1.To develop a deep learning-based approach for extracting pathological features from histological images of ovarian cancer patients.
2.To investigate the prognostic value of these features in predicting the clinical outcomes of ovarian cancer patients.
3.To develop a predictive model using the extracted features to identify patients with homologous recombination deficiency phenotype.
4.To evaluate the performance of the proposed model using an independent validation cohort of ovarian cancer patients.
5.To derivation histopathological features associated with prognosis and homologous recombination-deficient phenotypes.

Meng Zhou, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
Jie Sun, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
Zijian Yang, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
Yibo Zhang, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
Liwei Chen, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
Liujin Zhang, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
Yitong Zheng, School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China