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Machine learning to predict overall short-term mortality in cutaneous melanoma.

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Artificial intelligence (AI) enhances cutaneous malignant melanoma (CMM) staging by developing a machine learning tool to predict short-term survival. This AI model, utilizing routine clinicopathological data, offers high reliability for improved patient prognosis.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Cutaneous malignant melanoma (CMM) is a frequent malignancy where accurate staging is crucial for prognosis.
  • Artificial intelligence (AI) is increasingly utilized for developing reliable prognostic staging systems for CMM.
  • This study aimed to create a machine learning-based tool for predicting short-term survival in CMM patients.

Purpose of the Study:

  • To develop and validate an AI-driven tool for predicting 3-year mortality in cutaneous malignant melanoma (CMM).
  • To identify key clinicopathological variables influencing CMM survival beyond current staging systems.
  • To deploy the best-performing AI model as an accessible online tool for clinical use.

Main Methods:

  • Utilized CMM data from the Veneto Cancer Registry and regional health service.
  • Employed univariate Cox regression to assess variable prognostic strength.
  • Trained and evaluated multiple machine learning models, including Deep Neural Networks and Random Forests, using cross-validation and hyperparameter optimization.
  • Assessed model performance using balanced accuracy, precision, recall, and F1 score on a separate test set.

Main Results:

  • Univariate analysis confirmed the prognostic value of TNM staging and identified additional significant variables (sex, tumor site, histotype, growth phase, age).
  • The Neural Network and Random Forest models demonstrated superior prognostic performance, achieving balanced accuracies of 91% and 88%, respectively.
  • Key predictors of survival included age, T and M stages, mitotic count, and ulceration, as indicated by Gini importance scores.

Conclusions:

  • An AI algorithm with high staging reliability for CMM was developed using routinely collected clinicopathological data.
  • A web-based tool implementing this AI algorithm is available (unipd.link/melanomaprediction).
  • The tool's minimal implementation requirements facilitate its testing and validation in clinical practice for enhanced patient management.