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Research ArticleORIGINAL RESEARCH

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma

Jikai Zhang, Dominic LaBella, Dylan Zhang, Jessica L. Houk, Jeffrey D. Rudie, Haotian Zou, Pranav Warman, Maciej A. Mazurowski and Evan Calabrese
American Journal of Neuroradiology November 2024, ajnr.A8580; DOI: https://doi.org/10.3174/ajnr.A8580
Jikai Zhang
1 Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
3 Duke Center for Artificial Intelligence in Radiology, Duke University Medical Center, Durham, NC, USA
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Dominic LaBella
4 Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
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Dylan Zhang
6 Department of Radiology, Duke University Medical Center, Durham, NC, USA
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Jessica L. Houk
6 Department of Radiology, Duke University Medical Center, Durham, NC, USA
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Jeffrey D. Rudie
7 Department of Radiology, University of California San Diego, San Diego, CA, USA
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Haotian Zou
5 Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
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Pranav Warman
8 Duke University School of Medicine, Durham, NC, USA
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Maciej A. Mazurowski
1 Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
2 Department of Computer Science, Duke University, Durham, NC, USA
5 Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
6 Department of Radiology, Duke University Medical Center, Durham, NC, USA
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Evan Calabrese
3 Duke Center for Artificial Intelligence in Radiology, Duke University Medical Center, Durham, NC, USA
6 Department of Radiology, Duke University Medical Center, Durham, NC, USA
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ABSTRACT

BACKGROUND AND PURPOSE: To develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center.

MATERIALS AND METHODS: We retrospectively analyzed data from 634 patients treated for glioblastoma at a single brain tumor center over a 5-year period (2017-2021). The mean age was 56 +/-13 years. 372/634 (59%) patients were male, and 262/634 (41%) patients were female. Study data consisted of 3,403 brain MRI exams and corresponding standardized, radiologist-based brain tumor response assessments (BT-RADS). An artificial intelligence (AI)-based brain tumor response assessment algorithm was developed using automated, volumetric tumor segmentation. AI-based response assessments were evaluated for agreement with radiologist-based response assessments and ability to stratify patients by overall survival. Metrics were computed to assess the agreement using BTRADS as the ground-truth, fixed-time point survival analysis was conducted to evaluate the survival stratification, and associated P-values were calculated.

RESULTS: For all BT-RADS categories, AI-based response assessments showed moderate agreement with radiologists’ response assessments (F1 = 0.587-0.755). Kaplan-Meier survival analysis revealed statistically worse overall fixed time point survival for patients assessed as image worsening equivalent to RANO progression by human alone compared to by AI alone (log-rank P=0.007). Cox proportional hazard model analysis showed a disadvantage to AI-based assessments for overall survival prediction (P=0.012).

CONCLUSIONS: AI-based volumetric glioblastoma MRI response assessment following BT-RADS criteria yielded moderate agreement for replicating human response assessments and slightly worse stratification by overall survival.

ABBREVIATIONS: GBM= Glioblastoma; RANO= Response Assessment in Neuro-Oncology; BTRADS= Brain Tumor Reporting and Data System; NLP = Natural Language Processing.

Footnotes

  • We declare no conflict of interest.

  • © 2024 by American Journal of Neuroradiology
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Cite this article
Accepted Manuscript
Jikai Zhang, Dominic LaBella, Dylan Zhang, Jessica L. Houk, Jeffrey D. Rudie, Haotian Zou, Pranav Warman, Maciej A. Mazurowski, Evan Calabrese
Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma
American Journal of Neuroradiology Nov 2024, ajnr.A8580; DOI: 10.3174/ajnr.A8580

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Accepted Manuscript
Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma
Jikai Zhang, Dominic LaBella, Dylan Zhang, Jessica L. Houk, Jeffrey D. Rudie, Haotian Zou, Pranav Warman, Maciej A. Mazurowski, Evan Calabrese
American Journal of Neuroradiology Nov 2024, ajnr.A8580; DOI: 10.3174/ajnr.A8580
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