Abstract
Purpose
The prognosis of early-stage hepatocellular carcinoma (HCC) patients after radical resection has received widespread attention, but reliable prediction methods are lacking. Radiomics derived from enhanced computed tomography (CT) imaging offers a potential avenue for practical prognostication in HCC patients.
Methods
We recruited early-stage HCC patients undergoing radical resection. Statistical analyses were performed to identify clinicopathological and radiomic features linked to recurrence. Clinical, radiomic, and combined models (incorporating clinicopathological and radiomic features) were built using four algorithms. The performance of these models was scrutinized via fivefold cross-validation, with evaluation metrics including the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) being calculated and compared. Ultimately, an integrated nomogram was devised by combining independent clinicopathological predictors with the Radscore.
Results
From January 2016 through December 2020, HCC recurrence was observed in 167 cases (64.5%), with a median time to recurrence of 26.7 months following initial resection. Combined models outperformed those solely relying on clinicopathological or radiomic features. Notably, among the combined models, those employing support vector machine (SVM) algorithms exhibited the most promising predictive outcomes (AUC: 0.840 (95% Confidence interval (CI): [0.696, 0.984]), ACC: 0.805, SEN: 0.849, SPE: 0.733). Hepatitis B infection, tumour size > 5 cm, and alpha-fetoprotein (AFP) > 400 ng/mL were identified as independent recurrence predictors and were subsequently amalgamated with the Radscore to create a visually intuitive nomogram, delivering robust and reliable predictive performance.
Conclusion
Machine learning models amalgamating clinicopathological and radiomic features provide a valuable tool for clinicians to predict postoperative HCC recurrence, thereby informing early preventative strategies.
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Data availability
The datasets analyzed during the current study are available from the corresponding author on reasonable request.
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Funding
The authors are grateful for the financial support from the Zhejiang Traditional Chinese Medicine Scientific Research Fund, Category B (Grant 20212B037) and Zhejiang Basic Public Welfare Research Program (Grant LGF22H160084).
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QX, ZZ, and CL contributed conception and design of the study; QX and YY organized the database; QX, ZZ, and DL performed the statistical analysis; QX, ZZ, YY, DL, CL wrote sections of the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.
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This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Zhejiang Cancer Hospital (approval number IRB-2022–503). Since this is a retrospective study, the study was conducted with the exception of informed consent.
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Xie, Q., Zhao, Z., Yang, Y. et al. Radiomics-guided prognostic assessment of early-stage hepatocellular carcinoma recurrence post-radical resection. J Cancer Res Clin Oncol 149, 14983–14996 (2023). https://doi.org/10.1007/s00432-023-05291-z
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DOI: https://doi.org/10.1007/s00432-023-05291-z