1
|
Feng M, Zhao Y, Chen J, Zhao T, Mei J, Fan Y, Lin Z, Yao J, Bu H. A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer. Quant Imaging Med Surg 2023; 13:1899-1913. [PMID: 36915334 PMCID: PMC10006118 DOI: 10.21037/qims-22-220] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 11/07/2022] [Indexed: 01/11/2023]
Abstract
Background The current study aimed to develop a deep learning (DL) model for prediction of lymph node metastasis (LNM) based on hematoxylin and eosin (HE)-stained histopathological images of endometrial cancer (EC). The model was validated using external data. Methods A total of 2,104 whole slide image (WSI) from 564 patients with pathologically confirmed LNM status were collated from West China Second University Hospital. An artificial intelligence (AI) model was built on the multiple instance-learning (MIL) framework for automatic prediction of the probability of LNM and its performance compared with "Mayo criteria". An additional external data source comprising 533 WSI was collected from two independent medical institutions to validate the model's robustness. Heatmaps were generated to demonstrate regions of the WSI that made the greatest contributions to the DL network output to improve understanding of these processes. Results The proposed MIL model achieved an area under the curve (AUC) of 0.938, a sensitivity of 0.830 and a specificity of 0.911 for LNM prediction to EC. The AUC according to Mayo criteria was 0.666 for the same test dataset. For types I, II and mixed EC, AUCs were 0.927, 0.979 and 0.929, respectively. The predictive performance of the MIL model also achieved an AUC of 0.921 for early staging. In external validation data, the proposed model achieved an AUC of 0.770, a sensitivity of 0.814 and a specificity of 0.520 for LNM prediction. AUCs were 0.783 for type I and 0.818 for early stage EC. Conclusions The proposed MIL model generated from histopathological images of EC has a much better LNM predictive performance than that of Mayo criteria. A novel DL-based biomarker trained on different histological subtypes of EC slides was revealed to predict metastatic status with improved accuracy, especially for early staging patients. The current study proves the concept of MIL-based prediction of LNM in EC for the first time, and brought a new sight to improve the accuracy of LNM prediction. Multicenter prospective validation data is required to further confirm the clinical utility.
Collapse
Affiliation(s)
- Min Feng
- Department of Pathology, West China Second University Hospital, Sichuan University & Key Laboratory of Birth Defect and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.,Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, China.,Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Zhao
- AI Lab, Tencent, Shenzhen, China
| | - Jie Chen
- Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Tingyu Zhao
- Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Juan Mei
- Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Yingying Fan
- Department of Pathology, West China Second University Hospital, Sichuan University & Key Laboratory of Birth Defect and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | | | | | - Hong Bu
- Laboratory of Pathology, West China Hospital, Sichuan University, Chengdu, China.,Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|