1. Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma
- Author
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Oshima, Sonoko, Yao, Jingwen, Bobholz, Samuel, Nagaraj, Raksha, Raymond, Catalina, Teraishi, Ashley, Guenther, Anna-Marie, Kim, Asher, Sanvito, Francesco, Cho, Nicholas S, Eldred, Blaine SC, Connelly, Jennifer M, Nghiemphu, Phioanh L, Lai, Albert, Salamon, Noriko, Cloughesy, Timothy F, LaViolette, Peter S, and Ellingson, Benjamin M
- Subjects
Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Rare Diseases ,Cancer ,Brain Disorders ,Humans ,Glioblastoma ,Brain Neoplasms ,Male ,Female ,Middle Aged ,Neoplasm Recurrence ,Local ,Magnetic Resonance Imaging ,Aged ,Adult ,Machine Learning ,Prognosis ,MRI ,rad-path ,radiopathomic mapping ,recurrent glioblastoma ,survival ,tumor growth rate ,Oncology and carcinogenesis - 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
- Published
- 2024