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Introduction: This study determines the relationship between diffusion and perfusion-based MRI signatures and radio-pathomic maps of tumor pathology in a large, multi-site cohort.
Methods: This study included perfusion imaging from pre-surgical relative cerebral blood volume (rCBV) images from the UPenn-GBM dataset and pre-surgical arterial spin labeling (ASL) imaging from the UCSF-PDGM dataset. Diffusion imaging included fractional anisotropy (FA) estimates derived from diffusion tensor imaging (DTI) for each subject from each institution. A previously validated autopsy-based model was applied to the structural images from each patient to generate quantitative radio-pathomic maps of cell density and extracellular fluid (ECF). Mean cell density, ECF density, FA, rCBV calculated from DSC imaging, and rCBF calculated from ASL were computed for each patient and statistically compared within contrast-enhancement (CE) and the non-enhancing peritumor FLAIR hyperintensity (FH).
Results: Both rCBV and ASL showed positive correlation with cell density within CE (rCBV: R=0.280, p<0.001; ASL: R=0.117, p=0.023). However, both perfusion metrics also showed no association with cell density within the FH region at the group level (rCBV: R=0.0162, p=0.731; ASL: R=-0.020, p=0.652). Negative correlations were observed between FA and ECF density across both CE and FH in both the UPenn-GBM (CE: r = -0.204, p<0.001, FH: r=-0.332, p<0.001) and the UCSF-PDGM (CE:r=-0.179, p<0.001, FH:-0.355, p<0.001). Additionally, a positive ASL-cell density association per subject within FH was associated with worse survival prognosis (HR=5.58, p=0.022).
Discussion: These results suggest that radio-pathomic maps of tumor pathology provide complementary information to other MR signatures that reveal prognostically valuable signatures of the non-enhancing tumor margin.
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http://dx.doi.org/10.1093/neuonc/noaf044 | DOI Listing |
J Neurooncol
October 2025
UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
Purpose: In brain gliomas, non-invasive biomarkers reflecting tumor cellularity would be useful to guide supramarginal resections and to plan stereotactic biopsies. We aim to validate a previously-trained machine learning algorithm that generates cellularity prediction maps (CPM) from multiparametric MRI data to an independent, retrospective external cohort of gliomas undergoing image-guided biopsies, and to compare the performance of CPM and diffusion MRI apparent diffusion coefficient (ADC) in predicting cellularity.
Methods: A cohort of patients with treatment-naïve or recurrent gliomas were prospectively studied.
Neuro Oncol
February 2025
Department of Radiology, Medical College of Wisconsin, Milwaukee WI.
Introduction: This study determines the relationship between diffusion and perfusion-based MRI signatures and radio-pathomic maps of tumor pathology in a large, multi-site cohort.
Methods: This study included perfusion imaging from pre-surgical relative cerebral blood volume (rCBV) images from the UPenn-GBM dataset and pre-surgical arterial spin labeling (ASL) imaging from the UCSF-PDGM dataset. Diffusion imaging included fractional anisotropy (FA) estimates derived from diffusion tensor imaging (DTI) for each subject from each institution.
Sci Rep
December 2024
IRCCS SYNLAB SDN, Via E. Gianturco 113, 80143, Naples, Italy.
Uterine corpus endometrial carcinoma (EC) is one of the most common malignancies in the female reproductive system, characterized by tumor heterogeneity at both radiological and pathological scales. Both radiomics and pathomics have the potential to assess this heterogeneity and support EC diagnosis. This study examines the correlation between radiomics features from Apparent Diffusion Coefficient (ADC) maps and post-contrast T1 (T1C) images with pathomic features from pathology images in 32 patients from the CPTAC-UCEC database.
View Article and Find Full Text PDFCNS Oncol
December 2024
UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision & Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA 90024, USA.
A radio-pathomic machine learning (ML) model has been developed to estimate tumor cell density, cytoplasm density (Cyt) and extracellular fluid density (ECF) from multimodal MR images and autopsy pathology. In this multicenter study, we implemented this model to test its ability to predict survival in patients with recurrent glioblastoma (rGBM) treated with chemotherapy. Pre- and post-contrast T-weighted, FLAIR and ADC images were used to generate radio-pathomic maps for 51 patients with longitudinal pre- and post-treatment scans.
View Article and Find Full Text PDFNeurosurgery
September 2024
Department of Radiology, Medical College of Wisconsin, Milwaukee , Wisconsin , USA.
Background And Objectives: This study identified a clinically significant subset of patients with glioma with tumor outside of contrast enhancement present at autopsy and subsequently developed a method for detecting nonenhancing tumor using radio-pathomic mapping. We tested the hypothesis that autopsy-based radio-pathomic tumor probability maps would be able to noninvasively identify areas of infiltrative tumor beyond traditional imaging signatures.
Methods: A total of 159 tissue samples from 65 subjects were aligned to MRI acquired nearest to death for this retrospective study.