TY - JOUR
T1 - Applying a new computer-aided detection scheme generated imaging marker to predict short-term breast cancer risk
AU - Mirniaharikandehei, Seyedehnafiseh
AU - Hollingsworth, Alan B.
AU - Patel, Bhavika
AU - Heidari, Morteza
AU - Liu, Hong
AU - Zheng, Bin
N1 - Funding Information:
This work is supported in part by Grants R01 CA160205 and R01 CA197150 from the National Cancer Institute, National Institutes of Health, USA. The authors would also like to acknowledge the support from the Peggy and Charles Stephenson Cancer Center, University of Oklahoma, USA.
Publisher Copyright:
© 2018 Institute of Physics and Engineering in Medicine.
PY - 2018/5/15
Y1 - 2018/5/15
N2 - This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All mammograms were originally interpreted as negative by radiologists. In the next subsequent mammography screening, 402 women were diagnosed with breast cancer and 642 remained negative. An existing CAD scheme was applied 'as is' to process each image. From CAD-generated results, four detection features including the total number of (1) initial detection seeds and (2) the final detected false-positive regions, (3) average and (4) sum of detection scores, were computed from each image. Then, by combining the features computed from two bilateral images of left and right breasts from either craniocaudal or mediolateral oblique view, two logistic regression models were trained and tested using a leave-one-case-out cross-validation method to predict the likelihood of each testing case being positive in the next subsequent screening. The new prediction model yielded the maximum prediction accuracy with an area under a ROC curve of AUC = 0.65 ± 0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of (2.95, 6.83). The results also showed an increasing trend in the adjusted odds ratio and risk prediction scores (p < 0.01). Thus, this study demonstrated that CAD-generated false-positives might include valuable information, which needs to be further explored for identifying and/or developing more effective imaging markers for predicting short-term breast cancer risk.
AB - This study aims to investigate the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to help predict short-term breast cancer risk. An image dataset including four view mammograms acquired from 1044 women was retrospectively assembled. All mammograms were originally interpreted as negative by radiologists. In the next subsequent mammography screening, 402 women were diagnosed with breast cancer and 642 remained negative. An existing CAD scheme was applied 'as is' to process each image. From CAD-generated results, four detection features including the total number of (1) initial detection seeds and (2) the final detected false-positive regions, (3) average and (4) sum of detection scores, were computed from each image. Then, by combining the features computed from two bilateral images of left and right breasts from either craniocaudal or mediolateral oblique view, two logistic regression models were trained and tested using a leave-one-case-out cross-validation method to predict the likelihood of each testing case being positive in the next subsequent screening. The new prediction model yielded the maximum prediction accuracy with an area under a ROC curve of AUC = 0.65 ± 0.017 and the maximum adjusted odds ratio of 4.49 with a 95% confidence interval of (2.95, 6.83). The results also showed an increasing trend in the adjusted odds ratio and risk prediction scores (p < 0.01). Thus, this study demonstrated that CAD-generated false-positives might include valuable information, which needs to be further explored for identifying and/or developing more effective imaging markers for predicting short-term breast cancer risk.
KW - breast cancer screening
KW - computer-aided detection (CAD)
KW - false-positive detection
KW - mammography screening
KW - prediction of short-term cancer risk
KW - quantitative imaging marker
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U2 - 10.1088/1361-6560/aabefe
DO - 10.1088/1361-6560/aabefe
M3 - Article
C2 - 29667606
AN - SCOPUS:85048015081
SN - 0031-9155
VL - 63
JO - Physics in Medicine and Biology
JF - Physics in Medicine and Biology
IS - 10
M1 - 105005
ER -