Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer

Background and PurposeThis study aims to develop a risk model to predict esophageal fistula in esophageal cancer (EC) patients by learning from both clinical data and computerized tomography (CT) radiomic features.Materials and MethodsIn this retrospective study, computerized tomography (CT) images...

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Autores principales:	Yiyue Xu, Hui Cui, Taotao Dong, Bing Zou, Bingjie Fan, Wanlong Li, Shijiang Wang, Xindong Sun, Jinming Yu, Linlin Wang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	esophageal cancer esophageal fistula radiomics deep learning prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/fcf5aba1cfac43928a6f6bcb67f44353
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spelling	oai:doaj.org-article:fcf5aba1cfac43928a6f6bcb67f443532021-11-30T11:18:48ZIntegrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer2234-943X10.3389/fonc.2021.688706https://doaj.org/article/fcf5aba1cfac43928a6f6bcb67f443532021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.688706/fullhttps://doaj.org/toc/2234-943XBackground and PurposeThis study aims to develop a risk model to predict esophageal fistula in esophageal cancer (EC) patients by learning from both clinical data and computerized tomography (CT) radiomic features.Materials and MethodsIn this retrospective study, computerized tomography (CT) images and clinical data of 186 esophageal fistula patients and 372 controls (1:2 matched by the diagnosis time of EC, sex, marriage, and race) were collected. All patients had esophageal cancer and did not receive esophageal surgery. 70% patients were assigned into training set randomly and 30% into validation set. We firstly use a novel attentional convolutional neural network for radiographic descriptor extraction from nine views of planes of contextual CT, segmented tumor and neighboring structures. Then clinical factors including general, diagnostic, pathologic, therapeutic and hematological parameters are fed into neural network for high-level latent representation. The radiographic descriptors and latent clinical factor representations are finally associated by a fully connected layer for patient level risk prediction using SoftMax classifier.Results512 deep radiographic features and 32 clinical features were extracted. The integrative deep learning model achieved C-index of 0.901, sensitivity of 0.835, and specificity of 0.918 on validation set with superior performance than non-integrative model using CT imaging alone (C-index = 0.857) or clinical data alone (C-index = 0.780).ConclusionThe integration of radiomic descriptors from CT and clinical data significantly improved the esophageal fistula prediction. We suggest that this model has the potential to support individualized stratification and treatment planning for EC patients.Yiyue XuYiyue XuYiyue XuHui CuiTaotao DongBing ZouBingjie FanWanlong LiShijiang WangXindong SunJinming YuLinlin WangFrontiers Media S.A.articleesophageal canceresophageal fistularadiomicsdeep learningprediction modelNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	esophageal cancer esophageal fistula radiomics deep learning prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	esophageal cancer esophageal fistula radiomics deep learning prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Yiyue Xu Yiyue Xu Yiyue Xu Hui Cui Taotao Dong Bing Zou Bingjie Fan Wanlong Li Shijiang Wang Xindong Sun Jinming Yu Linlin Wang Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer
description	Background and PurposeThis study aims to develop a risk model to predict esophageal fistula in esophageal cancer (EC) patients by learning from both clinical data and computerized tomography (CT) radiomic features.Materials and MethodsIn this retrospective study, computerized tomography (CT) images and clinical data of 186 esophageal fistula patients and 372 controls (1:2 matched by the diagnosis time of EC, sex, marriage, and race) were collected. All patients had esophageal cancer and did not receive esophageal surgery. 70% patients were assigned into training set randomly and 30% into validation set. We firstly use a novel attentional convolutional neural network for radiographic descriptor extraction from nine views of planes of contextual CT, segmented tumor and neighboring structures. Then clinical factors including general, diagnostic, pathologic, therapeutic and hematological parameters are fed into neural network for high-level latent representation. The radiographic descriptors and latent clinical factor representations are finally associated by a fully connected layer for patient level risk prediction using SoftMax classifier.Results512 deep radiographic features and 32 clinical features were extracted. The integrative deep learning model achieved C-index of 0.901, sensitivity of 0.835, and specificity of 0.918 on validation set with superior performance than non-integrative model using CT imaging alone (C-index = 0.857) or clinical data alone (C-index = 0.780).ConclusionThe integration of radiomic descriptors from CT and clinical data significantly improved the esophageal fistula prediction. We suggest that this model has the potential to support individualized stratification and treatment planning for EC patients.
format	article
author	Yiyue Xu Yiyue Xu Yiyue Xu Hui Cui Taotao Dong Bing Zou Bingjie Fan Wanlong Li Shijiang Wang Xindong Sun Jinming Yu Linlin Wang
author_facet	Yiyue Xu Yiyue Xu Yiyue Xu Hui Cui Taotao Dong Bing Zou Bingjie Fan Wanlong Li Shijiang Wang Xindong Sun Jinming Yu Linlin Wang
author_sort	Yiyue Xu
title	Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer
title_short	Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer
title_full	Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer
title_fullStr	Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer
title_full_unstemmed	Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer
title_sort	integrating clinical data and attentional ct imaging features for esophageal fistula prediction in esophageal cancer
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/fcf5aba1cfac43928a6f6bcb67f44353
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Integrating Clinical Data and Attentional CT Imaging Features for Esophageal Fistula Prediction in Esophageal Cancer

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