Purpose

The Athena Breast Health Network collaboration is a University of California system-wide project initiated with the intent to drive innovation in breast cancer prevention, screening, and treatment. This qualitative research examines provider perceptions and expectations of posttreatment breast cancer care across five network sites with the goal of better understanding provider behavior during the posttreatment phase of the cancer care trajectory.

Methods

Investigators at each site conducted semi-structured interviews with oncology specialists and primary care providers (PCPs). Interviews used case study examples and open- and closed-ended questions on the delivery of posttreatment breast cancer care. Informant responses were manually recorded by the interviewer, compiled in a database, then coded and analyzed using NVivo 9 software.

Results

There were 39 key informants across the sites: 14 medical oncologists, 7 radiation oncologists, 11 surgeons, 3 oncology nurses, and 4 PCPs. Care coordination was a major unprompted theme identified in the interviews. There was a perceived need for greater care coordination across institutions in order to improve delivery of posttreatment health care services and a need for greater care coordination within oncology, particularly to help avoid duplication of follow-up care and services. Participants expect frequent follow-up visits and to use biomarker tests and advanced imaging services as part of routine surveillance care. Implementing survivorship care programs was perceived as a way to improve care delivery.

Conclusions

These results identify a need for increased focus on care coordination during the posttreatment phase of breast cancer care within the University of California system and the potential for system and provider-level interventions that could help increase coordination of posttreatment care.

Implications for cancer survivors

Breast cancer survivors do not always receive evidence-based care. This research helps to better understand what motivates provider behavior during the posttreatment phase and lays a foundation for targeted interventions to increase adherence to evidence-based recommendations.

Introduction

Multiple oncology providers are involved in the initial breast cancer treatment. To better understand the patterns and quality of posttreatment breast cancer care, we surveyed patients who had been treated at each of the 5 University of California (UC) cancer centers.

Patients and methods

We identified breast cancer patients diagnosed in 2008-2009 from hospital tumor registries; invitations for the mailed survey on posttreatment care were sent between September 2011 and November 2012. The survey requested information on the number and type of provider visits, discussion of key topics, use of treatment summaries, and survivorship care plans (SCP).

Results

A total of 329 patients completed the survey. The mean age of respondents was 60.5 years, and they were 3.2 years since diagnosis (range, 1.6-4.8 years). A total of 82% had continued posttreatment care at a UC facility, and they reported high numbers of clinical follow-up visits, with an average of > 2 providers (range, 1-5). Surgery-only patients reported an average of 4 to 5 office visits a year; patients who received surgery, radiation, and chemotherapy reported 5 to 6 office visits a year. Overall, 45% of women reported receiving a treatment summary; receipt of a SCP was reported by 59%, occurring significantly more often among those in follow-up at a UC (P = .004).

Conclusion

Patients reported visits to multiple providers during their follow-up care, in excess of what is recommended by current guidelines. This was in spite of many women reporting that they had received a SCP.

Background and objectives

To investigate accuracy of magnetic resonance imaging (MRI) for measuring residual tumor size in breast cancer patients receiving neoadjuvant chemotherapy (NAC).

Methods

Ninety-eight patients were studied. Several MRI were performed during NAC for response monitoring, and the residual tumor size was measured on last MRI after completing NAC. Covariates, including age, tumor characteristics, biomarkers, NAC regimens, MRI scanners, and time from last MRI to operation, were analyzed. Univariate and Multivariate linear regression models were used to determine the predictive value of these covariates for MRI-pathology size discrepancy as the outcome measure.

Results

The mean (±SD) of the absolute difference between MRI and pathological residual tumor size was 1.0 ± 2.0 cm (range, 0-14 cm). Univariate regression analysis showed tumor type, morphology, HR status, HER2 status, and MRI scanner (1.5 T or 3.0 T) were significantly associated with MRI-pathology size discrepancy (all P < 0.05). Multivariate regression analyses demonstrated that only tumor type, tumor morphology, and biomarker status considering both HR and HER-2 were independent predictors (P = 0.0014, 0.0032, and 0.0286, respectively).

Conclusion

The accuracy of MRI in evaluating residual tumor size depends on tumor type, morphology, and biomarker status. The information may be considered in surgical planning for NAC patients.

AIMS

This study aimed to evaluate the influence of hormonal receptor and Ki-67 proliferation marker in predicting MRI accuracy of measuring residual tumor size in HER2 negative breast cancer receiving neoadjuvant chemotherapy (NAC).

METHODS

Fifty-four women were studied. Patients received AC and/or taxane-based regimens. The accuracy of MR determined clinical complete response (CCR) was compared to pathological complete response (pCR). The size of detectable residual tumor on MRI was correlated with pathology-diagnosed tumor size using Pearson’s correlation.

RESULTS

MRI correctly diagnosed 16 of the 17 pCR patients. There were 8 false negative diagnoses, 7 hormonal receptor (HR) positive and one HR negative. The overall sensitivity, specificity, and accuracy of MRI were 78%, 94%, and 83%, respectively. The positive predictive value was 97% and the negative predictive value was 67%. For MRI-pathology tumor size correlation, HR negative cancers showed a higher correlation (R=0.79) than HR positive cancers (R= 0.58). A worse MRI-pathology size discrepancy was found in HR positive cancer than in HR negative cancer (1.6±2.8 cm vs. 0.56±0.9 cm, p=0.05). Tumors with a low Ki-67 proliferation (<40%) showed a larger size discrepancy than those with a high Ki-67 proliferation (≥40%) (1.2±2.0 cm vs. 0.4±0.8 cm p=0.05).

CONCLUSIONS

The results showed that the diagnostic performance of MRI for breast cancer undergoing NAC is associated with molecular biomarker profile. Among HER2 negative tumors, the accuracy of MRI was worse in HR positive than negative cancers, and also worse in low proliferative than high proliferative tumors. These findings may help in surgical planning.

Background and objectives

To investigate accuracy of magnetic resonance imaging (MRI) for measuring residual tumor size in breast cancer patients receiving neoadjuvant chemotherapy (NAC).

Methods

Ninety-eight patients were studied. Several MRI were performed during NAC for response monitoring, and the residual tumor size was measured on last MRI after completing NAC. Covariates, including age, tumor characteristics, biomarkers, NAC regimens, MRI scanners, and time from last MRI to operation, were analyzed. Univariate and Multivariate linear regression models were used to determine the predictive value of these covariates for MRI-pathology size discrepancy as the outcome measure.

Results

The mean (±SD) of the absolute difference between MRI and pathological residual tumor size was 1.0 ± 2.0 cm (range, 0-14 cm). Univariate regression analysis showed tumor type, morphology, HR status, HER2 status, and MRI scanner (1.5 T or 3.0 T) were significantly associated with MRI-pathology size discrepancy (all P < 0.05). Multivariate regression analyses demonstrated that only tumor type, tumor morphology, and biomarker status considering both HR and HER-2 were independent predictors (P = 0.0014, 0.0032, and 0.0286, respectively).

Conclusion

The accuracy of MRI in evaluating residual tumor size depends on tumor type, morphology, and biomarker status. The information may be considered in surgical planning for NAC patients.