Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: a feasibility study
Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: a feasibility study
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DOI：
10.1007/s11547-018-0935-y
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发表时间：
2019-01-01
影响因子：
8.9
通讯作者：
Valentini, Vincenzo
中科院分区：
文献类型：
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作者：
Filograna, Laura;Lenkowicz, Jacopo;Valentini, Vincenzo
目标最近，放射组学分析作为肿瘤患者管理的宝贵工具而受到关注。本研究的目的是找出基于磁共振成像 (MRI) 的放射组学分析的哪些特征被认为是脊柱骨髓转移性疾病肿瘤患者转移的最重要预测因素。材料和方法对 8 名肿瘤患者（3 名肺癌；1 名前列腺癌；1 名食道癌；1 名鼻咽癌；1 名肝癌；1 名乳腺癌）进行放疗前 MR 成像，总共包括 58 个背椎体，其中 29 个是转移性的，29 个是非转移性的。在放射治疗描绘控制台上，用 T1 和 T2 加权图像绘制每个椎体的轮廓。将获得的数据转移到自动数据提取系统中进行形态、统计和结构分析。 T1 和 T2 图像中每个病变的 89 个特征被计算为逐切片值的中值。对 89 个特征应用 Wilcoxon 检验，并对其中最具统计意义的特征进行逐步特征选择，以在逻辑回归模型中找到表现最佳的转移预测因子。通过 bootstrap 进行内部交叉验证，以评估接收者操作特征的曲线下面积 (AUC) 方面的模型性能。结果在测试的 89 个纹理特征中，发现 16 个在转移性组与非转移性组中存在统计显着性差异。性能最佳的模型由 T1 和 T2 图像的两个预测变量构成，即一个形态特征（质心偏移）（p 值
ObjectivesRecently, radiomic analysis has gained attention as a valuable instrument for the management of oncological patients. The aim of the study is to isolate which features of magnetic resonance imaging (MRI)-based radiomic analysis have to be considered the most significant predictors of metastasis in oncological patients with spinal bone marrow metastatic disease.Materials and methodsEight oncological patients (3 lung cancer; 1 prostatic cancer; 1 esophageal cancer; 1 nasopharyngeal cancer; 1 hepatocarcinoma; 1 breast cancer) with pre-radiotherapy MR imaging for a total of 58 dorsal vertebral bodies, 29 metastatic and 29 non-metastatic were included. Each vertebral body was contoured in T1 and T2 weighted images at a radiotherapy delineation console. The obtained data were transferred to an automated data extraction system for morphological, statistical and textural analysis. Eighty-nine features for each lesion in both T1 and T2 images were computed as the median of by-slice values. A Wilcoxon test was applied to the 89 features and the most statistically significant of them underwent to a stepwise feature selection, to find the best performing predictors of metastasis in a logistic regression model. An internal cross-validation via bootstrap was conducted for estimating the model performance in terms of the area under the curve (AUC) of the receiver operating characteristic.ResultsOf the 89 textural features tested, 16 were found to differ with statistical significance in the metastatic vs non-metastatic group. The best performing model was constituted by two predictors for T1 and T2 images, namely one morphological feature (center of mass shift) (p value