An Online Prognostic Application for Melanoma Based on Machine Learning and Statistics.

Area of Science:

• Oncology
• Bioinformatics
• Machine Learning

Background:

• Melanoma presents a significant global socioeconomic challenge.
• Accurate prognosis is crucial for patient management following surgical treatment.

Purpose of the Study:

• To develop and deploy accurate machine learning models for predicting melanoma patient survival.
• To provide an accessible online tool for prognostic assessment.

Main Methods:

• Utilized Surveillance, Epidemiology, and End Results (SEER) database data from over 156,000 patients.
• Applied and compared nine machine learning models for 5-year survival and overall survival prediction.
• Selected best-performing deep learning and random survival forest models for online deployment.

Main Results:

• A deep learning model achieved an area under the curve of 0.915 for 5-year survival prediction.
• A random survival forest model demonstrated a concordance index of 0.894 for overall survival.
• An interactive online calculator was launched at www.make-a-difference.top/melanoma.html.

Conclusions:

• The developed online prognostic tool offers valuable insights into melanoma patient survival.
• Clinical decisions should integrate prognostic application results with comprehensive patient data and specialist consultations.