发表机构
Université de Lorraine; CNRS; CRAN; Inria; IECL; Luxembourg Center for Systems of Biomedicine, University of Luxembourg; CHRU-Nancy, Service de Neuro-oncologie（洛林大学; 法国国家科学研究中心; 自动控制与计算研究实验室; 法国国家信息与自动化研究所; 洛林数学与逻辑研究所; 卢森堡大学生物医学系统中心; 南锡大学医院神经肿瘤科）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于MRI的新特征ESVR量化弥漫性低级别胶质瘤脑浸润，结合机器学习分析发现年龄、ESVR和病理结果对区分患者模式重要，有助于治疗管理。
AI 中文摘要
胶质瘤是最常见的原发性脑肿瘤类型。弥漫性低级别胶质瘤（DLGG）是生长缓慢的肿瘤，在很长一段时间内通常症状轻微。它们会进展为更高级别，导致患者死亡。治疗方法包括手术、化疗和放疗以控制肿瘤进展。患者对治疗的反应差异很大。此外，这种肿瘤实体的弥漫性成分已被观察到，但尚未得到很好的测量。在此，我们提出一个新的变量来量化胶质瘤形态，称为ESVR（额外球体体积比）。然后我们使用机器学习方法研究不同变量在区分患者模式方面的重要性：患者特定变量、肿瘤组织的遗传改变，以及一些基于图像的变量，如ESVR和通过MRI分析获得的肿瘤体积。我们的机器学习方法表明，诊断时的患者年龄和ESVR，以及病理结果似乎发挥重要作用。考虑这些突出变量可能有助于模拟肿瘤行为。结合其他经典生物标志物，我们的新ESVR特征可帮助临床医生管理治疗，因为它携带了关于DLGG侵袭性的重要信息。
英文摘要
Gliomas are the most common type of primary brain tumors. Diffuse low-grade gliomas (DLGGs) are slow-growing tumors that are often minimally symptomatic for a long period of time. They progress to a higher grade, resulting in the patient's death. Treatments include surgery, chemotherapy and radiation therapy to control tumor progression. Responses to treatments are highly variable among patients. Furthermore, the diffuse component of this tumor entity has been observed but is not yet well measured. Here, we propose a new variable to quantify glioma morphology, called ESVR (Extra Sphere Volume Ratio). We use then a machine learning approach to study the importance of different variables for discriminating patterns of patients: patient-specific variables, genetic alterations of tumor tissue, and some image-based variables like ESVR and the volume of the tumor, obtained by MRI analysis. Our machine learning approach shows that the patient's age and ESVR at diagnosis, as well as the pathology results seem to play an important role. Taking the highlighted variables into account could thus help to model the tumor behavior. In association with other classical biomarkers, our new ESVR feature could help the clinician to manage the treatment, as it carries significant information in terms of aggressivity of the DLGG.
Journal refCIBB - Rome Italy - 2026, International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics, Sep 2026, Rome Sapienza Universita di Roma, Italy
