Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer

Abstract

Background. Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, but clinical staging system is not a good predictor of patients’ survival. We aimed to develop a novel prognostic model to predict the prognosis for operable cervical cancer patients with better accuracy than clinical staging system. Methods. A total of 13,952 operable cervical cancer patients were retrospectively enrolled in this study. The whole dataset was randomly split into a training set ( n = 9,068 , 65%), validation set ( n = 2,442 , 17.5%), and testing set ( n = 2,442 , 17.5%). Cox proportional hazard (CPH) model and random survival forest (RSF) model were used as baseline models for the prediction of overall survival (OS). Then, a deep survival learning model (DSLM) was developed for OS prediction. Finally, a novel prognostic model was explored based on this DSLM. Results. The C-indexes for the CPH and RSF model were 0.731 and 0.753, respectively. DSLM, which had four layers that had 50 neurons in each layer, achieved a C-index of 0.782 in the validation set and a C-index of 0.758 in the testing set. The novel prognostic model based on DSLM showed better performances than the conventional clinical staging system (area under receiver operating curves were 0.826 and 0.689, respectively). Personalized survival curves for individual patient using this novel model also showed notably different survival slopes. Conclusions. Our study developed a novel, practical, personalized prognostic model for operable cervical cancer patients. This novel prognostic model may have the potential to provide a more prognostic information to oncologists.

Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT

“…In conclusion, a holistic approach to data integration emerged as a disruptive transformation in the field of CC research. Studies like those of Dong et al [25] and Zhang et al [27], emphasizing this paradigm shift, amalgamated diverse data sources into a unified model, demonstrating the heightened accuracy and robustness this integration brings to diagnostic and prognostic models. The focus on integrative data-synthesis methods underscores potential trajectories for future research, heralding the advent of a new era in the domain of CC diagnosis and treatment.…”

“…In contrast, studies by Al Mudawi et al [23] and Zhang et al [24] showcased the prowess of deep-learning methods. Furthermore, research by Dong et al [25] attested to the power of data integration, successfully amalgamating medical-imaging data with other clinical data to enhance prognostic evaluations.…”