Can 3D T1 Post-Contrast MRI in A Radiomics-Machine Learning Model Distinguish Infective from Neoplastic...

Background/Objectives: Rapid and accurate classification of ring-enhancing brain lesions (REBLs) into infection or neoplasm is key to clinical triaging for expedited diagnostics in the former to enhance treatment outcomes, especially in the immunocompromised patients. High-resolution three-dimensional (3D) T1 post-contrast (T1+C) MRI provides high-dimensional volumetric data for radiomics analysis. While radiomics is useful in brain neoplasm characterization, its utility in central nervous system infection remains under-explored. In this exploratory study, we aim to determine if a radiomics-machine learning model, based solely on a 3D T1+C MRI dataset, can distinguish infective from neoplastic REBLs. Methods: 92 patients (infection, n = 26; neoplasm, n = 66) with 402 REBLs, who fulfilled criteria for "definite" or "probable" infective or neoplastic REBLs, were identified from scans performed at our hospital over four years and formed the training/validation dataset. All REBLs were manually annotated on T1+C MRI images under radiological supervision. In total, 1197 radiomics features were extracted, feature selection performed using mutual information, and nine machine learning classifiers applied to assess patient-level infection vs. neoplasm classification performance. End-to-end 2D CNN baselines and hybrid radiomics-CNN configurations were additionally evaluated under the same protocol for comparative benchmarking. Model performance was tested on an external holdout dataset of 57 patients (infection, n = 25; neoplasm, n = 32) with 454 REBLs from another hospital. Results: The Multi-layer Perceptron (MLP) model using the Original + LoG + Wavelet feature group demonstrated superior performance. In the cross-validation cohort, it achieved a mean AUC of 0.80 ± 0.02, sensitivity of 0.83 ± 0.09, specificity of 0.77 ± 0.08, and balanced accuracy of 0.80 ± 0.02. On external holdout data, the same configuration showed stable and sustainable performance with an AUC of 0.84, sensitivity of 0.84, specificity of 0.75, and balanced accuracy of 0.80. Conclusions: Our radiomics-machine learning model, based solely on a high-resolution 3D T1+C dataset, shows potential for distinguishing infective REBLs from neoplastic REBLs. Further study, with additional MR sequences and clinical data in a multimodal MRI radiomics-machine learning model, is warranted.