Literature DB >> 31309573

Use of Memorial Sloan Kettering Cancer Center nomogram to guide intraoperative sentinel lymph node frozen sections in patients with early breast cancer.

Yang Houpu1, Xie Fei1, Yang Yang1, Tong Fuzhong1, Liu Peng1, Zhou Bo1, Cheng Lin1, Cao Yingming1, Liu Miao1, Liu Hongjun1, Wang Siyuan1, Peng Yuan1, Shen Danhua2, Wang Shu1.   

Abstract

BACKGROUND: We implemented selective use of frozen section (FS) to optimize accuracy and cost control in the intraoperative diagnosis of sentinel lymph node (SLN) in patients with breast cancer, guided by the Memorial Sloan Kettering Cancer Center (MSKCC) nodal metastasis risk prediction nomogram.
METHODS: Surgical pathology records were reviewed, examining 2582 consecutive biopsies from 2552 patients with breast cancer to compare intraoperative FS diagnoses with postoperative final reports. We calculated sensitivity, specificity, and false-negative rates (FNRs) for various MSKCC risk levels, also analyzing axillary reoperation rates, with and without FS, and the number needed to treat (NNT) to avoid separate axillary lymph node dissection.
RESULTS: The sensitivity, specificity, and FNR of FS were 84.7%, 99.9%, and 15.3%, respectively. FNR and MSKCC risk level negatively correlated (r = -0.86; P = .002). Axillary reoperation rate significantly declined if FS was done (FS: 4.0%; no FS: 36.4%; P = .002). In grouping patients by quartile of MSKCC risk, axillary reoperation rates were 16.7%, 25.1%, 38.7%, and 58.7% without FS, compared with 4.3%, 3.2%, 5.6%, 3.3% with FS and NNT correspondingly fell from 8.1 to 4.6, 3.0, and 1.8.
CONCLUSIONS: A stratified decision-making algorithm based on the MSKCC risk prediction model improved the effectiveness of FS during SLN biopsy to avoid axillary reoperation.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  breast neoplasms; frozen section; nomogram; sentinel lymph node

Mesh:

Year:  2019        PMID: 31309573     DOI: 10.1002/jso.25638

Source DB:  PubMed          Journal:  J Surg Oncol        ISSN: 0022-4790            Impact factor:   3.454


  2 in total

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Authors:  Young-Gon Kim; In Hye Song; Hyunna Lee; Sungchul Kim; Dong Hyun Yang; Namkug Kim; Dongho Shin; Yeonsoo Yoo; Kyowoon Lee; Dahye Kim; Hwejin Jung; Hyunbin Cho; Hyungyu Lee; Taeu Kim; Jong Hyun Choi; Changwon Seo; Seong Il Han; Young Je Lee; Young Seo Lee; Hyung-Ryun Yoo; Yongju Lee; Jeong Hwan Park; Sohee Oh; Gyungyub Gong
Journal:  Cancer Res Treat       Date:  2020-06-30       Impact factor: 4.679

2.  Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases.

Authors:  Cancan Chen; Shan Zheng; Lei Guo; Xuebing Yang; Yan Song; Zhuo Li; Yanwu Zhu; Xiaoqi Liu; Qingzhuang Li; Huijuan Zhang; Ning Feng; Zuxuan Zhao; Tinglin Qiu; Jun Du; Qiang Guo; Wensheng Zhang; Wenzhao Shi; Jianhui Ma; Fenglong Sun
Journal:  Sci Rep       Date:  2022-08-05       Impact factor: 4.996

  2 in total

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