Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy

Fatimah Binta Abdullahi; Akanbi Muhammed Bello; Abel Blessing Olorunsola; Ishola Olabisi Bolarinwa; Olamide Aisha Akanbi; Adamu Yusuf Atumoshi

doi:10.5455/EJCSI.20260423112928

Original Article
Online Published: 08 Jul 2026

Eu J Comp Sci Informatics. 2026; 3(3): 167-180

doi: 10.5455/EJCSI.20260423112928

Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy

Fatimah Binta Abdullahi, Akanbi Muhammed Bello, Abel Blessing Olorunsola, Ishola Olabisi Bolarinwa, Olamide Aisha Akanbi, Adamu Yusuf Atumoshi.

Abstract
Aim: This study aimed to develop an accurate and clinically useful framework for predicting organ-specific uptake, absorbed radiation dose, and treatment response of copper-64 (⁶⁴Cu), a theranostic radioisotope used for both cancer imaging and targeted therapy. Reliable prediction remains challenging due to patient variability and limited pharmacokinetic data.
Method: A hybrid computational framework was designed by integrating Monte Carlo radiation transport simulation (GATE v9.2), two-compartment pharmacokinetic modeling, and a deep learning CNN-LSTM network. The model was evaluated using clinical data from 47 patients diagnosed with glioblastoma multiforme (GBM), colorectal cancer, and non-small cell lung cancer (NSCLC). The dataset consisted of 1,240 in vitro uptake measurements, PET/CT time-activity curves acquired at seven time points, and 10 pharmacokinetic parameters. Performance was compared with Random Forest, XGBoost, and conventional LSTM models. SHAP analysis was applied for model interpretability.
Results: The proposed CNN-LSTM framework demonstrated strong predictive performance across all tasks. It achieved an AUC-ROC of 0.942 for treatment response classification, indicating high discrimination accuracy. For tumour absorbed dose estimation, the model produced an R² value of 0.918 with a mean absolute error of 1.21 Gy. Biodistribution prediction was also highly accurate, with an average error of 3.2%ID/g. These outcomes outperformed all benchmark machine learning models tested. SHAP interpretation identified SUVmax, tumour-to-background ratio, and the transfer rate constant k₁ as the most influential predictors.
Conclusion: The developed hybrid framework provides a robust, physics-consistent, and clinically useful tool for personalised ⁶⁴Cu dosimetry, treatment planning, and response prediction, pending prospective validation, with potential to improve precision oncology outcomes.

Key words: Absorbed dose prediction, biodistribution modelling, CNN-LSTM, ⁶⁴Cu-ATSM, dosimetry, GATE simulation, Monte Carlo

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How to Cite this Article

Pubmed Style

Abdullahi FB, Bello AM, Olorunsola AB, Bolarinwa IO, Akanbi OA, Atumoshi AY. Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy. Eu J Comp Sci Informatics. 2026; 3(3): 167-180. doi:10.5455/EJCSI.20260423112928

Web Style

Abdullahi FB, Bello AM, Olorunsola AB, Bolarinwa IO, Akanbi OA, Atumoshi AY. Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy. https://www.wisdomgale.com/ejcsi/?mno=318450 [Access: July 11, 2026]. doi:10.5455/EJCSI.20260423112928

AMA (American Medical Association) Style

Abdullahi FB, Bello AM, Olorunsola AB, Bolarinwa IO, Akanbi OA, Atumoshi AY. Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy. Eu J Comp Sci Informatics. 2026; 3(3): 167-180. doi:10.5455/EJCSI.20260423112928

Vancouver/ICMJE Style

Abdullahi FB, Bello AM, Olorunsola AB, Bolarinwa IO, Akanbi OA, Atumoshi AY. Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy. Eu J Comp Sci Informatics. (2026), [cited July 11, 2026]; 3(3): 167-180. doi:10.5455/EJCSI.20260423112928

Harvard Style

Abdullahi, F. B., Bello, . A. M., Olorunsola, . A. B., Bolarinwa, . I. O., Akanbi, . O. A. & Atumoshi, . A. Y. (2026) Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy. Eu J Comp Sci Informatics, 3 (3), 167-180. doi:10.5455/EJCSI.20260423112928

Turabian Style

Abdullahi, Fatimah Binta, Akanbi Muhammed Bello, Abel Blessing Olorunsola, Ishola Olabisi Bolarinwa, Olamide Aisha Akanbi, and Adamu Yusuf Atumoshi. 2026. Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy. European Journal of Computer Sciences and Informatics, 3 (3), 167-180. doi:10.5455/EJCSI.20260423112928

Chicago Style

Abdullahi, Fatimah Binta, Akanbi Muhammed Bello, Abel Blessing Olorunsola, Ishola Olabisi Bolarinwa, Olamide Aisha Akanbi, and Adamu Yusuf Atumoshi. "Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy." European Journal of Computer Sciences and Informatics 3 (2026), 167-180. doi:10.5455/EJCSI.20260423112928

MLA (The Modern Language Association) Style

Abdullahi, Fatimah Binta, Akanbi Muhammed Bello, Abel Blessing Olorunsola, Ishola Olabisi Bolarinwa, Olamide Aisha Akanbi, and Adamu Yusuf Atumoshi. "Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy." European Journal of Computer Sciences and Informatics 3.3 (2026), 167-180. Print. doi:10.5455/EJCSI.20260423112928

APA (American Psychological Association) Style

Abdullahi, F. B., Bello, . A. M., Olorunsola, . A. B., Bolarinwa, . I. O., Akanbi, . O. A. & Atumoshi, . A. Y. (2026) Computational Modeling and Machine Learning-Based Prediction of Copper-64 Radiopharmaceutical Biodistribution in Cancer Therapy. European Journal of Computer Sciences and Informatics, 3 (3), 167-180. doi:10.5455/EJCSI.20260423112928