Quality of Life Predictors in Patients With Melanoma: A Machine Learning Approach

Monica Pinto; Nicola Marotta; Corrado Caracò; Ester Simeone; Antonio Ammendolia; Alessandro de Sire

doi:10.3389/fonc.2022.843611

Quality of Life Predictors in Patients With Melanoma: A Machine Learning Approach

Front Oncol. 2022 Mar 25:12:843611. doi: 10.3389/fonc.2022.843611. eCollection 2022.

Authors

Monica Pinto¹, Nicola Marotta², Corrado Caracò³, Ester Simeone⁴, Antonio Ammendolia², Alessandro de Sire²

Affiliations

¹ Rehabilitation Medicine Unit, Strategic Health Services Department, Istituto Nazionale Tumori-Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS)-Fondazione G. Pascale, Naples, Italy.
² Physical Medicine and Rehabilitation Unit, Department of Medical and Surgical Sciences, University of Catanzaro "Magna Graecia", Catanzaro, Italy.
³ Melanoma and Skin Cancer Surgery Unit, Department of Melanoma, Cancer Immunotherapy and Development Therapeutics, Istituto Nazionale Tumori-Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS)-Fondazione G. Pascale, Naples, Italy.
⁴ Department of Melanoma, Cancer Immunotherapy and Development Therapeutics, Istituto Nazionale Tumori-Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS)-Fondazione G. Pascale, Naples, Italy.

Abstract

Health related quality of life (HRQoL) is an important recognized health outcome for cancer treatments, but also disease course with slower recovery and increased morbidity. These issues are of implication in melanoma, which maintains a risk of disease progression for many years after diagnosis. This study aimed to explore and weigh factors in the perception of the quality of life and possible relationships with demographic-clinical characteristics in people with melanoma via a machine learning approach. In this observational study, patients with melanoma, without metastatic disease, were recruited from January 2020 to December 2021 with a follow-up of at least one year. Demographic variables and clinics were collected, and the 12-Item Short-Form Health Survey (SF-12) was adopted as the physical and mental aspects of the Health-Related Quality of Life (HRQoL) measure. All the variables were processed in a random forest model to weigh at each node of each tree of this machine learning regression model, their actual weight in SF-12 score. We included 203 melanoma patients, mean aged 59.25 ± 15.1 years: 56 (27%) affecting the upper limbs and 147 (73%) affecting the trunk. The model of 142 patients with no missing value, generating 92 trees (MSE = 0.45, R2 of 0.78), reported that the lesion site was the most influencing variable on HRQoL based on the decrease in Gini impurity in variable weighing at each node intersection in forest generation. In this scenario, we built two distinct models for lesion sites and demonstrated that the variable that most influenced the quality of life in upper limb melanoma was lymphedema, while BMI was in the trunk. Given these results, random forest regressions could play a crucial role in the clinical and rehabilitation approach. The machine-learning model for detecting the HRQoL predictor in melanoma patients indicates that the experienced lymphedema and BMI may influence the HRQoL perception. This study suggests that the prevention and treatment of lymphedema and bodyweight reduction might improve the quality of life in melanoma.

Keywords: body mass index; cancer rehabilitation; lymphedema; machine learning; melanoma; quality of life.