Prediction early recurrence of hepatocellular carcinoma eligible for curative ablation using a Radiomics nomogram
文献类型：期刊论文
| 作者 | Yuan,Chunwang1,2; Wang,Zhenchang1; Gu,Dongsheng3; Tian,Jie3,5,6,7 ; Zhao,Peng2; Wei,Jingwei3; Yang,Xiaozhen2; Hao,Xiaohan3; Dong,Di3; He,Ning2
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| 刊名 | Cancer Imaging
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| 出版日期 | 2019-04-26 |
| 卷号 | 19期号:1 |
| 关键词 | Hepatocellular carcinoma Radiomics Recurrence forecasting Ablation techniques |
| ISSN号 | 1470-7330 |
| DOI | 10.1186/s40644-019-0207-7 |
| 通讯作者 | Wang,Zhenchang(cjr.wzhch@vip.163.com) ; Tian,Jie(tian@ieee.org) |
| 英文摘要 | AbstractBackgroundPredicting 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? |
| 语种 | 英语 |
| WOS记录号 | BMC:10.1186/S40644-019-0207-7 |
| 出版者 | BioMed Central |
| 源URL | [http://ir.ia.ac.cn/handle/173211/24458]  |
| 专题 | 自动化研究所_中国科学院分子影像重点实验室 |
| 通讯作者 | Wang,Zhenchang; Tian,Jie |
| 作者单位 | 1. 2. 3. 4. 5. 6. 7. |
| 推荐引用方式 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). |
入库方式： OAI收割
来源：自动化研究所
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