Radiomics features for the discrimination of tuberculomas from high grade gliomas and metastasis: a multimodal study

• Published in: Neuroradiology Vol. 66; no. 11; pp. 1979 - 1992
• Main Authors: Indoria, Abhilasha, Kulanthaivelu, Karthik, Prasad, Chandrajit, Srinivas, Dwarakanath, Rao, Shilpa, Sinha, Neelam, Potluri, Vivek, Netravathi, M., Nalini, Atchayaram, Saini, Jitender
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024; Springer Nature B.V
• Subjects: Advanced Neuroimaging; Algorithms; Developing countries; Feature extraction; Glioma; Image contrast; Imaging; LDCs; Lesions; Machine learning; Magnetic resonance imaging; Medical imaging; Medicine; Medicine & Public Health; Metastases; Metastasis; Morbidity; Neuroimaging; Neurology; Neuroradiology; Neurosciences; Neurosurgery; Phenotypes; Radiology; Radiomics; Tuberculoma; Explainable artificial intelligence; Tuberculoma; Multi-modality radiomics; High-grade glioma; Metastasis; Machine learning
• ISSN: 0028-3940; 1432-1920; 1432-1920
• DOI: 10.1007/s00234-024-03435-7

Background Tuberculomas are prevalent in developing countries and demonstrate variable signals on MRI resulting in the overlap of the conventional imaging phenotype with other entities including glioma and brain metastasis. An accurate MRI diagnosis is important for the early institution of anti-tubercular therapy, decreased patient morbidity, mortality, and prevents unnecessary neurosurgical excision. This study aims to assess the potential of radiomics features of regular contrast images including T1W, T2W, T2W FLAIR, T1W post contrast images, and ADC maps, to differentiate between tuberculomas, high-grade-gliomas and metastasis, the commonest intra parenchymal mass lesions encountered in the clinical practice. Methods This retrospective study includes 185 subjects. Images were resampled, co-registered, skull-stripped, and zscore-normalized. Automated lesion segmentation was performed followed by radiomics feature extraction, train-test split, and features reduction. All machine learning algorithms that natively support multiclass classification were trained and assessed on features extracted from individual modalities as well as combined modalities. Model explainability of the best performing model was calculated using the summary plot obtained by SHAP values. Results Extra tree classifier trained on the features from ADC maps was the best classifier for the discrimination of tuberculoma from high-grade-glioma and metastasis with AUC-score of 0.96, accuracy-score of 0.923, Brier-score of 0.23. Conclusion This study demonstrates that radiomics features are effective in discriminating between tuberculoma, metastasis, and high-grade-glioma with notable accuracy and AUC scores. Features extracted from the ADC maps surfaced as the most robust predictors of the target variable.