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18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer

dc.contributor.authorXingxing Zheng
dc.contributor.authorYuhong Huang
dc.contributor.authorYingyi Lin
dc.contributor.authorTeng Zhu
dc.contributor.authorJiachen Zou
dc.contributor.authorShuxia Wang
dc.contributor.authorKun Wang
dc.contributor.otherDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
dc.contributor.otherDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
dc.contributor.otherShantou University Medical College
dc.contributor.otherDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
dc.contributor.otherDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
dc.contributor.otherDepartment of Nuclear Medicine and PET Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
dc.contributor.otherDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University
dc.date.accessioned2023-12-10T12:31:27Z
dc.date.accessioned2025-10-08T08:07:15Z
dc.date.available2025-10-08T08:07:15Z
dc.date.issued2023-12
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/35636
dc.description.abstractAbstract Background This study aimed to assess whether a combined model incorporating radiomic and depth features extracted from PET/CT can predict disease-free survival (DFS) in patients who failed to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy. Results This study retrospectively included one hundred and five non-pCR patients. After a median follow-up of 71 months, 15 and 7 patients experienced recurrence and death, respectively. The primary tumor volume underwent feature extraction, yielding a total of 3644 radiomic features and 4096 depth features. The modeling procedure employed Cox regression for feature selection and utilized Cox proportional-hazards models to make predictions on DFS. Time-dependent receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to evaluate and compare the predictive performance of different models. 2 clinical features (RCB, cT), 4 radiomic features, and 7 depth features were significant predictors of DFS and were included to develop models. The integrated model incorporating RCB, cT, and radiomic and depth features extracted from PET/CT images exhibited the highest accuracy for predicting 5-year DFS in the training (AUC 0.943) and the validation cohort (AUC 0.938). Conclusion The integrated model combining radiomic and depth features extracted from PET/CT images can accurately predict 5-year DFS in non-pCR patients. It can help identify patients with a high risk of recurrence and strengthen adjuvant therapy to improve survival.
dc.language.isoEN
dc.publisherSpringerOpen
dc.subject.lccMedical physics. Medical radiology. Nuclear medicine
dc.title18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer
dc.typeArticle
dc.description.keywordsBreast cancer
dc.description.keywordsDeep learning
dc.description.keywordsRadiomics
dc.description.keywordsPET/CT
dc.description.keywordsNeoadjuvant chemotherapy
dc.description.pages1-11
dc.description.doi10.1186/s13550-023-01053-7
dc.title.journalEJNMMI Research
dc.identifier.e-issn2191-219X
dc.identifier.oai6f3cac4b98ae45779368c06ae228d729
dc.journal.infoVolume 13, Issue 1


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