Background and Objective: Non-invasively predicting the risk of cancer metastasis before surgery plays an essential role in determining optimal treatment methods for cancer patients (including who can benefit from neoadjuvant chemotherapy). Although developing radiomics based machine learning (ML) models has attracted broad research interest for this purpose, it often faces a challenge of how to build a highly performed and robust ML model using small and imbalanced image datasets. Methods: In this study, we explore a new approach to build an optimal ML model. A retrospective dataset involving abdominal computed tomography (CT) images acquired from 159 patients diagnosed with gastric cancer is assembled. Among them, 121 cases have peritoneal metastasis (PM), while 38 cases do not have PM. A computer-aided detection (CAD) scheme is first applied to segment primary gastric tumor volumes and initially computes 315 image features. Then, two Gradient Boosting Machine (GBM) models embedded with two different feature dimensionality reduction methods, namely, the principal component analysis (PCA) and a random projection algorithm (RPA) and a synthetic minority oversampling technique, are built to predict the risk of the patients having PM. All GBM models are trained and tested using a leave-one-case-out cross-validation method. Results: Results show that the GBM embedded with RPA yielded a significantly higher prediction accuracy (71.2%) than using PCA (65.2%) (p<0.05). Conclusions: The study demonstrated that CT images of the primary gastric tumors contain discriminatory information to predict the risk of PM, and RPA is a promising method to generate optimal feature vector, improving the performance of ML models of medical images.
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