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Updated: Apr 27, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Assessing treatment response of osteolytic lesions by manual volumetry, automatic segmentation, and RECIST in
Maren Bretschi1, Andrea Fränzle2, Maximilian Merz3
1Department of Medical Physics in Radiology, German Cancer Research Center, Heidelberg, Germany.
Rationale And Objectives:
Aim of the study was to compare between volumetric and unidimensional approaches for treatment response monitoring in a nude rat model of experimental bone metastases. For the volumetric approach, an automated segmentation algorithm of osteolytic lesions was introduced and compared to manual volumetry.
Material And Methods:
Nude rats bearing osteolytic metastases were treated with zoledronate and sunitinib and compared to controls. Treatment response was assessed longitudinally in vivo using flat-panel volumetric computed tomography at days 30, 35, 45, and 55 after tumor cell inoculation. The mean sizes and volumes of osteolytic lesions were determined according to response evaluation criteria in solid tumors (RECIST) and by automated and manual volumetry (software: MITK [The Medical Imaging Interaction Toolkit, Heidelberg, Germany] and VIRTUOS, Heidelberg, Germany).
Results:
In contrary to RECIST, the manual volumetric approach indicated a significant decrease in osteolytic lesion volume in response to treatment. The presented automatic segmentation algorithm for treatment monitoring identified bone metastases adequately and assessed changes in the osteolytic lesion volume over time according to manual volumetry.
Conclusions:
In an animal model, volumetric treatment response assessment of osteolytic bone metastases is superior to unidimensional measurements, and automated volumetric segmentation may be a valuable alternative to manual volume determination.
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