Faculty Collaboration Database

Independent histological validation of MR-derived radio-pathomic maps of tumor cell density using image-guided biopsies in human brain tumors. J Neurooncol 2025 Oct;175(1):111-122

Date

06/21/2025

Pubmed ID

40542949

Pubmed Central ID

PMC12367939

DOI

10.1007/s11060-025-05105-x

Scopus ID

2-s2.0-105008481709 (requires institutional sign-in at Scopus site)

Abstract

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. All patients underwent pre-surgical MRI according to the standardized brain tumor imaging protocol. The surgical sampling site was planned based on image-guided biopsy targets and tissue was stained with hematoxylin-eosin for cell density count. The correlation between MRI-derived CPM values and histological cellularity, and between ADC and histological cellularity, was evaluated both assuming independent observations and accounting for non-independent observations.

RESULTS: Sixty-six samples from twenty-seven patients were collected. Thirteen patients had treatment-naïve tumors and fourteen had recurrent lesions. CPM value accurately predicted histological cellularity in treatment-naïve patients (b = 1.4, R² = 0.2, p = 0.009, rho = 0.41, p = 0.016, RMSE = 1503 cell/mm²), but not in the recurrent sub-cohort. Similarly, ADC values showed a significant association with histological cellularity only in treatment-naive patients (b = 1.3, R² = 0.22, p = 0.007; rho = -0.37, p = 0.03), not statistically different from the CPM correlation. These findings were confirmed with statistical tests accounting for non-independent observations.

CONCLUSION: MRI-derived machine learning generated cellularity prediction maps (CPM) enabled a non-invasive evaluation of tumor cellularity in treatment-naïve glioma patients, although CPM did not clearly outperform ADC alone in this cohort.

Author List

Nocera G, Sanvito F, Yao J, Oshima S, Bobholz SA, Teraishi A, Raymond C, Patel K, Everson RG, Liau LM, Connelly J, Castellano A, Mortini P, Salamon N, Cloughesy TF, LaViolette PS, Ellingson BM

Authors

Samuel Bobholz PhD Assistant Professor in the Radiology department at Medical College of Wisconsin
Jennifer M. Connelly MD Professor in the Neurology department at Medical College of Wisconsin
Peter LaViolette PhD Vice Chair, Professor in the Radiology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Adult
Aged
Brain Neoplasms
Cell Count
Diffusion Magnetic Resonance Imaging
Female
Glioma
Humans
Image-Guided Biopsy
Machine Learning
Magnetic Resonance Imaging
Male
Middle Aged
Prospective Studies
Retrospective Studies
Young Adult