Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma. (January 2021)

• Record Type: Journal Article
• Authors: Hu, Yihuai; Xie, Chenyi; Yang, Hong; Ho, Joshua W.K.; Wen, Jing; Han, Lujun; Lam, Ka-On; Wong, Ian Y.H.; Law, Simon Y.K.; Chiu, Keith W.H.; Vardhanabhuti, Varut; Fu, Jianhua
• Is Part Of: Radiotherapy and oncology. Volume 154(2021)
• Page Start: 6
• Page End: 13
• Publication Date: 2021-01
• Subjects: Esophageal squamous cell carcinoma -- Neoadjuvant chemoradiotherapy -- Deep learning -- Radiomics -- Computed tomography; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2020.09.014
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: Deep learning provides efficient and accurate prediction of treatment response. Transfer learning can be used in radiological task with insufficient image datasets. Tumor microenvironment and signaling pathways are linked with radiological prediction. Abstract: Background: Deep learning is promising to predict treatment response. We aimed to evaluate and validate the predictive performance of the CT-based model using deep learning features for predicting pathologic complete response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). Materials and methods: Patients were retrospectively enrolled between April 2007 and December 2018 from two institutions. We extracted deep learning features of six pre-trained convolutional neural networks, respectively, from pretreatment CT images in the training cohort ( n = 161). Support vector machine was adopted as the classifier. Validation was performed in an external testing cohort ( n = 70). We assessed the performance using the area under the receiver operating characteristics curve (AUC) and selected an optimal model, which was compared with a radiomics model developed from the training cohort. A clinical model consisting of clinical factors only was also built for baseline comparison. We further conducted a radiogenomics analysis using gene expression profiles to reveal underlying biology associated with radiological prediction. Results: The optimal model with features extracted from ResNet50 achieved an AUC and accuracy of 0.805 (95% CI, 0.696–0.913) and 77.1% (65.6%-86.3%) in the testing cohort, compared with 0.725 (0.605–0.846)) and 67.1% (54.9%-77.9%) for the radiomics model. All the radiological models showed better predictive performance than the clinical model. Radiogenomics analysis suggested a potential association mainly with WNT signaling pathway and tumor microenvironment. Conclusions: The novel and noninvasive deep learning approach could provide efficient and accurate prediction of treatment response to nCRT in ESCC, and benefit clinical decision making of therapeutic strategy.