This paper is in the following e-collection/theme issue:

Machine Learning (1780) Artificial Intelligence (1786) Imaging Informatics (240)

Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19805, first published .
Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation

Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation

Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation

Authors of this article:

Yang Gao1 Author Orcid Image ;   Xiong Xiao2 Author Orcid Image ;   Bangcheng Han3 Author Orcid Image ;   Guilin Li4 Author Orcid Image ;   Xiaolin Ning3 Author Orcid Image ;   Defeng Wang3 Author Orcid Image ;   Weidong Cai5 Author Orcid Image ;   Ron Kikinis6, 7, 8 Author Orcid Image ;   Shlomo Berkovsky9 Author Orcid Image ;   Antonio Di Ieva10 Author Orcid Image ;   Liwei Zhang2 Author Orcid Image ;   Nan Ji2 Author Orcid Image ;   Sidong Liu9 Author Orcid Image
Article Authors Cited by (25) Tweetations Metrics

Journals

  1. Liu S, Meng T, Russo C, Di Ieva A, Berkovsky S, Peng L, Dou W, Qian L. Brain volumetric and fractal analysis of synthetic MRI: A comparative study with conventional 3D T1-weighted images. European Journal of Radiology 2021;141:109782 View
  2. Feng Y, Liu S, Cheng Z, Quiroz J, Rezazadegan D, Chen P, Lin Q, Qian L, Liu X, Berkovsky S, Coiera E, Song L, Qiu X, Cai X. Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT. Information 2021;12(11):471 View
  3. Palaniandy K, Kamarudin Z, Wong Y, Mohamed Mukari S, Jiau W, Bakar A. Case report: Triple whammy: Synchronous radiotherapy induced glioblastoma multiforme and papillary thyroid cancer following nasopharyngeal carcinoma. Frontiers in Oncology 2022;12 View
  4. Bhandari A, Scott L, Weilbach M, Marwah R, Lasocki A. Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM). Neuroradiology 2023;65(5):907 View
  5. Bhandari A, Marwah R, Smith J, Nguyen D, Bhatti A, Lim C, Lasocki A. Machine learning imaging applications in the differentiation of true tumour progression from treatment‐related effects in brain tumours: A systematic review and meta‐analysis. Journal of Medical Imaging and Radiation Oncology 2022;66(6):781 View
  6. Karami G, Pascuzzo R, Figini M, Del Gratta C, Zhang H, Bizzi A. Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning. Cancers 2023;15(2):482 View
  7. Ruckli A, Nanavati A, Meier M, Lerch T, Steppacher S, Vuilleumier S, Boschung A, Vuillemin N, Tannast M, Siebenrock K, Gerber N, Schmaranzer F. A Deep Learning Method for Quantification of Femoral Head Necrosis Based on Routine Hip MRI for Improved Surgical Decision Making. Journal of Personalized Medicine 2023;13(1):153 View
  8. Zhang Y, Liang K, He J, Ma H, Chen H, Zheng F, Zhang L, Wang X, Ma X, Chen X. Deep Learning With Data Enhancement for the Differentiation of Solitary and Multiple Cerebral Glioblastoma, Lymphoma, and Tumefactive Demyelinating Lesion. Frontiers in Oncology 2021;11 View
  9. Tanaka K, Russo C, Liu S, Stoodley M, Di Ieva A. Use of deep learning in the MRI diagnosis of Chiari malformation type I. Neuroradiology 2022;64(8):1585 View
  10. Jaspers J, Taal W, van Norden Y, Zindler J, Swaak A, Habraken S, Hoogeman M, Nout R, van den Bent M, Méndèz Romero A. Early and late contrast enhancing lesions after photon radiotherapy for IDH mutated grade 2 diffuse glioma. Radiotherapy and Oncology 2023;184:109674 View
  11. Tabassum M, Suman A, Suero Molina E, Pan E, Di Ieva A, Liu S. Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review. Cancers 2023;15(15):3845 View
  12. Eidex Z, Ding Y, Wang J, Abouei E, Qiu R, Liu T, Wang T, Yang X. Deep learning in MRI‐guided radiation therapy: A systematic review. Journal of Applied Clinical Medical Physics 2024;25(2) View
  13. Pal S, Singh R, Kumar A. Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further Evaluation Based on the Institute Test Data. Journal of Medical Physics 2024;49(1):22 View
  14. Mut M, Zhang M, Gupta I, Fletcher P, Farzad F, Nwafor D. Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice. Frontiers in Neurology 2024;15 View
  15. Shershever A, Daineko E, Soloveva S, Surova E, Askarova E. Development of a method for differential diagnostic of a recurrent glial tumor from radiation necrosis by clinical decision support system for MRI images. Diagnostic radiology and radiotherapy 2024;15(2):35 View
  16. Dorfner F, Patel J, Kalpathy-Cramer J, Gerstner E, Bridge C. A review of deep learning for brain tumor analysis in MRI. npj Precision Oncology 2025;9(1) View
  17. Kocak B, Mese I, Ates Kus E. Radiomics for differentiating radiation-induced brain injury from recurrence in gliomas: systematic review, meta-analysis, and methodological quality evaluation using METRICS and RQS. European Radiology 2025 View
  18. Ratiphunpong P, Inmutto N, Angkurawaranon S, Wantanajittikul K, Suwannasak A, Yarach U. A Pilot Study on Deep Learning With Simplified Intravoxel Incoherent Motion Diffusion-Weighted MRI Parameters for Differentiating Hepatocellular Carcinoma From Other Common Liver Masses. Topics in Magnetic Resonance Imaging 2025;34(1):e0316 View
  19. Li Q, Li X, Jiang H, Qian X. A Spatial-Transformation-Based Causality-Enhanced Model for Glioblastoma Progression Diagnosis. IEEE Transactions on Artificial Intelligence 2025;6(6):1529 View
  20. Ying Y, Cai X, Yang H, Huang H, Zheng D, Li H, Dong G, Wang Y, Jiang Z, An Z, Zhang G. Development and validation of a deep learning algorithm for discriminating glioma recurrence from radiation necrosis on MRI. Frontiers in Oncology 2025;15 View

Books/Policy Documents

  1. Jian A, Jang K, Russo C, Liu S, Di Ieva A. Machine Learning in Clinical Neuroscience. View
  2. Noorani I, Di Ieva A. Advanced Imaging and Therapy in Neuro-Oncology. View
  3. Suero Molina E, Azemi G, Russo C, Liu S, Di Ieva A. Computational Neurosurgery. View
  4. Cepeda S. Computational Neurosurgery. View

Conference Proceedings

  1. Hossain T, Qureshi Z, Jayakumar N, Muttikkal T, Patel S, Schiff D, Zhang M, Kundu B. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). Multimodal Deep Learning to Differentiate Tumor Recurrence from Treatment Effect in Human Glioblastoma View