目的:预测深肌层浸润临床风险类别,组织学类型,使用基于T2加权MR图像的临床和图像签名的机器学习分类方法,以及子宫内膜癌女性的淋巴血管间隙侵犯(LVSI)。
方法:本回顾性研究采用包含413例患者的训练数据集和包含82例患者的独立测试数据集。在矢状T2加权MRI上对整个肿瘤体积进行手动分割。提取临床和影像学特征以预测:(i)子宫内膜癌患者的MI,(二)子宫内膜癌临床高危水平,(iii)肿瘤的组织学亚型,和(Iv)存在LVSI。创建了具有不同自动选择的超参数值的分类模型。受试者工作特征(ROC)曲线的曲线下面积(AUC),F1得分,平均召回,并计算平均精度来评估不同的模型。
结果:基于独立的外部测试数据集,MDI的AUC,高危子宫内膜癌,子宫内膜组织学类型,LVSI分类分别为0.79、0.82、0.91和0.85。AUC的相应95%置信区间(CI)为[0.69,0.89],[0.75,0.91],[0.83,0.97],和[0.77,0.93],分别。
结论:可以对子宫内膜癌MI进行分类,风险,组织学类型,和LVSI使用不同的机器学习方法。
OBJECTIVE: To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images.
METHODS: A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models.
RESULTS: Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively.
CONCLUSIONS: It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.