber-first |
R - Medicine |
author |
Fang Yan |
spellingShingle |
Fang Yan misc RC254-282 misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
authorStr |
Fang Yan |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)884384454 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut aut |
collection |
DOAJ |
remote_str |
true |
callnumber-label |
RC254-282 |
illustrated |
Not Illustrated |
issn |
2397768X |
topic_title |
RC254-282 Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
topic |
misc RC254-282 misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
topic_unstemmed |
misc RC254-282 misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
topic_browse |
misc RC254-282 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 |
npj Precision Oncology |
hierarchy_parent_id |
884384454 |
hierarchy_top_title |
npj Precision Oncology |
isfreeaccess_txt |
true |
familylinks_str_mv |
(DE-627)884384454 (DE-600)2891458-2 |
title |
Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
ctrlnum |
(DE-627)DOAJ097280119 (DE-599)DOAJ1ce1530f669a4a3ba193648ec48199e2 |
title_full |
Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
author_sort |
Fang Yan |
journal |
npj Precision Oncology |
journalStr |
npj Precision Oncology |
callnumber-first-code |
R |
lang_code |
eng |
isOA_bool |
true |
recordtype |
marc |
publishDateSort |
2024 |
contenttype_str_mv |
txt |
container_start_page |
12 |
author_browse |
Fang Yan Qian Da Hongmei Yi Shijie Deng Lifeng Zhu Mu Zhou Yingting Liu Ming Feng Jing Wang Xuan Wang Yuxiu Zhang Wenjing Zhang Xiaofan Zhang Jingsheng Lin Shaoting Zhang Chaofu Wang |
container_volume |
8 |
class |
RC254-282 |
format_se |
Elektronische Aufsätze |
author-letter |
Fang Yan |
doi_str_mv |
10.1038/s41698-024-00577-y |
author2-role |
verfasserin |
title_sort |
artificial intelligence-based assessment of pd-l1 expression in diffuse large b cell lymphoma |
callnumber |
RC254-282 |
title_auth |
Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
abstract |
Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients. |
abstractGer |
Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients. |
abstract_unstemmed |
Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients. |
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_2014 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 |
1 |
title_short |
Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
url |
https://doi.org/10.1038/s41698-024-00577-y https://doaj.org/article/1ce1530f669a4a3ba193648ec48199e2 https://doaj.org/toc/2397-768X |
remote_bool |
true |
author2 |
Qian Da Hongmei Yi Shijie Deng Lifeng Zhu Mu Zhou Yingting Liu Ming Feng Jing Wang Xuan Wang Yuxiu Zhang Wenjing Zhang Xiaofan Zhang Jingsheng Lin Shaoting Zhang Chaofu Wang |
author2Str |
Qian Da Hongmei Yi Shijie Deng Lifeng Zhu Mu Zhou Yingting Liu Ming Feng Jing Wang Xuan Wang Yuxiu Zhang Wenjing Zhang Xiaofan Zhang Jingsheng Lin Shaoting Zhang Chaofu Wang |
ppnlink |
884384454 |
callnumber-subject |
RC - Internal Medicine |
mediatype_str_mv |
c |
isOA_txt |
true |
hochschulschrift_bool |
false |
doi_str |
10.1038/s41698-024-00577-y |
callnumber-a |
RC254-282 |
up_date |
2024-07-04T00:34:53.305Z |
_version_ |
1803606602082680832 |
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">DOAJ097280119</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413180605.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1038/s41698-024-00577-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ097280119</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ1ce1530f669a4a3ba193648ec48199e2</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">Fang Yan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</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">Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients.</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">Qian Da</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hongmei Yi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shijie Deng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Lifeng Zhu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mu Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yingting Liu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ming Feng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jing Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xuan Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yuxiu Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Wenjing Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Xiaofan Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jingsheng Lin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Shaoting Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Chaofu Wang</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">npj Precision Oncology</subfield><subfield code="d">Nature Portfolio, 2017</subfield><subfield code="g">8(2024), 1, Seite 12</subfield><subfield code="w">(DE-627)884384454</subfield><subfield code="w">(DE-600)2891458-2</subfield><subfield code="x">2397768X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1038/s41698-024-00577-y</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/1ce1530f669a4a3ba193648ec48199e2</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1038/s41698-024-00577-y</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2397-768X</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_2014</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">8</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield><subfield code="h">12</subfield></datafield></record></collection>
|
score |
7.4007006 |