Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients
Abstract Background Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). Methods A total of 478 patients with confirmed stage II CRC, with 313 f...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hui Zhu [verfasserIn] Muni Hu [verfasserIn] Yanru Ma [verfasserIn] Xun Yao [verfasserIn] Xiaozhu Lin [verfasserIn] Menglei Li [verfasserIn] Yue Li [verfasserIn] Zhiyuan Wu [verfasserIn] Debing Shi [verfasserIn] Tong Tong [verfasserIn] Haoyan Chen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Übergeordnetes Werk: |
In: Cancer Imaging - BMC, 2014, 23(2023), 1, Seite 11 |
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| Übergeordnetes Werk: |
volume:23 ; year:2023 ; number:1 ; pages:11 |
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| DOI / URN: |
10.1186/s40644-023-00588-1 |
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| Katalog-ID: |
DOAJ092871402 |
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| 520 | |a Abstract Background Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). Methods A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). Results A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). Conclusion The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. Trial registration Retrospectively registered. | ||
| 650 | 4 | |a Radiomics | |
| 650 | 4 | |a Computed tomography | |
| 650 | 4 | |a Prognosis | |
| 650 | 4 | |a Stage II colorectal cancer | |
| 650 | 4 | |a Adjuvant chemotherapy | |
| 653 | 0 | |a Medical physics. Medical radiology. Nuclear medicine | |
| 653 | 0 | |a Neoplasms. Tumors. Oncology. Including cancer and carcinogens | |
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| 700 | 0 | |a Zhiyuan Wu |e verfasserin |4 aut | |
| 700 | 0 | |a Debing Shi |e verfasserin |4 aut | |
| 700 | 0 | |a Tong Tong |e verfasserin |4 aut | |
| 700 | 0 | |a Haoyan Chen |e verfasserin |4 aut | |
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10.1186/s40644-023-00588-1 doi (DE-627)DOAJ092871402 (DE-599)DOAJ46a4dc88dfbe4194add8f61e57d759dc DE-627 ger DE-627 rakwb eng R895-920 RC254-282 Hui Zhu verfasserin aut Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). Methods A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). Results A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). Conclusion The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. Trial registration Retrospectively registered. Radiomics Computed tomography Prognosis Stage II colorectal cancer Adjuvant chemotherapy Medical physics. Medical radiology. Nuclear medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Muni Hu verfasserin aut Yanru Ma verfasserin aut Xun Yao verfasserin aut Xiaozhu Lin verfasserin aut Menglei Li verfasserin aut Yue Li verfasserin aut Zhiyuan Wu verfasserin aut Debing Shi verfasserin aut Tong Tong verfasserin aut Haoyan Chen verfasserin aut In Cancer Imaging BMC, 2014 23(2023), 1, Seite 11 (DE-627)36374732X (DE-600)2104862-9 14707330 nnns volume:23 year:2023 number:1 pages:11 https://doi.org/10.1186/s40644-023-00588-1 kostenfrei https://doaj.org/article/46a4dc88dfbe4194add8f61e57d759dc kostenfrei https://doi.org/10.1186/s40644-023-00588-1 kostenfrei https://doaj.org/toc/1470-7330 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 23 2023 1 11 |
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10.1186/s40644-023-00588-1 doi (DE-627)DOAJ092871402 (DE-599)DOAJ46a4dc88dfbe4194add8f61e57d759dc DE-627 ger DE-627 rakwb eng R895-920 RC254-282 Hui Zhu verfasserin aut Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). Methods A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). Results A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). Conclusion The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. Trial registration Retrospectively registered. Radiomics Computed tomography Prognosis Stage II colorectal cancer Adjuvant chemotherapy Medical physics. Medical radiology. Nuclear medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Muni Hu verfasserin aut Yanru Ma verfasserin aut Xun Yao verfasserin aut Xiaozhu Lin verfasserin aut Menglei Li verfasserin aut Yue Li verfasserin aut Zhiyuan Wu verfasserin aut Debing Shi verfasserin aut Tong Tong verfasserin aut Haoyan Chen verfasserin aut In Cancer Imaging BMC, 2014 23(2023), 1, Seite 11 (DE-627)36374732X (DE-600)2104862-9 14707330 nnns volume:23 year:2023 number:1 pages:11 https://doi.org/10.1186/s40644-023-00588-1 kostenfrei https://doaj.org/article/46a4dc88dfbe4194add8f61e57d759dc kostenfrei https://doi.org/10.1186/s40644-023-00588-1 kostenfrei https://doaj.org/toc/1470-7330 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 23 2023 1 11 |
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10.1186/s40644-023-00588-1 doi (DE-627)DOAJ092871402 (DE-599)DOAJ46a4dc88dfbe4194add8f61e57d759dc DE-627 ger DE-627 rakwb eng R895-920 RC254-282 Hui Zhu verfasserin aut Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). Methods A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). Results A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). Conclusion The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. Trial registration Retrospectively registered. Radiomics Computed tomography Prognosis Stage II colorectal cancer Adjuvant chemotherapy Medical physics. Medical radiology. Nuclear medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Muni Hu verfasserin aut Yanru Ma verfasserin aut Xun Yao verfasserin aut Xiaozhu Lin verfasserin aut Menglei Li verfasserin aut Yue Li verfasserin aut Zhiyuan Wu verfasserin aut Debing Shi verfasserin aut Tong Tong verfasserin aut Haoyan Chen verfasserin aut In Cancer Imaging BMC, 2014 23(2023), 1, Seite 11 (DE-627)36374732X (DE-600)2104862-9 14707330 nnns volume:23 year:2023 number:1 pages:11 https://doi.org/10.1186/s40644-023-00588-1 kostenfrei https://doaj.org/article/46a4dc88dfbe4194add8f61e57d759dc kostenfrei https://doi.org/10.1186/s40644-023-00588-1 kostenfrei https://doaj.org/toc/1470-7330 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 23 2023 1 11 |
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10.1186/s40644-023-00588-1 doi (DE-627)DOAJ092871402 (DE-599)DOAJ46a4dc88dfbe4194add8f61e57d759dc DE-627 ger DE-627 rakwb eng R895-920 RC254-282 Hui Zhu verfasserin aut Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). Methods A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). Results A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). Conclusion The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. Trial registration Retrospectively registered. Radiomics Computed tomography Prognosis Stage II colorectal cancer Adjuvant chemotherapy Medical physics. Medical radiology. Nuclear medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Muni Hu verfasserin aut Yanru Ma verfasserin aut Xun Yao verfasserin aut Xiaozhu Lin verfasserin aut Menglei Li verfasserin aut Yue Li verfasserin aut Zhiyuan Wu verfasserin aut Debing Shi verfasserin aut Tong Tong verfasserin aut Haoyan Chen verfasserin aut In Cancer Imaging BMC, 2014 23(2023), 1, Seite 11 (DE-627)36374732X (DE-600)2104862-9 14707330 nnns volume:23 year:2023 number:1 pages:11 https://doi.org/10.1186/s40644-023-00588-1 kostenfrei https://doaj.org/article/46a4dc88dfbe4194add8f61e57d759dc kostenfrei https://doi.org/10.1186/s40644-023-00588-1 kostenfrei https://doaj.org/toc/1470-7330 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 23 2023 1 11 |
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10.1186/s40644-023-00588-1 doi (DE-627)DOAJ092871402 (DE-599)DOAJ46a4dc88dfbe4194add8f61e57d759dc DE-627 ger DE-627 rakwb eng R895-920 RC254-282 Hui Zhu verfasserin aut Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). Methods A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). Results A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). Conclusion The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. Trial registration Retrospectively registered. Radiomics Computed tomography Prognosis Stage II colorectal cancer Adjuvant chemotherapy Medical physics. Medical radiology. Nuclear medicine Neoplasms. Tumors. Oncology. Including cancer and carcinogens Muni Hu verfasserin aut Yanru Ma verfasserin aut Xun Yao verfasserin aut Xiaozhu Lin verfasserin aut Menglei Li verfasserin aut Yue Li verfasserin aut Zhiyuan Wu verfasserin aut Debing Shi verfasserin aut Tong Tong verfasserin aut Haoyan Chen verfasserin aut In Cancer Imaging BMC, 2014 23(2023), 1, Seite 11 (DE-627)36374732X (DE-600)2104862-9 14707330 nnns volume:23 year:2023 number:1 pages:11 https://doi.org/10.1186/s40644-023-00588-1 kostenfrei https://doaj.org/article/46a4dc88dfbe4194add8f61e57d759dc kostenfrei https://doi.org/10.1186/s40644-023-00588-1 kostenfrei https://doaj.org/toc/1470-7330 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_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 23 2023 1 11 |
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Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients |
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Abstract Background Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). Methods A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). Results A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). Conclusion The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. Trial registration Retrospectively registered. |
| abstractGer |
Abstract Background Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). Methods A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). Results A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). Conclusion The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. Trial registration Retrospectively registered. |
| abstract_unstemmed |
Abstract Background Our study aimed to explore the potential of radiomics features derived from CT images in predicting the prognosis and response to adjuvant chemotherapy (ACT) in patients with Stage II colorectal cancer (CRC). Methods A total of 478 patients with confirmed stage II CRC, with 313 from Shanghai (Training set) and 165 from Beijing (Validation set) were enrolled. Optimized features were selected using GridSearchCV and Iterative Feature Elimination (IFE) algorithm. Subsequently, we developed an ensemble random forest classifier to predict the probability of disease relapse.We evaluated the performance of the model using the concordance index (C-index), precision-recall curves, and area under the precision-recall curves (AUCPR). Results A radiomic model (namely the RF5 model) consisting of four radiomics features and T stage were developed. The RF5 model performed better than simple radiomics features or T stage alone, with higher C-index and AUCPR, as well as better sensitivity and specificity (C-indexRF5: 0.836; AUCPR = 0.711; Sensitivity = 0.610; Specificity = 0.935). We identified an optimal cutoff value of 0.1215 to split patients into high- or low-score subgroups, with those in the low-score group having better disease-free survival (DFS) (Training Set: P = 1.4e-11; Validation Set: P = 0.015). Furthermore, patients in the high-score group who received ACT had better DFS compared to those who did not receive ACT (P = 0.04). However, no statistical difference was found in low-score patients (P = 0.17). Conclusion The radiomic model can serve as a reliable tool for assessing prognosis and identifying the optimal candidates for ACT in Stage II CRC patients. Trial registration Retrospectively registered. |
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Multi-center evaluation of machine learning-based radiomic model in predicting disease free survival and adjuvant chemotherapy benefit in stage II colorectal cancer patients |
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https://doi.org/10.1186/s40644-023-00588-1 https://doaj.org/article/46a4dc88dfbe4194add8f61e57d759dc https://doaj.org/toc/1470-7330 |
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Muni Hu Yanru Ma Xun Yao Xiaozhu Lin Menglei Li Yue Li Zhiyuan Wu Debing Shi Tong Tong Haoyan Chen |
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Muni Hu Yanru Ma Xun Yao Xiaozhu Lin Menglei Li Yue Li Zhiyuan Wu Debing Shi Tong Tong Haoyan Chen |
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2024-07-03T14:03:49.614Z |
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