Literature DB >> 22100905

Scoring to predict the possibility of upgrades to malignancy in atypical ductal hyperplasia diagnosed by an 11-gauge vacuum-assisted biopsy device: an external validation study.

S Bendifallah1, S Defert, N Chabbert-Buffet, N Maurin, J Chopier, M Antoine, C Bezu, D Touche, S Uzan, O Graesslin, R Rouzier.   

Abstract

BACKGROUND: Ko's scoring system was developed to predict malignancy upgrades in patients diagnosed with atypical ductal hyperplasia by core needle biopsy. The Ko algorithm was able to identify a subset of patients who were eligible for exclusively clinical follow-up. The current study statistically investigated the patient outcomes to determine whether this scoring system could be translated and used safely in clinical practice.
METHODS: We tested the statistical performance of the Ko scoring system against an external independent multicentre population. One hundred and seven cases of atypical ductal hyperplasia diagnosed by an 11-gauge biopsy needle were available for inclusion in this study. The discrimination, calibration and clinical utility of the scoring system were quantified. In addition, we tested the underestimation rate, sensitivity, specificity, and positive and negative predictive values according to the score threshold.
RESULTS: The overall underestimation rate was 19% (20/107). The area under the receiver operating characteristic curve for the logistic regression model was 0.51 (95% confidence interval: 0.47-0.53). The model was not well calibrated. The lowest predicted underestimation rate was 11%. The sensitivity, specificity, positive predictive value, and negative predictive values were 90%, 22%, 20%, and 89%, respectively, according to the most accurate threshold proposed in the original study.
CONCLUSION: The scoring system was not sufficiently accurate to safely define a subset of patients who would be eligible for follow-up only and no additional treatment. These results demonstrate a lack of reproducibility in an external population. A multidisciplinary approach that correlates clinicopathological and mammographic features should be recommended for the management of these patients.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 22100905     DOI: 10.1016/j.ejca.2011.08.011

Source DB:  PubMed          Journal:  Eur J Cancer        ISSN: 0959-8049            Impact factor:   9.162


  8 in total

1.  Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast.

Authors:  Simukayi Mutasa; Peter Chang; John Nemer; Eduardo Pascual Van Sant; Mary Sun; Alison McIlvride; Maham Siddique; Richard Ha
Journal:  Clin Breast Cancer       Date:  2020-06-07       Impact factor: 3.225

2.  Active surveillance of women diagnosed with atypical ductal hyperplasia on core needle biopsy may spare many women potentially unnecessary surgery, but at the risk of undertreatment for a minority: 10-year surgical outcomes of 114 consecutive cases from a single center.

Authors:  Gelareh Farshid; Suzanne Edwards; James Kollias; Peter Grantley Gill
Journal:  Mod Pathol       Date:  2017-11-03       Impact factor: 7.842

3.  Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data.

Authors:  Richard Ha; Simukayi Mutasa; Eduardo Pascual Van Sant; Jenika Karcich; Christine Chin; Michael Z Liu; Sachin Jambawalikar
Journal:  AJR Am J Roentgenol       Date:  2019-03-12       Impact factor: 3.959

4.  Vacuum assisted breast biopsy (VAB) excision of subcentimeter microcalcifications as an alternative to open biopsy for atypical ductal hyperplasia.

Authors:  Simone Schiaffino; Elena Massone; Licia Gristina; Piero Fregatti; Giuseppe Rescinito; Alessandro Villa; Daniele Friedman; Massimo Calabrese
Journal:  Br J Radiol       Date:  2018-02-23       Impact factor: 3.039

5.  Diagnostic value of radiomics and machine learning with dynamic contrast-enhanced magnetic resonance imaging for patients with atypical ductal hyperplasia in predicting malignant upgrade.

Authors:  Roberto Lo Gullo; Kerri Vincenti; Carolina Rossi Saccarelli; Peter Gibbs; Michael J Fox; Isaac Daimiel; Danny F Martinez; Maxine S Jochelson; Elizabeth A Morris; Jeffrey S Reiner; Katja Pinker
Journal:  Breast Cancer Res Treat       Date:  2021-01-20       Impact factor: 4.872

6.  Prediction of Atypical Ductal Hyperplasia Upgrades Through a Machine Learning Approach to Reduce Unnecessary Surgical Excisions.

Authors:  Lia Harrington; Roberta diFlorio-Alexander; Katherine Trinh; Todd MacKenzie; Arief Suriawinata; Saeed Hassanpour
Journal:  JCO Clin Cancer Inform       Date:  2018-12

7.  Development and Validation of a Simple-to-Use Nomogram for Predicting the Upgrade of Atypical Ductal Hyperplasia on Core Needle Biopsy in Ultrasound-Detected Breast Lesions.

Authors:  Yun-Xia Huang; Ya-Ling Chen; Shi-Ping Li; Ju-Ping Shen; Ke Zuo; Shi-Chong Zhou; Cai Chang
Journal:  Front Oncol       Date:  2021-03-31       Impact factor: 6.244

8.  Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast.

Authors:  Masatoshi Yamada; Akira Saito; Yoichiro Yamamoto; Eric Cosatto; Atsushi Kurata; Toshitaka Nagao; Ayako Tateishi; Masahiko Kuroda
Journal:  J Pathol Inform       Date:  2016-01-29
  8 in total

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