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

Yiyue Xu; Hui Cui; Taotao Dong; Bing Zou; Bingjie Fan; Wanlong Li; Shijiang Wang; Xindong Sun; Jinming Yu; Linlin Wang

doi:10.3389/fonc.2021.688706

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

Front Oncol. 2021 Nov 23:11:688706. doi: 10.3389/fonc.2021.688706. eCollection 2021.

Authors

Yiyue Xu^{1

2

3}, Hui Cui⁴, Taotao Dong⁵, Bing Zou¹, Bingjie Fan¹, Wanlong Li¹, Shijiang Wang¹, Xindong Sun¹, Jinming Yu¹, Linlin Wang¹

Affiliations

¹ Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
² Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
³ Department of Oncology, Tianjin Medical University, Tianjin, China.
⁴ Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia.
⁵ Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, China.

Abstract

Background and purpose: This 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 methods: In 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.

Results: 512 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).

Conclusion: The 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.

Keywords: deep learning; esophageal cancer; esophageal fistula; prediction model; radiomics.