Breast cancer risk prediction and mammography biopsy decisions: a model-based study.

BACKGROUND: Controversy continues about screening mammography, in part because of the risk of false-negative and false-positive mammograms. Pre-test breast cancer risk factors may improve the positive and negative predictive value of screening. PURPOSE: To create a model that estimates the potential impact of pre-test risk prediction using clinical and genomic information on the reclassification of women with abnormal mammograms (BI-RADS3 and BI-RADS4 [Breast Imaging-Reporting and Data System]) above and below the threshold for breast biopsy. METHODS: The current study modeled 1-year breast cancer risk in women with abnormal screening mammograms using existing data on breast cancer risk factors, 12 validated breast cancer single-nucleotide polymorphisms (SNPs), and probability of cancer given the BI-RADS category. Examination was made of reclassification of women above and below biopsy thresholds of 1%, 2%, and 3% risk. The Breast Cancer Surveillance Consortium data were collected from 1996 to 2002. Data analysis was conducted in 2010 and 2011. RESULTS: Using a biopsy risk threshold of 2% and the standard risk factor model, 5% of women with a BI-RADS3 mammogram had a risk above the threshold, and 3% of women with BI-RADS4A mammograms had a risk below the threshold. The addition of 12 SNPs in the model resulted in 8% of women with a BI-RADS3 mammogram above the threshold for biopsy and 7% of women with BI-RADS4A mammograms below the threshold. CONCLUSIONS: The incorporation of pre-test breast cancer risk factors could change biopsy decisions for a small proportion of women with abnormal mammograms. The greatest impact comes from standard breast cancer risk factors.

Cost effectiveness of personalized therapy for first-line treatment of stage IV and recurrent incurable adenocarcinoma of the lung.

PURPOSE: Patients with epidermal growth factor receptor (EGFR) mutation-positive stage IV adenocarcinoma have improved survival with tyrosine kinase inhibitor (TKI) treatments, but the cost effectiveness of personalized first-line therapy using EGFR mutation testing is unknown. METHODS: We created a decision analytic model comparing the costs and effects of platinum combination chemotherapy with personalized therapy in which patients with EGFR mutation-positive tumors were treated with erlotinib. We used two testing strategies: testing only those with tissue available and performing a repeat biopsy if tissue was not available versus three nontargeted chemotherapy regimens (ie, carboplatin and paclitaxel; carboplatin and pemetrexed; and carboplatin, pemetrexed, and bevacizumab). RESULTS: Compared with a carboplatin plus paclitaxel regimen, targeted therapy based on testing available tissue yielded an incremental cost-effectiveness ratio (ICER) of $110,644 per quality-adjusted life year (QALY), and the rebiopsy strategy yielded an ICER of $122,219 per QALY. Probabilistic sensitivity analysis revealed substantial uncertainty around these point estimates. With a willingness to pay of $100,000 per QALY, the testing strategy was cost effective 58% of the time, and the rebiopsy strategy was cost effective 54% of the time. Personalized therapy with an EGFR TKI was more favorable when the nontargeted chemotherapy regimen was more expensive. Compared with carboplatin, pemetrexed, and bevacizumab, ICERs were $25,547 per QALY for the testing strategy and $44,036 per QALY for the rebiopsy strategy. CONCLUSION: Although specific clinical circumstances should guide therapy, our cost-effectiveness analysis supports the strategy of testing for EGFR mutations in patients with stage IV or recurrent adenocarcinoma of the lung, rebiopsying patients if insufficient tissue is available for testing, and treating patients with EGFR mutations with erlotinib as first-line therapy.