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

被引:11
|
作者
Qu, Jinrong [1 ,2 ]
Ma, Ling [3 ]
Lu, Yanan [1 ,2 ]
Wang, Zhaoqi [1 ,2 ]
Guo, Jia [1 ,2 ]
Zhang, Hongkai [1 ,2 ]
Yan, Xu [6 ]
Liu, Hui [1 ,2 ]
Kamel, Ihab R. [7 ]
Qin, Jianjun [4 ,5 ]
Li, Hailiang [1 ,2 ]
机构
[1] Zhengzhou Univ, Dept Radiol, Affiliated Canc Hosp, Zhengzhou 450008, Henan, Peoples R China
[2] Henan Canc Hosp, Zhengzhou 450008, Henan, Peoples R China
[3] GE Healthcare, Adv Applicat Team, Shanghai 201203, Peoples R China
[4] Zhengzhou Univ, Dept Thorac Surg, Affiliated Canc Hosp, Zhengzhou 450008, Henan, Peoples R China
[5] Chinese Acad Med Sci & Peking Union Med Coll, Natl Canc Ctr Canc Hosp, Dept Thorac Surg, Beijing 100021, Peoples R China
[6] Siemens Ltd, NEA MR Collaborat, Shanghai 201318, Peoples R China
[7] Johns Hopkins Univ, Dept Radiol, Sch Med, Baltimore, MD 21205 USA
基金
中国国家自然科学基金;
关键词
Esophageal cancer; Magnetic Resonance Imaging; Adjuvant chemotherapy; Nomograms; Precision medicine; CONTRAST-ENHANCED MRI; CHEMORADIOTHERAPY; CARCINOMA; MANAGEMENT; SURVIVAL; TUMOR;
D O I
10.1007/s12672-022-00464-7
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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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页数:13
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