• Poisson, Laila
• Sako, Chiharu
• Cepeda, Santiago
• Booth, Thomas
• Balana, Carmen
• Baid, Ujjwal
• Bilello, Michel
• Rudie, Jeffrey
• Chang, Jong Hee
• Sloan, Andrew
• Lamontagne, Pamela
• Lee, Matthew Dongwoo
• Wiestler, Benedikt
• Chakravarti, Arnab
• Flanders, Adam
• Villanueva-Meyer, Javier
• Mohan, Suyash
• Puig, Josep
• Palmer, Joshua
• Shukla, Gaurav
• Colen, Rivka
• Garcia, Jose
• Nasrallah, Maclean
• Barnholtz-Sloan, Jill
• Marcus, Daniel
• Griffith, Brent
• Bakas, Spyridon
• Capellades, Jaume
• Lustig, Robert
• Kazerooni, Anahita Fathi
• Taylor, William
• Rogers, Lisa
• Brem, Steven
• Bagley, Stephen
• Choi, Yoon Seong
• Jain, Rajan
• Calabrese, Evan
• Orourke, Donald
• Akbari, Hamed
• Ak, Murat
• Mahajan, Abhishek
• Dicker, Adam
• Badve, Chaitra
• Ahn, Sung Soo
• Davatzikos, Christos
• Shi, Wenyin
• Lee, Seung-Koo

Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>PURPOSE</jats:title><jats:p>Glioblastoma is extremely infiltrative with malignant cells extending beyond the enhancing rim where recurrence inevitably occurs, despite aggressive multimodal therapy. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured and analyzed by multi-parametric MRI and artificial intelligence (AI) methods are generalizable in the updated multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium and predictive of neoplastic infiltration and future recurrence.</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>We used the most recent update of the ReSPOND consortium to evaluate and further refine generalizability of our methods with different scanners and acquisition settings. 179 de novo glioblastoma patients with available T1, T1Gd, T2, T2-FLAIR, and ADC sequences at pre-resection baseline and after complete resection with subsequent pathology-confirmed recurrence were included. To establish generalizability of the predictive models, training and testing of the refined AI model was performed through Leave-One-Institution-Out-Cross-Validation schema. The multi-institutional cohort consisted of the Hospital of the University of Pennsylvania (UPenn, 124), Case Western Reserve University/University Hospitals (CWRU/UH, 27), New York University (NYU, 13), Ohio State University (OSU, 13), and University Hospital Río Hortega (RH, 2). Features extracted from pre-resection MRI were used to build the model predicting the spatial pattern of subsequent tumor recurrence. These predictions were evaluated against regions of pathology-confirmed post-resection recurrence.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>Our model predicted the locations that later harbored tumor recurrence with overall odds ratio (99% CI)/AUC (99% CI), 12.0(11.8-12.2)/0.80(0.76-0.85), and per institute, CWRU/UH, 11.0(10.7-11.3)/0.80 (0.64-0.97); NYU, 7.0(6.7-7.3)/0.78(0.56-1.00); OSU, 18.3(17.5-19.1)/0.83(0.54-1.00); RH, 40.0(35.3-45.5)/0.93(0.00-1.00); UPenn, 8.00(7.7-8.3)/0.80(0.75-0.84).</jats:p></jats:sec><jats:sec><jats:title>CONCLUSION</jats:title><jats:p>This study provides extensive multi-institutional validated evidence that machine learning tools can identify peritumoral neoplastic infiltration and predict location of future recurrence, by decrypting the MRI signal heterogeneity in peritumoral tissue. Our analyses leveraged the unique dataset of the ReSPOND consortium, which aims to develop and validate AI-based biomarkers for individualized prediction and prognostication and establish generalizability in a multi-institutional setting.</jats:p></jats:sec>

Topics

• impedance spectroscopy
• size-exclusion chromatography
• chemical ionisation
• machine learning