guest ::
| Home > Publications database > Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study > print |
| Home > Publications database > Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study > print |
| 001 | 909633 | ||
| 005 | 20231027114345.0 | ||
| 024 | 7 | _ | |a 10.1093/neuonc/noac189 |2 doi |
| 024 | 7 | _ | |a 1522-8517 |2 ISSN |
| 024 | 7 | _ | |a 1523-5866 |2 ISSN |
| 024 | 7 | _ | |a 2128/34156 |2 Handle |
| 024 | 7 | _ | |a 35917833 |2 pmid |
| 024 | 7 | _ | |a WOS:000843014300001 |2 WOS |
| 037 | _ | _ | |a FZJ-2022-03304 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Vollmuth, Philipp |0 0000-0002-6224-0064 |b 0 |
| 245 | _ | _ | |a Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study |
| 260 | _ | _ | |a Oxford |c 2023 |b Oxford Univ. Press |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1679045188_15079 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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).Keywords: AI-based decision support; RANO; tumor response assessment; tumor volumetry. |
| 536 | _ | _ | |a 5252 - Brain Dysfunction and Plasticity (POF4-525) |0 G:(DE-HGF)POF4-5252 |c POF4-525 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Foltyn, Martha |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Huang, Raymond Y |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Galldiks, Norbert |0 P:(DE-Juel1)143792 |b 3 |e Corresponding author |
| 700 | 1 | _ | |a Petersen, Jens |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Isensee, Fabian |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a van den Bent, Martin J |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Barkhof, Frederik |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Park, Ji Eun |0 P:(DE-Juel1)176252 |b 8 |u fzj |
| 700 | 1 | _ | |a Park, Yae Won |0 0000-0001-8907-5401 |b 9 |
| 700 | 1 | _ | |a Ahn, Sung Soo |0 0000-0002-0503-5558 |b 10 |
| 700 | 1 | _ | |a Brugnara, Gianluca |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Meredig, Hagen |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Jain, Rajan |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Smits, Marion |0 0000-0001-5563-2871 |b 14 |
| 700 | 1 | _ | |a Pope, Whitney B |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Maier-Hein, Klaus |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Weller, Michael |0 0000-0002-1748-174X |b 17 |
| 700 | 1 | _ | |a Wen, Patrick Y |0 P:(DE-HGF)0 |b 18 |
| 700 | 1 | _ | |a Wick, Wolfgang |0 0000-0002-6171-634X |b 19 |
| 700 | 1 | _ | |a Bendszus, Martin |0 0000-0002-9094-6769 |b 20 |
| 773 | _ | _ | |a 10.1093/neuonc/noac189 |g p. noac189 |0 PERI:(DE-600)2094060-9 |n 3 |p 533–543 |t Neuro-Oncology |v 25 |y 2023 |x 1522-8517 |
| 856 | 4 | _ | |y Published on 2022-08-02. Available in OpenAccess from 2023-08-02. |u https://juser.fz-juelich.de/record/909633/files/POSTPRINT.pdf |
| 856 | 4 | _ | |y Published on 2022-08-02. Available in OpenAccess from 2023-08-02. |u https://juser.fz-juelich.de/record/909633/files/Postprint.pdf |
| 909 | C | O | |o oai:juser.fz-juelich.de:909633 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)143792 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 8 |6 P:(DE-Juel1)176252 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5252 |x 0 |
| 914 | 1 | _ | |y 2023 |
| 915 | _ | _ | |a Embargoed OpenAccess |0 StatID:(DE-HGF)0530 |2 StatID |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2022-11-08 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2022-11-08 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2023-10-24 |w ger |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b NEURO-ONCOLOGY : 2022 |d 2023-10-24 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-10-24 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-10-24 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |d 2023-10-24 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-10-24 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-10-24 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1110 |2 StatID |b Current Contents - Clinical Medicine |d 2023-10-24 |
| 915 | _ | _ | |a IF >= 15 |0 StatID:(DE-HGF)9915 |2 StatID |b NEURO-ONCOLOGY : 2022 |d 2023-10-24 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-3-20090406 |k INM-3 |l Kognitive Neurowissenschaften |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)INM-3-20090406 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|