School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, China.
Abdom Radiol (NY). 2017 Jul;42(7):1896-1905. doi: 10.1007/s00261-017-1079-6.
To determine radiomic features which are capable of reflecting muscular invasiveness of bladder cancer (BC) and propose a non-invasive strategy for the differentiation of muscular invasiveness preoperatively.
Sixty-eight patients with clinicopathologically confirmed BC were included in this retrospective study. A total of 118 cancerous volumes of interest (VOI) were segmented from patients' T2 weighted MR images (T2WI), including 34 non-muscle invasive bladder carcinomas (NMIBCs, stage <T2) and 84 muscle invasive ones (MIBCs, stage ≥T2). The radiomic features quantifying tumor signal intensity and textures were extracted from each VOI and its high-order derivative maps to characterize heterogeneity of tumor tissues. Statistical analysis was used to build radiomic signatures with significant inter-group differences of NMIBCs and MIBCs. The synthetic minority oversampling technique (SMOTE) and a support vector machine (SVM)-based feature selection and classification strategy were proposed to first rebalance the imbalanced sample size and then further select the most predictive and compact signature subset to verify its differentiation capability.
From each tumor VOI, a total of 63 radiomic features were derived and 30 of them showed significant inter-group differences (P ≤ 0.01). By using the SVM-based feature selection algorithm with rebalanced samples, an optimal subset including 13 radiomic signatures was determined. The area under receiver operating characteristic curve and Youden index were improved to 0.8610 and 0.7192, respectively.
3D radiomic signatures derived from T2WI and its high-order derivative maps could reflect muscular invasiveness of bladder cancer, and the proposed strategy can be used to facilitate the preoperative prediction of muscular invasiveness in patients with bladder cancer.
确定能够反映膀胱癌（BC）肌肉侵犯程度的放射组学特征，并提出一种术前非侵入性鉴别肌肉侵犯程度的策略。
本回顾性研究纳入了 68 例经临床病理证实的 BC 患者。从患者的 T2 加权磁共振成像（T2WI）中对 118 个感兴趣的癌症体积（VOI）进行分段，包括 34 个非肌层浸润性膀胱癌（NMIBC，T2 期）和 84 个肌层浸润性膀胱癌（MIBC，T2 期）。从每个 VOI 及其高阶导数图中提取定量肿瘤信号强度和纹理的放射组学特征，以描述肿瘤组织的异质性。统计分析用于构建具有 NMIBC 和 MIBC 之间显著组间差异的放射组学特征。采用合成少数过采样技术（SMOTE）和基于支持向量机（SVM）的特征选择和分类策略，首先重新平衡不平衡的样本量，然后进一步选择最具预测性和紧凑的特征子集，以验证其鉴别能力。
从每个肿瘤 VOI 中提取了总共 63 个放射组学特征，其中 30 个特征显示出显著的组间差异（P≤0.01）。通过使用基于 SVM 的特征选择算法和重新平衡的样本，确定了包括 13 个放射组学特征的最佳子集。受试者工作特征曲线下面积和 Youden 指数分别提高到 0.8610 和 0.7192。
从 T2WI 及其高阶导数图中提取的 3D 放射组学特征可以反映膀胱癌的肌肉侵犯程度，所提出的策略可以用于促进膀胱癌患者术前对肌肉侵犯程度的预测。
