A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging
A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging
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DOI：
10.1186/s41747-019-0119-0
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发表时间：
2019-10-17
影响因子：
3.8
通讯作者：
Braren, Rickmer
中科院分区：
文献类型：
--
作者：
Kaissis, Georgios;Ziegelmayer, Sebastian;Braren, Rickmer
开发一种监督式机器学习（ML）算法，根据胰腺导管腺癌（PDAC）患者的弥散加权成像衍生放射组学特征预测高于或低于中位总生存期（OS）。方法回顾性分析102例经病理证实的PDAC患者作为训练队列，30例前瞻性和回顾性入组的PDAC患者作为独立验证队列（IVC）。在术前表观扩散系数（ADC）图上分割肿瘤，并提取放射组学特征。随机森林ML算法适合于训练队列，并在IVC中进行测试。采用免疫组化方法对21例下腔静脉癌患者的肿瘤标本进行了组织学分型。个体辐射组学特征的重要性通过评估树节点基尼杂质减少和递归特征消除来评估。使用Fisher精确检验、95%置信区间（CI）和受试者工作特征曲线下面积（ROC-AUC）。结果ML算法预测IVC中高于或低于中位OS的敏感性为87%（95% IC 67. 3 - 92. 7），特异性为80%（95% CI 74. 0 - 86. 7），ROC AUC为90%。异质性相关的功能高度排名的模型。在21例具有确定的组织病理学亚型的患者中，8/9例预测经历低于中值OS的患者表现出准间充质亚型，而11/12例预测经历高于中值OS的患者表现出非准间充质亚型（p < 0.001）。结论ML应用于ADC放射组学可以预测IVC的OS，具有较高的诊断准确性。临床相关组织病理学亚型与模型预测的高度重叠强调了定量成像在PDAC术前亚型和预后中的潜力。
Background To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC). Methods One hundred two patients with histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued and retrospectively enrolled patients served as independent validation cohort (IVC). Tumors were segmented on preoperative apparent diffusion coefficient (ADC) maps, and radiomic features were extracted. A random forest ML algorithm was fit to the training cohort and tested in the IVC. Histopathological subtype of tumor samples was assessed by immunohistochemistry in 21 IVC patients. Individual radiomic feature importance was evaluated by assessment of tree node Gini impurity decrease and recursive feature elimination. Fisher's exact test, 95% confidence intervals (CI), and receiver operating characteristic area under the curve (ROC-AUC) were used. Results The ML algorithm achieved 87% sensitivity (95% IC 67.3-92.7), 80% specificity (95% CI 74.0-86.7), and ROC-AUC 90% for the prediction of above- versus below-median OS in the IVC. Heterogeneity-related features were highly ranked by the model. Of the 21 patients with determined histopathological subtype, 8/9 patients predicted to experience below-median OS exhibited the quasi-mesenchymal subtype, whilst 11/12 patients predicted to experience above-median OS exhibited a non-quasi-mesenchymal subtype (p < 0.001). Conclusion ML application to ADC radiomics allowed OS prediction with a high diagnostic accuracy in an IVC. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging in PDAC pre-operative subtyping and prognosis.