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 Park^2,4,5, and Hyo Soon Lim^2,3

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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