A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study. (April 2022)

• Record Type: Journal Article
• Authors: Cui, Yanfen; Zhang, Jiayi; Li, Zhenhui; Wei, Kaikai; Lei, Ye; Ren, Jialiang; Wu, Lei; Shi, Zhenwei; Meng, Xiaochun; Yang, Xiaotang; Gao, Xin
• Is Part Of: EClinicalMedicine. Volume 46(2022)
• Publication Date: 2022-04
• Subjects: Deep learning -- Radiomics nomogram -- Locally advanced gastric cancer -- Neoadjuvant chemotherapy; LAGC locally advanced gastric cancer -- NACT neoadjuvant chemotherapy -- CT computed tomography -- DLRN deep learning radiomics nomogram -- TRG tumor regression grade -- GR good response -- PR poor response -- ROI regions of interest -- LASSO least absolute shrinkage and selection operator -- ICC interclass correlation coefficient -- ROC Receiver operating characteristic -- NRI Net reclassification index -- IDI integrated discrimination improvement -- AIC Akaike information criterion -- DCA decision curve analysis -- DFS disease free survival; Medicine -- Research -- Periodicals; Medical policy -- Periodicals; Clinical Medicine; Health Policy; Public Health; Medical policy; Medicine -- Research; Periodical; Electronic journals; Periodicals; 613
• DOI: 10.1016/j.eclinm.2022.101348
• Languages: English
• ISSNs: 2589-5370

Abstract

Summary: Background: Accurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC. Methods: 719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort ( n = 300). Findings: The DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739–0.920), 0.804 (95% CI, 0.732–0.877), and 0.827 (95% CI, 0.755–0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts ( p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model ( p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC ( p < 0.05). Interpretation: A deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment. Funding: This study was supported by the National Natural Science Foundation of China (No. 82001789, 82171923, 82001986, 81871439, and 82002702), the China Postdoctoral Science Foundation (No. 2021M700897), the Project of Shanxi Provincial Health Commission (No. 2021XM51, 2020064, and 2019058), the Applied Basic Research Projects of Yunnan Province, China, Outstanding Youth Foundation (202101AW070001), and the Youth Project of Natural Science Foundation of Hunan Science (No. 2020JJ5905).