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MOSAIC: An Artificial Intelligence-Based Framework for Multimodal Analysis, Classification, and Personalized

Saverio D'Amico1,2, Lorenzo Dall'Olio3, Cesare Rollo4

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JCO Clinical Cancer Informatics
|June 14, 2024
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Summary

MOSAIC, an AI framework, enhances rare cancer classification and prognosis. It offers superior accuracy and personalized insights compared to traditional methods, with federated learning ensuring data protection and broad application.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Rare cancers represent over 20% of neoplasms and present significant unmet medical needs.
  • Effective classification and prognostication are essential for improving patient outcomes and guiding treatment strategies.
  • Myelodysplastic syndrome (MDS) serves as a model for rare hematologic cancers with complex clinical and genomic heterogeneity.

Purpose of the Study:

  • To introduce MOSAIC, an artificial intelligence (AI)-based framework for multimodal analysis, classification, and personalized prognostic assessment in rare cancers.
  • To clinically validate MOSAIC using myelodysplastic syndrome (MDS) as a representative rare cancer.
  • To compare AI-driven approaches with conventional methods for improved patient stratification and survival prediction.

Main Methods:

  • Analysis of 4,427 MDS patients using training and validation cohorts.
  • Integration and imputation of clinical/genomic features via deep learning.
  • Clustering using Uniform Manifold Approximation and Projection for Dimension Reduction + Hierarchical Density-Based Spatial Clustering of Applications with Noise (UMAP + HDBSCAN), compared to Hierarchical Dirichlet Process (HDP).
  • Survival prediction using linear and AI-based nonlinear models, with Explainable AI (Shapley Additive Explanations [SHAP]) and federated learning for interpretation and performance enhancement.

Main Results:

  • UMAP + HDBSCAN achieved more granular patient stratification with a higher average silhouette coefficient (0.16 vs. 0.01 for HDP) and improved Random Forest classification accuracy (92.7% ± 1.3%).
  • AI survival prediction models, particularly Nonlinear Gradient Boosting Survival, outperformed conventional methods, showing high Concordance-Index (C-Index) in internal (0.77) and external (0.74) validation.
  • SHAP analysis confirmed consistent feature importance across cohorts, and federated implementation enhanced model accuracy.

Conclusions:

  • MOSAIC offers an explainable and robust AI framework for optimizing rare cancer classification and prognosis.
  • AI approaches demonstrate superior accuracy in identifying genomic similarities and providing individual prognostic information.
  • Federated implementation of MOSAIC facilitates broad clinical adoption while ensuring data privacy and high performance.