Cozzolino C, Buja A, Rugge M, Miatton A, Zorzi M, Vecchiato A, Del Fiore P, Tropea S, Brazzale A, Damiani G, dall'Olmo L, Rossi C R, Mocellin S
Soft-Tissue, Peritoneum and Melanoma Surgical Oncology Unit, Veneto Institute of Oncology IOV-IRCCS, Via Gattamelata, 64, 35128, Padua, PD, Italy.
Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padua, Padua, Italy.
Discov Oncol. 2023 Jan 31;14(1):13. doi: 10.1007/s12672-023-00622-5.
Cutaneous malignant melanoma (CMM) ranks among the ten most frequent malignancies, clinicopathological staging being of key importance to predict prognosis. Artificial intelligence (AI) has been recently applied to develop prognostically reliable staging systems for CMM. This study aims to provide a useful machine learning based tool to predict the overall CMM short-term survival.
CMM records as collected at the Veneto Cancer Registry (RTV) and at the Veneto regional health service were considered. A univariate Cox regression validated the strength and direction of each independent variable with overall mortality. A range of machine learning models (Logistic Regression classifier, Support-Vector Machine, Random Forest, Gradient Boosting, and k-Nearest Neighbors) and a Deep Neural Network were then trained to predict the 3-years mortality probability. Five-fold cross-validation and Grid Search were performed to test the best data preprocessing procedures, features selection, and to optimize models hyperparameters. A final evaluation was carried out on a separate test set in terms of balanced accuracy, precision, recall and F1 score. The best model was deployed as online tool.
The univariate analysis confirmed the significant prognostic value of TNM staging. Adjunctive clinicopathological variables not included in the AJCC 8th melanoma staging system, i.e., sex, tumor site, histotype, growth phase, and age, were significantly linked to overall survival. Among the models, the Neural Network and the Random Forest models featured the best prognostic performance, achieving a balanced accuracy of 91% and 88%, respectively. According to the Gini importance score, age, T and M stages, mitotic count, and ulceration appeared to be the variables with the greatest impact on survival prediction.
Using data from patients with CMM, we developed an AI algorithm with high staging reliability, on top of which a web tool was implemented ( unipd.link/melanomaprediction ). Being essentially based on routinely recorded clinicopathological variables, it can already be implemented with minimal effort and further tested in the current clinical practice, an essential phase for validating the model's accuracy beyond the original research context.
皮肤恶性黑色素瘤（CMM）位列十大常见恶性肿瘤，临床病理分期对预测预后至关重要。人工智能（AI）最近已被应用于开发用于CMM的预后可靠分期系统。本研究旨在提供一种有用的基于机器学习的工具来预测CMM的总体短期生存率。
考虑在威尼托癌症登记处（RTV）和威尼托地区卫生服务机构收集的CMM记录。单变量Cox回归验证了每个自变量与总体死亡率之间的强度和方向。然后训练一系列机器学习模型（逻辑回归分类器、支持向量机、随机森林、梯度提升和k近邻）以及深度神经网络来预测3年死亡概率。进行五折交叉验证和网格搜索以测试最佳数据预处理程序、特征选择，并优化模型超参数。在单独的测试集上根据平衡准确率、精确率、召回率和F1分数进行最终评估。最佳模型被部署为在线工具。
单变量分析证实了TNM分期的显著预后价值。美国癌症联合委员会（AJCC）第8版黑色素瘤分期系统未包括的辅助临床病理变量，即性别、肿瘤部位、组织类型、生长阶段和年龄，与总生存率显著相关。在这些模型中，神经网络和随机森林模型具有最佳的预后性能，平衡准确率分别达到91%和88%。根据基尼重要性得分，年龄、T和M分期、有丝分裂计数和溃疡似乎是对生存预测影响最大的变量。
利用CMM患者的数据，我们开发了一种具有高分期可靠性的AI算法，并在此基础上实现了一个网络工具（unipd.link/melanomaprediction）。该算法基本上基于常规记录的临床病理变量，已经可以轻松实施，并在当前临床实践中进一步测试，这是在原始研究背景之外验证模型准确性的关键阶段。
