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Updated: Mar 15, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
The Walker 256 Breast Cancer Cell- Induced Bone Pain Model in Rats
Priyank A Shenoy1, Andy Kuo2, Irina Vetter3
1School of Biomedical Sciences, The University of QueenslandBrisbane, QLD, Australia; Centre for Integrated Preclinical Drug Development, The University of QueenslandBrisbane, QLD, Australia.
Abstract:
The majority of patients with terminal breast cancer show signs of bone metastasis, the most common cause of pain in cancer. Clinically available drug treatment options for the relief of cancer-associated bone pain are limited due to either inadequate pain relief and/or dose-limiting side-effects. One of the major hurdles in understanding the mechanism by which breast cancer causes pain after metastasis to the bones is the lack of suitable preclinical models. Until the late twentieth century, all animal models of cancer induced bone pain involved systemic injection of cancer cells into animals, which caused severe deterioration of animal health due to widespread metastasis. In this mini-review we have discussed details of a recently developed and highly efficient preclinical model of breast cancer induced bone pain: Walker 256 cancer cell- induced bone pain in rats. The model involves direct localized injection of cancer cells into a single tibia in rats, which avoids widespread metastasis of cancer cells and hence animals maintain good health throughout the experimental period. This model closely mimics the human pathophysiology of breast cancer induced bone pain and has great potential to aid in the process of drug discovery for treating this intractable pain condition.
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