Podlipnik S, Hernandez C, Kiroglu A, GarcĂ­a S, Ficapal J, Burgos J, et al.. Personalized medicine in melanoma patients aided by artificial intelligence. In Clinical Translation of Medical Image Computing and Computer Assisted Interventions (CLINICCAI) Workshop at MICCAI. 2021.


The 8th Edition of the American Joint Committee on Cancer (AJCC) staging system1 is the current standard for classifying patients into prognostic and treatment groups. This classification is used to predict the evolution of the patient, and therefore treatment actions provided to the individual. However, patients at the same stage behave differently, indicating that the current classification system is often insufficient to provide a customized prognosis for each patient2. It is, therefore, necessary to improve patient classification into prognostic groups. Furthermore, patients' systemic and surgical treatments often involve significant toxicities and morbidities that impact their quality of life (i.e., sentinel node biopsy is not needed for 80% of the melanoma patients, 50% of patients do not benefit from adjuvant treatment)3. Therefore, melanoma patients should benefit from a more precise risk estimation.

We create a survival dataset for melanoma risk estimation and train survival XGBoost algorithms4 to predict the mortality, relapse, and metastasis risk. We compare their performance to the AJCC 2018 risk stratification system. Furthermore, we train classifiers to predict the risk of a positive lymph node biopsy and distant metastasis on melanoma patients and compare the performance of the proposed system to the clinical practice.