Humanitas Research Hospital attend ASH 2024 in representation of GenoMed4All

Bone marrow (BM) cytology and histopathology images are crucial for diagnosing and prognosticating myeloid neoplasms (MNs), but their high-dimensional data are underused. Artificial Intelligence (AI) applied to tumor morphology (digital pathology, DP) has improved the use of tumor biopsies’ data for various types of malignancies, accurately detecting patterns and converting complex image information into numerical features. Here, we explored the potential of AI-based DP to improve personalized medicine in MNs which are characterized by high heterogeneity and a significant proportion of patients with unmet clinical needs.
This project was conducted by the GenoMed4All and Synthema consortia, to build AI-based features extraction tools from BM histopathological and cytological Whole Slide Images (WSI). High-dimensional data were used to 1) assess diagnostic accuracy in MN patients; 2) elucidate the association between morphologic features, clinical variables and molecular genetics, and 3) create an innovative tool for personalized risk assessments integrating morphological features with clinical and genomic information.

Most oncological and non-oncological hematological conditions fall under the category of rare diseases. Rare diseases present unique challenges due to the limited availability of data, which impacts diagnostic rates and the generation of clinical evidence. Overall, they constitute a public health concern, highlighting the urgent need to develop new methods for improving data accessibility. In this context, Federated Learning (FL) is a Machine Learning approach that allows multiple centers to collaborate on complex research questions without the need to centralize or share data.
This project was conducted by the Genomed4all and Synthema consortia with the goal of developing an innovative FL platform for rare hematological diseases. This platform enables the development of novel Artificial Intelligence (AI) models for personalized medicine without data sharing, to be implemented in the referral centers of EuroBloodNET, the European Reference Network for rare hematological diseases. The aims of the project were: 1) to develop robust federated models for personalized prediction using multicentric, real-world datasets; 2) to protect patients’ privacy; and 3) to enhance collaboration between institutions while avoiding the creation of centralized data repositories.

Personalized medicine in hematology requires extensive real-world and comprehensive data, including clinical and genomic information. However, integrating, processing and managing such complex data layers in large populations presents significant challenges. Development of patient-tailored models by Artificial Intelligence (AI), known as Digital Twins (DT) offers a novel approach to precision medicine. DT are virtual representations of patients created from multimodal information that can be used to improve diagnosis, prognosis and treatment outcome, improving clinical decision-making. This project aims to advance research by using AI to develop a DT platform for personalized medicine in hematology, with myelodysplastic syndromes (MDS) as case study. MDS are hematological diseases with high clinical and genomic heterogeneity, presenting a challenging scenario for new technologies implementation.

In hematology, leveraging real-world multimodal data at large scale is crucial for developing personalized medicine to address unmet clinical needs, particularly for rare diseases. Generative AI in healthcare shows great promise by generating multimodal synthetic data (SD) to improve patients’ diagnosis and prognosis while accelerating clinical research (PMID: 34131324). The challenges in generating SD include accessing complete real-world datasets for model training, maintaining intrinsic relationships among different data layers, and ensuring clinical accuracy and privacy protection.
This project conducted by GenoMed4All and Synthema consortia, aimed to: 1) implement an innovative approach for generating high-fidelity multimodal SD from patients with myeloid neoplasms (MN); 2) develop a comprehensive multimodal Synthetic Validation Framework (SVF) to assess the SD clinical and statistical fidelity and privacy preservation; 3) verify the SD technology capability to accelerate research and enhance predictive models through multimodal data integration.

Recent advancements in genome characterization have transformed the study of myelodysplastic syndromes (MDS). Accordingly, there has been a shift from traditional classification and prognostication methods, which relied mainly on morphological and clinical data, to next-generation systems that incorporate genomic features. However, genetic abnormalities account for only part of the overall risk related to survival, disease progression, and individual response to hypomethylating agents (HMA), indicating that a significant portion of these risks is still tied to clinical and non-mutational factors. Increasing evidence suggests that transcriptomics, immune dysfunctions, and high-dimensional tumor morphology data extracted by Artificial Intelligence (AI) may play a crucial role in predicting clinical outcomes in human cancers, thereby improving the implementation of personalized medicine programs.
In this scenario, we developed MEGAERA, an innovative, deep learning-based framework for multimodal analysis of hematological malignancies. MEGAERA integrates clinical, multi-omics, and histopathological data, using specific strategies to ensure full clinical explainability and interpretability of predictions. This study was conducted by the GenoMed4All and Synthema EU consortia, with MDS included as use case, to improve personalized predictions of patient outcomes.