Chiesa S, Russo R, Beghella Bartoli F, Palumbo I, Sabatino G, Cannatà M C, Gigli R, Longo S, Tran H E, Boldrini L, Dinapoli N, Votta C, Cusumano D, Pignotti F, Lupattelli M, Camilli F, Della Pepa G M, D'Alessandris G Q, Olivi A, Balducci M, Colosimo C, Gambacorta M A, Valentini V, Aristei C, Gaudino S
Department of Radiology, Radiation Oncology and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.
Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Institute of Radiology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.
Front Med (Lausanne). 2023 Jan 19;10:1059712. doi: 10.3389/fmed.2023.1059712. eCollection 2023.
The glioblastoma's bad prognosis is primarily due to intra-tumor heterogeneity, demonstrated from several studies that collected molecular biology, cytogenetic data and more recently radiomic features for a better prognostic stratification. The GLIFA project (GLIoblastoma Feature Analysis) is a multicentric project planned to investigate the role of radiomic analysis in GB management, to verify if radiomic features in the tissue around the resection cavity may guide the radiation target volume delineation.
We retrospectively analyze from three centers radiomic features extracted from 90 patients with total or near total resection, who completed the standard adjuvant treatment and for whom we had post-operative images available for features extraction. The Manual segmentation was performed on post gadolinium T1w MRI sequence by 2 radiation oncologists and reviewed by a neuroradiologist, both with at least 10 years of experience. The Regions of interest (ROI) considered for the analysis were: the surgical cavity ± post-surgical residual mass (CTV_cavity); the CTV a margin of 1.5 cm added to CTV_cavity and the volume resulting from subtracting the CTV_cavity from the CTV was defined as CTV_Ring. Radiomic analysis and modeling were conducted in RStudio. Z-score normalization was applied to each radiomic feature. A radiomic model was generated using features extracted from the Ring to perform a binary classification and predict the PFS at 6 months. A 3-fold cross-validation repeated five times was implemented for internal validation of the model.
Two-hundred and seventy ROIs were contoured. The proposed radiomic model was given by the best fitting logistic regression model, and included the following 3 features: F_cm_merged.contrast, F_cm_merged.info.corr.2, F_rlm_merged.rlnu. A good agreement between model predicted probabilities and observed outcome probabilities was obtained (-value of 0.49 by Hosmer and Lemeshow statistical test). The ROC curve of the model reported an AUC of 0.78 (95% CI: 0.68-0.88).
This is the first hypothesis-generating study which applies a radiomic analysis focusing on healthy tissue ring around the surgical cavity on post-operative MRI. This study provides a preliminary model for a decision support tool for a customization of the radiation target volume in GB patients in order to achieve a margin reduction strategy.
胶质母细胞瘤预后不佳主要归因于肿瘤内异质性，多项研究已证实这一点，这些研究收集了分子生物学、细胞遗传学数据，以及最近用于更好地进行预后分层的放射组学特征。GLIFA项目（胶质母细胞瘤特征分析）是一个多中心项目，旨在研究放射组学分析在胶质母细胞瘤治疗中的作用，以验证切除腔周围组织中的放射组学特征是否可指导放射治疗靶区勾画。
我们回顾性分析了来自三个中心的90例接受了全切除或近全切除的患者的放射组学特征，这些患者完成了标准辅助治疗，且我们有术后影像可供提取特征。由2名放射肿瘤学家在钆增强T1加权MRI序列上进行手动分割，并由一名神经放射学家进行审核，他们均有至少10年工作经验。分析所考虑的感兴趣区域（ROI）为：手术腔±术后残留肿块（CTV_cavity）；在CTV_cavity基础上增加1.5 cm边缘的CTV，从CTV中减去CTV_cavity得到的体积定义为CTV_Ring。在RStudio中进行放射组学分析和建模。对每个放射组学特征应用Z分数归一化。使用从Ring中提取的特征生成一个放射组学模型，以进行二元分类并预测6个月时的无进展生存期（PFS）。对该模型进行内部验证，实施了重复5次的3折交叉验证。
勾勒出270个ROI。所提出的放射组学模型由最佳拟合逻辑回归模型给出，包括以下3个特征：F_cm_merged.contrast、F_cm_merged.info.corr.2、F_rlm_merged.rlnu。模型预测概率与观察到的结果概率之间取得了良好的一致性（Hosmer和Lemeshow统计检验的P值为0.49）。该模型的ROC曲线报告的AUC为0.78（95%CI：0.68 - 0.88）。
这是第一项产生假设的研究，该研究应用了一种放射组学分析，重点关注术后MRI上手术腔周围的健康组织环。本研究为胶质母细胞瘤患者定制放射治疗靶区的决策支持工具提供了一个初步模型，以实现边缘缩小策略。
