Predictive Value of Breast MRI Background Parenchymal Enhancement for Neoadjuvant Treatment Response among HER2− Patients. Issue 4 (22nd July 2020)

• Record Type: Journal Article
• Authors: Arasu, Vignesh A; Kim, Paul; Li, Wen; Strand, Fredrik; McHargue, Cody; Harnish, Roy; Newitt, David C; Jones, Ella F; Glymour, M Maria; Kornak, John; Esserman, Laura J; Hylton, Nola M
• Is Part Of: Journal of breast imaging. Volume 2:Issue 4(2020)
• Page Start: 352
• Page End: 360
• Publication Date: 2020-07-22
• Subjects: background parenchymal enhancement -- breast cancer -- HER2− breast cancer -- magnetic resonance imaging -- neoadjuvant chemotherapy -- tumor response; Breast -- Imaging -- Periodicals; Breast -- Cancer -- Diagnosis -- Periodicals; 616.994490
• DOI: 10.1093/jbi/wbaa028
• Languages: English
• ISSNs: 2631-6110

Abstract

Abstract: Objective: Women with advanced HER2− breast cancer have limited treatment options. Breast MRI functional tumor volume (FTV) is used to predict pathologic complete response (pCR) to improve treatment efficacy. In addition to FTV, background parenchymal enhancement (BPE) may predict response and was explored for HER2− patients in the I-SPY-2 TRIAL. Methods: Women with HER2− stage II or III breast cancer underwent prospective serial breast MRIs during four neoadjuvant chemotherapy timepoints. BPE was quantitatively calculated using whole-breast manual segmentation. Logistic regression models were systematically explored using pre-specified and optimized predictor selection based on BPE or combined with FTV. Results: A total of 352 MRI examinations in 88 patients (29 with pCR, 59 non-pCR) were evaluated. Women with hormone receptor (HR)+HER2− cancers who achieved pCR demonstrated a significantly greater decrease in BPE from baseline to pre-surgery compared to non-pCR patients (odds ratio 0.64, 95% confidence interval (CI): 0.39–0.92, P = 0.04). The associated BPE area under the curve (AUC) was 0.77 (95% CI: 0.56–0.98), comparable to the range of FTV AUC estimates. Among multi-predictor models, the highest cross-validated AUC of 0.81 (95% CI: 0.73–0.90) was achieved with combined FTV+HR predictors, while adding BPE to FTV+HR models had an estimated AUC of 0.82 (95% CI: 0.74–0.92). Conclusion: Among women with HER2− cancer, BPE alone demonstrated association with pCR in women with HR+HER2− breast cancer, with similar diagnostic performance to FTV. BPE predictors remained significant in multivariate FTV models, but without added discrimination for pCR prediction. This may be due to small sample size limiting ability to create subtype-specific multivariate models.