Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists' decisions of deep myometrial invasion
Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists' decisions of deep myometrial invasion
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DOI：
10.1016/j.mri.2021.10.024
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发表时间：
2021-10-29
影响因子：
2.5
通讯作者：
Kido, Aki
中科院分区：
文献类型：
--
作者：
Otani, Satoshi;Himoto, Yuki;Kido, Aki
目的：评估基于多参数磁共振图像（MRI）的放射组学机器学习（ML）分类器在子宫内膜癌（EC）风险因素预处理评估中的作用，并检查对放射科医生解释子宫深肌层浸润（dMI）的影响。研究方法：这项回顾性研究检查了2004年1月至2017年3月期间的200例连续EC患者，随机分为Discovery（n = 150）和Test（n = 50）数据集。从T2加权像、表观扩散系数图和增强T1加权像中提取肿瘤的放射组学特征。使用Discovery数据集，进行XGBoost的特征选择和超参数调整。建立了10个分类器，分别用于预测dMI、组织学分级、淋巴管浸润（LVI）和盆腔/主动脉旁淋巴结转移（PLNM/PALNM）。使用测试数据集，通过受试者操作特征曲线（AUC）下的面积评估10个分类器的诊断性能。接下来，四名放射科医生在参考测试数据集的ML分类器的推断之前和之后使用具有Likert量表的MRI独立评估dMI。然后，比较参比前后的AUC。结果：在测试数据集中，ML分类器的dMI、组织学分级、LVI、PLNM和PALNM的平均AUC分别为0.83、0.77、0.81、0.72和0.82。所有放射科医师的dMI AUC（0.83-0.88）均优于或等于ML分类器的平均AUC，其在参考前后无统计学显著差异。结论：放射组学分类显示了对EC危险因素的预处理评估的前景。放射科医生的推断优于ML分类器的dMI，并显示没有改善的审查。
Purpose: To evaluate radiomic machine learning (ML) classifiers based on multiparametric magnetic resonance images (MRI) in pretreatment assessment of endometrial cancer (EC) risk factors and to examine effects on radiologists' interpretation of deep myometrial invasion (dMI). Methods: This retrospective study examined 200 consecutive patients with EC during January 2004 -March 2017, divided randomly to Discovery (n = 150) and Test (n = 50) datasets. Radiomic features of tumors were extracted from T2-weighted images, apparent diffusion coefficient map, and contrast enhanced T1-weighed images. Using the Discovery dataset, feature selection and hyperparameter tuning for XGBoost were performed. Ten classifiers were built to predict dMI, histological grade, lymphovascular invasion (LVI), and pelvic/paraaortic lymph node metastasis (PLNM/PALNM), respectively. Using the Test dataset, the diagnostic performances of ten classifiers were assessed by the area under the receiver operator characteristic curve (AUC). Next, four radiologists assessed dMI independently using MRI with a Likert scale before and after referring to inference of the ML classifier for the Test dataset. Then, AUCs obtained before and after reference were compared. Results: In the Test dataset, mean AUC of ML classifiers for dMI, histological grade, LVI, PLNM, and PALNM were 0.83, 0.77, 0.81, 0.72, and 0.82. AUCs of all radiologists for dMI (0.83-0.88) were better than or equal to mean AUC of the ML classifier, which showed no statistically significant difference before and after the reference. Conclusion: Radiomic classifiers showed promise for pretreatment assessment of EC risk factors. Radiologists' inferences outperformed the ML classifier for dMI and showed no improvement by review.