CASIA OpenIR
Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram
Yuan,Chunwang1,2; Wang,Zhenchang1; Gu,Dongsheng3; Tian,Jie3,5,6,7; Zhao,Peng2; Wei,Jingwei3; Yang,Xiaozhen2; Hao,Xiaohan3; Dong,Di3; He,Ning2; Sun,Yu2; Gao,Wenfeng2; Feng,Jiliang4
Source PublicationCancer Imaging
ISSN1470-7330
2019-04-26
Volume19Issue:1
Corresponding AuthorWang,Zhenchang(cjr.wzhch@vip.163.com) ; Tian,Jie(tian@ieee.org)
AbstractAbstractBackgroundPredicting early recurrence (ER) after radical therapy for HCC patients is critical for the decision of subsequent follow-up and treatment. Radiomic features derived from the medical imaging show great potential to predict prognosis. Here we aim to develop and validate a radiomics nomogram that could predict ER after curative ablation.MethodsTotal 184 HCC patients treated from August 2007 to August 2014 were included in the study and were divided into the training (n?=?129) and validation(n?=?55) cohorts randomly. The endpoint was recurrence free survival (RFS). A set of 647 radiomics features were extracted from the 3 phases contrast enhanced computed tomography (CECT) images. The minimum redundancy maximum relevance algorithm (MRMRA) was used for feature selection. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to build a radiomics signature. Recurrence prediction models were built using clinicopathological factors and radiomics signature, and a prognostic nomogram was developed and validated by calibration.ResultsAmong the four radiomics models, the portal venous phase model obtained the best performance in the validation subgroup (C-index?=?0.736 (95%CI:0.726–0.856)). When adding the clinicopathological factors to the models, the portal venous phase combined model also yielded the best predictive performance for training (C-index?=?0.792(95%CI:0.727–0.857) and validation (C-index?=?0.755(95%CI:0.651–0.860) subgroup. The combined model indicated a more distinct improvement of predictive power than the simple clinical model (ANOVA, P?
KeywordHepatocellular carcinoma Radiomics Recurrence forecasting Ablation techniques
DOI10.1186/s40644-019-0207-7
Language英语
WOS IDBMC:10.1186/s40644-019-0207-7
PublisherBioMed Central
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24459
Collection中国科学院自动化研究所
Corresponding AuthorWang,Zhenchang; Tian,Jie
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Recommended Citation
GB/T 7714
Yuan,Chunwang,Wang,Zhenchang,Gu,Dongsheng,et al. Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram[J]. Cancer Imaging,2019,19(1).
APA Yuan,Chunwang.,Wang,Zhenchang.,Gu,Dongsheng.,Tian,Jie.,Zhao,Peng.,...&Feng,Jiliang.(2019).Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram.Cancer Imaging,19(1).
MLA Yuan,Chunwang,et al."Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram".Cancer Imaging 19.1(2019).
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