DCE-MRI radiomics nomogram can predict response to neoadjuvant chemotherapy in esophageal cancer

Springer Science and Business Media LLC - 2022
Jinrong Qu1, Ling Ma2, Yanan Lu1, Zhaoqi Wang1, Jia Guo1, Hongkai Zhang1, Yan Xu3, Hui Liu1, Ihab R. Kamel4, Jianjun Qin5, Hailiang Li1
1Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, 450008, Henan, China
2Advanced Application Team, GE Healthcare, Shanghai, 201203, China
3NEA MR Collaboration, Siemens Ltd., China, Shanghai, 201318, China
4Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205-2196, USA
5Department of Thoracic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, 450008, Henan, China

Tóm tắt

Abstract Objectives To assess volumetric DCE-MRI radiomics nomogram in predicting response to neoadjuvant chemotherapy (nCT) in EC patients. Methods This retrospective analysis of a prospective study enrolled EC patients with stage cT1N + M0 or cT2-4aN0-3M0 who received DCE-MRI within 7 days before chemotherapy, followed by surgery. Response assessment was graded from 1 to 5 according to the tumor regression grade (TRG). Patients were stratified into responders (TRG1 + 2) and non-responders (TRG3 + 4 + 5). 72 radiomics features and vascular permeability parameters were extracted from DCE-MRI. The discriminating performance was assessed with ROC. Decision curve analysis (DCA) was used for comparing three different models. Results This cohort included 82 patients, and 72 tumor radiomics features and vascular permeability parameters acquired from DCE-MRI. mRMR and LASSO were performed to choose the optimized subset of radiomics features, and 3 features were selected to create the radiomics signature that were significantly associated with response (P < 0.001). AUC of combining radiomics signature and DCE-MRI performance in the training (n = 41) and validation (n = 41) cohort was 0.84 (95% CI 0.57–1) and 0.86 (95% CI 0.74–0.97), respectively. This combined model showed the best discrimination between responders and non-responders, and showed the highest positive and positive predictive value in both training set and test set. Conclusions The radiomics features are useful for nCT response prediction in EC patients.

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