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.

Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma

“…Holdbrook et al 28 successfully quantified nuclear pleomorphic patterns using DL using cRCC pathologic slides. Other researchers have also shown that a specific texture pattern, which can be learned using DL of abdominal CT images, can successfully predict tumor grade of cRCC patients 25 , 29 . Ning et al reported that image texture differentiation using convolutional neural network could be useful to classify risk of recurrence of cRCC patients 16 .…”

Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

“…Similarly, Han et al exploited a DL framework to distinguish between RCC subtypes using CECT images. Three-phasic input images were fed to an ANN; its performance was tested with a dataset of 169 biopsy-proven cases and showed an AUC of 0.90 regardless of subtypes [34]. Li et al developed ML-based radiomics models with CECT images for differentiating ccRCC from non-clear-cell variants and investigated a potential radiogenomics link between imaging features and the von Hippel-Lindau gene mutation.…”

Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature

“…Radical cystectomy has been the surgical standard to treat patients suffering from muscle-invasive bladder cancer. Though there is a significant reduction in the estimated blood loss (EBL), the blood transfusion rate, and the length of stay in robotic-assisted radical cystectomy (RARC) compared to those in open radical cystectomy (ORC), the complications and the positive margin status have been found to be similar [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ]. Although the role of RARC is controversial, it has become an acceptable alternative to open surgery by some guideline organizations, including the European Association of Urology [ 62 ].…”