An Online Prognostic Application for Melanoma Based on Machine Learning and Statistics. Issue 10 (October 2022)

• Record Type: Journal Article
• Authors: Liu, Wenhui; Zhu, Ying; Lin, Chong; Liu, Linbo; Li, Guangshuai
• Is Part Of: Journal of plastic, reconstructive & aesthetic surgery. Volume 75:Issue 10(2022)
• Page Start: 3853
• Page End: 3858
• Publication Date: 2022-10
• Subjects: Melanoma -- Prognosis -- Machine learning -- Big data -- Survival analysis; Surgery, Plastic -- Great Britain -- Periodicals; Reconstructive Surgical Procedures -- Periodicals; Surgery, Plastic -- Great Britain -- Periodicals; 617.9505
• DOI: 10.1016/j.bjps.2022.06.069
• Languages: English
• ISSNs: 1748-6815

Abstract

Summary: Background: Melanoma is a common cancer that causes a severe socioeconomic burden. Patients usually turn to plastic surgeons to determine their prognosis after surgery. Methods: Data from hundreds of thousands of real-world patients were downloaded from the Surveillance, Epidemiology, and End Results database. Nine mainstream machine learning models were applied to predict 5-year survival probability and three survival analysis models for overall survival prediction. Models that outperformed were deployed online. Results: After manual review, 156, 154 real-world patients were included. The deep learning model was chosen for predicting the probability of 5-year survival, based on its area under the receiver operating characteristic curve (0.915) and its accuracy (84.8%). The random survival forest model was chosen for predicting overall survival, with a concordance index of 0.894. These models were deployed at www.make-a-difference.top/melanoma.html as an online calculator with an interactive interface and an explicit outcome for everyone. Conclusions: Users should make decisions based on not only this online prognostic application but also multidimensional information and consult with multidiscipline specialists.