Cited 0 times in Scipus Cited Count

Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation

DC Field Value Language
dc.contributor.authorPark, YW-
dc.contributor.authorShin, SJ-
dc.contributor.authorEom, J-
dc.contributor.authorLee, H-
dc.contributor.authorYou, SC-
dc.contributor.authorAhn, SS-
dc.contributor.authorLim, SM-
dc.contributor.authorPark, RW-
dc.contributor.authorLee, SK-
dc.date.accessioned2023-02-13T06:23:10Z-
dc.date.available2023-02-13T06:23:10Z-
dc.date.issued2022-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/24509-
dc.description.abstractThe heterogeneity of MRI is one of the major reasons for decreased performance of a radiomics model on external validation, limiting the model's generalizability and clinical application. We aimed to establish a generalizable radiomics model to predict meningioma grade on external validation through leveraging Cycle-Consistent Adversarial Networks (CycleGAN). In this retrospective study, 257 patients with meningioma were included in the institutional training set. Radiomic features (n = 214) were extracted from T2-weighted (T2) and contrast-enhanced T1 (T1C) images. After radiomics feature selection, extreme gradient boosting classifiers were developed. The models were validated in the external validation set consisting of 61 patients with meningiomas. To reduce the gap in generalization associated with the inter-institutional heterogeneity of MRI, the smaller image set style of the external validation was translated into the larger image set style of the institutional training set using CycleGAN. On external validation before CycleGAN application, the performance of the combined T2 and T1C models showed an area under the curve (AUC), accuracy, and F1 score of 0.77 (95% confidence interval 0.63-0.91), 70.7%, and 0.54, respectively. After applying CycleGAN, the performance of the combined T2 and T1C models increased, with an AUC, accuracy, and F1 score of 0.83 (95% confidence interval 0.70-0.97), 73.2%, and 0.59, respectively. Quantitative metrics (by Frechet Inception Distance) showed that CycleGAN can decrease inter-institutional image heterogeneity while preserving predictive information. In conclusion, leveraging CycleGAN may be helpful to increase the generalizability of a radiomics model in differentiating meningioma grade on external validation.-
dc.language.isoen-
dc.subject.MESHArea Under Curve-
dc.subject.MESHHumans-
dc.subject.MESHMagnetic Resonance Imaging-
dc.subject.MESHMeningeal Neoplasms-
dc.subject.MESHMeningioma-
dc.subject.MESHRetrospective Studies-
dc.titleCycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation-
dc.typeArticle-
dc.identifier.pmid35488007-
dc.identifier.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9055063-
dc.contributor.affiliatedAuthorPark, RW-
dc.type.localJournal Papers-
dc.identifier.doi10.1038/s41598-022-10956-9-
dc.citation.titleScientific reports-
dc.citation.volume12-
dc.citation.number1-
dc.citation.date2022-
dc.citation.startPage7042-
dc.citation.endPage7042-
dc.identifier.bibliographicCitationScientific reports, 12(1). : 7042-7042, 2022-
dc.identifier.eissn2045-2322-
dc.relation.journalidJ020452322-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Biomedical Informatics
Files in This Item:
35488007.pdfDownload

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse