Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging.
Background: We attempted to train and validate a model of deep learning for the preoperative prediction of the response of patients with intermediate-stage hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE).Method: All computed tomography (CT) images were acquired for 5...
| Publicado en: | European Radiology Vol. 30; no. 1; pp. 413 - 425 |
|---|---|
| Autores principales: | , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2020
|
| 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=140064720&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140064720 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jan2020 vid: 30 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 140064720 140064720 NLM31332558 10.1007/s00330-019-06318-1 NLM31332558 140064720 ppf: 413 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. aug: au: Peng, Jie Kang, Shuai Ning, Zhengyuan Deng, Hangxia Shen, Jingxian Xu, Yikai Zhang, Jing Zhao, Wei Li, Xinling Gong, Wuxing Huang, Jinhua Liu, Li affil: Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China sug: subj: Carcinoma, Hepatocellular Chemoembolization, Therapeutic Tomography, X-Ray Computed Carcinoma, Hepatocellular Therapy Liver Neoplasms Liver Neoplasms Therapy Middle Age Female Carcinoma, Hepatocellular Pathology Retrospective Design Male Disease Progression Liver Neoplasms Pathology Scales Middle Aged: 45-64 years Female Male ab: Background: We attempted to train and validate a model of deep learning for the preoperative prediction of the response of patients with intermediate-stage hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE).Method: All computed tomography (CT) images were acquired for 562 patients from the Nan Fang Hospital (NFH), 89 patients from Zhu Hai Hospital Affiliated with Jinan University (ZHHAJU), and 138 patients from the Sun Yat-sen University Cancer Center (SYUCC). We built a predictive model from the outputs using the transfer learning techniques of a residual convolutional neural network (ResNet50). The prediction accuracy for each patch was revaluated in two independent validation cohorts.Results: In the training set (NFH), the deep learning model had an accuracy of 84.3% and areas under curves (AUCs) of 0.97, 0.96, 0.95, and 0.96 for complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD), respectively. In the other two validation sets (ZHHAJU and SYUCC), the deep learning model had accuracies of 85.1% and 82.8% for CR, PR, SD, and PD. The ResNet50 model also had high AUCs for predicting the objective response of TACE therapy in patches and patients of three cohorts. Decision curve analysis (DCA) showed that the ResNet50 model had a high net benefit in the two validation cohorts.Conclusion: The deep learning model presented a good performance for predicting the response of TACE therapy and could help clinicians in better screening patients with HCC who can benefit from the interventional treatment.Key Points: • Therapy response of TACE can be predicted by a deep learning model based on CT images. • The probability value from a trained or validation deep learning model showed significant correlation with different therapy responses. • Further improvement is necessary before clinical utilization. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|