RESUMO
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
RESUMO
Human epidermal growth factor receptor 2 (HER2) is involved in the malignant progression of several human cancers, including esophageal adenocarcinoma (EAC). The purpose of this study was to evaluate HER2 overexpression and to explore the feasibility of confocal laser endomicroscopy for in vivo molecular imaging of HER2 status in an animal model of Barrett's-related EAC. Rats underwent esophagojejunostomy with gastric preservation. At 30 weeks post-surgery, the esophagus of 46 rats was studied; endoscopic and histological findings were correlated with HER2 immunofluorescence on excised biopsies and gross specimens. At this age, 23/46 rats developed Barrett's esophagus (BE), and 6/46 had cancer (four EAC and two squamous cell carcinomas). A significant overexpression of HER2 was observed in esophageal adenocarcinoma compared with normal squamous esophagus (9.4-fold) and BE (6.0-fold). AKT and its phosphorylated form were also overexpressed in cancer areas. Molecular imaging was performed at 80 weeks post-surgery in four rats after tail injection of fluorescent-labeled anti-HER2 antibody. At this age, 3/4 rats developed advance adenocarcinoma and showed in vivo overexpression of HER2 by molecular confocal laser endomicroscopy with heterogeneous distribution within cancer; no HER2 signal was observed in normal or Barrett's tissues. Therefore, HER2 overexpression is a typical feature of the surgical induced model of EAC that can be easily quantified in vivo using an innovative mini-invasive approach including confocal endomicroscopy; this approach may avoid limits of histological evaluation of HER2 status on 'blinded' biopsies.