A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker

Cancers

Cheng-Chang Chang¹  Po-Chao Hsu¹  Chih-Hung Wang²  Yi-Jia Lin³  Tai-Kuang Chao³  Yu-Ching Lee⁴  Aung-Kyaw-Oo Sai⁵  Ching-Wei Wang⁵  Yi-An Liou⁵  Chun-Chieh Chang⁵ 

[1] Department of Gynecology and Obstetrics, Tri-Service General Hospital, Taipei 11490, Taiwan;Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, Taipei 11490, Taiwan;Department of Pathology, Tri-Service General Hospital, Taipei 11490, Taiwan;Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 106335, Taiwan;Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan;

关键词: weakly supervised learning;    ovarian cancer;    precision oncology;    deep learning;   

DOI  :  10.3390/cancers14071651

来源: DOAJ

【 摘 要 】

Ovarian cancer is a common malignant gynecological disease. Molecular target therapy, i.e., antiangiogenesis with bevacizumab, was found to be effective in some patients of epithelial ovarian cancer (EOC). Although careful patient selection is essential, there are currently no biomarkers available for routine therapeutic usage. To the authors’ best knowledge, this is the first automated precision oncology framework to effectively identify and select EOC and peritoneal serous papillary carcinoma (PSPC) patients with positive therapeutic effect. From March 2013 to January 2021, we have a database, containing four kinds of immunohistochemical tissue samples, including AIM2, c3, C5 and NLRP3, from patients diagnosed with EOC and PSPC and treated with bevacizumab in a hospital-based retrospective study. We developed a hybrid deep learning framework and weakly supervised deep learning models for each potential biomarker, and the experimental results show that the proposed model in combination with AIM2 achieves high accuracy 0.92, recall 0.97, F-measure 0.93 and AUC 0.97 for the first experiment (66% training and 34%testing) and high accuracy 0.86 ± 0.07, precision 0.9 ± 0.07, recall 0.85 ± 0.06, F-measure 0.87 ± 0.06 and AUC 0.91 ± 0.05 for the second experiment using five-fold cross validation, respectively. Both Kaplan-Meier PFS analysis and Cox proportional hazards model analysis further confirmed that the proposed AIM2-DL model is able to distinguish patients gaining positive therapeutic effects with low cancer recurrence from patients with disease progression after treatment (p < 0.005).

【 授权许可】

Unknown