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

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Pages (from-to)638-643
Number of pages6
JournalJournal of Digital Imaging
Volume32
Issue number4
DOIs
StatePublished - 15 Aug 2019

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Kidney Neoplasms
Tomography
Learning
Biopsy
Renal Cell Carcinoma
Injections
Cells
Neural networks
Benchmarking
Image acquisition
Contrast Media
Area Under Curve
Labels
Kidney
Weights and Measures
Sensitivity and Specificity
Deep learning
Research
Radiologists

Keywords

  • Deep learning
  • Linear combination
  • Renal cancer
  • Subtype classification

Cite this

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title = "The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method",
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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The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method. / Han, Seokmin; Hwang, Sung Il; Lee, Hak Jong.

In: Journal of Digital Imaging, Vol. 32, No. 4, 15.08.2019, p. 638-643.

Research output: Contribution to journalArticleResearchpeer-review

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