Performance of a clinical and imaging-based multivariate model as decision support tool to help save unnecessary...

To investigate the performance of an imaging and biopsy parameters-based multivariate model in decreasing unnecessary surgeries for high-risk breast lesions.

In an IRB-approved study, we retrospectively reviewed all high-risk lesions (HRL) identified at imaging-guided biopsy in our institution between July 1, 2014-July 1, 2017. Lesions were categorized high-risk-I (HR-I = atypical ductal hyperplasia, atypical lobular hyperplasia, lobular carcinoma in situ and atypical papillary lesion) and II (HR-II = Flat epithelial atypia, radial scar, benign papilloma). Patient risk factors, lesion features, detection and biopsy modality, excision and cancer upgrade rates were collected. Reference standard for upgrade was either excision or at least 2-year imaging follow-up. Multiple logistic regression analysis was performed to develop a multivariate model using HRL type, lesion and biopsy needle size for surgical cancer upgrade with performance assessed using ROC analysis.

Of 699 HRL in 652 patients, 525(75%) had reference standard available, and 48/525(9.1%) showed cancer at surgical excision. Excision (84.5% vs 51.1%) and upgrade (17.6%vs1.8%) rates were higher in HR-I compared to HR-II (p < 0.01). In HR-I, small needle size < 12G vs ≥ 12G [32.1% vs 13.2%, p < 0.01] and less cores [< 6 vs ≥ 6, 28.6%vs13.7%, p = 0.01] were significantly associated with higher cancer upgrades. Our multivariate model had an AUC = 0.87, saving 28.1% of benign surgeries with 100% sensitivity, based on HRL subtype, lesion size(mm, continuous), needle size (< 12G vs ≥ 12G) and biopsy modality (US vs MRI vs stereotactic) CONCLUSION: Our multivariate model using lesion size, needle size and patient age had a high diagnostic performance in decreasing unnecessary surgeries and shows promise as a decision support tool.