Lei Ruilin, Yu Yunfang, Li Qingjian, Yao Qinyue, Wang Jin, Gao Ming, Wu Zhuo, Ren Wei, Tan Yujie, Zhang Bingzhong, Chen Liliang, Lin Zhongqiu, Yao Herui
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Department of Gynecological Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Front Oncol. 2022 Nov 23;12:895177. doi: 10.3389/fonc.2022.895177. eCollection 2022.
The aim of the study is to develop and validate a deep learning model to predict the platinum sensitivity of patients with epithelial ovarian cancer (EOC) based on contrast-enhanced magnetic resonance imaging (MRI).
In this retrospective study, 93 patients with EOC who received platinum-based chemotherapy (≥4 cycles) and debulking surgery at the Sun Yat-sen Memorial Hospital from January 2011 to January 2020 were enrolled and randomly assigned to the training and validation cohorts (2:1). Two different models were built based on either the primary tumor or whole volume of the abdomen as the volume of interest (VOI) within the same cohorts, and then a pre-trained convolutional neural network Med3D (Resnet 10 version) was transferred to automatically extract 1,024 features from two MRI sequences (CE-T1WI and T2WI) of each patient to predict platinum sensitivity. The performance of the two models was compared.
A total of 93 women (mean age, 50.5 years ± 10.5 [standard deviation]) were evaluated (62 in the training cohort and 31 in the validation cohort). The AUCs of the whole abdomen model were 0.97 and 0.98 for the training and validation cohorts, respectively, which was better than the primary tumor model (AUCs of 0.88 and 0.81 in the training and validation cohorts, respectively). In k-fold cross-validation and stratified analysis, the whole abdomen model maintained a stable performance, and the decision function value generated by the model was a prognostic indicator that successfully discriminates high- and low-risk recurrence patients.
The non-manually segmented whole-abdomen deep learning model based on MRI exhibited satisfactory predictive performance for platinum sensitivity and may assist gynecologists in making optimal treatment decisions.
本研究旨在开发并验证一种深度学习模型，以基于对比增强磁共振成像（MRI）预测上皮性卵巢癌（EOC）患者的铂敏感性。
在这项回顾性研究中，纳入了2011年1月至2020年1月在中山大学附属孙逸仙纪念医院接受铂类化疗（≥4个周期）和肿瘤减灭术的93例EOC患者，并将其随机分配至训练组和验证组（2:1）。在同一组内，基于原发肿瘤或整个腹部体积作为感兴趣体积（VOI）构建了两种不同的模型，然后转移预训练的卷积神经网络Med3D（Resnet 10版本）以自动从每位患者的两个MRI序列（CE-T1WI和T2WI）中提取1024个特征来预测铂敏感性。比较了这两种模型的性能。
共评估了93名女性（平均年龄50.5岁±10.5[标准差]）（训练组62例，验证组31例）。全腹部模型在训练组和验证组中的AUC分别为0.97和0.98，优于原发肿瘤模型（训练组和验证组的AUC分别为0.88和0.81）。在k折交叉验证和分层分析中，全腹部模型保持了稳定的性能，并且模型生成的决策函数值是成功区分高风险和低风险复发患者的预后指标。
基于MRI的非手动分割全腹部深度学习模型对铂敏感性表现出令人满意的预测性能，可能有助于妇科医生做出最佳治疗决策。
