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Journal of Imaging Informatics in Medicine
Published in: Journal of Digital Imaging 4/2019

Open Access 01-08-2019 | Computed Tomography

The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method

Authors: Seokmin Han, Sung Il Hwang, Hak Jong Lee

Published in: Journal of Imaging Informatics in Medicine | Issue 4/2019

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Abstract

In this research, we exploit an image-based deep learning framework to distinguish three major subtypes of renal cell carcinoma (clear cell, papillary, and chromophobe) using images acquired with computed tomography (CT). A biopsy-proven benchmarking dataset was built from 169 renal cancer cases. In each case, images were acquired at three phases(phase 1, before injection of the contrast agent; phase 2, 1 min after the injection; phase 3, 5 min after the injection). After image acquisition, rectangular ROI (region of interest) in each phase image was marked by radiologists. After cropping the ROIs, a combination weight was multiplied to the three-phase ROI images and the linearly combined images were fed into a deep learning neural network after concatenation. A deep learning neural network was trained to classify the subtypes of renal cell carcinoma, using the drawn ROIs as inputs and the biopsy results as labels. The network showed about 0.85 accuracy, 0.64–0.98 sensitivity, 0.83–0.93 specificity, and 0.9 AUC. The proposed framework which is based on deep learning method and ROIs provided by radiologists showed promising results in renal cell subtype classification. We hope it will help future research on this subject and it can cooperate with radiologists in classifying the subtype of lesion in real clinical situation.
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Metadata
Title
The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method
Authors
Seokmin Han
Sung Il Hwang
Hak Jong Lee
Publication date
01-08-2019
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 4/2019
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-019-00230-2

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