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Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning

This study aimed to develop and validate a prognostic nomogram for recurrence-free survival (RFS) after surgery in the absence of adjuvant therapy to guide the selection for adjuvant imatinib therapy based on Residual Neural Network (ResNet). The ResNet model was developed based on contrast-enhanced...

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Autores principales: Chen, Tao, Liu, Shangqing, Li, Yong, Feng, Xingyu, Xiong, Wei, Zhao, Xixi, Yang, Yali, Zhang, Cangui, Hu, Yanfeng, Chen, Hao, Lin, Tian, Zhao, Mingli, Liu, Hao, Yu, Jiang, Xu, Yikai, Zhang, Yu, Li, Guoxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6355433/
https://www.ncbi.nlm.nih.gov/pubmed/30587460
http://dx.doi.org/10.1016/j.ebiom.2018.12.028
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author Chen, Tao
Liu, Shangqing
Li, Yong
Feng, Xingyu
Xiong, Wei
Zhao, Xixi
Yang, Yali
Zhang, Cangui
Hu, Yanfeng
Chen, Hao
Lin, Tian
Zhao, Mingli
Liu, Hao
Yu, Jiang
Xu, Yikai
Zhang, Yu
Li, Guoxin
author_facet Chen, Tao
Liu, Shangqing
Li, Yong
Feng, Xingyu
Xiong, Wei
Zhao, Xixi
Yang, Yali
Zhang, Cangui
Hu, Yanfeng
Chen, Hao
Lin, Tian
Zhao, Mingli
Liu, Hao
Yu, Jiang
Xu, Yikai
Zhang, Yu
Li, Guoxin
author_sort Chen, Tao
collection PubMed
description This study aimed to develop and validate a prognostic nomogram for recurrence-free survival (RFS) after surgery in the absence of adjuvant therapy to guide the selection for adjuvant imatinib therapy based on Residual Neural Network (ResNet). The ResNet model was developed based on contrast-enhanced computed tomography (CE-CT) in a training cohort consisted of 80 patients pathologically diagnosed gastrointestinal sromal tumors (GISTs) and validated in internal and external validation cohort respectively. Independent clinicopathologic factors were integrated with the ResNet model to construct the individualized nomogram. The performance of the nomogram was evaluated in regard to discrimination, calibration, and clinical usefulness. The ResNet model was significantly associated with RFS. Integrable predictors in the individualized ResNet nomogram included the tumor site, size, and mitotic count. Compared with modified NIH, AFIP, and clinicopathologic nomogram, both ResNet nomogram and ResNet model showed a better discrimination capability with AUCs of 0·947(95%CI, 0·910–0·984) for 3-year-RFS, 0·918(0·852–0·984) for 5-year-RFS, and AUCs of 0·912 (0·851–0·973) for 3-year-RFS, 0·887(0·816–0·960) for 5-year-RFS, respectively. Calibration curve shows the good calibration of the nomogram in terms of the agreement between the estimated and the observed 3- and 5- year outcomes. Decision curve analysis showed that the ResNet nomogram had a higher overall net benefit. In conclusion, we presented a deep learning-based prognostic nomogram to predict RFS after resection of localized primary GISTs with excellent performance and could be a potential tool to select patients for adjuvant imatinib therapy.
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spelling pubmed-63554332019-02-07 Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning Chen, Tao Liu, Shangqing Li, Yong Feng, Xingyu Xiong, Wei Zhao, Xixi Yang, Yali Zhang, Cangui Hu, Yanfeng Chen, Hao Lin, Tian Zhao, Mingli Liu, Hao Yu, Jiang Xu, Yikai Zhang, Yu Li, Guoxin EBioMedicine Research paper This study aimed to develop and validate a prognostic nomogram for recurrence-free survival (RFS) after surgery in the absence of adjuvant therapy to guide the selection for adjuvant imatinib therapy based on Residual Neural Network (ResNet). The ResNet model was developed based on contrast-enhanced computed tomography (CE-CT) in a training cohort consisted of 80 patients pathologically diagnosed gastrointestinal sromal tumors (GISTs) and validated in internal and external validation cohort respectively. Independent clinicopathologic factors were integrated with the ResNet model to construct the individualized nomogram. The performance of the nomogram was evaluated in regard to discrimination, calibration, and clinical usefulness. The ResNet model was significantly associated with RFS. Integrable predictors in the individualized ResNet nomogram included the tumor site, size, and mitotic count. Compared with modified NIH, AFIP, and clinicopathologic nomogram, both ResNet nomogram and ResNet model showed a better discrimination capability with AUCs of 0·947(95%CI, 0·910–0·984) for 3-year-RFS, 0·918(0·852–0·984) for 5-year-RFS, and AUCs of 0·912 (0·851–0·973) for 3-year-RFS, 0·887(0·816–0·960) for 5-year-RFS, respectively. Calibration curve shows the good calibration of the nomogram in terms of the agreement between the estimated and the observed 3- and 5- year outcomes. Decision curve analysis showed that the ResNet nomogram had a higher overall net benefit. In conclusion, we presented a deep learning-based prognostic nomogram to predict RFS after resection of localized primary GISTs with excellent performance and could be a potential tool to select patients for adjuvant imatinib therapy. Elsevier 2018-12-23 /pmc/articles/PMC6355433/ /pubmed/30587460 http://dx.doi.org/10.1016/j.ebiom.2018.12.028 Text en © 2018 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research paper
Chen, Tao
Liu, Shangqing
Li, Yong
Feng, Xingyu
Xiong, Wei
Zhao, Xixi
Yang, Yali
Zhang, Cangui
Hu, Yanfeng
Chen, Hao
Lin, Tian
Zhao, Mingli
Liu, Hao
Yu, Jiang
Xu, Yikai
Zhang, Yu
Li, Guoxin
Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning
title Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning
title_full Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning
title_fullStr Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning
title_full_unstemmed Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning
title_short Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning
title_sort developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning
topic Research paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6355433/
https://www.ncbi.nlm.nih.gov/pubmed/30587460
http://dx.doi.org/10.1016/j.ebiom.2018.12.028
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