Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data

Zhe Zhu; Michael Harowicz D.D.S.; Jun Zhang; Ashirbani Saha; Lars J. Grimm; Shelley Hwang; Maciej A. Mazurowski

doi:10.1117/12.2295470

27 February 2018 Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data

Zhe Zhu, Michael Harowicz D.D.S., Jun Zhang, Ashirbani Saha, Lars J. Grimm, Shelley Hwang, Maciej A. Mazurowski

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 105752W (2018) https://doi.org/10.1117/12.2295470
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Approximately 25% of patients with ductal carcinoma in situ (DCIS) diagnosed from core needle biopsy are subsequently upstaged to invasive cancer at surgical excision. Identifying patients with occult invasive disease is important as it changes treatment and precludes enrollment in active surveillance for DCIS. In this study, we investigated upstaging of DCIS to invasive disease using deep features. While deep neural networks require large amounts of training data, the available data to predict DCIS upstaging is sparse and thus directly training a neural network is unlikely to be successful. In this work, a pre-trained neural network is used as a feature extractor and a support vector machine (SVM) is trained on the extracted features. We used the dynamic contrast-enhanced (DCE) MRIs of patients at our institution from January 1, 2000, through March 23, 2014 who underwent MRI following a diagnosis of DCIS. Among the 131 DCIS patients, there were 35 patients who were upstaged to invasive cancer. Area under the ROC curve within the 10-fold cross-validation scheme was used for validation of our predictive model. The use of deep features was able to achieve an AUC of 0.68 (95% CI: 0.56-0.78) to predict occult invasive disease. This preliminary work demonstrates the promise of deep features to predict surgical upstaging following a diagnosis of DCIS.

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Zhe Zhu, Michael Harowicz D.D.S., Jun Zhang, Ashirbani Saha, Lars J. Grimm, Shelley Hwang, and Maciej A. Mazurowski "Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: preliminary data", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105752W (27 February 2018); https://doi.org/10.1117/12.2295470

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