Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI
Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI
复制标题
基于 DCE-MRI 的放射组学和深度学习鉴别源自肺癌和其他癌症的脊柱转移瘤
DOI：
10.1016/j.mri.2019.02.013
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发表时间：
2019-12-01
影响因子：
2.5
通讯作者：
Su, Min-Ying
中科院分区：
文献类型：
--
作者：
Lang, Ning;Zhang, Yang;Su, Min-Ying
用途：与传统的热点ROI分析相比，使用放射组学和深度学习来区分原发性肺癌和其他癌症的脊柱转移性病变。方法：在使用动态对比增强（DCE）序列对临床脊柱MRI数据库进行回顾性审查时，共有61例患者既往未诊断为癌症，后来证实有转移（30例肺癌; 31例非肺癌）。对于热点分析，放置手动ROI以从DCE动力学中的洗入、最大和洗出阶段计算三个启发式参数。对于每种情况，通过使用归一化切割算法生成3D肿瘤掩模。进行放射组学分析，从三个DCE参数图中提取直方图和纹理特征。使用这些图作为传统卷积神经网络（CNN）的输入进行深度学习，并将所有12组DCE图像用于卷积长短期记忆（CLSTM）网络。结果：对于热点ROI分析，肺转移瘤的平均洗脱斜率为0.25 +/- 10%，其他肿瘤为-9.8 +/- 12.9%。CHAID分类使用-6.6%的洗脱斜率，然后是98%的洗脱增强比，诊断准确率为0.79。使用代表肿瘤异质性的特征的放射组学分析仅达到0.71的最高准确度。使用CNN分类的平均准确度为0.71 +/- 0.043，而CLSTM将准确度提高到0.81 +/-0.034。结论：DCE-MRI机器学习分析方法有潜力预测脊柱中的肺癌转移，这可用于指导后续的确诊检查。
Purpose: To differentiate metastatic lesions in the spine originated from primary lung cancer and other cancers using radiomics and deep learning, compared to traditional hot-spot ROI analysis.Methods: In a retrospective review of clinical spinal MRI database with a dynamic contrast enhanced (DCE) sequence, a total of 61 patients without prior cancer diagnosis and later confirmed to have metastases (30 lung; 31 non-lung cancers) were identified. For hot-spot analysis, a manual ROI was placed to calculate three heuristic parameters from the wash-in, maximum, and wash-out phases in the DCE kinetics. For each case, the 3D tumor mask was generated by using the normalized-cut algorithm. Radiomics analysis was performed to extract histogram and texture features from three DCE parametric maps. Deep learning was performed using these maps as inputs into a conventional convolutional neural network (CNN), as well as using all 12 sets of DCE images into a convolutional long short term memory (CLSTM) network.Results: For hot-spot ROI analysis, mean wash-out slope was 0.25 +/- 10% for lung metastases and -9.8 +/- 12.9% for other tumors. CHAID classification using a wash-out slope of -6.6% followed by wash-in enhancement ratio of 98% achieved a diagnostic accuracy of 0.79. Radiomics analysis using features representing tumor heterogeneity only reached the highest accuracy of 0.71. Classification using CNN achieved a mean accuracy of 0.71 +/- 0.043, whereas a CLSTM improved accuracy to 0.81 +/- 0.034.Conclusions: DCE-MRI machine-learning analysis methods have potential to predict lung cancer metastases in the spine, which may be used to guide subsequent workup for confirmed diagnosis.