nach oben

Erschienen in:

12.11.2018 | Ultrasound

Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma

verfasst von: Hang-tong Hu, Zhu Wang, Xiao-wen Huang, Shu-ling Chen, Xin Zheng, Si-min Ruan, Xiao-yan Xie, Ming-de Lu, Jie Yu, Jie Tian, Ping Liang, Wei Wang, Ming Kuang

Erschienen in: European Radiology | Ausgabe 6/2019

Einloggen, um Zugang zu erhalten

Abstract

Purpose

To develop an ultrasound (US)-based radiomics score for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Methods

Between January 1, 2012, and October 31, 2017, a total of 482 HCC patients who underwent contrast-enhanced ultrasound (CEUS) were retrospectively reviewed. The study population was divided into a training cohort (n = 341) and a validation cohort (n = 141) based on a cutoff time of January 1, 2016. Radiomics features were extracted from the grayscale US images of HCC. After features selection, a radiomics score was developed from the training cohort. The incremental value of the radiomics score to the clinic-pathological factors for MVI prediction was assessed in the validation cohort with respect to discrimination, calibration, and clinical usefulness.

Results

The US-based radiomics score consisted of six selected features. Multivariate logistic regression analysis showed that the radiomics score, alpha-fetoprotein (AFP), and tumor size were independent predictors of MVI. The radiomics nomogram (based on the three factors) showed better performance for MVI detection (area under the curve [AUC] 0.731[0.647, 0.815] than the clinical nomogram (based on AFP and tumor size) (0.634 [0.543, 0.724]) (p = 0.015). Both nomograms showed good calibration. Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the clinical nomogram.

Conclusion

The US-based radiomics score was an independent predictor of MVI in HCC. Combining the radiomics score with clinical factors improved the prediction efficacy.

Key points

• Radiomics can be applied in US images.

• US-based radiomics score was an independent predictor of MVI.

• Radiomics nomogram incorporated with the radiomics score showed good performance for MVI prediction.

Nur mit Berechtigung zugänglich

Forner A, Llovet JM, Bruix J (2012) Hepatocellular carcinoma. Lancet 379:1245–1255CrossRefPubMed

Fuks D, Dokmak S, Paradis V, Diouf M, Durand F, Belghiti J (2012) Benefit of initial resection of hepatocellular carcinoma followed by transplantation in case of recurrence: an intention-to-treat analysis. Hepatology 55:132–140CrossRefPubMed

Zimmerman MA, Ghobrial RM, Tong MJ et al (2008) Recurrence of hepatocellular carcinoma following liver transplantation: a review of preoperative and postoperative prognostic indicators. Arch Surg 143:182–188 discussion 188CrossRefPubMed

Bruix J, Gores GJ, Mazzaferro V (2014) Hepatocellular carcinoma: clinical frontiers and perspectives. Gut 63:844–855CrossRefPubMed

Llovet JM, Schwartz M, Mazzaferro V (2005) Resection and liver transplantation for hepatocellular carcinoma. Semin Liver Dis 25:181–200CrossRefPubMed

Roayaie S, Blume IN, Thung SN et al (2009) A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology 137:850–855CrossRefPubMed

Lim KC, Chow PK, Allen JC et al (2011) Microvascular invasion is a better predictor of tumor recurrence and overall survival following surgical resection for hepatocellular carcinoma compared to the Milan criteria. Ann Surg 254:108–113CrossRefPubMed

Pawlik TM, Gleisner AL, Anders RA, Assumpcao L, Maley W, Choti MA (2007) Preoperative assessment of hepatocellular carcinoma tumor grade using needle biopsy: implications for transplant eligibility. Ann Surg 245:435–442CrossRefPubMedPubMedCentral

Yao FY, Xiao L, Bass NM, Kerlan R, Ascher NL, Roberts JP (2007) Liver transplantation for hepatocellular carcinoma: validation of the UCSF-expanded criteria based on preoperative imaging. Am J Transplant 7:2587–2596CrossRefPubMed

10.

Shindoh J, Andreou A, Aloia TA et al (2013) Microvascular invasion does not predict long-term survival in hepatocellular carcinoma up to 2 cm: reappraisal of the staging system for solitary tumors. Ann Surg Oncol 20:1223–1229CrossRefPubMed

11.

Rodriguez-Peralvarez M, Luong TV, Andreana L, Meyer T, Dhillon AP, Burroughs AK (2013) A systematic review of microvascular invasion in hepatocellular carcinoma: diagnostic and prognostic variability. Ann Surg Oncol 20:325–339CrossRefPubMed

12.

Chou CT, Chen RC, Lee CW, Ko CJ, Wu HK, Chen YL (2012) Prediction of microvascular invasion of hepatocellular carcinoma by pre-operative CT imaging. Br J Radiol 85:778–783CrossRefPubMedPubMedCentral

13.

Chou CT, Chen RC, Lin WC, Ko CJ, Chen CB, Chen YL (2014) Prediction of microvascular invasion of hepatocellular carcinoma: preoperative CT and histopathologic correlation. AJR Am J Roentgenol 203:W253–W259CrossRefPubMed

14.

Renzulli M, Brocchi S, Cucchetti A et al (2016) Can current preoperative imaging be used to detect microvascular invasion of hepatocellular carcinoma? Radiology 279:432–442

15.

Wu TH, Hatano E, Yamanaka K et al (2016) A non-smooth tumor margin on preoperative imaging predicts microvascular invasion of hepatocellular carcinoma. Surg Today. https://doi.org/10.1007/s00595-016-1320-x:1-7

16.

Limkin EJ, Sun R, Dercle L et al (2017) Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. https://doi.org/10.1093/annonc/mdx034

17.

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMed

18.

Esteva A, Kuprel B, Novoa RA et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118CrossRefPubMedPubMedCentral

19.

Braman NM, Etesami M, Prasanna P et al (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 19:57CrossRefPubMedPubMedCentral

20.

Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. https://doi.org/10.1200/JCO.2015.65.9128

21.

Zhang X, Li J, Shen F, Lau WY (2017) Significance of presence of microvascular invasion in specimens obtained after surgical treatment of hepatocellular carcinoma. J Gastroenterol Hepatol. https://doi.org/10.1111/jgh.13843

22.

Claudon M, Dietrich CF, Choi BI et al (2013) Guidelines and good clinical practice recommendations for contrast enhanced ultrasound (CEUS) in the liver--update 2012: a WFUMB-EFSUMB initiative in cooperation with representatives of AFSUMB, AIUM, ASUM, FLAUS and ICUS. Ultraschall Med 34:11–29CrossRefPubMed

23.

Wang W, Chen LD, Lu MD et al (2013) Contrast-enhanced ultrasound features of histologically proven focal nodular hyperplasia: diagnostic performance compared with contrast-enhanced CT. Eur Radiol 23:2546–2554CrossRefPubMed

24.

Sauerbrei W, Royston P, Binder H (2007) Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med 26:5512–5528CrossRefPubMed

25.

Coutant C, Olivier C, Lambaudie E et al (2009) Comparison of models to predict nonsentinel lymph node status in breast cancer patients with metastatic sentinel lymph nodes: a prospective multicenter study. J Clin Oncol 27:2800–2808CrossRefPubMed

26.

Vickers AJ, Cronin AM, Elkin EB, Gonen M (2008) Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak 8:53CrossRefPubMedPubMedCentral

27.

Lei Z, Li J, Wu D et al (2016) Nomogram for preoperative estimation of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma within the Milan criteria. JAMA Surg 151:356–363CrossRefPubMed

28.

Zhao WC, Fan LF, Yang N, Zhang HB, Chen BD, Yang GS (2013) Preoperative predictors of microvascular invasion in multinodular hepatocellular carcinoma. Eur J Surg Oncol 39:858–864CrossRefPubMed

29.

Kim KA, Kim MJ, Jeon HM et al (2012) Prediction of microvascular invasion of hepatocellular carcinoma: usefulness of peritumoral hypointensity seen on gadoxetate disodium-enhanced hepatobiliary phase images. J Magn Reson Imaging 35:629–634CrossRefPubMed

30.

Kornberg A, Freesmeyer M, Barthel E et al (2009) 18F-FDG-uptake of hepatocellular carcinoma on PET predicts microvascular tumor invasion in liver transplant patients. Am J Transplant 9:592–600

31.

Shan J, Alam SK, Garra B, Zhang Y, Ahmed T (2016) Computer-aided diagnosis for breast ultrasound using computerized BI-RADS features and machine learning methods. Ultrasound Med Biol 42:980–988CrossRefPubMed

32.

Sugimoto K, Shiraishi J, Tanaka H et al (2016) Computer-aided diagnosis for estimating the malignancy grade of hepatocellular carcinoma using contrast-enhanced ultrasound: an ROC observer study. Liver Int 36:1026–1032CrossRefPubMed

33.

Zhang Q, Xiao Y, Dai W et al (2016) Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 72:150–157CrossRefPubMed

34.

Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336CrossRefPubMed

35.

Grossmann P, Stringfield O, El-Hachem N et al (2017) Defining the biological basis of radiomic phenotypes in lung cancer. Elife 6

36.

Segal E, Sirlin CB, Ooi C et al (2007) Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 25:675–680CrossRefPubMed

37.

Guo Y, Hu Y, Qiao M et al (2017) Radiomics analysis on ultrasound for prediction of biologic behavior in breast invasive ductal carcinoma. Clin Breast Cancer. https://doi.org/10.1016/j.clbc.2017.08.002

38.

Campbell PJ, Yachida S, Mudie LJ et al (2010) The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467:1109–1113CrossRefPubMedPubMedCentral

39.

Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRefPubMedPubMedCentral

40.

Kuo MD, Gollub J, Sirlin CB, Ooi C, Chen X (2007) Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. J Vasc Interv Radiol 18:821–831CrossRefPubMed

41.

Rutman AM, Kuo MD (2009) Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol 70:232–241CrossRefPubMed

42.

Tran B, Dancey JE, Kamel-Reid S et al (2012) Cancer genomics: technology, discovery, and translation. J Clin Oncol 30:647–660CrossRefPubMed

43.

Huang Y, Liu Z, He L et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957CrossRefPubMed

Titel: Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma
verfasst von: Hang-tong Hu
Zhu Wang
Xiao-wen Huang
Shu-ling Chen
Xin Zheng
Si-min Ruan
Xiao-yan Xie
Ming-de Lu
Jie Yu
Jie Tian
Ping Liang
Wei Wang
Ming Kuang
Publikationsdatum: 12.11.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 6/2019
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5797-0

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma