Abstract
AimsTo develop and validate a model for predicting major pathological response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on a machine learning algorithm.MethodA total of 221 patients who underwent NAC and radical gastrectomy between February 2013 and September 2020 were enrolled in this study. A total of 144 patients were assigned to the training cohort for model building, and 77 patients were assigned to the validation cohort. A major pathological response was defined as primary tumor regressing to ypT0 or T1. Radiomic features extracted from venous-phase computed tomography (CT) images were selected by machine learning algorithms to calculate a radscore. Together with other clinical variables selected by univariate analysis, the radscores were included in a binary logistic regression analysis to construct an integrated prediction model. The data obtained for the validation cohort were used to test the predictive accuracy of the model.ResultA total of 27.6% (61/221) patients achieved a major pathological response. Five features of 572 radiomic features were selected to calculate the radscores. The final established model incorporates adenocarcinoma differentiation and radscores. The model showed satisfactory predictive accuracy with a C-index of 0.763 and good fitting between the validation data and the model in the calibration curve.ConclusionA prediction model incorporating adenocarcinoma differentiation and radscores was developed and validated. The model helps stratify patients according to their potential sensitivity to NAC and could serve as an individualized treatment strategy-making tool for AGC patients.
Highlights
Gastric cancer is the fifth most common malignancy in the world and the third leading cause of cancer-related death [1]
We reviewed the gastric cancer database of our institution and included patients according to the following criteria: Inclusion criteria: (i) patients with histologically confirmed adenocarcinoma of the stomach or esophagogastric junction who received neoadjuvant chemotherapy (NAC) and radical gastrectomy; (ii) patients who underwent abdominal multidetector computed tomography (CT) inspection before any intervention started; and (iii) tumor lesions that are assessable according to The Response Evaluation Criteria in Solid Tumors Version 1.1 [16]
From February 2013 to September 2020, 221 patients who received NAC and D2 radical gastrectomy were enrolled in the study
Summary
Gastric cancer is the fifth most common malignancy in the world and the third leading cause of cancer-related death [1]. Some scholars stated that NAC could result in tumor downstaging and a higher curative resection rate and may eventually prolong survival for AGC patients [3, 4]. Previous studies have found that the survival benefit of NAC vastly depends on the pathological response of the tumor. Those with a major pathological response and significant downstaging gained more survival benefit than others [7, 8]. For those with a minor response, NAC offers no survival benefit but only toxicity and the risk of tumor progression during chemotherapy that may hinder surgical resection. To achieve personalized precision medicine, a pre-intervention prediction model to identify major responders and minor responders is needed
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