Developing imaging biomarkers for mammographically-occult cancer in dense breasts using a radiologist's progress rating on cancer development: a preliminary analysis

Juhun Lee; Sung Eun Song; Robert M. Nishikawa

doi:10.1117/12.2318324

6 July 2018 Developing imaging biomarkers for mammographically-occult cancer in dense breasts using a radiologist's progress rating on cancer development: a preliminary analysis

Juhun Lee, Sung Eun Song, Robert M. Nishikawa

Author Affiliations +

Proceedings Volume 10718, 14th International Workshop on Breast Imaging (IWBI 2018); 107180G (2018) https://doi.org/10.1117/12.2318324
Event: The Fourteenth International Workshop on Breast Imaging, 2018, Atlanta, Georgia, United States

Abstract

We have developed imaging biomarkers for mammographically-occult (MO) cancer in women with dense breasts using a radiologist’s progress ratings on cancer development. MO cancer is a cancer that is occluded by dense breast tissue, or especially visually subtle that radiologists fail to recognize the cancer. We used screening mammograms of 242 normal women (half training) with dense breasts and 116 women (66 training and 50 testing) with dense breasts who had unilateral MO cancer, i.e., negative consecutive mammograms followed by a diagnosis of cancer. With full cancer diagnosis information of the cancer-diagnosed in the current year’s mammogram, an experienced breast radiologist reviewed 1- 3 prior consecutive mammograms in the training set and rated the progress score on cancer development over prior mammograms, using a [0 100] scale. We segmented a dense area in those mammograms and extracted 42 image features (5 histogram, 16 texture, and 21 bilateral asymmetries). We conducted a Pearson’s correlation analysis between image features and the radiologist’s cancer development ratings. We found 23 features correlated with the radiologist’s ratings (p-values < 0.05). We used the top six correlated image features (p-values < 0.01) with the radiologist’s ratings to develop a classifier to identify women with MO cancer. The features included three histogram, one texture, and two bilateral histogram asymmetry features. Using training and testing sets, we trained and tested a logistic regression classifier. The mean and 95% confidence interval of the area under the receiver-operating characteristic curve (AUC) of the classifier was 0.79 [0.7, 0.86].

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Juhun Lee, Sung Eun Song, and Robert M. Nishikawa "Developing imaging biomarkers for mammographically-occult cancer in dense breasts using a radiologist's progress rating on cancer development: a preliminary analysis", Proc. SPIE 10718, 14th International Workshop on Breast Imaging (IWBI 2018), 107180G (6 July 2018); https://doi.org/10.1117/12.2318324

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KEYWORDS

Cancer

Mammography

Breast

Breast cancer

Tissues

Biological research

Radiology

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