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Phys. Eng. Express"],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Gliomas, one of the most prevalent primary tumors of the central nervous system, have IDH molecular subtypes that are strongly linked to patient survival, prognosis, and treatment plans. However, there are currently few noninvasive preoperative prediction techniques, and postoperative pathology and genetic testing are the main approaches used to assess IDH status. Radiomics features can quantitatively characterize intratumoral heterogeneity, whereas spatial semantic features may provide additional information related to tumor location and macroscopic growth patterns. When it comes to characteristics like glioma invasiveness, growth patterns, and angiogenesis, spatial semantic features can enhance radiomics features. In order to predict IDH molecular subtypes in gliomas, this study suggests a combined analytical approach that incorporates clinical data, spatial semantic features, and multiparametric magnetic resonance imaging (MRI) radiomic features. This technique uses the IBSI standard to extract radiomic data from raw multiparametric MRI images and combines morphological analysis, gradient statistics, and 3D geometric modeling to create spatial semantic information. To eliminate redundant features and account for multicollinearity, Pearson correlation analysis and the variance inflation factor were utilized. The best feature subset was then obtained through a multi-stage feature selection procedure that combined random forests (RFs), LASSO regression, and recursive feature elimination. Lastly, imaging characteristics and clinical data were combined using a RF model to identify IDH molecular subtypes in gliomas. According to experimental findings, the suggested model\u2019s area under the ROC curve (AUC) in a five-fold cross-validation was 96.08%. The model had the AUC of 95.62%, ACC of 91.67%, sensitivity of 88.89%, and specificity of 93.94% in the final test set. The limitations of radiomics in conveying tumor macroscopic development patterns and spatial invasion relationships can be somewhat compensated for by spatial semantic aspects, according to additional investigation. The source code at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Grace503\/IDH_prediction.git\">https:\/\/github.com\/Grace503\/IDH_prediction.git<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1088\/2057-1976\/aeaeef","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T22:48:11Z","timestamp":1790894891000},"page":"055045","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating radiomic features and spatial semantic features from multiparametric MRI for IDH genotyping in glioma"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1122-1410","authenticated-orcid":true,"given":"Yang","family":"Xi","sequence":"first","affiliation":[{"name":"Northeast Electric Power University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Bingjie","family":"Shi","sequence":"additional","affiliation":[{"name":"Northeast Electric Power University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"given":"Qingzhu","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeast Electric Power University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Xiaopeng","family":"Lv","sequence":"additional","affiliation":[{"name":"Jilin City Hospital of Chemical Industry"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"given":"Xuebing","family":"Jing","sequence":"additional","affiliation":[{"name":"Northeast Electric Power University"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]}],"member":"266","published-online":{"date-parts":[[2026,10,12]]},"reference":[{"key":"bpexaeaeefbib1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.22056","type":"journal-article","article-title":"Diagnostic accuracy of magnetic resonance spectroscopy in predicting the grade of glioma keeping histopathology as the gold standard","volume":"14","author":"Rafique","year":"2022","journal-title":"Cureus"},{"key":"bpexaeaeefbib2","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1093\/neuonc\/noac166","type":"journal-article","article-title":"Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning","volume":"25","author":"Voort","year":"2023","journal-title":"Neuro-Oncology"},{"key":"bpexaeaeefbib3","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1093\/neuonc\/noab106","type":"journal-article","article-title":"The 2021 WHO classification of tumors of the central nervous system: a summary","volume":"23","author":"Louis","year":"2021","journal-title":"Neuro-Oncology"},{"key":"bpexaeaeefbib4","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s11060-024-04826-9","type":"journal-article","article-title":"Determinants of long-term survival in patients with IDH mutant gliomas","volume":"170","author":"Katzendobler","year":"2024","journal-title":"J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-06-15","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-10-01","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-10-12","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}