Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma

Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the s...
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Autor*in:	Wang, Yun [verfasserIn] Lyu, Deng [verfasserIn] Hu, Su [verfasserIn] Ma, Yanqing [verfasserIn] Duan, Shaofeng [verfasserIn] Geng, Yayuan [verfasserIn] Zhou, Taohu [verfasserIn] Tu, Wenting [verfasserIn] Xiao, Yi [verfasserIn] Fan, Li [verfasserIn] Liu, Shiyuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Lung cancer Adenocarcinoma Visceral pleural invasion Radiomics Nomogram

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Journal of cardiothoracic surgery - BioMed Central, 2006, 19(2024), 1 vom: 31. Mai
Übergeordnetes Werk:	volume:19 ; year:2024 ; number:1 ; day:31 ; month:05

Links:	Volltext

DOI / URN:	10.1186/s13019-024-02807-7

Katalog-ID:	SPR056076606

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520			\|a Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the $ GPTV_{10} $-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.
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allfields	10.1186/s13019-024-02807-7 doi (DE-627)SPR056076606 (SPR)s13019-024-02807-7-e DE-627 ger DE-627 rakwb eng 610 VZ Wang, Yun verfasserin aut Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the $ GPTV_{10} $-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma. Lung cancer (dpeaa)DE-He213 Adenocarcinoma (dpeaa)DE-He213 Visceral pleural invasion (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Lyu, Deng verfasserin aut Hu, Su verfasserin aut Ma, Yanqing verfasserin aut Duan, Shaofeng verfasserin aut Geng, Yayuan verfasserin aut Zhou, Taohu verfasserin aut Tu, Wenting verfasserin aut Xiao, Yi verfasserin aut Fan, Li verfasserin aut Liu, Shiyuan verfasserin aut Enthalten in Journal of cardiothoracic surgery BioMed Central, 2006 19(2024), 1 vom: 31. Mai (DE-627)509401260 (DE-600)2227224-0 1749-8090 nnns volume:19 year:2024 number:1 day:31 month:05 https://dx.doi.org/10.1186/s13019-024-02807-7 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2522 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 19 2024 1 31 05
spelling	10.1186/s13019-024-02807-7 doi (DE-627)SPR056076606 (SPR)s13019-024-02807-7-e DE-627 ger DE-627 rakwb eng 610 VZ Wang, Yun verfasserin aut Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the $ GPTV_{10} $-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma. Lung cancer (dpeaa)DE-He213 Adenocarcinoma (dpeaa)DE-He213 Visceral pleural invasion (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Lyu, Deng verfasserin aut Hu, Su verfasserin aut Ma, Yanqing verfasserin aut Duan, Shaofeng verfasserin aut Geng, Yayuan verfasserin aut Zhou, Taohu verfasserin aut Tu, Wenting verfasserin aut Xiao, Yi verfasserin aut Fan, Li verfasserin aut Liu, Shiyuan verfasserin aut Enthalten in Journal of cardiothoracic surgery BioMed Central, 2006 19(2024), 1 vom: 31. Mai (DE-627)509401260 (DE-600)2227224-0 1749-8090 nnns volume:19 year:2024 number:1 day:31 month:05 https://dx.doi.org/10.1186/s13019-024-02807-7 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2522 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 19 2024 1 31 05
allfields_unstemmed	10.1186/s13019-024-02807-7 doi (DE-627)SPR056076606 (SPR)s13019-024-02807-7-e DE-627 ger DE-627 rakwb eng 610 VZ Wang, Yun verfasserin aut Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the $ GPTV_{10} $-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma. Lung cancer (dpeaa)DE-He213 Adenocarcinoma (dpeaa)DE-He213 Visceral pleural invasion (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Lyu, Deng verfasserin aut Hu, Su verfasserin aut Ma, Yanqing verfasserin aut Duan, Shaofeng verfasserin aut Geng, Yayuan verfasserin aut Zhou, Taohu verfasserin aut Tu, Wenting verfasserin aut Xiao, Yi verfasserin aut Fan, Li verfasserin aut Liu, Shiyuan verfasserin aut Enthalten in Journal of cardiothoracic surgery BioMed Central, 2006 19(2024), 1 vom: 31. Mai (DE-627)509401260 (DE-600)2227224-0 1749-8090 nnns volume:19 year:2024 number:1 day:31 month:05 https://dx.doi.org/10.1186/s13019-024-02807-7 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2522 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 19 2024 1 31 05
allfieldsGer	10.1186/s13019-024-02807-7 doi (DE-627)SPR056076606 (SPR)s13019-024-02807-7-e DE-627 ger DE-627 rakwb eng 610 VZ Wang, Yun verfasserin aut Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the $ GPTV_{10} $-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma. Lung cancer (dpeaa)DE-He213 Adenocarcinoma (dpeaa)DE-He213 Visceral pleural invasion (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Lyu, Deng verfasserin aut Hu, Su verfasserin aut Ma, Yanqing verfasserin aut Duan, Shaofeng verfasserin aut Geng, Yayuan verfasserin aut Zhou, Taohu verfasserin aut Tu, Wenting verfasserin aut Xiao, Yi verfasserin aut Fan, Li verfasserin aut Liu, Shiyuan verfasserin aut Enthalten in Journal of cardiothoracic surgery BioMed Central, 2006 19(2024), 1 vom: 31. Mai (DE-627)509401260 (DE-600)2227224-0 1749-8090 nnns volume:19 year:2024 number:1 day:31 month:05 https://dx.doi.org/10.1186/s13019-024-02807-7 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2522 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 19 2024 1 31 05
allfieldsSound	10.1186/s13019-024-02807-7 doi (DE-627)SPR056076606 (SPR)s13019-024-02807-7-e DE-627 ger DE-627 rakwb eng 610 VZ Wang, Yun verfasserin aut Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the $ GPTV_{10} $-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma. Lung cancer (dpeaa)DE-He213 Adenocarcinoma (dpeaa)DE-He213 Visceral pleural invasion (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213 Lyu, Deng verfasserin aut Hu, Su verfasserin aut Ma, Yanqing verfasserin aut Duan, Shaofeng verfasserin aut Geng, Yayuan verfasserin aut Zhou, Taohu verfasserin aut Tu, Wenting verfasserin aut Xiao, Yi verfasserin aut Fan, Li verfasserin aut Liu, Shiyuan verfasserin aut Enthalten in Journal of cardiothoracic surgery BioMed Central, 2006 19(2024), 1 vom: 31. Mai (DE-627)509401260 (DE-600)2227224-0 1749-8090 nnns volume:19 year:2024 number:1 day:31 month:05 https://dx.doi.org/10.1186/s13019-024-02807-7 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_2522 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 19 2024 1 31 05
language	English
source	Enthalten in Journal of cardiothoracic surgery 19(2024), 1 vom: 31. Mai volume:19 year:2024 number:1 day:31 month:05
sourceStr	Enthalten in Journal of cardiothoracic surgery 19(2024), 1 vom: 31. Mai volume:19 year:2024 number:1 day:31 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Lung cancer Adenocarcinoma Visceral pleural invasion Radiomics Nomogram
dewey-raw	610
isfreeaccess_bool	true
container_title	Journal of cardiothoracic surgery
authorswithroles_txt_mv	Wang, Yun @@aut@@ Lyu, Deng @@aut@@ Hu, Su @@aut@@ Ma, Yanqing @@aut@@ Duan, Shaofeng @@aut@@ Geng, Yayuan @@aut@@ Zhou, Taohu @@aut@@ Tu, Wenting @@aut@@ Xiao, Yi @@aut@@ Fan, Li @@aut@@ Liu, Shiyuan @@aut@@
publishDateDaySort_date	2024-05-31T00:00:00Z
hierarchy_top_id	509401260
dewey-sort	3610
id	SPR056076606
language_de	englisch
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We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the $ GPTV_{10} $-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. 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author	Wang, Yun
spellingShingle	Wang, Yun ddc 610 misc Lung cancer misc Adenocarcinoma misc Visceral pleural invasion misc Radiomics misc Nomogram Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma
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topic_title	610 VZ Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma Lung cancer (dpeaa)DE-He213 Adenocarcinoma (dpeaa)DE-He213 Visceral pleural invasion (dpeaa)DE-He213 Radiomics (dpeaa)DE-He213 Nomogram (dpeaa)DE-He213
topic	ddc 610 misc Lung cancer misc Adenocarcinoma misc Visceral pleural invasion misc Radiomics misc Nomogram
topic_unstemmed	ddc 610 misc Lung cancer misc Adenocarcinoma misc Visceral pleural invasion misc Radiomics misc Nomogram
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title	Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma
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title_full	Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma
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author_browse	Wang, Yun Lyu, Deng Hu, Su Ma, Yanqing Duan, Shaofeng Geng, Yayuan Zhou, Taohu Tu, Wenting Xiao, Yi Fan, Li Liu, Shiyuan
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title_sort	nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage ia lung adenocarcinoma
title_auth	Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma
abstract	Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the $ GPTV_{10} $-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma. © The Author(s) 2024
abstractGer	Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the $ GPTV_{10} $-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma. © The Author(s) 2024
abstract_unstemmed	Background Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the $ GPTV_{10} $-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma. © The Author(s) 2024
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container_issue	1
title_short	Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma
url	https://dx.doi.org/10.1186/s13019-024-02807-7
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author2	Lyu, Deng Hu, Su Ma, Yanqing Duan, Shaofeng Geng, Yayuan Zhou, Taohu Tu, Wenting Xiao, Yi Fan, Li Liu, Shiyuan
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up_date	2024-07-03T20:02:52.432Z
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We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume ($ GPTV_{5} $, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The $ GPTV_{10} $ radiomics model exhibited superior predictive performance compared to GTV, $ GPTV_{5} $, and $ GPTV_{15} $, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. 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Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma

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