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Background and Objective: Non-invasively predicting the risk of cancermetastasis before surgery plays an essential role in determining optimaltreatment methods for cancer patients (including who can benefit fromneoadjuvant chemotherapy). Although developing radiomics based machine learning(ML) models has attracted broad research interest for this purpose, it oftenfaces a challenge of how to build a highly performed and robust ML model usingsmall and imbalanced image datasets. Methods: In this study, we explore a newapproach to build an optimal ML model. A retrospective dataset involvingabdominal computed tomography (CT) images acquired from 159 patients diagnosedwith gastric cancer is assembled. Among them, 121 cases have peritonealmetastasis (PM), while 38 cases do not have PM. A computer-aided detection(CAD) scheme is first applied to segment primary gastric tumor volumes andinitially computes 315 image features. Then, two Gradient Boosting Machine(GBM) models embedded with two different feature dimensionality reductionmethods, namely, the principal component analysis (PCA) and a random projectionalgorithm (RPA) and a synthetic minority oversampling technique, are built topredict the risk of the patients having PM. All GBM models are trained andtested using a leave-one-case-out cross-validation method. Results: Resultsshow that the GBM embedded with RPA yielded a significantly higher predictionaccuracy (71.2%) than using PCA (65.2%) (p<0.05). Conclusions: The studydemonstrated that CT images of the primary gastric tumors containdiscriminatory information to predict the risk of PM, and RPA is a promisingmethod to generate optimal feature vector, improving the performance of MLmodels of medical images.

Applying a random projection algorithm to optimize machine learning  model for predicting peritoneal metastasis in gastric cancer patients using  CT images