Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Aug;129(8):1130-1142.
doi: 10.1007/s11547-024-01845-4. Epub 2024 Jul 13.

Prediction of microvascular invasion in hepatocellular carcinoma patients with MRI radiomics based on susceptibility weighted imaging and T2-weighted imaging

Zhijun Geng #  1 , Shutong Wang #  2 , Lidi Ma #  1 , Cheng Zhang  1 , Zeyu Guan  3 , Yunfei Zhang  4 , Shaohan Yin  1 , Shanshan Lian  1 , Chuanmiao Xie  5
Affiliations

Prediction of microvascular invasion in hepatocellular carcinoma patients with MRI radiomics based on susceptibility weighted imaging and T2-weighted imaging

Zhijun Geng et al. Radiol Med. 2024 Aug.

Abstract

Background: The accurate identification of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance.

Purpose: To develop a radiomics nomogram based on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) for predicting MVI in early-stage (Barcelona Clinic Liver Cancer stages 0 and A) HCC patients.

Materials and methods: A prospective cohort of 189 participants with HCC was included for model training and testing, and an additional 34 participants were enrolled for external validation. ITK-SNAP was used to manually segment the tumour, and PyRadiomics was used to extract radiomic features from the SWI and T2W images. Variance filtering, student's t test, least absolute shrinkage and selection operator regression and random forest (RF) were applied to select meaningful features. Four machine learning classifiers, including K-nearest neighbour, RF, logistic regression and support vector machine-based models, were established. Independent clinical and radiological risk factors were also determined to establish a clinical model. The best radiomics and clinical models were further evaluated in the validation set. In addition, a nomogram was constructed from the radiomic model and independent clinical factors. Diagnostic efficacy was evaluated by receiver operating characteristic curve analysis with fivefold cross-validation.

Results: AFP levels greater than 400 ng/mL [odds ratio (OR) 2.50; 95% confidence interval (CI) 1.239-5.047], tumour diameter greater than 5 cm (OR 2.39; 95% CI 1.178-4.839), and absence of pseudocapsule (OR 2.053; 95% CI 1.007-4.202) were found to be independent risk factors for MVI. The areas under the curve (AUCs) of the best radiomic model were 1.000 and 0.882 in the training and testing cohorts, respectively, while those of the clinical model were 0.688 and 0.6691. In the validation set, the radiomic model achieved better diagnostic performance (AUC = 0.888) than the clinical model (AUC = 0.602). The combination of clinical factors and the radiomic model yielded a nomogram with the best diagnostic performance (AUC = 0.948).

Conclusion: SWI and T2WI-derived radiomic features are valuable for noninvasively and accurately identifying MVI in early-stage HCC. Furthermore, the integration of radiomics and clinical factors yielded a predictive nomogram with satisfactory diagnostic performance and potential clinical benefits.

Keywords: Hepatocellular carcinoma; Microvascular invasion; Radiomics; Susceptibility-weighted imaging (SWI); T2-weighted imaging (T2WI).

PubMed Disclaimer

References

    1. Villanueva A (2019) Hepatocellular carcinoma. N Engl J Med 380(15):1450–1462. https://doi.org/10.1056/NEJMra1713263 - DOI - PubMed
    1. Lee S, Kang TW, Song KD, Lee MW, Rhim H, Lim HK, Kim SY, Sinn DH, Kim JM, Kim K, Ha SY (2021) Effect of Microvascular invasion risk on early recurrence of hepatocellular carcinoma after surgery and radiofrequency ablation. Ann Surg 273(3):564–571. https://doi.org/10.1097/sla.0000000000003268 - DOI - PubMed
    1. Xu XF, Diao YK, Zeng YY, Li C, Li FW, Sun LY, Wu H, Lin KY, Yao LQ, Wang MD, Zhang CW, Lau WY, Shen F, Yang T (2023) Association of severity in the grading of microvascular invasion with long-term oncological prognosis after liver resection for early-stage hepatocellular carcinoma: a multicenter retrospective cohort study from a hepatitis B virus-endemic area. Int J surg (London, England) 109(4):841–849. https://doi.org/10.1097/js9.0000000000000325 - DOI
    1. Chan AWH, Zhong J, Berhane S, Toyoda H, Cucchetti A, Shi K, Tada T, Chong CCN, Xiang BD, Li LQ, Lai PBS, Mazzaferro V, García-Fiñana M, Kudo M, Kumada T, Roayaie S, Johnson PJ (2018) Development of pre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection. J Hepatol 69(6):1284–1293. https://doi.org/10.1016/j.jhep.2018.08.027 - DOI - PubMed
    1. Yang P, Si A, Yang J, Cheng Z, Wang K, Li J, Xia Y, Zhang B, Pawlik TM, Lau WY, Shen F (2019) A wide-margin liver resection improves long-term outcomes for patients with HBV-related hepatocellular carcinoma with microvascular invasion. Surgery 165(4):721–730. https://doi.org/10.1016/j.surg.2018.09.016 - DOI - PubMed

LinkOut - more resources