Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors

• 成果类型: Article
• 署名作者: Chhatwal, Jagpreet; Alagoz, Oguzhan; Burnside, Elizabeth S.
• 署名单位: Merck & Company; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.1100.0877
• 发表日期: 2010
• 页码: 1577-1591
• 关键词: cancer-society guidelines; computer-aided diagnosis; false-negative rate; core-needle-biopsy; carcinoma in-situ; screening mammography; initial-experience; database format; lesions; radiologists

摘要

Breast cancer is the most common non-skin cancer affecting women in the United States, where every year more than 20 million mammograms are performed. Breast biopsy is commonly performed on the suspicious findings on mammograms to confirm the presence of cancer. Currently, 700,000 biopsies are performed annually in the U. S.; 55%-85% of these biopsies ultimately are found to be benign breast lesions, resulting in unnecessary treatments, patient anxiety, and expenditures. This paper addresses the decision problem faced by radiologists: When should a woman be sent for biopsy based on her mammographic features and demographic factors? This problem is formulated as a finite-horizon discrete-time Markov decision process. The optimal policy of our model shows that the decision to biopsy should take the age of patient into account; particularly, an older patient's risk threshold for biopsy should be higher than that of a younger patient. When applied to the clinical data, our model outperforms radiologists in the biopsy decision-making problem. This study also derives structural properties of the model, including sufficiency conditions that ensure the existence of a control-limit type policy and nondecreasing control-limits with age.