Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT.
Background: We hypothesize generative adversarial networks (GAN) combined with self‐attention (SA) and aggregated residual transformations (ResNeXt) perform better than conventional deep learning models in differentiating hepatocellular carcinoma (HCC). Attention modules facilitate concentrating on...
| Publicado en: | Precision Radiation Oncology Vol. 9; no. 1; pp. 13 - 23 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Mar2025
|
| 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=184139648&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184139648 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23987324 L3KI jtl: Precision Radiation Oncology issn: 23987324 maglogo: N pubinfo: dt: Mar2025 vid: 9 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 184139648 183891208 184139648 184139648 10.1002/pro6.70003 184139648 ppf: 13 ppct: 10 formats: tig: atl: Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT. aug: au: Wang, Yuenan Jian, Wanwei Yuan, Zhidong Guan, Fada Carlson, David affil: Department of Therapeutic Radiology, Yale University School of Medicine, New Haven Connecticut,, USA sug: subj: Carcinoma, Hepatocellular Diagnosis Deep Learning Prediction Models Tomography, X-Ray Computed Human Sensitivity and Specificity Retrospective Design Descriptive Statistics Data Analysis Software Male Female Adult Middle Age Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Background: We hypothesize generative adversarial networks (GAN) combined with self‐attention (SA) and aggregated residual transformations (ResNeXt) perform better than conventional deep learning models in differentiating hepatocellular carcinoma (HCC). Attention modules facilitate concentrating on salient features and suppressing redundancies, while residual transformations can reuse relevant features. Therefore, we aim to propose a GAN+SA+ResNeXt deep learning model to improve HCC prediction accuracy. Methods: 228 multiphase CTs from 57 patients were retrospectively analyzed with local IRB's approval, where 30 patients were pathologically confirmed with HCC and the rest 27 were non‐HCC. Pre‐processing of automatic liver segmentation and Hounsfield unit (HU) normalization was performed, followed by deep learning training with five‐fold cross validation in a conventional 3D GAN, a 3D GAN+A, and a 3D GAN+A+ ResNeXt, respectively (training: testing ∼ 4:1). Area under receiver operating characteristics curves (AUROC), accuracy, sensitivity and specificity of HCC prediction were evaluated. Results: Results showed the proposed method had larger AUROC (95%), better accuracy (91%) and sensitivity (93%) with acceptable specificity (88%) and prediction time (0.04s). Deep GAN with attentions and residual transformations for HCC diagnosis using multiphase CT is feasible and favorable with improved accuracy and efficiency, which harbors clinical potentials in differentiating HCC from other benign or malignant liver lesions. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|