Yuta Oi, Haruki Minamoto, Ichita Taniyama, Masayuki Fukuzawa, Koji Sakai, Takahiro Ogawa, Takumi Yamanaka, Yoshinobu Takahashi, Naoya Hashimoto
{"title":"Novel predictors of tumor growth by exploratory quantitative analysis of radiomics features from MRI data for incidentally discovered meningioma.","authors":"Yuta Oi, Haruki Minamoto, Ichita Taniyama, Masayuki Fukuzawa, Koji Sakai, Takahiro Ogawa, Takumi Yamanaka, Yoshinobu Takahashi, Naoya Hashimoto","doi":"10.1007/s11060-025-05186-8","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Predicting future tumor growth from initial imaging of incidentally discovered meningioma (IDM) could inform treatment decisions. However, most factors identified in prior studies on meningioma growth are qualitative. The aim of this study is to identify factors associated with tumor growth using quantitative radiomics features from MRI data.</p><p><strong>Methods: </strong>MRI T2 features from initial imaging of 24 tumor growth cases were compared with those of 25 cases without growth. An in-house program was developed to reduce the time required for data analysis. This program is based on the open-source software 3D Slicer 5.6.2 and PyRadiomics 3.1.0. It enables semi-automatic batch t-test analyses for each feature to compare tumor growth and non-growth groups. Regions of interest (ROIs) were placed in the tumor, outer tumor edge, whole brain, and white matter contralateral to the tumor. A total of 107 features were analyzed across seven classifications: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, Gray Level Dependence Matrix, and Neighboring Gray Tone Difference Matrix. A t-test was used to identify significant predictors.</p><p><strong>Results: </strong>Ten features across five classifications showed significant differences (p < 0.05): 2 First Order statistics, 2 Shape features, 4 Gy Level Co-occurrence Matrices, 1 Gy Level Size Zone Matrix, and 1 Neighboring Gray Tone Difference Matrix.</p><p><strong>Conclusions: </strong>Potential predictors of IDM growth were identified using radiomics features. Future studies with larger cohorts and validation will be essential to confirm the clinical utility and improve the predictive accuracy of these features.</p>","PeriodicalId":16425,"journal":{"name":"Journal of Neuro-Oncology","volume":" ","pages":"1177-1186"},"PeriodicalIF":3.1000,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neuro-Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11060-025-05186-8","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/9/16 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Predicting future tumor growth from initial imaging of incidentally discovered meningioma (IDM) could inform treatment decisions. However, most factors identified in prior studies on meningioma growth are qualitative. The aim of this study is to identify factors associated with tumor growth using quantitative radiomics features from MRI data.
Methods: MRI T2 features from initial imaging of 24 tumor growth cases were compared with those of 25 cases without growth. An in-house program was developed to reduce the time required for data analysis. This program is based on the open-source software 3D Slicer 5.6.2 and PyRadiomics 3.1.0. It enables semi-automatic batch t-test analyses for each feature to compare tumor growth and non-growth groups. Regions of interest (ROIs) were placed in the tumor, outer tumor edge, whole brain, and white matter contralateral to the tumor. A total of 107 features were analyzed across seven classifications: First Order, Shape, Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix, Gray Level Size Zone Matrix, Gray Level Dependence Matrix, and Neighboring Gray Tone Difference Matrix. A t-test was used to identify significant predictors.
Results: Ten features across five classifications showed significant differences (p < 0.05): 2 First Order statistics, 2 Shape features, 4 Gy Level Co-occurrence Matrices, 1 Gy Level Size Zone Matrix, and 1 Neighboring Gray Tone Difference Matrix.
Conclusions: Potential predictors of IDM growth were identified using radiomics features. Future studies with larger cohorts and validation will be essential to confirm the clinical utility and improve the predictive accuracy of these features.
期刊介绍:
The Journal of Neuro-Oncology is a multi-disciplinary journal encompassing basic, applied, and clinical investigations in all research areas as they relate to cancer and the central nervous system. It provides a single forum for communication among neurologists, neurosurgeons, radiotherapists, medical oncologists, neuropathologists, neurodiagnosticians, and laboratory-based oncologists conducting relevant research. The Journal of Neuro-Oncology does not seek to isolate the field, but rather to focus the efforts of many disciplines in one publication through a format which pulls together these diverse interests. More than any other field of oncology, cancer of the central nervous system requires multi-disciplinary approaches. To alleviate having to scan dozens of journals of cell biology, pathology, laboratory and clinical endeavours, JNO is a periodical in which current, high-quality, relevant research in all aspects of neuro-oncology may be found.