Department of Medical Imaging, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, China.
Department of Cardiovascularology, Tungwah Hospital of Sun Yat-Sen University, Dong cheng East Road, Dong guan, 523110, Guangdong, China.
Cancer Imaging. 2020 Oct 23;20(1):76. doi: 10.1186/s40644-020-00353-8.
Radiation-induced insufficiency fractures (IF) is frequently occult without fracture line, which may be mistaken as metastasis. Quantitative apparent diffusion coefficient (ADC) shows potential value for characterization of benign and malignant bone marrow diseases. The purpose of this study was to develop a nomogram based on multi-parametric ADCs in the differntiation of occult IF from bone metastasis after radiotherapy (RT) for cervical cancer.
This study included forty-seven patients with cervical cancer that showed emerging new bone lesions in RT field during the follow-up. Multi-parametric quantitative ADC values were measured for each lesion by manually setting region of interests (ROIs) on ADC maps, and the ROIs were copied to adjacent normal muscle and bone marrow. Six parameters were calculated, including ADC, ADC, ADC, ADC, ADC ratio (lesion/normal bone) and ADC ratio (lesion/muscle). For univariate analysis, receiver operating characteristic curve (ROC) analysis was performed to assess the performance. For combined diagnosis, a nomogram model was developed by using a multivariate logistic regression analysis.
A total of 75 bone lesions were identified, including 48 occult IFs and 27 bone metastases. There were significant differences in the six ADC parameters between occult IFs and bone metastases (p < 0.05), the ADC ratio (lesion/ muscle) showed an optimal diagnostic efficacy, with an area under ROC (AUC) of 0.887, the sensitivity of 95.8%, the specificity of 81.5%, respectively. Regarding combined diagnosis, ADC and ADC ratio (lesion/muscle) were identified as independent factors and were selected to generate a nomogram model. The nomogram model showed a better performance, yielded an AUC of 0.92, the sensitivity of 91.7%, the specificity of 96.3%, positive predictive value (PPV) of 97.8% and negative predictive value (NPV) of 86.7%, respectively.
Multi-parametric ADC values demonstrate potential value for differentiating occult IFs from bone metastasis, a nomogram based on the combination of ADC and ADC ratio (lesion/muscle) may provide an improved classification performance.
放射性诱导的不全骨折（IF）常无骨折线，易误诊为转移瘤。定量表观扩散系数（ADC）对良恶性骨髓疾病的特征具有潜在价值。本研究旨在建立一个基于多参数 ADC 的列线图，以区分宫颈癌放疗后隐匿性 IF 与骨转移。
本研究纳入 47 例宫颈癌患者，在随访中发现放疗野中新出现骨病变。通过在 ADC 图上手动设置感兴趣区（ROI），对每个病变进行多参数定量 ADC 值测量，然后将 ROI 复制到相邻正常肌肉和骨髓中。计算 6 个参数，包括 ADC、ADC、ADC、ADC、ADC 比值（病变/正常骨）和 ADC 比值（病变/肌肉）。对于单变量分析，采用受试者工作特征曲线（ROC）分析评估效能。对于联合诊断，采用多元逻辑回归分析建立列线图模型。
共发现 75 个骨病变，其中 48 个为隐匿性 IF，27 个为骨转移。隐匿性 IF 与骨转移之间的 6 个 ADC 参数存在显著差异（p<0.05），其中病变/肌肉 ADC 比值诊断效能最佳，ROC 曲线下面积（AUC）为 0.887，敏感度为 95.8%，特异度为 81.5%。对于联合诊断，ADC 和病变/肌肉 ADC 比值被确定为独立因素，并被选择纳入列线图模型。该模型的性能更好，AUC 为 0.92，敏感度为 91.7%，特异度为 96.3%，阳性预测值（PPV）为 97.8%，阴性预测值（NPV）为 86.7%。
多参数 ADC 值对区分隐匿性 IF 与骨转移具有潜在价值，基于 ADC 和病变/肌肉 ADC 比值的列线图模型可能提供更好的分类性能。
