Discussion
tumor, early-stage cervical cancer, diabetes
TUMOR, CERVICAL CANCER, HYPERTENSION, DISEASES, DIABETES
In this retrospective study, we demonstrated that the utilization of pathological diagnostic images to predict 5-year survival outcomes via a deep-learning approach performed superior ability in non-surgical cervical cancer patients. Most of this research mainly focused on either the analyzing radiographic images for predicting treatment response or detecting abnormal cells from tissue slides for classification and biomarkers discovery. The study of predicting 5 years of survival in non-surgical cervical cancer by deep-learning model remains limited. In the field of cervical cancer, the application of the deep-learning model has been studied for detecting human papillomavirus (HPV) (Kahng et al. Although surgery is the cornerstone in early-stage cervical cancer, the prognostic of postoperation has been significantly affected by surgical techniques, equipment, nutritional level, and nursing competence. Dr. Kluska and his colleagues found that utilization of deep-learning model predicted 5 years of survival outcomes based on clinicopathologic characteristics from cervical cancer patients treated with radical hysterectomy. Nevertheless, with the development of radiochemotherapy, radical surgery and radiochemotherapy achieved similar prognostic in early-stage cervical cancer patients (Landoni et al. In recent years, deep learning has been used in various fields, such as predicting chemotherapy response, lymph node metastasis via radiomics (Peng et al. Although the results of our 5-year predictive models are very promising for translation into clinical practice, there is still room for improvement in the future. (i) This retrospective study has a relatively small sample size from a single institution. A large amount of sample size should be included from multi-centers to design randomized clinical trials. (ii) The standardization of sample preparation is critical and closely related to the accuracy of the model, such as tissue sectioning, staining, acquisition of representative tumor areas, and the quality of the pathological image. How to distinguish different cellular components (tumor cells, lymphocytes, red blood cells, etc.) in the same patch should be further investigated. (iii) More specific biomarkers and clinical parameters (diabetes, hypertension, and other diseases) should be integrated into the survival prediction model to improve the accuracy.The deep-learning model based on representative pathologic features and clinical data had a superior performance for the predicting 5-year OS in cervical cancer and showed great clinical application promising.