Jikai Zhang, Dominic LaBella, Dylan Zhang, Jessica L Houk, Jeffrey D Rudie, Haotian Zou, Pranav Warman, Maciej A Mazurowski, Evan Calabrese
{"title":"Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.","authors":"Jikai Zhang, Dominic LaBella, Dylan Zhang, Jessica L Houk, Jeffrey D Rudie, Haotian Zou, Pranav Warman, Maciej A Mazurowski, Evan Calabrese","doi":"10.3174/ajnr.A8580","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and purpose: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p><p><strong>Abbreviations: </strong>GBM= Glioblastoma; RANO= Response Assessment in Neuro-Oncology; BTRADS= Brain Tumor Reporting and Data System; NLP = Natural Language Processing.</p>","PeriodicalId":93863,"journal":{"name":"AJNR. American journal of neuroradiology","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AJNR. American journal of neuroradiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3174/ajnr.A8580","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.