Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features

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

doi:10.1117/12.2293809

27 February 2018 Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features

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

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105750R (2018) https://doi.org/10.1117/12.2293809
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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).

Conference Presentation

Citation Download Citation

Rui Hou, Bibo Shi, Lars J. Grimm, Maciej A. Mazurowski, Jeffrey R. Marks, Lorraine M. King, Carlo C. Maley, E. Shelley Hwang, and Joseph Y. Lo "Improving classification with forced labeling of other related classes: application to prediction of upstaged ductal carcinoma in situ using mammographic features", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105750R (27 February 2018); https://doi.org/10.1117/12.2293809

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available