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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Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Public Library Science
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
ObjectivesThe 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.
Açıklama
Anahtar Kelimeler
Lymph-Node Metastasis, Survival
Kaynak
Plos One
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
18
Sayı
9
