Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Fudan University, Shanghai, China.
Mod Pathol. 2023 Dec;36(12):100316. doi: 10.1016/j.modpat.2023.100316. Epub 2023 Aug 26.
We developed a deep learning framework to accurately predict the lymph node status of patients with cervical cancer based on hematoxylin and eosin-stained pathological sections of the primary tumor. In total, 1524 hematoxylin and eosin-stained whole slide images (WSIs) of primary cervical tumors from 564 patients were used in this retrospective, proof-of-concept study. Primary tumor sections (1161 WSIs) were obtained from 405 patients who underwent radical cervical cancer surgery at the Fudan University Shanghai Cancer Center (FUSCC) between 2008 and 2014; 165 and 240 patients were negative and positive for lymph node metastasis, respectively (including 166 with positive pelvic lymph nodes alone and 74 with positive pelvic and para-aortic lymph nodes). We constructed and trained a multi-instance deep convolutional neural network based on a multiscale attention mechanism, in which an internal independent test set (100 patients, 228 WSIs) from the FUSCC cohort and an external independent test set (159 patients, 363 WSIs) from the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program database were used to evaluate the predictive performance of the network. In predicting the occurrence of lymph node metastasis, our network achieved areas under the receiver operating characteristic curve of 0.87 in the cross-validation set, 0.84 in the internal independent test set of the FUSCC cohort, and 0.75 in the external test set of the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program. For patients with positive pelvic lymph node metastases, we retrained the network to predict whether they also had para-aortic lymph node metastases. Our network achieved areas under the receiver operating characteristic curve of 0.91 in the cross-validation set and 0.88 in the test set of the FUSCC cohort. Deep learning analysis based on pathological images of primary foci is very likely to provide new ideas for preoperatively assessing cervical cancer lymph node status; its true value must be validated with cervical biopsy specimens and large multicenter datasets.
我们开发了一个深度学习框架，能够基于原发性肿瘤的苏木精和伊红染色病理切片准确预测宫颈癌患者的淋巴结状态。在这项回顾性概念验证研究中，总共使用了来自 564 名患者的 1524 张原发性宫颈癌苏木精和伊红染色全玻片图像（WSI）。原发性肿瘤切片（1161 张 WSI）来自于 2008 年至 2014 年期间在复旦大学附属肿瘤医院（FUSCC）接受根治性宫颈癌手术的 405 名患者；其中 165 名患者淋巴结转移阴性，240 名患者淋巴结转移阳性（包括 166 名单纯盆腔淋巴结阳性和 74 名盆腔和主动脉旁淋巴结阳性）。我们基于多尺度注意机制构建和训练了一个多实例深度卷积神经网络，其中使用了来自 FUSCC 队列的内部独立测试集（100 名患者，228 张 WSI）和癌症基因组图谱计划数据库中的宫颈鳞状细胞癌和子宫内膜腺癌队列的外部独立测试集（159 名患者，363 张 WSI）来评估网络的预测性能。在预测淋巴结转移的发生方面，我们的网络在交叉验证集中的受试者工作特征曲线下面积为 0.87，在 FUSCC 队列的内部独立测试集中为 0.84，在癌症基因组图谱计划的宫颈鳞状细胞癌和子宫内膜腺癌队列的外部测试集中为 0.75。对于盆腔淋巴结转移阳性的患者，我们重新训练网络以预测他们是否也有主动脉旁淋巴结转移。我们的网络在交叉验证集中和 FUSCC 队列的测试集中的受试者工作特征曲线下面积分别为 0.91 和 0.88。基于原发性病灶的病理图像的深度学习分析很可能为术前评估宫颈癌淋巴结状态提供新的思路；其真正价值必须通过宫颈活检标本和大型多中心数据集进行验证。
