Pinto Monica, Marotta Nicola, Caracò Corrado, Simeone Ester, Ammendolia Antonio, de Sire Alessandro
Rehabilitation Medicine Unit, Strategic Health Services Department, Istituto Nazionale Tumori-Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS)-Fondazione G. Pascale, Naples, Italy.
Physical Medicine and Rehabilitation Unit, Department of Medical and Surgical Sciences, University of Catanzaro "Magna Graecia", Catanzaro, Italy.
Front Oncol. 2022 Mar 25;12:843611. doi: 10.3389/fonc.2022.843611. eCollection 2022.
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 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.
健康相关生活质量(HRQoL)是癌症治疗中一个重要的、已得到认可的健康结果,对于疾病进程而言也是如此,疾病恢复较慢且发病率增加。这些问题在黑色素瘤中具有重要意义,黑色素瘤在诊断后多年仍有疾病进展的风险。本研究旨在采用机器学习方法,探索并权衡黑色素瘤患者生活质量认知中的因素以及与人口统计学 - 临床特征的可能关系。在这项观察性研究中,2020年1月至2021年12月招募了无转移性疾病的黑色素瘤患者,并进行了至少一年的随访。收集了人口统计学变量和临床数据,采用12项简短健康调查问卷(SF - 12)作为健康相关生活质量(HRQoL)测量的身体和心理方面指标。所有变量均在随机森林模型中进行处理,以权衡该机器学习回归模型每棵树每个节点处它们在SF - 12评分中的实际权重。我们纳入了203例黑色素瘤患者,平均年龄为59.25±15.1岁:56例(27%)累及上肢,147例(73%)累及躯干。在142例无缺失值的患者模型中生成了92棵树(均方误差 = 0.45,R²为0.78),报告显示基于森林生成过程中每个节点交叉处变量权重计算中基尼杂质的减少情况,病变部位是对HRQoL影响最大的变量。在这种情况下,我们针对病变部位构建了两个不同的模型,并证明在上肢黑色素瘤中对生活质量影响最大的变量是淋巴水肿,而在躯干黑色素瘤中是体重指数(BMI)。鉴于这些结果,随机森林回归在临床和康复方法中可能发挥关键作用。用于检测黑色素瘤患者HRQoL预测因子的机器学习模型表明,经历的淋巴水肿和BMI可能会影响HRQoL认知。本研究表明,淋巴水肿的预防和治疗以及体重减轻可能会改善黑色素瘤患者的生活质量。