Vollmuth, Philipp;
Foltyn, Martha;
Huang, Raymond Y;
Galldiks, Norbert;
Petersen, Jens;
Isensee, Fabian;
van den Bent, Martin J;
... Bendszus, Martin; + view all
(2023)
Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study.
Neuro-Oncology
, 25
(3)
pp. 533-543.
10.1093/neuonc/noac189.
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Vollmuth - AI-based decision support improves reproducibility of tumor response assessment in neuro-oncology - N-O-D-22-00258R2.pdf - Accepted Version Download (2MB) | Preview |
Abstract
BACKGROUND: To assess whether 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 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 1 st round the TTP was evaluated based on the RANO-criteria, whereas in the 2 nd 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=0.005). This effect was significantly greater (p=0.008) 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=0.02). 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).
| Type: | Article |
|---|---|
| Title: | Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/neuonc/noac189 |
| Publisher version: | https://doi.org/10.1093/neuonc/noac189 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | AI-based decision support, RANO, tumor response assessment, tumor volumetry |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10154209 |
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