Deep learning for staging liver fibrosis on CT: a pilot study

Yasaka, Koichiro; Akai, Hiromu; Kunimatsu, Akira; Abe, Osamu; Kiryu, Shigeru

doi:10.1007/s00330-018-5499-7

Cited by 89 publications

(52 citation statements)

References 29 publications

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“…More importantly, the accuracy with test data set 1 was high (area under the receiver operating characteristic curve range, 0.94-0.97), exceeding the accuracy of serum fibrosis tests (area under the receiver operating characteristic curve range, 0.65-0.85) and the accuracy of four independent radiologists subjectively evaluating liver morphologic findings (area under the receiver operating characteristic curve range, 0.74-0.88). This accuracy far exceeds that from a comparative deep learning algorithm for staging liver fibrosis at CT (area under the receiver operating characteristic curve range, 0.73-0.76) in a smaller patient cohort (7).…”

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confidence: 77%

Enter the Era of Quantitative Liver CT

Smith

2018

Radiology

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confidence: 77%

Enter the Era of Quantitative Liver CT

Smith

2018

Radiology

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“…The accurate and non-invasive classification of liver fibrosis is of crucial importance in clinical practice. Deep learning system for staging liver fibrosis using CT images has been reported recently and showed good performance [29,30]. US is a more common and non-invasive imaging modality for routine examination, and there have been few reports of deep learning used in the analysis of US images.…”

Section: Discussionmentioning

confidence: 99%

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis

Xue

Jiang

et al. 2020

Eur Radiol

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Objectives To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound images for accurate liver fibrosis grading. Methods Totally 466 patients undergoing partial hepatectomy were enrolled, including 401 with chronic hepatitis B and 65 without fibrosis pathologically. All patients received elastography and got liver stiffness measurement (LSM) 2-3 days before surgery. We proposed a deep convolutional neural network by TL to analyze images of gray scale modality (GM) and elastogram modality (EM). The TL process was used for liver fibrosis classification by Inception-V3 network which pretrained on ImageNet. The diagnostic performance of TL and non-TL was compared. The value of single modalities, including GM and EM alone, and multimodalities, including GM + LSM and GM + EM, was evaluated and compared with that of LSM and serological indexes. Receiver operating characteristic curve analysis was performed to calculate the optimal area under the curve (AUC) for classifying fibrosis of S4, ≥ S3, and ≥ S2. Results TL in GM and EM demonstrated higher diagnostic accuracy than non-TL, with significantly higher AUCs (all p < .01). Single-modal GM and EM both performed better than LSM and serum indexes (all p < .001). Multimodal GM + EM was the most accurate prediction model (AUCs are 0.950, 0.932, and 0.930 for classifying S4, ≥ S3, and ≥ S2, respectively) compared with GM + LSM, GM and EM alone, LSM, and biomarkers (all p < .05). Conclusions Liver fibrosis can be staged by a transfer learning modal based on the combination of gray scale and elastogram ultrasound images, with excellent performance. Key Points • Transfer learning consists in applying to a specific deep learning algorithm that pretrained on another relevant problem, expected to reduce the risk of overfitting due to insufficient medical images. • Liver fibrosis can be staged by transfer learning radiomics with excellent performance. • The most accurate prediction model of transfer learning by Inception-V3 network is the combination of gray scale and elastogram ultrasound images.

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“…In that report, CNN was used only for feature extraction whereas classification was performed with the SVM because of the small amount of training data. Yasaka et al (56) developed CNN algorithms for liver fibrosis staging using cropped CT images and cropped gadoxetic acid-enhanced hepatobiliary phase MR images (57). They reported area under the curves (AUCs) of 0.73-0.76 for the CT-based algorithm and 0.84-0.85 for the MRI-based algorithm in staging liver fibrosis.…”

Section: Liver Fibrosis Stagingmentioning

confidence: 99%

Radiomics and Deep Learning: Hepatic Applications

Park

Lee

2020

Korean J Radiol

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Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

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Deep learning for staging liver fibrosis on CT: a pilot study

Cited by 89 publications

References 29 publications

Enter the Era of Quantitative Liver CT

Enter the Era of Quantitative Liver CT

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis

Radiomics and Deep Learning: Hepatic Applications

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