Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma

Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma

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 surv...

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Bibliographic Details
Main Authors: Sonoko Oshima, Jingwen Yao, Samuel Bobholz, Raksha Nagaraj, Catalina Raymond, Ashley Teraishi, Anna-Marie Guenther, Asher Kim, Francesco Sanvito, Nicholas S Cho, Blaine S. C. Eldred, Jennifer M Connelly, Phioanh L Nghiemphu, Albert Lai, Noriko Salamon, Timothy F Cloughesy, Peter S LaViolette, Benjamin M Ellingson
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:CNS Oncology
Subjects:
MRI
rad-path
radiopathomic mapping
recurrent glioblastoma
survival
tumor growth rate
Online Access:https://www.tandfonline.com/doi/10.1080/20450907.2024.2415285
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