Histopathology‐validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo‐progression in glioblastoma

• Published in: Cancer Vol. 126; no. 11; pp. 2625 - 2636
• Main Authors: Akbari, Hamed, Rathore, Saima, Bakas, Spyridon, Nasrallah, MacLean P., Shukla, Gaurav, Mamourian, Elizabeth, Rozycki, Martin, Bagley, Stephen J., Rudie, Jeffrey D., Flanders, Adam E., Dicker, Adam P., Desai, Arati S., O’Rourke, Donald M., Brem, Steven, Lustig, Robert, Mohan, Suyash, Wolf, Ronald L., Bilello, Michel, Martinez‐Lage, Maria, Davatzikos, Christos
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.06.2020
• Subjects: Accuracy; Adult; Aged; Aged, 80 and over; Angiogenesis; Biomarkers; Biomarkers, Tumor; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Chemoradiotherapy; Disease Progression; Feature extraction; Female; Glioblastoma; Glioblastoma - diagnostic imaging; Glioblastoma - pathology; Histopathology; Humans; Image acquisition; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Middle Aged; Multivariate analysis; Oncology; Patients; pseudo‐progression; radiographic biomarker; Replication; Toolkits; true progression; pseudo-progression; true progression; glioblastoma; machine learning; radiographic biomarker

Background Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% of patients, these enhancements primarily represent the effects of treatment, or pseudo‐progression (...