Prediction of Ki-67 expression in hepatocellular carcinoma: a dual-center study based on T2-weighted imaging habitat analysis

Xiaojun Zheng; Lihong Huang; Mengjie Huang; Bin Yu; Shiji Qin; Deyou Huang

doi:10.2478/raon-2026-0032

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Prediction of Ki-67 expression in hepatocellular carcinoma: a dual-center study based on T2-weighted imaging habitat analysis

Radiology and Oncology

Volume 60 (2026): Issue 2 (June 2026)

By: Xiaojun Zheng, Lihong Huang, Mengjie Huang, Bin Yu, Shiji Qin and Deyou Huang

Open Access

|Jun 2026

Figures & Tables

Patient inclusion and exclusion flowchart for the hepatocellular carcinoma (HCC) dual-center study cohort.

T2WI-based habitat segmentation of hepatocellular carcinoma (HCC) and its correlation with Ki-67 expression status. (B1-B3): HCC with Ki-67 -high expression (20%); (C1-C3): HCC with Ki-67 low expression (10%). Representative T2WI images and corresponding habitat segmentation of HCC with different Ki-67 expression levels. The red area indicates the tumor high-proliferation potential region (correlated with high Ki-67 expression), and the blue area indicates the low-proliferation potential region (correlated with low Ki-67 expression).

ROC curves, DeLong test results and decision curve analysis (DCA) for evaluating the predictive performance of three models in predicting HCC Ki-67 expression. Receiver operating characteristic (ROC) curves showing the diagnostic performance of the habitat, radiomics and clinical models in the training and external validation cohorts, with key area under the curve (AUC) values labeled. DeLong test results verifying the statistical significance of performance differences between models (P values labeled) in the training and external validation cohorts. Decision curve analysis (DCA) curves demonstrating the net clinical benefit of the three models in the training and external validation cohorts; the habitat model yielded the highest net benefit across all threshold probabilities, with notable clinical value when the threshold probability exceeded 0.2. The habitat model outperformed the radiomics and clinical models in all assessments (P<0.05).

SHAP visualizations for model interpretability. (A) SHAP summary plot (beeswarm plot) displaying feature importance ranked by mean absolute SHAP values. Each point represents a single patient, with horizontal displacement indicating the magnitude and direction of the feature’s effect on model output. (B) SHAP waterfall plot illustrating how each feature’s contribution (SHAP value) accumulates from the base value to the final prediction for an individual case. Each bar represents the magnitude and direction (positive or negative) of a feature’s contribution to the prediction. (C) SHAP force plot demonstrating how features combine to push the model output from the base value (average prediction) to the final predicted value for a specific instance. Red arrows indicate features increasing the prediction, while blue arrows represent features decreasing the prediction, with arrow length corresponding to contribution strength.

Predictive performance of various models for Ki-67 expression in hepatocellular carcinoma

Model	Cohort	Accuracy	Area under the curve	95% confidence interval	Sensitivity	Specificity
Habitat analysis	Training Cohort	0.938	0.984	0.9647-1.0000	0.976	0.897
Habitat analysis	External Test Cohort	0.850	0.910	0.8355-0.9835	0.919	0.739
Radiomics	Training Cohort	0.713	0.814	0.7234-0.9045	0.561	0.872
Radiomics	External Test Cohort	0.750	0.746	0.6102-0.8810	0.757	0.739
Clinical features	Training Cohort	0.750	0.775	0.6715-0.8782	0.854	0.641
Clinical features	External Test Cohort	0.617	0.615	0.4597-0.7706	0.649	0.565

Comparative analysis of clinical baseline data across hepatocellular carcinoma patient datasets

Clinical Data	Training Cohort		P-value	External Validation Cohort		P-value
Clinical Data	Ki-67low-expression group	Ki-67high-expression group	P-value	Ki-67low-expression group	Ki-67high-expression group	P-value
Age(years old x¯ \overline {\rm{x}} ± s)	54.03±11.45	54.44±11.60	0.873	55.17±12.80	53.76±11.91	0.665
Sex(number of cases, (%))			0.901			1.0
Male	29 (74.36)	32 (78.05)		19 (82.61)	31 (83.78)
Female	10 (25.64)	9 (21.95)		4 (17.39)	6 (16.22)
HBsAg	1006.1±266.41	1108.43±910.28	0.209	404.61±774.98	670.74±1560.00	0.745
AFP	237.50±456.96	547.53±516.66	0.001	273.98±562.51	4128.08±17293.40	0.073
ALB	37.62±4.42	38.16±3.60	0.55	39.75±4.34	39.11±4.92	0.61
AST	57.38±60.05	56.68±59.60	0.889	91.83±139.89	51.43±56.38	0.879
ALT	57.52±81.88	58.88±72.24	0.321	103.63±161.34	69.98±91.43	0.749
PLT	210.00±98.92	207.90±91.08	0.806	183.37±76.63	202.96±84.05	0.648
PT	11.99±1.35	12.29±2.55	0.942	12.79±1.42	12.67±1.27	0.749

Articles in this issue

DOI: https://doi.org/10.2478/raon-2026-0032 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099

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Language: English

Page range: 217 - 226

Submitted on: Nov 15, 2025

Accepted on: Apr 11, 2026

Published on: Jun 26, 2026

Published by: Association of Radiology and Oncology

In partnership with: Paradigm Publishing Services

Keywords:

hepatocellular carcinoma,

habitat imaging,

radiomics,

prognostic prediction

Related subjects:

Medicine,

Clinical medicine,

Radiology

© 2026 Xiaojun Zheng, Lihong Huang, Mengjie Huang, Bin Yu, Shiji Qin, Deyou Huang, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 60 (2026): Issue 2 (June 2026)

Prediction of Ki-67 expression in hepatocellular carcinoma: a dual-center study based on T2-weighted imaging habitat analysis

Figures & Tables

FIGURE 1.

FIGURE 2.

FIGURE 3.

FIGURE 4.

Predictive performance of various models for Ki-67 expression in hepatocellular carcinoma

Comparative analysis of clinical baseline data across hepatocellular carcinoma patient datasets

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