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

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...

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Bibliographic Details
Published in:Journal of digital imaging 2019-08, Vol.32 (4), p.638-643
Main Authors: Han, Seokmin, Hwang, Sung Il, Lee, Hak Jong
Format: Article
Language:English
Subjects:
Biopsy
Cancer
Classification
Clear cell-type renal cell carcinoma
Computed tomography
Contrast agents
Deep learning
Image acquisition
Image classification
Imaging
Injection
Kidney cancer
Medical imaging
Medicine
Medicine & Public Health
Neural networks
Radiology
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Summary: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.
ISSN:0897-1889
1618-727X
DOI:10.1007/s10278-019-00230-2