Artificial Intelligence-Powered Digital Pathology to Improve Diagnosis and Personalized Prognostic Assessment in Patient with Myeloid Neoplasms
Authors: Asti Gianluca , Curti Nico , Maggioni Giulia , Carlini Gianluca , Lanino Luca , Campagna Alessia , D'Amico Saverio , Sauta Elisabetta , Delleani Mattia , Bonometti Arturo , Lancellotti Cesare , Rahal Daoud , Ubezio Marta , Todisco Gabriele , Tentori Cristina Astrid , Russo Antonio , Crespi Alessandra , Figini Giulia , Buizza Alessandro , Riva Elena , Zampini Matteo , Brindisi Matteo , Ficara Francesca , Crisafulli Laura , Ventura Denise , Pinocchio Nicole , Zazzetti Elena , Bicchieri Marilena , Grondelli Maria Chiara , Forcina Barrero Alessandro , Savevski Victor , Santoro Armando , Santini Valeria , Sole Francesc , Platzbecker Uwe , Fenaux Pierre , Diez-Campelo Maria , Komrokji Rami S. , Haferlach Torsten , Kordasti Shahram , Di Tommaso Luca , Zeidan Amer M. , Loghavi Sanam , Garcia-Manero Guillermo , Castellani Gastone , Della Porta Matteo Giovanni 
Published: December 2024

BACKGROUND. 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.
AIMS. 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.
METHODS. The study population included 1167 MN patients(18% AML; 44% MDS, 29% MPN, 9% MDS/MPN) from Humanitas Research Hospital, with available demographic, clinical, cytogenetic and genomic data, alongside with treatments, outcomes information and WSI collected from BM aspiration and biopsy at different timepoints during disease history. AI-based features extraction models were developed for different staining (May-Grunwald Giemsa (MGG), Perls, Hematoxylin-Eosin, Gomori, CD34, MPO, CD117, CD71, CD61, CD20, CD3, and P53). Different models and training strategies were employed to segment structures in each staining, like nuclei, cells and fibrosis; morphological and textural features were then extracted. Hematologists and hemato-pathologists feedback was included in the training process. Moreover, a classification model was developed to analyze cells on MGG smears, predicting 12 different cell types. The percentages of markers, cell types, and tissue components, combined with their spatial organization, were integrated to address the clinical aims of the project. Explainability and interpretability were implemented by SHapley Additive exPlanations approach (SHAP).
RESULTS. Multiple Gradient Boosting binary classifiers were trained using the WSI features to discriminate specific clinical entities among MN. The models predicted a correct diagnosis with an F1 Score &gt; 90%, suggesting that extracted features capture clinically relevant information. MDS/MPN phenotype cases resulted in the most diagnostic disagreement between AI-models and pathologists.
Then we analysed the morphologic and molecular features association. Specific genomic profiles were predicted from WSI features with specialized XGBOOST models with high accuracy, in particular for SF3B1, JAK/STAT, TP53 and RUNX1 mutations (all with F1 Score &gt; 90%).
These findings underline the capability of the morphological features to capture the biological background of MN.
On these bases, we integrated morphological features into an innovative prognostic tool for personalized prediction of overall survival (OS) and leukemia-free survival (LFS) in MN. After the feature selection process (by using a L1-penalized Cox regression) 71 morphological features were included in the model together with demographic, clinical and genomic information. Model discrimination was assessed using Harrell's concordance index (CI).
Sequential integration of data layers into the model showed an increasing CI for OS and LFS, starting with 0.68 and 0.59, respectively with clinical and cytogenetic features alone; then raising CI up to 0.75 and 0.64 including somatic gene mutations; and finally reaching CI of 0.87 and 0.85 further integrating morphological features.
Validation is currently ongoing on an independent MN population from MD Anderson Cancer Center, US.
CONCLUSION . Our developed AI models were capable of automatically extract textural, cell features and markers from WSI. The morphological features integration significantly improved personalized patient stratification and prognostication in MN. The proposed solution provided a fully explainable interpretation of high dimensional features, thus facilitating a wide clinical implementation.