Xianfu Sun1, Qiang Zhang1, Lianjie Niu1, Tao Huang1, Yingjie Wang2, Shengze Zhang3. 1. Department of Breast Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China. 2. Department of Oncology, Affiliated Zhengzhou Cancer Hospital of Henan University, Zhengzhou Cancer Hospital, Zhengzhou, China. 3. Department of Thyroid and Breast III, Cangzhou Central Hospital, Cangzhou, China.
Abstract
BACKGROUND: Axillary lymph node (ALN) management in early-stage breast cancer (ESBC) patients has become less invasive during the past decades. Here, we tried to explore whether high nodal burden (HNB) in ESBC patients could be predicted preoperatively, so as to avoid unnecessary sentinel lymph node biopsy (SLNB). METHODS: The clinicopathological and imaging data of patients with early invasive breast cancer (cT1-2N0M0) were analyzed retrospectively. Univariate and multivariate analyses were performed for the risk factors of axillary HNB in ESBC patients, and a risk prediction model of HNB was established. RESULTS: HNB was identified in 105 (8.0%) of 1,300 ESBC patients. Multivariate analysis showed that estrogen receptors (ER) status, human epidermal growth factor receptor 2 (HER2) status, number of abnormal lymph nodes (LNs) on computed tomography (CT), and axillary score on ultrasound (US) were the risk factors of HNB (all P<0.05). The area under the receiver operating characteristic (ROC) curve in the prediction model was 0.914, with the sensitivity being 85.7% and the specificity being 82.4%. The calibration curve showed that the prediction model had good performance. CONCLUSIONS: As a valuable tool for predicting HNB in ESBC patients, this newly established model helps clinicians to make reasonable axillary surgery decisions and thus avoid unnecessary SLNB. 2021 Gland Surgery. All rights reserved.
BACKGROUND: Axillary lymph node (ALN) management in early-stage breast cancer (ESBC) patients has become less invasive during the past decades. Here, we tried to explore whether high nodal burden (HNB) in ESBC patients could be predicted preoperatively, so as to avoid unnecessary sentinel lymph node biopsy (SLNB). METHODS: The clinicopathological and imaging data of patients with early invasive breast cancer (cT1-2N0M0) were analyzed retrospectively. Univariate and multivariate analyses were performed for the risk factors of axillary HNB in ESBC patients, and a risk prediction model of HNB was established. RESULTS: HNB was identified in 105 (8.0%) of 1,300 ESBC patients. Multivariate analysis showed that estrogen receptors (ER) status, human epidermal growth factor receptor 2 (HER2) status, number of abnormal lymph nodes (LNs) on computed tomography (CT), and axillary score on ultrasound (US) were the risk factors of HNB (all P<0.05). The area under the receiver operating characteristic (ROC) curve in the prediction model was 0.914, with the sensitivity being 85.7% and the specificity being 82.4%. The calibration curve showed that the prediction model had good performance. CONCLUSIONS: As a valuable tool for predicting HNB in ESBC patients, this newly established model helps clinicians to make reasonable axillary surgery decisions and thus avoid unnecessary SLNB. 2021 Gland Surgery. All rights reserved.
Entities:
Keywords:
High nodal burden; axillary lymph node dissection (ALND); early-stage breast cancer (ESBC); sentinel lymph node (SLN)
Authors: Emmanuel Barranger; Charles Coutant; Antoine Flahault; Yann Delpech; Emile Darai; Serge Uzan Journal: Breast Cancer Res Treat Date: 2005-05 Impact factor: 4.872
Authors: William J Gradishar; Benjamin O Anderson; Jame Abraham; Rebecca Aft; Doreen Agnese; Kimberly H Allison; Sarah L Blair; Harold J Burstein; Chau Dang; Anthony D Elias; Sharon H Giordano; Matthew P Goetz; Lori J Goldstein; Steven J Isakoff; Jairam Krishnamurthy; Janice Lyons; P Kelly Marcom; Jennifer Matro; Ingrid A Mayer; Meena S Moran; Joanne Mortimer; Ruth M O'Regan; Sameer A Patel; Lori J Pierce; Hope S Rugo; Amy Sitapati; Karen Lisa Smith; Mary Lou Smith; Hatem Soliman; Erica M Stringer-Reasor; Melinda L Telli; John H Ward; Jessica S Young; Jennifer L Burns; Rashmi Kumar Journal: J Natl Compr Canc Netw Date: 2020-04 Impact factor: 11.908
Authors: Oldrich Coufal; Tomás Pavlík; Pavel Fabian; Rita Bori; Gábor Boross; István Sejben; Róbert Maráz; Jaroslav Koca; Eva Krejcí; Iva Horáková; Vendula Foltinová; Pavlína Vrtelová; Vojtech Chrenko; Wolde Eliza Tekle; Mária Rajtár; Mihály Svébis; Vuk Fait; Gábor Cserni Journal: Pathol Oncol Res Date: 2009-05-15 Impact factor: 3.201
Authors: José Luiz B Bevilacqua; Michael W Kattan; Jane V Fey; Hiram S Cody; Patrick I Borgen; Kimberly J Van Zee Journal: J Clin Oncol Date: 2007-07-30 Impact factor: 44.544