Faculty Collaboration Database

Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma. CNS Oncol 2024 Dec 31;13(1):2415285

Date

11/13/2024

Pubmed ID

39535237

Pubmed Central ID

PMC11562955

DOI

10.1080/20450907.2024.2415285

Scopus ID

2-s2.0-85209813692 (requires institutional sign-in at Scopus site)

Abstract

Aim: 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.Methods: Pre- and post-contrast T₁-weighted, FLAIR and ADC images were used to generate radio-pathomic maps for 51 patients with longitudinal pre- and post-treatment scans. Univariate and multivariate Cox regression analyses were used to test the influence of contrast-enhancing tumor volume, total cellularity, mean Cyt and mean ECF at baseline, immediately post-treatment and the pre- and post-treatment rate of change in volume and cellularity on overall survival (OS).Results: Smaller Cyt and larger ECF after treatment were significant predictors of OS, independent of tumor volume and other clinical prognostic factors (HR = 3.23 × 10^-6, p < 0.001 and HR = 2.39 × 10⁵, p < 0.001, respectively). Both post-treatment volumetric growth rate and the rate of change in cellularity were significantly correlated with OS (HR = 1.17, p = 0.003 and HR = 1.14, p = 0.01, respectively).Conclusion: Changes in histological characteristics estimated from a radio-pathomic ML model are a promising tool for evaluating treatment response and predicting outcome in rGBM.

Author List

Oshima S, Yao J, Bobholz S, Nagaraj R, Raymond C, Teraishi A, Guenther AM, Kim A, Sanvito F, Cho NS, C Eldred BS, Connelly JM, Nghiemphu PL, Lai A, 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
Female
Glioblastoma
Humans
Machine Learning
Magnetic Resonance Imaging
Male
Middle Aged
Neoplasm Recurrence, Local
Prognosis