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.
Xingxing Zheng, Yuhong Huang, Yingyi Lin, Teng Zhu, Jiachen Zou, Shuxia Wang, Kun Wang
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引用次数: 0
Abstract
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.
EJNMMI ResearchRADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING&nb-
CiteScore
5.90
自引率
3.10%
发文量
72
审稿时长
13 weeks
期刊介绍:
EJNMMI Research publishes new basic, translational and clinical research in the field of nuclear medicine and molecular imaging. Regular features include original research articles, rapid communication of preliminary data on innovative research, interesting case reports, editorials, and letters to the editor. Educational articles on basic sciences, fundamental aspects and controversy related to pre-clinical and clinical research or ethical aspects of research are also welcome. Timely reviews provide updates on current applications, issues in imaging research and translational aspects of nuclear medicine and molecular imaging technologies.
The main emphasis is placed on the development of targeted imaging with radiopharmaceuticals within the broader context of molecular probes to enhance understanding and characterisation of the complex biological processes underlying disease and to develop, test and guide new treatment modalities, including radionuclide therapy.