Deep learning for staging liver fibrosis on CT: a pilot study.
Objectives: To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images.Methods: This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluatio...
| Publicado en: | European Radiology Vol. 28; no. 11; pp. 4578 - 4586 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132188066&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132188066 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Nov2018 vid: 28 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132188066 132188066 NLM29761358 10.1007/s00330-018-5499-7 NLM29761358 132188066 ppf: 4578 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning for staging liver fibrosis on CT: a pilot study. aug: au: Yasaka, Koichiro Akai, Hiroyuki Kunimatsu, Akira Abe, Osamu Kiryu, Shigeru affil: Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, 108-8639, Tokyo, Japan sug: subj: Tomography, X-Ray Computed Methods Liver Cirrhosis Diagnosis Image Interpretation, Computer Assisted Methods Pilot Studies Aged Male Retrospective Design Middle Age Female ROC Curve Scales Aged: 65+ years Middle Aged: 45-64 years Male Female ab: Objectives: To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images.Methods: This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (FDLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance.Results: The FDLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p < 0.001). The areas under the receiver operating characteristic curves (with 95% confidence intervals) for diagnosing significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4) by using FDLCT scores were 0.74 (0.64-0.85), 0.76 (0.66-0.85) and 0.73 (0.62-0.84), respectively.Conclusions: Liver fibrosis can be staged by using a deep learning model based on CT images, with moderate performance.Key Points: • Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance. • Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging. • Further improvement are necessary before utilisation in clinical settings. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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