文章：

基于多维连接组学与影像组学的先进机器学习框架用于区分脑转移瘤中的放射性坏死与真实进展

A Multidimensional Connectomics- and Radiomics-Based Advanced Machine-Learning Framework to Distinguish Radiation Necrosis from True Progression in Brain Metastases

原文发布日期：15 August 2023

DOI: 10.3390/cancers15164113

类型: Article

开放获取: 是

英文摘要：

We introduce tumor connectomics, a novel MRI-based complex graph theory framework that describes the intricate network of relationships within the tumor and surrounding tissue, and combine this with multiparametric radiomics (mpRad) in a machine-learning approach to distinguish radiation necrosis (RN) from true progression (TP). Pathologically confirmed cases of RN vs. TP in brain metastases treated with SRS were included from a single institution. The region of interest was manually segmented as the single largest diameter of the T1 post-contrast (T1C) lesion plus the corresponding area of T2 FLAIR hyperintensity. There were 40 mpRad features and 6 connectomics features extracted, as well as 5 clinical and treatment factors. We developed an Integrated Radiomics Informatics System (IRIS) based on an Isomap support vector machine (IsoSVM) model to distinguish TP from RN using leave-one-out cross-validation. Class imbalance was resolved with differential misclassification weighting during model training using the IRIS. In total, 135 lesions in 110 patients were analyzed, including 43 cases (31.9%) of pathologically proven RN and 92 cases (68.1%) of TP. The top-performing connectomics features were three centrality measures of degree, betweenness, and eigenvector centralities. Combining these with the 10 top-performing mpRad features, an optimized IsoSVM model was able to produce a sensitivity of 0.87, specificity of 0.84, AUC-ROC of 0.89 (95% CI: 0.82–0.94), and AUC-PR of 0.94 (95% CI: 0.87–0.97).

摘要翻译： 

我们提出肿瘤连接组学，这是一种基于磁共振成像的新型复杂图论框架，用于描述肿瘤及其周围组织内部错综复杂的关系网络，并将其与多参数影像组学相结合，通过机器学习方法区分放射性坏死与真实进展。本研究纳入来自单一机构的经立体定向放射外科治疗的脑转移瘤病例，所有病例均经病理学证实为放射性坏死或真实进展。感兴趣区域通过手动勾画，定义为增强T1加权像上病灶最大直径区域及其对应的T2液体衰减反转恢复高信号区域。共提取了40个多参数影像组学特征、6个连接组学特征以及5个临床与治疗相关因素。我们基于等度量映射支持向量机模型开发了集成放射组学信息系统，采用留一法交叉验证来区分真实进展与放射性坏死。在模型训练过程中，通过集成放射组学信息系统采用差异错误分类加权法解决了类别不平衡问题。共分析了110名患者的135个病灶，其中经病理证实的放射性坏死43例（31.9%），真实进展92例（68.1%）。表现最优的连接组学特征为度中心性、介数中心性和特征向量中心性这三个中心性度量指标。将这些特征与10个表现最佳的多参数影像组学特征相结合，优化后的等度量映射支持向量机模型实现了0.87的敏感度、0.84的特异度，受试者工作特征曲线下面积为0.89（95%置信区间：0.82–0.94），精确率-召回率曲线下面积为0.94（95%置信区间：0.87–0.97）。

原文链接：

A Multidimensional Connectomics- and Radiomics-Based Advanced Machine-Learning Framework to Distinguish Radiation Necrosis from True Progression in Brain Metastases