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Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study

 Philipp Vollmuth  ;  Martha Foltyn  ;  Raymond Y Huang  ;  Norbert Galldiks  ;  Jens Petersen  ;  Fabian Isensee  ;  Martin J van den Bent  ;  Frederik Barkhof  ;  Ji Eun Park  ;  Yae Won Park  ;  Sung Soo Ahn  ;  Gianluca Brugnara  ;  Hagen Meredig  ;  Rajan Jain  ;  Marion Smits  ;  Whitney B Pope  ;  Klaus Maier-Hein  ;  Michael Weller  ;  Patrick Y Wen  ;  Wolfgang Wick  ;  Martin Bendszus 
 NEURO-ONCOLOGY, Vol.25(3) : 533-543, 2023-03 
Journal Title
Issue Date
Artificial Intelligence ; Brain Neoplasms* / diagnostic imaging ; Brain Neoplasms* / pathology ; Brain Neoplasms* / therapy ; Glioblastoma* / pathology ; Glioma* / diagnostic imaging ; Glioma* / pathology ; Glioma* / therapy ; Humans ; Reproducibility of Results
Artificial intelligence (AI)-based decision support ; RANO ; tumor response assessment ; tumor volumetry
Background. To assess whether artificial intelligence (AI)-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the Response Assessment in Neuro-Oncology (RANO) criteria.,Methods. A series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the first round the TTP was evaluated based on the RANO criteria, whereas in the second round the TTP was evaluated by incorporating additional information from AI-enhanced MRI sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and P-values obtained using bootstrap resampling.,Results. The CCC of TTP-measurements between investigators was 0.77 (95% CI = 0.69,0.88) with RANO alone and increased to 0.91 (95% CI = 0.82,0.95) with AI-based decision support (P = .005). This effect was significantly greater (P = .008) for patients with lower-grade gliomas (CCC = 0.70 [95% CI = 0.56,0.85] without vs. 0.90 [95% CI = 0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC = 0.83 [95% CI = 0.75,0.92] without vs. 0.86 [95% CI = 0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (P = .02).,Conclusions. AI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).,
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1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Park, Yae Won(박예원) ORCID logo https://orcid.org/0000-0001-8907-5401
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
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