MOSAIC: An Artificial Intelligence–Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers

Author:

D'Amico Saverio12ORCID,Dall’Olio Lorenzo3ORCID,Rollo Cesare4,Alonso Patricia5ORCID,Prada-Luengo Iñigo6,Dall’Olio Daniele3ORCID,Sala Claudia7ORCID,Sauta Elisabetta1ORCID,Asti Gianluca1ORCID,Lanino Luca1ORCID,Maggioni Giulia1,Campagna Alessia1,Zazzetti Elena1ORCID,Delleani Mattia1ORCID,Bicchieri Maria Elena1ORCID,Morandini Pierandrea1ORCID,Savevski Victor1,Arroyo Borja5,Parras Juan5ORCID,Zhao Lin Pierre8ORCID,Platzbecker Uwe9ORCID,Diez-Campelo Maria10ORCID,Santini Valeria11ORCID,Fenaux Pierre8,Haferlach Torsten12,Krogh Anders6,Zazo Santiago5,Fariselli Piero4ORCID,Sanavia Tiziana4ORCID,Della Porta Matteo Giovanni113ORCID,Castellani Gastone37ORCID

Affiliation:

1. Humanitas Clinical and Research Center—IRCCS, Milan, Italy

2. Train s.r.l., Milan, Italy

3. Department of Physics and Astronomy (DIFA), Bologna, Italy

4. Computational Biomedicine Unit, Department of Medical Sciences, University of Turin, Turin, Italy

5. Department of Signals, Systems and Radiocommunications, Polytechnic University of Madrid, Madrid, Spain

6. University of Copenhagen, Copenhagen, Denmark

7. Experimental, Diagnostic and Specialty Medicine—DIMES, Bologna, Italy

8. Hematology and Bone Marrow Transplantation, Hôpital Saint-Louis/University Paris 7, Paris, France

9. Medical Clinic and Policlinic 1, Hematology and Cellular Therapy, University Hospital Leipzig, Leipzig, Germany

10. Hematology Department, Hospital Universitario de Salamanca, Salamanca, Spain

11. Hematology, Azienda Ospedaliero-Universitaria Careggi & University of Florence, Florence, Italy

12. MLL Munich Leukemia Laboratory, Munich, Germany

13. Department of Biomedical Sciences, Humanitas University, Milan, Italy

Abstract

PURPOSE Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innovative treatment strategies. We have created and implemented MOSAIC, an artificial intelligence (AI)–based framework designed for multimodal analysis, classification, and personalized prognostic assessment in rare cancers. Clinical validation was performed on myelodysplastic syndrome (MDS), a rare hematologic cancer with clinical and genomic heterogeneities. METHODS We analyzed 4,427 patients with MDS divided into training and validation cohorts. Deep learning methods were applied to integrate and impute clinical/genomic features. Clustering was performed by combining Uniform Manifold Approximation and Projection for Dimension Reduction + Hierarchical Density-Based Spatial Clustering of Applications with Noise (UMAP + HDBSCAN) methods, compared with the conventional Hierarchical Dirichlet Process (HDP). Linear and AI-based nonlinear approaches were compared for survival prediction. Explainable AI (Shapley Additive Explanations approach [SHAP]) and federated learning were used to improve the interpretation and the performance of the clinical models, integrating them into distributed infrastructure. RESULTS UMAP + HDBSCAN clustering obtained a more granular patient stratification, achieving a higher average silhouette coefficient (0.16) with respect to HDP (0.01) and higher balanced accuracy in cluster classification by Random Forest (92.7% ± 1.3% and 85.8% ± 0.8%). AI methods for survival prediction outperform conventional statistical techniques and the reference prognostic tool for MDS. Nonlinear Gradient Boosting Survival stands in the internal (Concordance-Index [C-Index], 0.77; SD, 0.01) and external validation (C-Index, 0.74; SD, 0.02). SHAP analysis revealed that similar features drove patients' subgroups and outcomes in both training and validation cohorts. Federated implementation improved the accuracy of developed models. CONCLUSION MOSAIC provides an explainable and robust framework to optimize classification and prognostic assessment of rare cancers. AI-based approaches demonstrated superior accuracy in capturing genomic similarities and providing individual prognostic information compared with conventional statistical methods. Its federated implementation ensures broad clinical application, guaranteeing high performance and data protection.

Publisher

American Society of Clinical Oncology (ASCO)

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