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Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features

PURPOSE: To explore the potential factors influencing the malignancy risk of amorphous calcifications and establish a predictive nomogram for malignancy risk stratification. PATIENTS AND METHODS: Consecutive mammograms from January 2013 to December 2018 were retrospectively reviewed. Traditional cli...

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Autores principales: Shen, Lijuan, Ma, Xiaowen, Jiang, Tingting, Shen, Xigang, Yang, Wentao, You, Chao, Peng, Weijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7811441/
https://www.ncbi.nlm.nih.gov/pubmed/33469367
http://dx.doi.org/10.2147/CMAR.S286269
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author Shen, Lijuan
Ma, Xiaowen
Jiang, Tingting
Shen, Xigang
Yang, Wentao
You, Chao
Peng, Weijun
author_facet Shen, Lijuan
Ma, Xiaowen
Jiang, Tingting
Shen, Xigang
Yang, Wentao
You, Chao
Peng, Weijun
author_sort Shen, Lijuan
collection PubMed
description PURPOSE: To explore the potential factors influencing the malignancy risk of amorphous calcifications and establish a predictive nomogram for malignancy risk stratification. PATIENTS AND METHODS: Consecutive mammograms from January 2013 to December 2018 were retrospectively reviewed. Traditional clinical features were recorded, and mammographic features were estimated according to the 5th BI-RADS. Included calcifications were randomly divided into the training and validation cohorts. A nomogram was developed to graphically predict the risk of malignancy (risk) based on stepwise multivariate logistic regression analysis. The discrimination and calibration performance of the model were assessed in both the training and validation cohorts. RESULTS: Finally, 1018 amorphous calcifications with final pathological results in 907 women were identified with a malignancy rate of 28.4% (95% CI: 25.7%, 31.3%). The malignancy rates of subgroups divided by the distribution of calcifications, quantity of calcifications, age, menopausal status and family history of cancer were significantly different. There were 712 cases and 306 cases in the training and validation cohorts. The prediction nomogram was finally developed based on four risk factors, including age and distribution, maximum diameter and quantity of calcifications. The AUC of the nomogram was 0.799 (95% CI: 0.761, 0.836) in the training cohort and 0.795 (95% CI: 0.738, 0.852) in the validation cohort. CONCLUSION: On mammography, the distribution, maximum diameter and quantity of calcifications are independent predictors of malignant amorphous calcifications and can be easily obtained in the clinic. The nomogram developed in this study for individualized malignancy risk stratification of amorphous calcifications shows good discrimination performance.
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spelling pubmed-78114412021-01-18 Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features Shen, Lijuan Ma, Xiaowen Jiang, Tingting Shen, Xigang Yang, Wentao You, Chao Peng, Weijun Cancer Manag Res Original Research PURPOSE: To explore the potential factors influencing the malignancy risk of amorphous calcifications and establish a predictive nomogram for malignancy risk stratification. PATIENTS AND METHODS: Consecutive mammograms from January 2013 to December 2018 were retrospectively reviewed. Traditional clinical features were recorded, and mammographic features were estimated according to the 5th BI-RADS. Included calcifications were randomly divided into the training and validation cohorts. A nomogram was developed to graphically predict the risk of malignancy (risk) based on stepwise multivariate logistic regression analysis. The discrimination and calibration performance of the model were assessed in both the training and validation cohorts. RESULTS: Finally, 1018 amorphous calcifications with final pathological results in 907 women were identified with a malignancy rate of 28.4% (95% CI: 25.7%, 31.3%). The malignancy rates of subgroups divided by the distribution of calcifications, quantity of calcifications, age, menopausal status and family history of cancer were significantly different. There were 712 cases and 306 cases in the training and validation cohorts. The prediction nomogram was finally developed based on four risk factors, including age and distribution, maximum diameter and quantity of calcifications. The AUC of the nomogram was 0.799 (95% CI: 0.761, 0.836) in the training cohort and 0.795 (95% CI: 0.738, 0.852) in the validation cohort. CONCLUSION: On mammography, the distribution, maximum diameter and quantity of calcifications are independent predictors of malignant amorphous calcifications and can be easily obtained in the clinic. The nomogram developed in this study for individualized malignancy risk stratification of amorphous calcifications shows good discrimination performance. Dove 2021-01-12 /pmc/articles/PMC7811441/ /pubmed/33469367 http://dx.doi.org/10.2147/CMAR.S286269 Text en © 2021 Shen et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Shen, Lijuan
Ma, Xiaowen
Jiang, Tingting
Shen, Xigang
Yang, Wentao
You, Chao
Peng, Weijun
Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features
title Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features
title_full Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features
title_fullStr Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features
title_full_unstemmed Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features
title_short Malignancy Risk Stratification Prediction of Amorphous Calcifications Based on Clinical and Mammographic Features
title_sort malignancy risk stratification prediction of amorphous calcifications based on clinical and mammographic features
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7811441/
https://www.ncbi.nlm.nih.gov/pubmed/33469367
http://dx.doi.org/10.2147/CMAR.S286269
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