Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma
Creators: name: Oshima, Sonoko
name: Yao, Jingwen
name: Bobholz, Samuel
name: Nagaraj, Raksha
name: Raymond, Catalina
name: Teraishi, Ashley
name: Guenther, Anna-Marie
name: Kim, Asher
name: Sanvito, Francesco
name: Cho, Nicholas S
name: Eldred, Blaine S C
name: Connelly, Jennifer M
name: Nghiemphu, Phioanh L
name: Lai, Albert
name: Salamon, Noriko
name: Cloughesy, Timothy F
name: LaViolette, Peter S
name: Ellingson, Benjamin M
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Medicine
subject: Cell Biology
subject: Neuroscience
subject: Biotechnology
subject: Cancer
subject: Plant Biology
subject: FOS: Biological sciences

description: <b>Aim:</b> 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. <b>Methods:</b> Pre- and post-contrast T<sub>1</sub>-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). <b>Results:</b> 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<sup>-6</sup>, p &lt; 0.001 and HR = 2.39 × 10<sup>5</sup>, p &lt; 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). <b>Conclusion:</b> Changes in histological characteristics estimated from a radio-pathomic ML model are a promising tool for evaluating treatment response and predicting outcome in rGBM. A “radio-pathomic” machine learning model was previously developed to estimate tumor cell density, cytoplasm density and extracellular fluid density from patient-specific MRI images and matching pathology at autopsy. However, it has not been evaluated as a tool for serial monitoring glioblastoma patients during treatment active treatment. This study revealed that smaller cytoplasm density and larger extracellular fluid density after treatment were associated with better survival, independent of tumor volume and other clinical factors, in patients with recurrent glioblastoma treated with cytotoxic chemotherapy with or without radiation. Post-treatment volumetric contrast enhancing growth rate on MRI and the rate of change in cellularity on radio-pathomic maps were independently associated with survival, suggesting this model may be a valuable tool for evaluating treatment response and predicting outcome in recurrent glioblastoma.