A Radiomics-based Framework for Liver Cancer Analysis using Explainable Artificial Intelligence (XAI) Methods

Authors

  • Bellary Chiterki Anil Department of CSE (AI & ML), JSS Academy of Technical Education, Bengaluru, India | Visvesvaraya Technological University, Belagavi, India
  • Jayasimha Sondekoppa Rajkumar Department of MCA, JSS Academy of Technical Education, Bengaluru, India | Visvesvaraya Technological University, Belagavi, India
  • T. L. Divya Department of MCA, RV College of Engineering, Bengaluru, India
  • Samitha khaiyum Department of MCA, Dayananda Sagar College of Engineering, Bangalore, India
  • Rakshitha Kiran P. Department of MCA, Dayananda Sagar College of Engineering, Bangalore, India
  • Balakrishnan Ramadoss Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India
Volume: 15 | Issue: 3 | Pages: 24098-24103 | June 2025 | https://doi.org/10.48084/etasr.10377

Received: 29 January 2025 | Revised: 19 February 2025, 24 February 2025, and 1 March 2025 | Accepted: 6 March 2025 | Online: 4 June 2025


Corresponding author: Jayasimha Sondekoppa Rajkumar

Abstract

This study presents a radiomics-based framework for liver cancer analysis, integrating imaging techniques with Explainable Artificial Intelligence (XAI) methods. The workflow involves collecting imaging data, extracting radiomics features to quantify tumor characteristics, and training Machine Learning (ML) models with Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to enhance interpretability. Its results demonstrate improved predictive performance, with significant imaging biomarkers identified for disease progression and classification. The integration of XAI ensures model transparency, allowing clinicians to derive actionable insights and support personalized treatment planning. This approach aim to bridge the gap between complex algorithms and clinical decision-making, advancing liver cancer diagnosis and care.

Keywords:

radiomics, liver cancer, XAI, ML, SHAP, LIME

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References

J. Peng et al., “Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma,” Frontiers in Oncology, vol. 12, Oct. 2022, Art. no. 853254.

A. B. Parimala and R. S. Shanmugasundaram, “Prediction of Liver Diseases using Radiomics Features and Convolutional Neural Networks,” in 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, Jan. 2025, pp. 1–5.

L. P. Hung, C. H. Xu, C. S. Wang, and C. L. Chen, “Applying the Shapley Value Method to Predict Mortality in Liver Cancer Based on Explainable AI,” in Smart Grid and Internet of Things, vol. 497, D. J. Deng, H. C. Chao, and J. C. Chen, Eds. Springer Nature Switzerland, 2023, pp. 133–143.

K. Miao et al., “Exploring explainable machine learning and Shapley additive exPlanations (SHAP) technique to uncover key factors of HNSC cancer: An analysis of the best practices,” Biomedical Signal Processing and Control, vol. 89, Mar. 2024, Art. no. 105752.

H. Raj, N. G, A. Kodipalli, and T. Rao, “Prediction of Chronic Liver Disease Using Machine Learning Algorithms and Interpretation with SHAP Kernels,” in 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), Bangalore, India, Jun. 2024, pp. 1–6.

K. Chaudhary, O. B. Poirion, L. Lu, and L. X. Garmire, “Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer,” Clinical Cancer Research, vol. 24, no. 6, pp. 1248–1259, Mar. 2018.

A. Raj, R. C. Petreaca, and G. Mirzaei, “Multi-Omics Integration for Liver Cancer Using Regression Analysis,” Current Issues in Molecular Biology, vol. 46, no. 4, pp. 3551–3562, Apr. 2024.

R. Gorle and A. Guttavelli, “Enhanced Image Tampering Detection using Error Level Analysis and CNN,” Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19683–19689, Feb. 2025.

V. B. S. Zossou, F. H. R. Gnangnon, O. Biaou, F. De Vathaire, R. S. Allodji, and E. C. Ezin, “Radiomics-Based Classification of Tumor and Healthy Liver on Computed Tomography Images,” Cancers, vol. 16, no. 6, Mar. 2024, Art. no. 1158.

S. Yao, Z. Ye, Y. Wei, H. Y. Jiang, and B. Song, “Radiomics in hepatocellular carcinoma: A state-of-the-art review,” World Journal of Gastrointestinal Oncology, vol. 13, no. 11, pp. 1599–1615, Nov. 2021.

R. Chen, Y. Fu, X. Yi, Q. Pei, H. Zai, and B. T. Chen, "Application of Radiomics in Predicting Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Strategies and Challenges," Journal of Oncology, vol. 2022, no. 1, 2022, Art. no. 1590620.

A. Pellat, M. Barat, R. Coriat, P. Soyer, and A. Dohan, "Artificial intelligence: A review of current applications in hepatocellular carcinoma imaging," Diagnostic and Interventional Imaging, vol. 104, no. 1, pp. 24–36, Jan. 2023.

L. Wang, M. Fatemi, and A. Alizad, "Artificial intelligence techniques in liver cancer," Frontiers in Oncology, vol. 14, Sep. 2024, Art. no. 1415859.

Y. S. Sung, B. Park, H. J. Park, and S. S. Lee, "Radiomics and deep learning in liver diseases," Journal of Gastroenterology and Hepatology, vol. 36, no. 3, pp. 561–568, Mar. 2021.

M. Haghshomar, D. Rodrigues, A. Kalyan, Y. Velichko, and A. Borhani, "Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies," Frontiers in Oncology, vol. 14, May 2024.

A. Castaldo et al., "State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma," Diagnostics, vol. 11, no. 7, Jul. 2021, Art. no. 1194.

J. Miranda et al., "Current status and future perspectives of radiomics in hepatocellular carcinoma," World Journal of Gastroenterology, vol. 29, no. 1, pp. 43–60, Jan. 2023.

M. Zhang et al., "Enhancing prognostic prediction in hepatocellular carcinoma post-TACE: a machine learning approach integrating radiomics and clinical features," Frontiers in Medicine, vol. 11, Jul. 2024.

K. Bera, N. Braman, A. Gupta, V. Velcheti, and A. Madabhushi, "Predicting cancer outcomes with radiomics and artificial intelligence in radiology," Nature Reviews Clinical Oncology, vol. 19, no. 2, pp. 132–146, Feb. 2022.

P. F. Christ et al., "Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks." arXiv, Feb. 23, 2017.

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How to Cite

[1]
Anil, B.C., Rajkumar, J.S., Divya, T.L., khaiyum, S., Kiran P., R. and Ramadoss, B. 2025. A Radiomics-based Framework for Liver Cancer Analysis using Explainable Artificial Intelligence (XAI) Methods. Engineering, Technology & Applied Science Research. 15, 3 (Jun. 2025), 24098–24103. DOI:https://doi.org/10.48084/etasr.10377.

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Copyright (c) 2025 Anil Bellary Chiterki, Jayasimha Sondekoppa Rajkumar, T. L. Divya, Samitha khaiyum, Rakshitha P. Kiran, Balakrishnan Ramadoss

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