Top

Published in:

Open Access 01-12-2018 | Research article

Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients

Authors: Bo-Hao Zheng, Long-Zi Liu, Zhi-Zhi Zhang, Jie-Yi Shi, Liang-Qing Dong, Ling-Yu Tian, Zhen-bin Ding, Yuan Ji, Sheng-Xiang Rao, Jian Zhou, Jia Fan, Xiao-Ying Wang, Qiang Gao

Published in: BMC Cancer | Issue 1/2018

Abstract

Background

Radiomics is an emerging field in oncological research. In this study, we aimed at developing a radiomics score (rad-score) to estimate postoperative recurrence and survival in patients with solitary hepatocellular carcinoma (HCC).

Methods

A total of 319 solitary HCC patients (training cohort: n = 212; validation cohort: n = 107) were enrolled. Radiomics features were extracted from the artery phase of preoperatively acquired computed tomography (CT) in all patients. A rad-score was generated by using the least absolute shrinkage and selection operator (lasso) logistic model. Kaplan-Meier and Cox’s hazard regression analyses were used to evaluate the prognostic significance of the rad-score. Final nomograms predicting recurrence and survival of solitary HCC patients were established based on the rad-score and clinicopathological factors. C-index and calibration statistics were used to assess the performance of nomograms.

Results

Six potential radiomics features were selected out of 110 texture features to formulate the rad-score. Low rad-score positively correlated with aggressive tumor phenotypes, like larger tumor size and vascular invasion. Meanwhile, low rad-score was significantly associated with increased recurrence and reduced survival. In addition, multivariate analysis identified the rad-score as an independent prognostic factor (recurrence: Hazard ratio (HR): 2.472, 95% confident interval (CI): 1.339–4.564, p = 0.004;survival: HR: 1.558, 95%CI: 1.022–2.375, p = 0.039). Notably, the nomogram integrating rad-score had a better prognostic performance as compared with traditional staging systems. These results were further confirmed in the validation cohort.

Conclusions

The preoperative CT image based rad-score was an independent prognostic factor for the postoperative outcome of solitary HCC patients. This score may be complementary to the current staging system and help to stratify individualized treatments for solitary HCC patients.

Available only for authorised users

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67(1):7–30. https://doi.org/10.3322/caac.21387.CrossRefPubMed

Forner A, Llovet JM, Bruix J. Hepatocellular carcinoma. Lancet. 2012;379(9822):1245–55. https://doi.org/10.1016/S0140-6736(11)61347-0.CrossRefPubMed

Grazi GL, Ercolani G, Pierangeli F, Del Gaudio M, Cescon M, Cavallari A, et al. Improved results of liver resection for hepatocellular carcinoma on cirrhosis give the procedure added value. Ann Surg. 2001;234(1):71–8.CrossRefPubMedPubMedCentral

Zimmerman MA, Ghobrial RM, Tong MJ, Hiatt JR, Cameron AM, Hong J, et al. Recurrence of hepatocellular carcinoma following liver transplantation: a review of preoperative and postoperative prognostic indicators. Arch Surg. 2008;143(2):182–8; discussion 188. https://doi.org/10.1001/archsurg.2007.39.CrossRefPubMed

Adhoute X, Penaranda G, Bronowicki JP, Raoul JL. Usefulness of the HKLC vs. the BCLC staging system in a European HCC cohort. J Hepatol. 2015;62(2):492–3. https://doi.org/10.1016/j.jhep.2014.08.035.CrossRefPubMed

Liu PH, Hsu CY, Hsia CY, Lee YH, Su CW, Huang YH, et al. Prognosis of hepatocellular carcinoma: assessment of eleven staging systems. J Hepatol. 2016;64(3):601–8. https://doi.org/10.1016/j.jhep.2015.10.029.CrossRefPubMed

Farinati F, Vitale A, Spolverato G, Pawlik TM, Huo TL, Lee YH, et al. Development and validation of a new prognostic system for patients with hepatocellular carcinoma. PLoS Med. 2016;13(4):e1002006. https://doi.org/10.1371/journal.pmed.1002006.CrossRefPubMedPubMedCentral

Parikh ND, Scaglione S, Li Y, Powell C, Yerokun OA, Devlin P, et al. A comparison of staging Systems for Hepatocellular Carcinoma in a multicenter US cohort. Clin Gastroenterol Hepatol. 2017. https://doi.org/10.1016/j.cgh.2017.10.001.CrossRefPubMed

Liu Z, Zhang XY, Shi YJ, Wang L, Zhu HT, Tang Z, et al. Radiomics analysis for evaluation of pathological complete response to neoadjuvant Chemoradiotherapy in locally advanced rectal Cancer. Clin Cancer Res. 2017;23(23):7253–62. https://doi.org/10.1158/1078-0432.CCR-17-1038.CrossRefPubMed

10.

Wu S, Zheng J, Li Y, Yu H, Shi S, Xie W, et al. A Radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder Cancer. Clin Cancer Res. 2017;23(22):6904–11. https://doi.org/10.1158/1078-0432.CCR-17-1510.CrossRefPubMed

11.

Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, They Are Data. Radiology. 2016;278(2):563–77. https://doi.org/10.1148/radiol.2015151169.CrossRefPubMed

12.

Chou R, Cuevas C, Fu R, Devine B, Wasson N, Ginsburg A, et al. Imaging techniques for the diagnosis of hepatocellular carcinoma: a systematic review and meta-analysis. Ann Intern Med. 2015;162(10):697–711. https://doi.org/10.7326/M14-2509.CrossRefPubMed

13.

Liang C, Huang Y, He L, Chen X, Ma Z, Dong D, et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer. Oncotarget. 2016;7(21):31401–12. https://doi.org/10.18632/oncotarget.8919.CrossRefPubMedPubMedCentral

14.

Zhang B, Tian J, Dong D, Gu D, Dong Y, Zhang L, et al. Radiomics features of multiparametric MRI as novel prognostic factors in advanced nasopharyngeal carcinoma. Clin Cancer Res. 2017;23(15):4259–69. https://doi.org/10.1158/1078-0432.CCR-16-2910.CrossRefPubMed

15.

Fu S, Chen S, Liang C, Liu Z, Zhu Y, Li Y, et al. Texture analysis of intermediate-advanced hepatocellular carcinoma: prognosis and patients’ selection of transcatheter arterial chemoembolization and sorafenib. Oncotarget. 2017;8(23):37855–65. https://doi.org/10.18632/oncotarget.13675.CrossRefPubMed

16.

Bakr S, Echegaray S, Shah R, Kamaya A, Louie J, Napel S, et al. Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study. J Med Imaging (Bellingham). 2017;4(4):041303. https://doi.org/10.1117/1.JMI.4.4.041303.CrossRef

17.

Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol. 2007;25(6):675–80. https://doi.org/10.1038/nbt1306.CrossRefPubMed

18.

Kickingereder P, Gotz M, Muschelli J, Wick A, Neuberger U, Shinohara RT, et al. Large-scale Radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-Angiogenic treatment response. Clin Cancer Res. 2016;22(23):5765–71. https://doi.org/10.1158/1078-0432.CCR-16-0702.CrossRefPubMedPubMedCentral

19.

Nie K, Shi L, Chen Q, Hu X, Jabbour SK, Yue N, et al. Rectal Cancer: assessment of neoadjuvant Chemoradiation outcome based on Radiomics of multiparametric MRI. Clin Cancer Res. 2016;22(21):5256–64. https://doi.org/10.1158/1078-0432.CCR-15-2997.CrossRefPubMed

20.

Daginawala N, Li B, Buch K, Yu H, Tischler B, Qureshi MM, et al. Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Eur J Radiol. 2016;85(3):511–7. https://doi.org/10.1016/j.ejrad.2015.12.009.CrossRefPubMed

21.

Gao Q, Wang XY, Qiu SJ, Yamato I, Sho M, Nakajima Y, et al. Overexpression of PD-L1 significantly associates with tumor aggressiveness and postoperative recurrence in human hepatocellular carcinoma. Clin Cancer Res. 2009;15(3):971–9. https://doi.org/10.1158/1078-0432.CCR-08-1608.CrossRefPubMed

22.

Xie DY, Ren ZG, Zhou J, Fan J, Gao Q. Critical appraisal of Chinese 2017 guideline on the management of hepatocellular carcinoma. Hepatobiliary Surg Nutr. 2017;6(6):387–96. https://doi.org/10.21037/hbsn.2017.11.01.CrossRefPubMedPubMedCentral

23.

Zhou J, Sun H-C, Wang Z, Cong W-M, Wang J-H, Zeng M-S, et al. Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China (2017 Edition). Liver Cancer. 2018;7:1–26. https://doi.org/10.1159/000488035.CrossRef

24.

Bartko JJ. The intraclass correlation coefficient as a measure of reliability. Psychol Rep. 1966;19(1):3–11. https://doi.org/10.2466/pr0.1966.19.1.3.CrossRefPubMed

25.

Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979;86(2):420–8.CrossRefPubMed

26.

Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. https://doi.org/10.1038/ncomms5006.CrossRefPubMed

27.

Huang X, Pan W. Linear regression and two-class classification with gene expression data. Bioinformatics. 2003;19(16):2072–8.CrossRefPubMed

28.

Tibshirani R. The lasso method for variable selection in the cox model. Stat Med. 1997;16(4):385–95.CrossRefPubMed

29.

Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004;10(21):7252–9. https://doi.org/10.1158/1078-0432.CCR-04-0713.CrossRefPubMed

30.

Zheng BH, Yang LX, Sun QM, Fan HK, Duan M, Shi JY, et al. A new preoperative prognostic system combining CRP and CA199 for patients with intrahepatic cholangiocarcinoma. Clin Transl Gastroenterol. 2017;8(10):e118. https://doi.org/10.1038/ctg.2017.45.CrossRefPubMedPubMedCentral

31.

Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26(8):1364–70. https://doi.org/10.1200/JCO.2007.12.9791.CrossRefPubMed

32.

Llovet JM, Bru C, Bruix J. Prognosis of hepatocellular carcinoma: the BCLC staging classification. Semin Liver Dis. 1999;19(3):329–38. https://doi.org/10.1055/s-2007-1007122.CrossRefPubMed

33.

Kudo M, Chung H, Osaki Y. Prognostic staging system for hepatocellular carcinoma (CLIP score): its value and limitations, and a proposal for a new staging system, the Japan integrated staging score (JIS score). J Gastroenterol. 2003;38(3):207–15. https://doi.org/10.1007/s005350300038.CrossRefPubMed

34.

Yau T, Tang VY, Yao TJ, Fan ST, Lo CM, Poon RT. Development of Hong Kong liver Cancer staging system with treatment stratification for patients with hepatocellular carcinoma. Gastroenterology. 2014;146(7):1691–700 e3. https://doi.org/10.1053/j.gastro.2014.02.032.CrossRefPubMed

35.

Schmitz J, Schwab J, Schwenck J, Chen Q, Quintanilla-Martinez L, Hahn M, et al. Decoding Intratumoral heterogeneity of breast Cancer by multiparametric in vivo imaging: a translational study. Cancer Res. 2016;76(18):5512–22. https://doi.org/10.1158/0008-5472.can-15-0642.CrossRefPubMedPubMedCentral

36.

Kuo MD, Gollub J, Sirlin CB, Ooi C, Chen X. Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol. 2007;18(7):821–31. https://doi.org/10.1016/j.jvir.2007.04.031.CrossRefPubMed

37.

Banerjee S, Wang DS, Kim HJ, Sirlin CB, Chan MG, Korn RL, et al. A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma. Hepatology. 2015;62(3):792–800. https://doi.org/10.1002/hep.27877.CrossRefPubMed

38.

European Association For The Study Of The L. European organisation for R, and treatment of C EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol. 2012;56(4):908–43. https://doi.org/10.1016/j.jhep.2011.12.001.CrossRef

39.

Hepp T, Schmid M, Gefeller O, Waldmann E, Mayr A. Approaches to regularized regression - a comparison between gradient boosting and the lasso. Methods Inf Med. 2016;55(5):422–30. https://doi.org/10.3414/ME16-01-0033.CrossRefPubMed

40.

Ndhlovu ZM, Chibnik LB, Proudfoot J, Vine S, McMullen A, Cesa K, et al. High-dimensional immunomonitoring models of HIV-1-specific CD8 T-cell responses accurately identify subjects achieving spontaneous viral control. Blood. 2013;121(5):801–11. https://doi.org/10.1182/blood-2012-06-436295.CrossRefPubMedPubMedCentral

Title: Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients
Authors: Bo-Hao Zheng
Long-Zi Liu
Zhi-Zhi Zhang
Jie-Yi Shi
Liang-Qing Dong
Ling-Yu Tian
Zhen-bin Ding
Yuan Ji
Sheng-Xiang Rao
Jian Zhou
Jia Fan
Xiao-Ying Wang
Qiang Gao
Publication date: 01-12-2018
Publisher: BioMed Central
Published in: BMC Cancer / Issue 1/2018
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-018-5024-z

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2018

Telomere length differences between colorectal polyp subtypes: a colonoscopy-based case-control study

Elevated serum RAS p21 is an independent prognostic factor in metastatic breast cancer

Individualized induction chemotherapy by pre-treatment plasma Epstein-Barr viral DNA in advanced nasopharyngeal carcinoma

Does fibrosis have an impact on survival of patients with hepatocellular carcinoma: evidence from the SEER database?

Topical steroid versus placebo for the prevention of radiation dermatitis in head and neck cancer patients receiving chemoradiotherapy: the study protocol of J-SUPPORT 1602 (TOPICS study), a randomized double-blinded phase 3 trial

Intra-arterial ethanol embolization augments response to TACE for treatment of HCC with portal venous tumor thrombus

Keynote webinar | Spotlight on antibody–drug conjugates in cancer