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Peer-reviewedCC BY 4.0Frontiers in Oncology

Intratumoral habitat combined with peritumoral radiomics to improve the predictive performance of initial TACE efficacy: a multicenter retrospective study

  • Zhefeng Wang1,2
  • Ningbo Yin2,3
  • Ruisi Su2,3
  • Minhui Zhou4
  • Zhichao Sun2,3
  • Jiaying Li2,3
  • Litian Chen1
4 affiliations
  1. 1Radiology Imaging Center, The First Affiliated Hospital of Wenzhou Medical University
  2. 2The First Clinical Medical College of Zhejiang Chinese Medical University
  3. 3Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine)
  4. 4Department of Radiology, The Central Affiliated Hospital of Shaoxing University (Shaoxing Central Hospital)
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Abstract

Background and purpose: Transcatheter arterial chemoembolization (TACE) is the primary treatment for intermediate-stage hepatocellular carcinoma (HCC). However, significant heterogeneity in patient response to TACE necessitates non-invasive and precise methods for evaluating treatment efficacy. This study aims to explore the predictive ability of combined intratumoral habitat imaging and peritumoral radiomics features for TACE response.

Materials and methods: We retrospectively enrolled HCC patients who underwent TACE treatment at four centers between January 2015 and December 2023. All patients underwent contrast-enhanced MRI. Patients from centers A and B formed the training cohort, while those from centers C and D comprised external validation cohorts. Using three-phase enhanced MRI (arterial, portal venous, and delayed phase), K-Means clustering divided intratumoral regions into three habitat subregions (Habitat 1, 2, and 3), and peritumoral features were extracted. Clinical variables were also collected. Key radiomics features were selected by LASSO regression, and clinical indicators were identified through univariable and multivariable logistic regression. The XGBoost algorithm constructed intratumoral, peritumoral, clinical, and combined models. Performance was evaluated using ROC curves, calibration curves, decision curve analysis (DCA), and DeLong tests.

Results: A total of 174 HCC patients who underwent initial TACE treatment were enrolled, including 104 in training and 70 in validation cohorts. Fifteen key intratumoral features from the habitat subregions and 24 radiomics features from the peritumoral region were identified, primarily derived from wavelet transforms and texture analysis. The wavelet-LLL_glszm_ZoneVariance_ PVP feature from Cluster 2 contributed significantly to the model and had a positive impact (highest SHAP value). Alpha-fetoprotein (AFP) (P = 0.015) and Tumor size (P = 0.016) remained significantly associated with TACE treatment response in multivariable analysis. The combined model based on clinical, intratumoral habitat and peritumoral radiomics showed superior predictive performance, with AUCs of 0.950 (95% CI: 0.89-1.00), 0.850 (0.79-0.91) and 0.830 (0.77-0.89) in training and validation cohorts, outperforming single models (Clinic: AUC = 0.700, Tumor_Habitat: AUC = 0.880, Peritumor_Radiomics: AUC = 0.870, P<0.05). Calibration and DCA demonstrated good agreement between predicted and observed outcomes and favorable net benefit across the evaluated threshold probabilities.

Conclusion: The combined model integrating AFP, Tumor size, intratumoral habitat imaging, and peritumoral radiomics could accurately predict initial TACE response in HCC patients. Cluster 2-derived features showed relatively greater contributions among the intratumoral habitat subregions. This non-invasive prediction tool may provide complementary information for pretreatment risk stratification. Future prospective multicenter studies are needed to verify the clinical applicability of the model.

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Cite

BibTeXRIS
BibTeX
@article{wang2026intratumoral,
  title = {{Intratumoral habitat combined with peritumoral radiomics to improve the predictive performance of initial TACE efficacy: a multicenter retrospective study}},
  author = {Zhefeng Wang and Ningbo Yin and Ruisi Su and Minhui Zhou and Zhichao Sun and Jiaying Li and Litian Chen},
  journal = {Frontiers in Oncology},
  year = {2026},
  month = oct,
  doi = {10.3389/fonc.2026.1772206},
  url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1772206/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Intratumoral habitat combined with peritumoral radiomics to improve the predictive performance of initial TACE efficacy: a multicenter retrospective study
AU  - Zhefeng Wang
AU  - Ningbo Yin
AU  - Ruisi Su
AU  - Minhui Zhou
AU  - Zhichao Sun
AU  - Jiaying Li
AU  - Litian Chen
PY  - 2026
DA  - 2026/10/14
JO  - Frontiers in Oncology
DO  - 10.3389/fonc.2026.1772206
UR  - https://www.frontiersin.org/articles/10.3389/fonc.2026.1772206/full
AB  - Background and purpose: Transcatheter arterial chemoembolization (TACE) is the primary treatment for intermediate-stage hepatocellular carcinoma (HCC). However, significant heterogeneity in patient response to TACE necessitates non-invasive and precise methods for evaluating treatment efficacy. This study aims to explore the predictive ability of combined intratumoral habitat imaging and peritumoral radiomics features for TACE response.  Materials and methods: We retrospectively enrolled HCC patients who underwent TACE treatment at four centers between January 2015 and December 2023. All patients underwent contrast-enhanced MRI. Patients from centers A and B formed the training cohort, while those from centers C and D comprised external validation cohorts. Using three-phase enhanced MRI (arterial, portal venous, and delayed phase), K-Means clustering divided intratumoral regions into three habitat subregions (Habitat 1, 2, and 3), and peritumoral features were extracted. Clinical variables were also collected. Key radiomics features were selected by LASSO regression, and clinical indicators were identified through univariable and multivariable logistic regression. The XGBoost algorithm constructed intratumoral, peritumoral, clinical, and combined models. Performance was evaluated using ROC curves, calibration curves, decision curve analysis (DCA), and DeLong tests.  Results: A total of 174 HCC patients who underwent initial TACE treatment were enrolled, including 104 in training and 70 in validation cohorts. Fifteen key intratumoral features from the habitat subregions and 24 radiomics features from the peritumoral region were identified, primarily derived from wavelet transforms and texture analysis. The wavelet-LLL_glszm_ZoneVariance_ PVP feature from Cluster 2 contributed significantly to the model and had a positive impact (highest SHAP value). Alpha-fetoprotein (AFP) (P = 0.015) and Tumor size (P = 0.016) remained significantly associated with TACE treatment response in multivariable analysis. The combined model based on clinical, intratumoral habitat and peritumoral radiomics showed superior predictive performance, with AUCs of 0.950 (95% CI: 0.89-1.00), 0.850 (0.79-0.91) and 0.830 (0.77-0.89) in training and validation cohorts, outperforming single models (Clinic: AUC = 0.700, Tumor_Habitat: AUC = 0.880, Peritumor_Radiomics: AUC = 0.870, P<0.05). Calibration and DCA demonstrated good agreement between predicted and observed outcomes and favorable net benefit across the evaluated threshold probabilities.  Conclusion: The combined model integrating AFP, Tumor size, intratumoral habitat imaging, and peritumoral radiomics could accurately predict initial TACE response in HCC patients. Cluster 2-derived features showed relatively greater contributions among the intratumoral habitat subregions. This non-invasive prediction tool may provide complementary information for pretreatment risk stratification. Future prospective multicenter studies are needed to verify the clinical applicability of the model.
SN  - 2234-943X
ER  -