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Background/purpose: We estimated loss-of-life expectancy (LE) and lifetime medical expenditures (LME) stratified by stages to evaluate the cost-effectiveness of breast cancer (BC) screening in Taiwan.
Methods: We interlinked four national databases- Cancer Registry, Mortality Registry, National Health Insurance Claim, and Mammography Screening. A cohort of 123,221 BC was identified during 2002-2015 and followed until December 31, 2017. We estimated LE and loss-of-LE by rolling extrapolation algorithm using age-, sex-, and calendar-year-matched referents simulated from vital statistics. LME was estimated by multiplying monthly cost with survival probability and adjusted for annual discount rate. We calculated incremental cost-effectiveness ratio (ICER) by comparing the loss-of-LE of those detected by screening versus non-screening after accounting for administration fees and radiation-related excess BC.
Results: The LEs of stages I, II, III, and IV were 31.4, 27.2, 20.0, and 5.2 years, respectively, while the loss-of-LEs were 1.2, 4.9, 11.7, and 25.0 years with corresponding LMEs of US$ 73,791, 79,496, 89,962, and 66,981, respectively. The difference in LE between stages I and IV was 26.2 years while that of loss-of-LE was 23.8 years, which implies that a potential lead time bias may exist if diagnosis at younger ages for earlier stages were not adjusted for. The ICER of mammography seemed cost-saving after the coverage exceeded half a million.
Conclusion: Mammography could detect BC early and be cost-saving after adjustment for different distributions of age and calendar year of diagnosis. Future studies exploring healthcare expenditure and impaired quality of life for false-positive cases are warranted.
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http://dx.doi.org/10.1016/j.jfma.2021.06.013 | DOI Listing |
J Med Screen
September 2025
The Cancer Registry of Norway, Department of Screening programs, Norwegian Institute of Public Health, Oslo, Norway.
ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening program concerning cost-effectiveness and time savings for radiologists.MethodsIn a decision tree model using recent data from AI vendors and the Cancer Registry of Norway, and assuming equal effectiveness of radiologists plus AI compared to standard practice, we simulated costs, effects and radiologist person-years over the next 20 years under different scenarios: 1) Assuming a €1 additional running cost of AI instead of the €3 assumed in the base case, 2) varying the AI-score thresholds for single vs. double readings, 3) varying the consensus and recall rates, and 4) reductions in the interval cancer rate compared to standard practice.
View Article and Find Full Text PDFIEEE Trans Med Imaging
September 2025
Mammography is a primary method for early screening, and developing deep learning-based computer-aided systems is of great significance. However, current deep learning models typically treat each image as an independent entity for diagnosis, rather than integrating images from multiple views to diagnose the patient. These methods do not fully consider and address the complex interactions between different views, resulting in poor diagnostic performance and interpretability.
View Article and Find Full Text PDFRadiology
September 2025
Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Korea.
Background The optimal surgical management of human epidermal growth factor receptor 2 (HER2)-positive breast cancer with calcifications remains controversial, particularly when pathologic complete response (pCR) is suspected. Purpose To identify factors associated with pCR after neoadjuvant chemotherapy in patients with HER2-positive breast cancer and assess whether calcifications affect the performance of radiologic complete response (rCR) at MRI for predicting pCR. Materials and Methods This retrospective study included patients with HER2-positive breast cancer who received neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab and underwent surgery between January 2021 and October 2023.
View Article and Find Full Text PDFPLoS One
September 2025
Korea University College of Medicine, Seoul, Republic of Korea.
Purpose: To develop and validate a deep learning-based model for automated evaluation of mammography phantom images, with the goal of improving inter-radiologist agreement and enhancing the efficiency of quality control within South Korea's national accreditation system.
Materials And Methods: A total of 5,917 mammography phantom images were collected from the Korea Institute for Accreditation of Medical Imaging (KIAMI). After preprocessing, 5,813 images (98.
Radiol Med
September 2025
Breast Imaging Division, Radiology Department, IEO European Institute of Oncology IRCCS, 20141, Milan, Italy.
Metastatic involvement (MB) of the breast from extramammary malignancies is rare, with an incidence of 0.09-1.3% of all breast malignancies.
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