Decoding Intratumoral Heterogeneity of Breast Cancer by Multiparametric In Vivo Imaging: A Translational Study

Abstract

Differential diagnosis and therapy of heterogeneous breast tumors poses a major clinical challenge. To address the need for a comprehensive, non-invasive strategy to define the molecular and functional profiles of tumors in vivo, we investigated a novel combination of metabolic positron emission tomography (PET) and diffusion-weighted (DW) magnetic resonance imaging (MRI) in the polyoma virus middle T transgenic mouse model of breast cancer. The implementation of a voxelwise analysis for the clustering of intra- and intertumoral heterogeneity in this model resulted in a multiparametric profile based on [18F]FDG-PET and DW-MRI which identified 3 distinct tumor phenotypes in vivo, including solid acinar and solid nodular malignancies as well as cystic hyperplasia. To evaluate the feasibility of this approach for clinical use, we examined estrogen receptor-positive (ER+) and progesterone receptor-positive (PR+) breast tumors from 5 patient cases using DW-MRI and [18F]FDG-PET in a simultaneous PET/MRI system. The post-surgical in vivo PET/MRI data was correlated to whole-slide histology using the latter traditional diagnostic standard to define phenotype. By this approach, we showed how molecular, structural (microscopic, anatomic) and functional information could be simultaneously obtained non-invasively to identify precancerous and malignant subtypes within heterogeneous tumors. Combined with an automatized analysis, our results suggest that multiparametric molecular and functional imaging may be capable of providing comprehensive tumor profiling for non-invasive cancer diagnostics.

Preparing data for multiparametric PET/MR imaging: Influence of PET point spread function modelling and EPI distortion correction on the spatial correlation of [18F]FDG-PET and diffusion-weighted MRI in head and neck cancer

“…It is suggested that FDG-PETby means of standardized uptake value (SUV) and DWIexpressed as apparent diffusion coefficient (ADC)correlate weakly, but with a statistically significant negative correlation on a voxel level within tumors of individual patients [3,4]. The voxelwise combination of SUV and ADC may further be used to identify tissue classes within the tumor by joint statistical modelling [5][6][7][8][9]. With the introduction of integrated PET/MR, it has become possible to acquire PET and DWI simultaneously, thereby potentially improving image coregistration.…”

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

“…Long-term survivors had a predominant tumor habitat with high Cancer December 15, 2018 enhancement and intermediate cell density, whereas the tumors of short-term survivors had habitats with low enhancement and high FLAIR signals (indicating necrosis). 194 More recently, these same authors presented a computational framework with which to quantitatively extract image features from the tumor habitats that demonstrated high accuracy to potentially predict the survival of patients with glioblastoma.…”

“…These subregions were validated with coregistered hematoxylin and eosin staining and ex vivo autoradiography in a preclinical setting. 194…”

Advancing Biomarker Development Through Convergent Engagement: Summary Report of the 2nd International Danube Symposium on Biomarker Development, Molecular Imaging and Applied Diagnostics; March 14–16, 2018; Vienna, Austria

“…This requires new methodological approaches towards the combination of multi-parametric image features (aka Bradiomics^) and multi-omics data from tissue or liquid biopsies so as to yield clinically relevant information. Here, artificial intelligence-based approaches have been proposed as one way to derive predictive biomarkers for therapeutic responses in cancer [45,46].…”