Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography

© 2019 The Authors Strain elastography was used to monitor response to neoadjuvant chemotherapy (NAC) in 92 patients with biopsy-proven, locally advanced breast cancer. Strain elastography data were collected before, during, and after NAC. Relative changes in tumor strain ratio (SR) were calculated over time, and responder status was classified according to tumor size changes. Statistical analyses determined the significance of changes in SR over time and between response groups. Machine learning techniques, such as a naïve Bayes classifier, were used to evaluate the performance of the SR as a marker for Miller-Payne pathological endpoints. With pathological complete response (pCR) as an endpoint, a significant difference (P < .01) in the SR was observed between response groups as early as 2 weeks into NAC. Naïve Bayes classifiers predicted pCR with a sensitivity of 84%, specificity of 85%, and area under the curve of 81% at the preoperative scan. This study demonstrates that strain elastography may be predictive of NAC response in locally advanced breast cancer as early as 2 weeks into treatment, with high sensitivity and specificity, granting it the potential to be used for active monitoring of tumor response to chemotherapy

Budget Impact Analysis of a Breast Rapid Diagnostic Unit

Background: The Odette Cancer Centre’s recent implementation of a rapid diagnostic unit (RDU) for breast lesions has significantly decreased wait times to diagnosis. However, the economic impact of the unit remains unknown. This project defined the development and implementation costs and the operational costs of a breast RDU in a tertiary care facility. Methods: From an institutional perspective, a budget impact analysis identified the direct costs associated with the breast RDU. A base-case model was also used to calculate the cost per patient to achieve a diagnosis. Sensitivity analyses computed costs based on variations in key components. Costs are adjusted to 2015 valuations using health care–specific consumer price indices and are reported in Canadian dollars. Results: Initiation cost for the RDU was $366,243. The annual operational cost for support staff was$111,803. The average per-patient clinical cost for achieving a diagnosis was $770. Sensitivity analyses revealed that, if running at maximal institutional capacity, the total annual clinical cost for achieving a diagnosis could range between$136,080 and $702,675. Conclusions: Establishment and maintenance of a breast rdu requires significant investment to achieve reductions in time to diagnosis. Expenditures ought to be interpreted in the context of institutional patient volumes and trade-offs in patient-centred outcomes, including lessened patient anxiety and possibly shorter times to definitive treatment. Our study can be used as a resource-planning tool for future rdus in health care systems wishing to improve diagnostic efficiency