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Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features
Author(s): Rui Hou; Bibo Shi; Lars J. Grimm; Maciej A. Mazurowski; Jeffrey R. Marks; Lorraine M. King; Carlo C. Maley; E. Shelley Hwang; Joseph Y. Lo
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Paper Abstract

Predicting whether ductal carcinoma in situ (DCIS) identified at core biopsy contains occult invasive disease is an import task since these “upstaged” cases will affect further treatment planning. Therefore, a prediction model that better classifies pure DCIS and upstaged DCIS can help avoid overtreatment and overdiagnosis. In this work, we propose to improve this classification performance with the aid of two other related classes: Atypical Ductal Hyperplasia (ADH) and Invasive Ductal Carcinoma (IDC). Our data set contains mammograms for 230 cases. Specifically, 66 of them are ADH cases; 99 of them are biopsy-proven DCIS cases, of whom 25 were found to contain invasive disease at the time of definitive surgery. The remaining 65 cases were diagnosed with IDC at core biopsy. Our hypothesis is that knowledge can be transferred from training with the easier and more readily available cases of benign but suspicious ADH versus IDC that is already apparent at initial biopsy. Thus, embedding both ADH and IDC cases to the classifier will improve the performance of distinguishing upstaged DCIS from pure DCIS. We extracted 113 mammographic features based on a radiologist’s annotation of clusters.Our method then added both ADH and IDC cases during training, where ADH were “force labeled” or treated by the classifier as pure DCIS (negative) cases, and IDC were labeled as upstaged DCIS (positive) cases. A logistic regression classifier was built based on the designed training dataset to perform a prediction of whether biopsy-proven DCIS cases contain invasive cancer. The performance was assessed by repeated 5-fold CrossValidation and Receiver Operating Characteristic(ROC) curve analysis. While prediction performance with only training on DCIS dataset had an average AUC of 0.607(%95CI, 0.479-0.721). By adding both ADH and IDC cases for training, we improved the performance to 0.691(95%CI, 0.581-0.801).

Paper Details

Date Published: 27 February 2018
PDF: 8 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750R (27 February 2018); doi: 10.1117/12.2293809
Show Author Affiliations
Rui Hou, Duke Univ. School of Medicine (United States)
Duke Univ. (United States)
Bibo Shi, Duke Univ. School of Medicine (United States)
Lars J. Grimm, Duke Univ. School of Medicine (United States)
Maciej A. Mazurowski, Duke Univ. School of Medicine (United States)
Duke Univ. (United States)
Jeffrey R. Marks, Duke Univ. School of Medicine (United States)
Lorraine M. King, Duke Univ. School of Medicine (United States)
Carlo C. Maley, Arizona State Univ. (United States)
E. Shelley Hwang, Duke Univ. School of Medicine (United States)
Joseph Y. Lo, Duke Univ. School of Medicine (United States)
Duke Univ. (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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