Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.
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 hepati...
| Publicado en: | European Radiology Vol. 30; no. 5; pp. 2973 - 2984 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2020
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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=142738704&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142738704 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: May2020 vid: 30 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142738704 142738704 NLM31965257 10.1007/s00330-019-06595-w NLM31965257 142738704 ppf: 2973 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis. aug: au: Xue, Li-Yun Jiang, Zhuo-Yun Fu, Tian-Tian Wang, Qing-Min Zhu, Yu-Li Dai, Meng Wang, Wen-Ping Yu, Jin-Hua Ding, Hong affil: Department of Ultrasound, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, 200032, Shanghai, China sug: subj: Hepatitis B, Chronic Liver Cirrhosis Liver Ultrasonography Methods Algorithms Adult Retrospective Design Middle Age Aged Male Female ROC Curve Liver Cirrhosis Pathology Hepatitis B, Chronic Pathology Pharmacokinetics Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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