X Gao1, X Wang2. 1. Shanghai Institute of Medical Imaging, 200032 Shanghai, China; Department of Interventional Radiology, Fudan University Zhongshan Hospital, 200032 Shanghai, China. 2. Shanghai Institute of Medical Imaging, 200032 Shanghai, China; Department of Interventional Radiology, Fudan University Zhongshan Hospital, 200032 Shanghai, China. Electronic address: fduwangxiaolin@hotmail.com.
Abstract
PURPOSE: The purpose of this study was to evaluate the ability of deep learning to differentiate pancreatic diseases on contrast-enhanced magnetic resonance (MR) images with the aid of generative adversarial network (GAN). MATERIALS AND METHODS: A total of 504 patients who underwent T1-weighted contrast-enhanced MR examinations before any treatments were included in this retrospective study. First, the MRI examinations of 398 patients (215 men, 183 women; mean age, 59.14±12.07 [SD] years [range: 16-85 years]) from one hospital were used as the training set. Then the MRI examinations of 50 (26 men, 24women; mean age, 58.58±13.64 [SD] years [range: 24-85 years]) and 56 (30 men, 26 women; mean age, 59.13±11.35 [SD] years [range: 26-80 years]) consecutive patients from two hospitals were separately collected as the internal and external validation sets. An InceptionV4 network was trained on the training set augmented by synthetic images from GANs. Classification performance of trained InceptionV4 network for every patch and every patient were made on both validation sets, respectively. The prediction agreement between convolutional neural network (CNN) and radiologist was measured by the Cohen's kappa coefficient. RESULTS: The patch-level average accuracy and the micro-averaging area under receiver operating characteristic curve (AUC) of InceptionV4 network were 71.56% and 0.9204 (95% confidence interval [CI]: 0.9165-0.9308) for the internal validation set, and 79.46% and 0.9451 (95%CI: 0.9320-0.9523) for the external validation set, respectively. The patient-level average accuracy and the micro-averaging AUC of InceptionV4 network were 70.00% and 0.8250 (95%CI: 0.8147-0.8326) for the internal validation, 76.79% and 0.8646 (95%CI: 0.8489-0.8772) for the external validation set, respectively. Evaluated by human reader, the average accuracy and micro-averaging AUC for internal and external validation sets were 82.00% and 0.8950 (95%CI: 0.8817-0.9083), 83.93% and 0.9063 (95%CI: 0.8968-0.9212), respectively. The Cohen's kappa coefficients between InceptionV4 network and human reader for the internal and external invalidation sets were 0.8339 (95%CI: 0.6991-0.9447) and 0.8862 (95%CI: 0.7759-0.9738), respectively. CONCLUSION: Deep learning using CNN and GAN had the potential to differentiate pancreatic diseases on contrast-enhanced MR images.
PURPOSE: The purpose of this study was to evaluate the ability of deep learning to differentiate pancreatic diseases on contrast-enhanced magnetic resonance (MR) images with the aid of generative adversarial network (GAN). MATERIALS AND METHODS: A total of 504 patients who underwent T1-weighted contrast-enhanced MR examinations before any treatments were included in this retrospective study. First, the MRI examinations of 398 patients (215 men, 183 women; mean age, 59.14±12.07 [SD] years [range: 16-85 years]) from one hospital were used as the training set. Then the MRI examinations of 50 (26 men, 24women; mean age, 58.58±13.64 [SD] years [range: 24-85 years]) and 56 (30 men, 26 women; mean age, 59.13±11.35 [SD] years [range: 26-80 years]) consecutive patients from two hospitals were separately collected as the internal and external validation sets. An InceptionV4 network was trained on the training set augmented by synthetic images from GANs. Classification performance of trained InceptionV4 network for every patch and every patient were made on both validation sets, respectively. The prediction agreement between convolutional neural network (CNN) and radiologist was measured by the Cohen's kappa coefficient. RESULTS: The patch-level average accuracy and the micro-averaging area under receiver operating characteristic curve (AUC) of InceptionV4 network were 71.56% and 0.9204 (95% confidence interval [CI]: 0.9165-0.9308) for the internal validation set, and 79.46% and 0.9451 (95%CI: 0.9320-0.9523) for the external validation set, respectively. The patient-level average accuracy and the micro-averaging AUC of InceptionV4 network were 70.00% and 0.8250 (95%CI: 0.8147-0.8326) for the internal validation, 76.79% and 0.8646 (95%CI: 0.8489-0.8772) for the external validation set, respectively. Evaluated by human reader, the average accuracy and micro-averaging AUC for internal and external validation sets were 82.00% and 0.8950 (95%CI: 0.8817-0.9083), 83.93% and 0.9063 (95%CI: 0.8968-0.9212), respectively. The Cohen's kappa coefficients between InceptionV4 network and human reader for the internal and external invalidation sets were 0.8339 (95%CI: 0.6991-0.9447) and 0.8862 (95%CI: 0.7759-0.9738), respectively. CONCLUSION: Deep learning using CNN and GAN had the potential to differentiate pancreatic diseases on contrast-enhanced MR images.
Authors: Thavavel Vaiyapuri; Ashit Kumar Dutta; I S Hephzi Punithavathi; P Duraipandy; Saud S Alotaibi; Hadeel Alsolai; Abdullah Mohamed; Hany Mahgoub Journal: Healthcare (Basel) Date: 2022-04-03