Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT

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dc.authoridBulut, Gulcan/0000-0001-7382-0972
dc.contributor.authorBulut, Gulcan
dc.contributor.authorAtilgan, Hasan Ikbal
dc.contributor.authorCinarer, Gokalp
dc.contributor.authorKilic, Kazim
dc.contributor.authorYikar, Deniz
dc.contributor.authorParlar, Tuba
dc.date.accessioned2024-09-18T21:06:34Z
dc.date.available2024-09-18T21:06:34Z
dc.date.issued2023
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractObjectivesThe aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC).IntroductionNAC is the standard treatment for locally advanced breast cancer (LABC). Pathological complete response (pCR) after NAC is considered a good predictor of disease-free survival (DFS) and overall survival (OS).Therefore, there is a need to develop methods that can predict the pCR at the time of diagnosis.MethodsThis article was designed as a retrospective chart study.For the convolutional neural network model, a total of 355 PET/CT images of 31 patients were used. All patients had primary breast surgery after completing NAC.ResultsPathological complete response was obtained in a total of 9 patients. The study results show that our proposed deep convolutional neural networks model achieved a remarkable success with an accuracy of 84.79% to predict pathological complete response.ConclusionIt was concluded that deep learning methods can predict breast cancer treatment.en_US
dc.identifier.doi10.1371/journal.pone.0290543
dc.identifier.issn1932-6203
dc.identifier.issue9en_US
dc.identifier.pmid37708209en_US
dc.identifier.scopus2-s2.0-85171344867en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0290543
dc.identifier.urihttps://hdl.handle.net/20.500.12483/13696
dc.identifier.volume18en_US
dc.identifier.wosWOS:001079087100072en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPublic Library Scienceen_US
dc.relation.ispartofPlos Oneen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLymph-Node Metastasisen_US
dc.subjectSurvivalen_US
dc.titlePrediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CTen_US
dc.typeArticleen_US

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