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

Indoria, A and Kulanthaivelu, K and Prasad, C and Srinivas, D and Rao, S and Sinha, N and Potluri, V and Netravathi, M and Nalini, A and Saini, J (2024) Radiomics features for the discrimination of tuberculomas from high grade gliomas and metastasis: a multimodal study. In: Neuroradiology .

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Abstract

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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Item Type:	Journal Article
Publication:	Neuroradiology
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to publisher
Department/Centre:	Autonomous Societies / Centres > Centre for Brain Research
Date Deposited:	09 Sep 2024 07:24
Last Modified:	09 Sep 2024 07:24
URI:	http://eprints.iisc.ac.in/id/eprint/86040

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