Literature DB >> 35502394

Comprehensive quantitative malignant risk prediction of pure grouped amorphous calcifications: clinico-mammographic nomogram.

Lijuan Shen1,2, Tingting Jiang1,3, Pengzhou Tang1,3, Huijuan Ge3,4, Chao You1,3, Weijun Peng1,3.   

Abstract

Background: Pure grouped amorphous calcifications are classified as Breast Imaging Reporting and Data System (BI-RADS) category 4B suspicious calcifications and recommended for biopsy. However, the biopsies often reveal benign findings, especially in screening mammograms (92.4-97.2%).
Methods: Mammograms of 699 pure grouped amorphous calcifications with final pathological results were analyzed in this retrospective study. The maximum span (MS) of the group of calcifications and the MS of the parallel/vertical direction of the mammary duct (MPS/MVS) were measured, and the MPS to MVS ratio was calculated. Based on the MS and ratio, 2 prediction nomograms with other clinic-mammographic features were developed. The discrimination performance of the models was assessed and compared by the area under the receiver operating characteristic curve (AUC). Scatterplots were created to determine the cutoff values with fewer misdiagnoses of malignant calcifications and fewer false positives.
Results: Ultimately, 2 prediction models were successfully developed based on the 4 risk factors of age, purpose of the mammogram, whether multiple or single calcifications, and the MS [odds ratio (OR) =1.06, P=0.02]/ratio (OR =6.05, P<0.001). Both models had good discrimination. The ratio model performed better than the MS model in the training cohort (AUC of 0.875 and 0.834, respectively, P=0.003) and validation cohort (AUC 0.908 and 0.867, respectively, P=0.047). For the group with probably benign calcifications (as detected by the ratio nomogram), the malignancy rates were 2.7% [95% confidence interval (CI): 1.00% to 6.53%] and 1.19% (95% CI: 0.06% to 7.37%) in the training and validation cohorts, respectively, and 44.12% and 47.70% of benign biopsies were detected in the training and validation cohorts, respectively. Conclusions: The clinico-mammographic quantitative malignancy risk prediction nomogram showed favorable discrimination and calibration performance. The ratio model showed better diagnostic efficiency than the MS model, and identified >40% of benign biopsies. 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Entities:  

Keywords:  Breast; amorphous calcification; mammography; nomogram; predictive value of tests

Year:  2022        PMID: 35502394      PMCID: PMC9014145          DOI: 10.21037/qims-21-797

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  30 in total

1.  A matrix of morphology and distribution of calcifications in the breast: Analysis of 849 vacuum-assisted biopsies.

Authors:  Benjamin Kaltenbach; Volker Brandenbusch; Volker Möbus; Gerhard Mall; Stephan Falk; Marcus van den Bergh; Frauke Chevalier; Markus Müller-Schimpfle
Journal:  Eur J Radiol       Date:  2016-11-23       Impact factor: 3.528

2.  Recommendations for Measuring Pulmonary Nodules at CT: A Statement from the Fleischner Society.

Authors:  Alexander A Bankier; Heber MacMahon; Jin Mo Goo; Geoffrey D Rubin; Cornelia M Schaefer-Prokop; David P Naidich
Journal:  Radiology       Date:  2017-06-26       Impact factor: 11.105

3.  External validation of AI algorithms in breast radiology: the last healthcare security checkpoint?

Authors:  Teodoro Martin-Noguerol; Antonio Luna
Journal:  Quant Imaging Med Surg       Date:  2021-06

4.  Grouped Amorphous Calcifications at Mammography: Frequently Atypical but Rarely Associated with Aggressive Malignancy.

Authors:  Hayley C Oligane; Wendie A Berg; Andriy I Bandos; Sue S Chen; Sahand Sohrabi; Maria Anello; Margarita L Zuley
Journal:  Radiology       Date:  2018-06-19       Impact factor: 11.105

5.  A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction.

Authors:  Adam Yala; Constance Lehman; Tal Schuster; Tally Portnoi; Regina Barzilay
Journal:  Radiology       Date:  2019-05-07       Impact factor: 11.105

6.  Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications.

Authors:  Chuqian Lei; Wei Wei; Zhenyu Liu; Qianqian Xiong; Ciqiu Yang; Mei Yang; Liulu Zhang; Teng Zhu; Xiaosheng Zhuang; Chunling Liu; Zaiyi Liu; Jie Tian; Kun Wang
Journal:  Eur J Radiol       Date:  2019-10-20       Impact factor: 3.528

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

Authors:  Lijuan Shen; Xiaowen Ma; Tingting Jiang; Xigang Shen; Wentao Yang; Chao You; Weijun Peng
Journal:  Cancer Manag Res       Date:  2021-01-12       Impact factor: 3.989

8.  Pathological and biological differences between screen-detected and interval ductal carcinoma in situ of the breast.

Authors:  Marnix A de Roos; Bert van der Vegt; Jaap de Vries; Jelle Wesseling; Geertruida H de Bock
Journal:  Ann Surg Oncol       Date:  2007-04-24       Impact factor: 5.344

9.  Suspicious amorphous microcalcifications detected on full-field digital mammography: correlation with histopathology.

Authors:  Vera Christina Camargo de Siqueira Ferreira; Elba Cristina Sá de Camargo Etchebehere; José Luiz Barbosa Bevilacqua; Nestor de Barros
Journal:  Radiol Bras       Date:  2018 Mar-Apr

10.  Nomogram to Predict Internal Mammary Lymph Nodes Metastasis in Patients With Breast Cancer.

Authors:  Xinhua Xie; Zhenchong Xiong; Xing Li; Xiaojia Huang; Feng Ye; Hailin Tang; Xiaoming Xie
Journal:  Front Oncol       Date:  2019-11-08       Impact factor: 6.244

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