Abstract 5428: Skeletal muscle gauge prediction by a machine learning model in patients with colorectal cancer

Abstract

Abstract Background: Skeletal muscle gauge (SMG) was recently introduced as an imaging indicator of sarcopenia for the prediction of clinical outcomes, including chemotherapy toxicity and prognosis, in patients with cancer. Computed tomography (CT) is essential for measuring SMG; thus, the use of SMG is limited to patients who undergo CT. Objective: We aimed to develop a machine learning algorithm using clinical and inflammatory markers to predict SMG in patients with colorectal cancer (CRC) Methods: The least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of the LASSO model, defined as LP-SMG was compared using the area under the receiver operating characteristic (AUROC) and decision curve analysis (DCA) in the test set. Results: A total of 1,094 patients with CRC were enrolled and randomly categorized into training (n=656) and test (n=438) sets. Low SMG was identified in 142 (21.6%) and 90 (20.5%) patients in the training and test sets, respectively. According to multivariable analysis of the test sets, LP-SMG was identified as an independent predictor of low SMG (OR: 1329.431, CI: 271.684-7667.996, p<.001). Its predictive performance was similar in the training and test sets (AUROC: 0.846 vs. 0.869, p=.427). In the test set, LP-SMG showed better outcomes in predicting SMG than single clinical variables, such as sex, height, weight, and hemoglobin, as measured by AUROC and DCA. Conclusions: LP-SMG, incorporating clinical variables and serum inflammatory indicators, showed superior performance compared to single variables in predicting low SMG. This machine learning model can be used as a screening tool to detect sarcopenic status without using CT during the treatment period. Applying a machine learning model might be beneficial in reducing the effort, cost, and radiation exposure from conventional CT-based diagnosis. Citation Format: Jeonghyun Kang. Skeletal muscle gauge prediction by a machine learning model in patients with colorectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5428.