Ying Zhou1,2,3, Lan He4, Yanqi Huang2, Shuting Chen1,2, Penqi Wu1,2, Weitao Ye2, Zaiyi Liu5,6, Changhong Liang7,8. 1. Graduate College, Southern Medical University, Guangzhou, 510515, China. 2. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China. 3. Department of Radiology, Mianyang Central Hospital, Mianyang, 621000, Sichuan Province, China. 4. School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China. 5. Graduate College, Southern Medical University, Guangzhou, 510515, China. zyliu@163.com. 6. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China. zyliu@163.com. 7. Graduate College, Southern Medical University, Guangzhou, 510515, China. cjr.lchh@vip.163.com. 8. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China. cjr.lchh@vip.163.com.
Abstract
PURPOSE: To develop a CT-based radiomics signature and assess its ability for preoperatively predicting the early recurrence (≤1 year) of hepatocellular carcinoma (HCC). METHODS: A total of 215 HCC patients who underwent partial hepatectomy were enrolled in this retrospective study, and all the patients were followed up at least within 1 year. Radiomics features were extracted from arterial- and portal venous-phase CT images, and a radiomics signature was built by the least absolute shrinkage and selection operator (LASSO) logistic regression model. Preoperative clinical factors associated with early recurrence were evaluated. A radiomics signature, a clinical model, and a combined model were built, and the area under the curve (AUC) of operating characteristics (ROC) was used to explore their performance to discriminate early recurrence. RESULTS: Twenty-one radiomics features were chosen from 300 candidate features to build a radiomics signature that was significantly associated with early recurrence (P < 0.001), and they presented good performance in the discrimination of early recurrence alone with an AUC of 0.817 (95% CI: 0.758-0.866), sensitivity of 0.794, and specificity of 0.699. The AUCs of the clinical and combined models were 0.781 (95% CI: 0.719-0.834) and 0.836 (95% CI: 0.779-0.883), respectively, with the sensitivity being 0.784 and 0.824, and the specificity being 0.619 and 0.708, respectively. Adding a radiomics signature into conventional clinical variables can significantly improve the accuracy of the preoperative model in predicting early recurrence (P = 0.01). CONCLUSIONS: The radiomics signature was a significant predictor for early recurrence in HCC. Incorporating radiomics signature into conventional clinical factors performed better for preoperative estimation of early recurrence than with clinical variables alone.
PURPOSE: To develop a CT-based radiomics signature and assess its ability for preoperatively predicting the early recurrence (≤1 year) of hepatocellular carcinoma (HCC). METHODS: A total of 215 HCC patients who underwent partial hepatectomy were enrolled in this retrospective study, and all the patients were followed up at least within 1 year. Radiomics features were extracted from arterial- and portal venous-phase CT images, and a radiomics signature was built by the least absolute shrinkage and selection operator (LASSO) logistic regression model. Preoperative clinical factors associated with early recurrence were evaluated. A radiomics signature, a clinical model, and a combined model were built, and the area under the curve (AUC) of operating characteristics (ROC) was used to explore their performance to discriminate early recurrence. RESULTS: Twenty-one radiomics features were chosen from 300 candidate features to build a radiomics signature that was significantly associated with early recurrence (P < 0.001), and they presented good performance in the discrimination of early recurrence alone with an AUC of 0.817 (95% CI: 0.758-0.866), sensitivity of 0.794, and specificity of 0.699. The AUCs of the clinical and combined models were 0.781 (95% CI: 0.719-0.834) and 0.836 (95% CI: 0.779-0.883), respectively, with the sensitivity being 0.784 and 0.824, and the specificity being 0.619 and 0.708, respectively. Adding a radiomics signature into conventional clinical variables can significantly improve the accuracy of the preoperative model in predicting early recurrence (P = 0.01). CONCLUSIONS: The radiomics signature was a significant predictor for early recurrence in HCC. Incorporating radiomics signature into conventional clinical factors performed better for preoperative estimation of early recurrence than with clinical variables alone.
Authors: Emmanuel Rios Velazquez; Chintan Parmar; Ying Liu; Thibaud P Coroller; Gisele Cruz; Olya Stringfield; Zhaoxiang Ye; Mike Makrigiorgos; Fiona Fennessy; Raymond H Mak; Robert Gillies; John Quackenbush; Hugo J W L Aerts Journal: Cancer Res Date: 2017-05-31 Impact factor: 12.701