Performance of a clinical and imaging-based multivariate model as decision support tool to help save unnecessary surgeri
- PDF / 1,069,668 Bytes
- 16 Pages / 595.276 x 790.866 pts Page_size
- 116 Downloads / 146 Views
EPIDEMIOLOGY
Performance of a clinical and imaging‑based multivariate model as decision support tool to help save unnecessary surgeries for high‑risk breast lesions Dogan S. Polat1 · Jennifer G. Schopp1 · Firouzeh Arjmandi1 · Jessica Porembka1 · Venetia Sarode2 · Deborah Farr3 · Yin Xi1 · Basak E. Dogan1 Received: 1 June 2020 / Accepted: 21 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Purpose To investigate the performance of an imaging and biopsy parameters-based multivariate model in decreasing unnecessary surgeries for high-risk breast lesions. Methods 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 followup. 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. Results 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
Data Loading...
