Danilov Gleb, Kalaeva Diana, Vikhrova Nina, Shugay Svetlana, Telysheva Ekaterina, Goraynov Sergey, Kosyrkova Alexandra, Pavlova Galina, Drozd Sergey, Samoylenkova Nadezhda, Pronin Igor, Usachev Dmitriy
Laboratory of Biomedical Informatics and Artificial Intelligence, Moscow, Russian Federation.
Department of Neuroimaging, Moscow, Russian Federation.
Stud Health Technol Inform. 2025 Apr 8;323:154-158. doi: 10.3233/SHTI250068.
Radiomics shows promise in enhancing predictions of overall survival (OS) and progression-free survival (PFS) in patients with glial brain tumors. The prognostic significance of imaging biomarkers derived from a whole-brain mask is still unclear. This study aimed to evaluate the potential of radiomics for predictive modeling of OS and PFS in patients with brain gliomas. We compared 13 prognostic models designed to predict OS and PFS, using clinical features alone, radiological biomarkers alone, and a combination of both. Our approach achieved C-index values of 0.900 for OS and 0.903 for PFS. Models built solely on imaging biomarkers exhibited the highest quality, whereas those based only on clinical signs showed the lowest quality. Given the limited data, it is unclear how reproducible the whole-brain radiomic features and corresponding models will be with new data. Nonetheless, there are reasons to view whole-brain radiomics as a promising avenue for further research.
放射组学在提高胶质脑肿瘤患者总生存期(OS)和无进展生存期(PFS)预测方面显示出前景。源自全脑掩码的影像生物标志物的预后意义仍不明确。本研究旨在评估放射组学对脑胶质瘤患者OS和PFS进行预测建模的潜力。我们比较了13种旨在预测OS和PFS的预后模型,这些模型分别仅使用临床特征、仅使用放射生物标志物以及两者结合。我们的方法在OS方面的C指数值为0.900,在PFS方面为0.903。仅基于影像生物标志物构建的模型质量最高,而仅基于临床体征的模型质量最低。鉴于数据有限,尚不清楚全脑放射组学特征及相应模型在新数据下的可重复性如何。尽管如此,仍有理由将全脑放射组学视为一个有前景的进一步研究途径。