Development and Validation of a Computed Tomography–Based Radiomics Signature to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Gastric Cancer

Abstract

IMPORTANCENeoadjuvant therapies have been shown to decrease tumor burden, increase resection rate, and improve the outcomes among patients with locally advanced gastric cancer (GC).However, not all patients are equally responsive; therefore, differentiating potential respondents from nonrespondents is clinically important. OBJECTIVETo use pretreatment computed tomography (CT)-pixelated feature-difference extraction techniques to identify diagnostically relevant features that could predict patients' response to neoadjuvant chemotherapy at diagnosis. DESIGN, SETTING, AND PARTICIPANTS This multicenter cohort study included patients with locally advanced GC who were treated from January 2010 to July 2017 at 2 hospitals in southern China (training cohort) and 1 hospital in northern China (external validation cohort). Their clinicopathological data, pretreatment CT images, and pathological reports were retrieved and analyzed. Data analysis was conducted from December 2017 to May 2021. EXPOSURES All patients underwent 2 to 4 cycles of fluorouracil in combination with a platinumbased neoadjuvant chemotherapy regimen. All gastrectomies were performed according to the Japanese Classification of Gastric Carcinoma (14th edition) guidelines. MAIN OUTCOMES AND MEASURES Reliability of clinicopathological and radiomics-based features were assessed with area under receiver operating characteristic curve (AUC) and Mann-Whitney U test. RESULTS A total of 323 patients (242 [74.9%] men; median [range] age, 58 [24-82] years) were included in the study, with 250 patients (77.4%) in the training cohort and 73 (22.6%) in the validation cohort. The baseline pretreatment characteristics of the training and validation cohorts were well-balanced. The number of respondents in the training and validation cohort was 122 (48.8%) and 40 (54.8%), respectively, and the number of nonrespondents was 128 (51.2%) and 33 (45.2%), respectively. No clinicopathological variables were significantly associated with treatment response. Using radiomics, 20 low-intercorrelated features from a total of 7477 features were used to construct a radiomics signature that demonstrated significant association with treatment response. Good discrimination performance of the radiomics signature for predicting treatment response in the training (AUC, 0.736; 95% CI, 0.675-0.798) and external validation (AUC, 0.679; 95% CI, 0.554-0.803) cohorts was observed. Decision curve analysis confirmed the clinical utility of the radiomics signature.(continued) Key Points Question Can computed tomographybased radiomics be used to predict patients' response to neoadjuvant chemotherapy at diagnosis? Findings In this cohort study of 323 patients with locally advanced gastric cancer, no pretreatment clinicopathological characteristics were associated with response or nonresponse to treatment. However, 20 radiomic features with low intercorrelation were significantly associated with treatment response and were used to create a radiomics signature with promising clinical reliability for ident...

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