Machine learning-based extraction of microstructural parameters from diffusion-weighted imaging starting from realistic in silico cellular substrates: Application in breast and prostate cancer.

BACKGROUND AND OBJECTIVE: Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is a key tool for probing tissue microstructure by measuring water motion. Given the invasiveness and limited sampling of biopsies, Apparent Diffusion Coefficient (ADC), derived from DW-MRI acquisitions, is clinically used as a non-invasive biomarker for tumor heterogeneity, although it provides a simplified representation of tissue microstructure. Recent work maps in vivo DW-MRI signals to simulated dictionaries generated from in silico cellular models, allowing estimation of sub-voxel microstructural properties using clinically feasible acquisitions. However, these methods remain constrained by oversimplified tissue models and require re-optimization when acquisition parameters change, limiting their generalizability. This work seeks to address these limitations by implementing a generalized Machine Learning framework adaptable to different acquisition protocols and trained on realistic in silico cellular models directly derived from real microscopy images. METHODS: We created a library of realistic and dynamic 3D in silico tumor models, derived from microscopy images of real cell lines, along with their corresponding in silico DW-MRI signals via GPU-accelerated Monte Carlo simulations. Using this library, we trained a Neural Network to estimate microstructural parameters from in vivo DW-MRI acquisitions of prostate and breast cancer patients, independently of the acquisition protocol. RESULTS: Results showed that the distributions of extracted microstructural parameters across lesions effectively stratified patients according to histological grade. Preliminary validation further indicated that breast-derived in silico substrates yielded the closest agreement with the parameters extracted from the breast cancer cohorts. CONCLUSIONS: This approach enables fast, non-invasive, and spatially resolved characterization of tumor heterogeneity, improving on ADC-based and simple-geometry approaches, ultimately providing a clinically applicable and robust framework for tumor microstructure characterization across different imaging protocols.