Radiomics machine learning study with small sample size: single random training-test set split may result in unreliable results

Abstract

Objective: This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model under different conditions, using real-world brain tumor radiomics data.Materials and Methods: We conducted two classification tasks of different difficulty levels with magnetic resonance imaging (MRI) radiomics features: (1) “Simple” task, glioblastomas [n=109] vs. brain metastasis [n=58] and (2) “difficult” task, low- [n=163] vs. high-grade [n=95] meningiomas. Additionally, two undersampled datasets were created by randomly sampling 50% from these datasets. We performed random training-test set splitting for each dataset repeatedly to create 1,000 different training and test set pairs. For each dataset pair, the least absolute shrinkage and selection operator model was trained by five-fold cross-validation (CV) or nested CV with or without repetitions in the training set and tested with the test set, using the area under the curve (AUC) as an evaluation metric.Results: The AUCs in CV and testing varied widely based on data composition, especially with the undersampled datasets and the difficult task. The mean (±standard deviation) AUC difference between CV and testing was 0.029 (±0.022) for the simple task without undersampling and 0.108 (±0.079) for the difficult task with undersampling. In a training-test set pair, the AUC was high in CV but much lower in testing (0.840 and 0.650, respectively); in another dataset pair with the same task, however, the AUC was low in CV but much higher in testing (0.702 and 0.836, respectively). None of the CV methods helped overcome this issue.Conclusions: Machine learning after a single random training-test set split may lead to unreliable results in radiomics studies, especially when the sample size is small.