A cost-effective breast cancer screening strategy for Urban China: Findings from a Shenzhen-based modeling study
Changqing Tu, Yuke Zhong, Huan Li, Haifeng Qi, Qian Lu, Xuesen He, Ruofei Du, Ruofei Du, Ruofei Du, Ruofei Du

TL;DR
The study finds that a specific breast cancer screening strategy in Shenzhen, China, is cost-effective and improves early detection.
Contribution
A new cost-effective breast cancer screening strategy combining CBE, BUS, and MAM is evaluated for urban China.
Findings
The (CBE + BUS) + MAM strategy is cost-effective with an ICUR of 140,915 CNY/QALY.
Early detection rates were high, with 88% of cases diagnosed at early stages.
The strategy remains robust in 76% of Monte Carlo simulations.
Abstract
Early detection through breast cancer screening significantly enhances survival rates and reduces mortality. However, financial constraints in low- and middle-income countries often limit the implementation of large-scale screening programs. This study evaluates the cost-effectiveness of a combined Clinical Breast Examination (CBE), Breast Ultrasound (BUS), and supplementary Mammography (MAM), screening strategy for women aged 35–65 in Shenzhen, China. It further identifies optimal screening protocols by analyzing variations in screening frequency, starting/ending ages, and long-term health outcomes. A Markov model was developed from a societal perspective to assess the lifetime cost-effectiveness of biennial (CBE + BUS)+MAM screening for women aged 35–65. A total of 27 strategies were simulated, varying screening frequency (annual, biennial, triennial), age at initiation (35, 40, 45),…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGlobal Cancer Incidence and Screening · Digital Radiography and Breast Imaging · AI in cancer detection
