Novel predictors of tumor growth by exploratory quantitative analysis of radiomics features from MRI data for incidentally discovered meningioma.

IF 3.1 2区 医学 Q2 CLINICAL NEUROLOGY
Journal of Neuro-Oncology Pub Date : 2025-12-01 Epub Date: 2025-09-16 DOI:10.1007/s11060-025-05186-8
Yuta Oi, Haruki Minamoto, Ichita Taniyama, Masayuki Fukuzawa, Koji Sakai, Takahiro Ogawa, Takumi Yamanaka, Yoshinobu Takahashi, Naoya Hashimoto
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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.

通过对偶然发现的脑膜瘤MRI数据放射组学特征的探索性定量分析,发现肿瘤生长的新预测因子。
目的:从偶然发现的脑膜瘤(IDM)的初始影像预测未来的肿瘤生长,为治疗决策提供依据。然而,在先前的脑膜瘤生长研究中发现的大多数因素都是定性的。本研究的目的是利用MRI数据的定量放射组学特征来确定与肿瘤生长相关的因素。方法:对24例肿瘤生长的首发MRI T2表现与25例无生长的首发MRI T2表现进行比较。开发了一个内部程序来减少数据分析所需的时间。本程序基于开源软件3D Slicer 5.6.2和PyRadiomics 3.1.0。它可以对每个特征进行半自动批量t检验分析,以比较肿瘤生长组和非生长组。感兴趣区域(roi)位于肿瘤、肿瘤外边缘、全脑和肿瘤对侧白质。共分析了107个特征,分为7个类别:一阶、形状、灰度共生矩阵、灰度运行长度矩阵、灰度大小区域矩阵、灰度依赖矩阵和相邻灰度色调差矩阵。使用t检验来识别显著的预测因子。结果:5个分类中的10个特征显示出显著差异(p)。结论:利用放射组学特征确定了IDM生长的潜在预测因素。未来更大规模的研究和验证对于确认临床应用和提高这些特征的预测准确性至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Neuro-Oncology
Journal of Neuro-Oncology 医学-临床神经学
CiteScore
6.60
自引率
7.70%
发文量
277
审稿时长
3.3 months
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.
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