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Abdom Radiol (NY). 2017 Jun;42(6):1695-1704. doi: 10.1007/s00261-017-1072-0.

CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma.

Zhou Y1,2,3, He L4, Huang Y2, Chen S1,2, Wu P1,2, Ye W2, Liu Z5,6, Liang C7,8.

Author information

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.

KEYWORDS:

Computed tomography; Hepatocellular carcinoma; Predictor; Radiomics signature; Recurrence

PMID:
28180924
DOI:
10.1007/s00261-017-1072-0
[Indexed for MEDLINE]

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