Vollmuth, Philipp, Foltyn, Martha, Huang, Raymond Y., Galldiks, Norbert, Petersen, Jens, Isensee, Fabian, van den Bent, Martin J., Barkhof, Frederik ORCID: 0000-0003-3543-3706, Park, Ji Eun ORCID: 0000-0002-4419-4682, Park, Yae Won ORCID: 0000-0001-8907-5401, Ahn, Sung Soo ORCID: 0000-0002-0503-5558, Brugnara, Gianluca, Meredig, Hagen, Jain, Rajan, Smits, Marion ORCID: 0000-0001-5563-2871, Pope, Whitney B., Maier-Hein, Klaus, Weller, Michael ORCID: 0000-0002-1748-174X, Wen, Patrick Y., Wick, Wolfgang and Bendszus, Martin . Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study. Neuro-Oncology. CARY: OXFORD UNIV PRESS INC. ISSN 1523-5866

Full text not available from this repository.

Abstract

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).

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Vollmuth, PhilippUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Foltyn, MarthaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Huang, Raymond Y.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Galldiks, NorbertUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Petersen, JensUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Isensee, FabianUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
van den Bent, Martin J.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Barkhof, FrederikUNSPECIFIEDorcid.org/0000-0003-3543-3706UNSPECIFIED
Park, Ji EunUNSPECIFIEDorcid.org/0000-0002-4419-4682UNSPECIFIED
Park, Yae WonUNSPECIFIEDorcid.org/0000-0001-8907-5401UNSPECIFIED
Ahn, Sung SooUNSPECIFIEDorcid.org/0000-0002-0503-5558UNSPECIFIED
Brugnara, GianlucaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Meredig, HagenUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Jain, RajanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Smits, MarionUNSPECIFIEDorcid.org/0000-0001-5563-2871UNSPECIFIED
Pope, Whitney B.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Maier-Hein, KlausUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Weller, MichaelUNSPECIFIEDorcid.org/0000-0002-1748-174XUNSPECIFIED
Wen, Patrick Y.UNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wick, WolfgangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bendszus, MartinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-696733
DOI: 10.1093/neuonc/noac189
Journal or Publication Title: Neuro-Oncology
Publisher: OXFORD UNIV PRESS INC
Place of Publication: CARY
ISSN: 1523-5866
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
HIGH-GRADE GLIOMAS; CRITERIAMultiple languages
Oncology; Clinical NeurologyMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/69673

Downloads

Downloads per month over past year

Altmetric

Export

Actions (login required)

View Item View Item