Access the full text.
Sign up today, get DeepDyve free for 14 days.
Ken Chang, Biqi Zhang, Xiaotao Guo, M. Zong, R. Rahman, D. Sanchez, Nicolette Winder, D. Reardon, Binsheng Zhao, P. Wen, Raymond Huang (2016)
Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab.Neuro-oncology, 18 12
G. Lombardi, A. Pambuku, L. Bellu, M. Farina, A. Puppa, L. Denaro, V. Zagonel (2017)
Effectiveness of antiangiogenic drugs in glioblastoma patients: A systematic review and meta-analysis of randomized clinical trials.Critical reviews in oncology/hematology, 111
Squibb Provides Update on Phase 3 Opdivo (nivolumab) CheckMate-548 Trial in Patients with Newly Diagnosed MGMT-Methylated Glioblastoma Multiforme
D. Reardon, T. Kaley, J. Dietrich, Jennifer Clarke, G. Dunn, M. Lim, T. Cloughesy, H. Gan, Andrew Park, P. Schwarzenberger, T. Ricciardi, M. Macri, A. Ryan, R. Venhaus (2017)
Phase 2 study to evaluate safety and efficacy of MEDI4736 (durvalumab [DUR]) in glioblastoma (GBM) patients: An update.Journal of Clinical Oncology, 35
Chunhao Wang, Wenzheng Sun, J. Kirkpatrick, Z. Chang, F. Yin (2017)
Assessment of concurrent stereotactic radiosurgery and bevacizumab treatment of recurrent malignant gliomas using multi-modality MRI imaging and radiomics analysis.Journal of radiosurgery and SBRT, 5 3
Raymond Huang, P. Wen (2016)
Response Assessment in Neuro-Oncology Criteria and Clinical Endpoints.Magnetic resonance imaging clinics of North America, 24 4
F. Rundo, C. Spampinato, G. Banna, S. Conoci (2019)
Advanced Deep Learning Embedded Motion Radiomics Pipeline for Predicting Anti-PD-1/PD-L1 Immunotherapy Response in the Treatment of Bladder Cancer: Preliminary ResultsElectronics
J. Zeng, A. See, J. Phallen, Christopher Jackson, Z. Belcaid, J. Ruzevick, N. Durham, C. Meyer, T. Harris, E. Albesiano, G. Pradilla, E. Ford, J. Wong, H. Hammers, Dimitris Mathios, B. Tyler, H. Brem, P. Tran, D. Pardoll, C. Drake, M. Lim (2013)
Anti-PD-1 blockade and stereotactic radiation produce long-term survival in mice with intracranial gliomas.International journal of radiation oncology, biology, physics, 86 2
Biqi Zhang, Ken Chang, S. Ramkissoon, S. Tanguturi, W. Bi, D. Reardon, K. Ligon, B. Alexander, P. Wen, Raymond Huang (2017)
Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomasNeuro-Oncology, 19
H. Ishwaran, U. Kogalur, E. Blackstone, M. Lauer (2008)
Random Survival ForestsWiley StatsRef: Statistics Reference Online
H. Ishwaran, UB. Kogalur, EH. Blackstone
Random survival forestsAnnals of Applied Statistics, 2
Image biomarker standardisation initiative
Dong Nie, Junfeng Lu, Han Zhang, E. Adeli, Jun Wang, Zhengda Yu, Luyan Liu, Qian Wang, Jinsong Wu, D. Shen (2019)
Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal NeuroimagesScientific Reports, 9
H. Okada, M. Weller, Raymond Huang, G. Finocchiaro, M. Gilbert, W. Wick, B. Ellingson, N. Hashimoto, I. Pollack, A. Brandes, E. Franceschi, C. Herold‐Mende, L. Nayak, A. Panigrahy, W. Pope, R. Prins, J. Sampson, P. Wen, D. Reardon (2015)
Immunotherapy response assessment in neuro-oncology: a report of the RANO working group.The Lancet. Oncology, 16 15
Khalid Jazieh, Mohammadhadi Khorrami, Anas Saad, M. Gad, V. Viswanathan, P. Fu, P. Rajiah, A. Madabhushi, N. Pennell (2021)
Novel imaging biomarkers predict progression-free survival in stage 3 NSCLC treated with chemoradiation and durvalumab.Journal of Clinical Oncology, 39
D. Reardon, T. Kaley, J. Dietrich, Jennifer Clarke, G. Dunn, M. Lim, T. Cloughesy, H. Gan, Andrew Park, P. Schwarzenberger, T. Ricciardi, M. Macri, A. Ryan, R. Venhaus (2019)
Phase II study to evaluate safety and efficacy of MEDI4736 (durvalumab) + radiotherapy in patients with newly diagnosed unmethylated MGMT glioblastoma (new unmeth GBM).Journal of Clinical Oncology
Luke Macyszyn, H. Akbari, J. Pisapia, Xiao Da, Mark Attiah, Vadim Pigrish, Y. Bi, Sharmistha Pal, R. Davuluri, L. Roccograndi, N. Dahmane, M. Martinez-Lage, G. Biros, R. Wolf, M. Bilello, D. O’Rourke, C. Davatzikos (2016)
Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.Neuro-oncology, 18 3
D. Reardon, A. Omuro, A. Brandes, J. Rieger, A. Wick, J. Sepúlveda, S. Phuphanich, P. Souza, M. Ahluwalia, M. Lim, G. Vlahović, J. Sampson (2017)
OS10.3 Randomized Phase 3 Study Evaluating the Efficacy and Safety of Nivolumab vs Bevacizumab in Patients With Recurrent Glioblastoma: CheckMate 143Neuro-oncology, 19
E. Lipson, P. Forde, H. Hammers, L. Emens, J. Taube, S. Topalian (2015)
Antagonists of PD-1 and PD-L1 in Cancer Treatment.Seminars in oncology, 42 4
M. Vallières, Emily Kay-Rivest, Léo Perrin, X. Liem, C. Furstoss, H. Aerts, N. Khaouam, P. Nguyen-Tan, Chang-Shu Wang, K. Sultanem, J. Seuntjens, I. Naqa (2017)
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancerScientific Reports, 7
F. Sforazzini, P. Salome, A. Kudak, M. Ulrich, L. König, Rolf Warta, N. Bougatf, J. Debus, C. Herold‐Mende, M. Knoll, A. Abdollahi (2021)
PD-L1-R: A MR based surrogate for PD-L1 expression in Glioblastoma multiforme.Journal of Clinical Oncology, 39
F. Felice, N. Pranno, F. Marampon, D. Musio, M. Salducci, A. Polimeni, V. Tombolini (2019)
Immune check-point in glioblastoma multiforme.Critical reviews in oncology/hematology, 138
Hung Hung, Chin‐Tsang Chiang (2009)
Estimation methods for time‐dependent AUC models with survival dataCanadian Journal of Statistics, 38
Junfei Zhao, Andrew Chen, R. Gartrell, A. Silverman, Luis Aparicio, T. Chu, Darius Bordbar, D. Shan, J. Samanamud, A. Mahajan, I. Filip, Rose Orenbuch, Morgan Goetz, Jonathan Yamaguchi, M. Cloney, C. Horbinski, R. Lukas, J. Raizer, A. Rae, Jinzhou Yuan, P. Canoll, J. Bruce, Y. Saenger, P. Sims, F. Iwamoto, A. Sonabend, R. Rabadán (2019)
Immune and genomic correlates of response to anti-PD-1 immunotherapy in glioblastomaNature Medicine, 25
P. Grossmann, V. Narayan, Ken Chang, R. Rahman, L. Abrey, D. Reardon, L. Schwartz, P. Wen, B. Alexander, Raymond Huang, H. Aerts (2017)
Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumabNeuro-Oncology, 19
Q. Ostrom, G. Cioffi, H. Gittleman, N. Patil, K. Waite, C. Kruchko, J. Barnholtz-Sloan (2019)
CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2012-2016.Neuro-oncology, 21 Supplement_5
Ken Chang, Andrew Beers, H. Bai, James Brown, K. Ly, Xuejun Li, J. Senders, V. Kavouridis, Alessandro Boaro, Chang Su, W. Bi, O. Rapalino, W. Liao, Q. Shen, Hao Zhou, Bo Xiao, Yin-yan Wang, Paul Zhang, M. Pinho, P. Wen, T. Batchelor, J. Boxerman, O. Arnaout, B. Rosen, E. Gerstner, Li Yang, Raymond Huang, Jayashree Kalpathy-Cramer (2019)
Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurementNeuro-Oncology, 21
P. Kickingereder, Sina Burth, A. Wick, M. Götz, Oliver Eidel, H. Schlemmer, Klaus Maier-Hein, W. Wick, M. Bendszus, A. Radbruch, D. Bonekamp (2016)
Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models.Radiology, 280 3
D. Reardon, A. Brandes, A. Omuro, P. Mulholland, M. Lim, A. Wick, J. Baehring, M. Ahluwalia, P. Roth, O. Bähr, S. Phuphanich, J. Sepúlveda, P. Souza, S. Sahebjam, M. Carleton, K. Tatsuoka, C. Taitt, R. Zwirtes, J. Sampson, M. Weller (2020)
Effect of Nivolumab vs Bevacizumab in Patients With Recurrent GlioblastomaJAMA Oncology, 6
D. Wainwright, Alan Chang, M. Dey, I. Balyasnikova, C. Kim, Alex Tobias, Y. Cheng, Julius Kim, J. Qiao, Lingjiao Zhang, Yu Han, M. Lesniak (2014)
Durable Therapeutic Efficacy Utilizing Combinatorial Blockade against IDO, CTLA-4, and PD-L1 in Mice with Brain TumorsClinical Cancer Research, 20
S. Burugu, Amanda Dancsok, T. Nielsen (2017)
Emerging targets in cancer immunotherapy.Seminars in cancer biology, 52 Pt 2
Anirban Sengupta, A. Ramaniharan, Rakesh Gupta, Sumeet Agarwal, Anup Singh (2019)
Glioma grading using a machine‐learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor componentsJournal of Magnetic Resonance Imaging, 50
E. Lotan, R. Jain, N. Razavian, G. Fatterpekar, Y. Lui (2019)
State of the Art: Machine Learning Applications in Glioma Imaging.AJR. American journal of roentgenology, 212 1
A. Zwanenburg, S. Leger, M. Vallières, S. Löck (2016)
Image biomarker standardisation initiative - feature definitionsArXiv, abs/1612.07003
P. Kickingereder, M. Götz, J. Muschelli, A. Wick, U. Neuberger, R. Shinohara, M. Sill, M. Nowosielski, H. Schlemmer, A. Radbruch, W. Wick, M. Bendszus, Klaus Maier-Hein, D. Bonekamp (2016)
Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment ResponseClinical Cancer Research, 22
M. Vallières, Carolyn Freeman, S. Skamene, I. Naqa, I. Naqa (2015)
A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremitiesPhysics in Medicine & Biology, 60
Parita Sanghani, B. Ang, N. King, Hongliang Ren (2018)
Overall survival prediction in glioblastoma multiforme patients from volumetric, shape and texture features using machine learning.Surgical oncology, 27 4
R. Stupp, M. Hegi, W. Mason, M. Bent, M. Taphoorn, R. Janzer, S. Ludwin, A. Allgeier, B. Fisher, K. Bélanger, P. Hau, A. Brandes, J. Gijtenbeek, C. Marosi, C. Vecht, K. Mokhtari, P. Wesseling, S. Villà, E. Eisenhauer, T. Gorlia, M. Weller, D. Lacombe, J. Cairncross, R. Mirimanoff (2009)
Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial.The Lancet. Oncology, 10 5
BACKGROUND AND PURPOSE: Imaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND METHODS: Post hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma ( n = 113). Radiomics tumor features on pretreatment and first on-treatment time point MR imaging were extracted. The random survival forest algorithm was applied to clinical and radiomics features from pretreatment and first on-treatment MR imaging from a subset of trial sites ( n = 60–74) to train a model to predict long overall survival and progression-free survival and was tested externally on data from the remaining sites ( n = 29–43). Model performance was assessed using the concordance index and dynamic area under the curve from different time points. RESULTS: The mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index = 0.472–0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692–0.750) and progression-free survival (concordance index = 0.680–0.715). CONCLUSIONS: A radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.
American Journal of Neuroradiology – American Journal of Neuroradiology
Published: May 1, 2022
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.