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Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer

Patients with a biopsy diagnosis of ductal carcinoma in situ (DCIS) may be diagnosed with invasive breast cancer after excision. We evaluated the preoperative clinical and imaging predictors of DCIS that were associated with an upgrade to invasive carcinoma on final pathology and also compared the d...

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Autores principales: Park, Ko Woon, Kim, Seon Woo, Han, Heewon, Park, Minsu, Han, Boo-Kyung, Ko, Eun Young, Choi, Ji Soo, Cho, Eun Yoon, Cho, Soo Youn, Ko, Eun Sook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760307/
https://www.ncbi.nlm.nih.gov/pubmed/35031626
http://dx.doi.org/10.1038/s41523-021-00364-z
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author Park, Ko Woon
Kim, Seon Woo
Han, Heewon
Park, Minsu
Han, Boo-Kyung
Ko, Eun Young
Choi, Ji Soo
Cho, Eun Yoon
Cho, Soo Youn
Ko, Eun Sook
author_facet Park, Ko Woon
Kim, Seon Woo
Han, Heewon
Park, Minsu
Han, Boo-Kyung
Ko, Eun Young
Choi, Ji Soo
Cho, Eun Yoon
Cho, Soo Youn
Ko, Eun Sook
author_sort Park, Ko Woon
collection PubMed
description Patients with a biopsy diagnosis of ductal carcinoma in situ (DCIS) may be diagnosed with invasive breast cancer after excision. We evaluated the preoperative clinical and imaging predictors of DCIS that were associated with an upgrade to invasive carcinoma on final pathology and also compared the diagnostic performance of various statistical models. We reviewed the medical records; including mammography, ultrasound (US), and magnetic resonance imaging (MRI) findings; of 644 patients who were preoperatively diagnosed with DCIS and who underwent surgery between January 2012 and September 2018. Logistic regression and three machine learning methods were applied to predict DCIS underestimation. Among 644 DCIS biopsies, 161 (25%) underestimated invasive breast cancers. In multivariable analysis, suspicious axillary lymph nodes (LNs) on US (odds ratio [OR], 12.16; 95% confidence interval [CI], 4.94–29.95; P < 0.001) and high nuclear grade (OR, 1.90; 95% CI, 1.24–2.91; P = 0.003) were associated with underestimation. Cases with biopsy performed using vacuum-assisted biopsy (VAB) (OR, 0.42; 95% CI, 0.27–0.65; P < 0.001) and lesion size <2 cm on mammography (OR, 0.45; 95% CI, 0.22–0.90; P = 0.021) and MRI (OR, 0.29; 95% CI, 0.09–0.94; P = 0.037) were less likely to be upgraded. No significant differences in performance were observed between logistic regression and machine learning models. Our results suggest that biopsy device, high nuclear grade, presence of suspicious axillary LN on US, and lesion size on mammography or MRI were independent predictors of DCIS underestimation.
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spelling pubmed-87603072022-01-26 Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer Park, Ko Woon Kim, Seon Woo Han, Heewon Park, Minsu Han, Boo-Kyung Ko, Eun Young Choi, Ji Soo Cho, Eun Yoon Cho, Soo Youn Ko, Eun Sook NPJ Breast Cancer Article Patients with a biopsy diagnosis of ductal carcinoma in situ (DCIS) may be diagnosed with invasive breast cancer after excision. We evaluated the preoperative clinical and imaging predictors of DCIS that were associated with an upgrade to invasive carcinoma on final pathology and also compared the diagnostic performance of various statistical models. We reviewed the medical records; including mammography, ultrasound (US), and magnetic resonance imaging (MRI) findings; of 644 patients who were preoperatively diagnosed with DCIS and who underwent surgery between January 2012 and September 2018. Logistic regression and three machine learning methods were applied to predict DCIS underestimation. Among 644 DCIS biopsies, 161 (25%) underestimated invasive breast cancers. In multivariable analysis, suspicious axillary lymph nodes (LNs) on US (odds ratio [OR], 12.16; 95% confidence interval [CI], 4.94–29.95; P < 0.001) and high nuclear grade (OR, 1.90; 95% CI, 1.24–2.91; P = 0.003) were associated with underestimation. Cases with biopsy performed using vacuum-assisted biopsy (VAB) (OR, 0.42; 95% CI, 0.27–0.65; P < 0.001) and lesion size <2 cm on mammography (OR, 0.45; 95% CI, 0.22–0.90; P = 0.021) and MRI (OR, 0.29; 95% CI, 0.09–0.94; P = 0.037) were less likely to be upgraded. No significant differences in performance were observed between logistic regression and machine learning models. Our results suggest that biopsy device, high nuclear grade, presence of suspicious axillary LN on US, and lesion size on mammography or MRI were independent predictors of DCIS underestimation. Nature Publishing Group UK 2022-01-14 /pmc/articles/PMC8760307/ /pubmed/35031626 http://dx.doi.org/10.1038/s41523-021-00364-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Park, Ko Woon
Kim, Seon Woo
Han, Heewon
Park, Minsu
Han, Boo-Kyung
Ko, Eun Young
Choi, Ji Soo
Cho, Eun Yoon
Cho, Soo Youn
Ko, Eun Sook
Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer
title Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer
title_full Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer
title_fullStr Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer
title_full_unstemmed Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer
title_short Ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer
title_sort ductal carcinoma in situ: a risk prediction model for the underestimation of invasive breast cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760307/
https://www.ncbi.nlm.nih.gov/pubmed/35031626
http://dx.doi.org/10.1038/s41523-021-00364-z
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