{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:16:08Z","timestamp":1774451768290,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Singhealth Duke-NUS Clinical and System Innovation Grant","award":["2025\/PG\/000003\/C2"],"award-info":[{"award-number":["2025\/PG\/000003\/C2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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 \u201cdefinite\u201d or \u201cprobable\u201d 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\u2013CNN 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 \u00b1 0.02, sensitivity of 0.83 \u00b1 0.09, specificity of 0.77 \u00b1 0.08, and balanced accuracy of 0.80 \u00b1 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.<\/jats:p>","DOI":"10.3390\/diagnostics16060926","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T10:49:58Z","timestamp":1774003798000},"page":"926","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Can 3D T1 Post-Contrast MRI in A Radiomics-Machine Learning Model Distinguish Infective from Neoplastic Ring-Enhancing Brain Lesions? An Exploratory Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6522-2614","authenticated-orcid":false,"given":"Edwin Chong Yu","family":"Sng","sequence":"first","affiliation":[{"name":"Department of Infectious Diseases, Changi General Hospital, 2 Simei Street 3, Singapore 529889, Singapore"},{"name":"Duke-NUS Medical School, 8 College Rd., Singapore 169857, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2683-2198","authenticated-orcid":false,"given":"Minh Bao","family":"Kha","sequence":"additional","affiliation":[{"name":"Bioinformatics Institute, A*STAR, 30 Biopolis St., Matrix, Singapore 138671, Singapore"},{"name":"School of Information and Communications Technology, Hanoi University of Science and Technology, No. 1 Dai Co Viet, Hanoi, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min Jia","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology, Singapore General Hospital, 31 Third Hospital Ave., Singapore 168753, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas Kuan Hsien","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology, Singapore General Hospital, 31 Third Hospital Ave., Singapore 168753, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan Cheng Yao","family":"Goh","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology, Singapore General Hospital, 31 Third Hospital Ave., Singapore 168753, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"So Jeong","family":"Park","sequence":"additional","affiliation":[{"name":"Bioinformatics Institute, A*STAR, 30 Biopolis St., Matrix, Singapore 138671, Singapore"},{"name":"School of Computing, National University of Singapore, COM1, 13, Computing Dr., Singapore 117417, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Darren Cheng Han","family":"Teo","sequence":"additional","affiliation":[{"name":"Department of Infectious Diseases, Sengkang General Hospital, 110 Sengkang E Wy, Singapore 544886, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2021-3586","authenticated-orcid":false,"given":"Wei Ming","family":"Chua","sequence":"additional","affiliation":[{"name":"Duke-NUS Medical School, 8 College Rd., Singapore 169857, Singapore"},{"name":"Department of Neuroradiology, Singapore General Hospital, 31 Third Hospital Ave., Singapore 168753, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2606-3632","authenticated-orcid":false,"given":"May Yi Shan","family":"Lim","sequence":"additional","affiliation":[{"name":"Duke-NUS Medical School, 8 College Rd., Singapore 169857, Singapore"},{"name":"Department of Neuroradiology, Singapore General Hospital, 31 Third Hospital Ave., Singapore 168753, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Septian","family":"Hartono","sequence":"additional","affiliation":[{"name":"Duke-NUS Medical School, 8 College Rd., Singapore 169857, Singapore"},{"name":"Department of Neuroradiology, Singapore General Hospital, 31 Third Hospital Ave., Singapore 168753, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lester Chee Hoe","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Changi General Hospital, 2 Simei Street 3, Singapore 529889, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7321-710X","authenticated-orcid":false,"given":"Candice Yuen Yue","family":"Chan","sequence":"additional","affiliation":[{"name":"Duke-NUS Medical School, 8 College Rd., Singapore 169857, Singapore"},{"name":"Department of Infectious Diseases, Singapore General Hospital, 31 Third Hospital Ave., Singapore 168753, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hwee Kuan","family":"Lee","sequence":"additional","affiliation":[{"name":"Bioinformatics Institute, A*STAR, 30 Biopolis St., Matrix, Singapore 138671, Singapore"},{"name":"School of Computing, National University of Singapore, COM1, 13, Computing Dr., Singapore 117417, Singapore"},{"name":"School of Biological Sciences, Nanyang Technological University, 60 Nanyang Dr., Singapore 637551, Singapore"},{"name":"International Research Laboratory on Artificial Intelligence, 1 Fusionopolis Way, #21-01 Connexis (South Tower), Singapore 138632, Singapore"},{"name":"A* Centre for Frontier AI Research (CFAR), 1 Fusionopolis Way #16-16 Connexis (North Tower), Singapore 138632, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7603-7334","authenticated-orcid":false,"given":"Ling Ling","family":"Chan","sequence":"additional","affiliation":[{"name":"Duke-NUS Medical School, 8 College Rd., Singapore 169857, Singapore"},{"name":"Department of Neuroradiology, Singapore General Hospital, 31 Third Hospital Ave., Singapore 168753, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"ofaf095","DOI":"10.1093\/ofid\/ofaf095","article-title":"Clinicodemographic and Radiological Features of Infective Ring-Enhancing Brain Lesions: A 4-Year Retrospective Study at a Tertiary Referral Center","volume":"12","author":"Sng","year":"2025","journal-title":"Open Forum Infect. 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