  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.
