CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma
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To develop a CT-based radiomics signature and assess its ability for preoperatively predicting the early recurrence (≤1 year) of hepatocellular carcinoma (HCC).
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.
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).
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.
KeywordsHepatocellular carcinoma Computed tomography Radiomics signature Predictor Recurrence
We deeply appreciated the Medical Record Management Center for its close cooperation in data collection, sorting, verification, and database creation.
Compliance with ethical standards
This study was funded by the National Natural Scientific Foundation of China (Grant Numbers: 81271569, 81271654 and U1301258).
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.
Statement of informed consent was not applicable since the article does not contain any patient data.
- 6.Torzilli G, Belghiti J, Kokudo N, et al. (2013) A snapshot of the effective indications and results of surgery for hepatocellular carcinoma in tertiary referral centers: is it adherent to the EASL/AASLD recommendations? An observational study of the HCC East-West study group. Ann Surg 257(5):929–937. doi: 10.1097/SLA.0b013e31828329b8 CrossRefPubMedGoogle Scholar
- 9.Poon RT, Fan ST, Lo CM, Liu CL, Wong J (2002) Long-term survival and pattern of recurrence after resection of small hepatocellular carcinoma in patients with preserved liver function: implications for a strategy of salvage transplantation. Ann Surg 235(3):373–382CrossRefPubMedPubMedCentralGoogle Scholar
- 25.Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K (2012) Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol 67(2):157–164. doi: 10.1016/j.crad.2011.08.012 CrossRefPubMedGoogle Scholar
- 29.Chang PE, Ong WC, Lui HF, Tan CK (2008) Is the prognosis of young patients with hepatocellular carcinoma poorer than the prognosis of older patients? A comparative analysis of clinical characteristics, prognostic features, and survival outcome. J Gastroenterol 43(11):881–888. doi: 10.1007/s00535-008-2238-x CrossRefPubMedGoogle Scholar
- 33.Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA (2010) Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 10:137–143. doi: 10.1102/1470-7330.2010.0021 CrossRefPubMedPubMedCentralGoogle Scholar
- 34.Jr HF (2015) Regression modeling strategies with applications to linear models, logistic and ordinal regression, and survival analysis. New York, NY: Springer-VerlagGoogle Scholar
- 40.Qi X, Liu L, Wang D, et al. (2015) Hepatic resection alone versus in combination with pre- and post-operative transarterial chemoembolization for the treatment of hepatocellular carcinoma: a systematic review and meta-analysis. Oncotarget 6(34):36838–36859. doi: 10.18632/oncotarget.5426 PubMedPubMedCentralGoogle Scholar
- 42.Agopian VG, Harlander-Locke M, Zarrinpar A, et al. (2015) A novel prognostic nomogram accurately predicts hepatocellular carcinoma recurrence after liver transplantation: analysis of 865 consecutive liver transplant recipients. J Am Coll Surg 220(4):416–427. doi: 10.1016/j.jamcollsurg.2014.12.025 CrossRefPubMedGoogle Scholar