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Abstract #1114

Predicting Underestimation of Invasive Cancer in Patients with Core-needle Biopsy-diagnosed Ductal Carcinoma in Situ using Deep Learning

Luu-Ngoc Do1, Chae Yeong Im2, Jae Hyuk Park2, So Yeon Ki3, Ilwoo Park2,4,5, and Hyo Soon Lim2,3
1Department of Radiology, Chonnam National University, Gwangju, Korea, Republic of, 2College of Medicine, Chonnam National University, Gwangju, Korea, Republic of, 3Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, Korea, Republic of, 4Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea, Republic of, 5Department of Radiology, Chonnam National University Hospital, Gwangju, Korea, Republic of

This study aims to explore the effectiveness of deep learning algorithms for distinguishing pure (noninvasive) ductal carcinoma in situ (DCIS) from invasive disease for patients showing DCIS in core-needle biopsy using MRI. Preoperative axial dynamic contrast-enhanced MRI data from 352 patients were used to train, validate and test the two-step convolutional neural network (CNN) utilizing a recurrent model. Our model produced an accuracy of 69.4% and AUC of 0.721. The comparison between the proposed model and a 2D or 3D model suggests that the sequential information may provide an important support for occult invasive cancer in patients diagnosed with DCIS.

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