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Updated: May 5, 2026

Assessment of Bone Fracture Healing Using Micro-Computed Tomography
Published on: December 9, 2022
A quantification strategy for missing bone mass in case of osteolytic bone lesions
Andrea Fränzle1, Maren Bretschi, Tobias Bäuerle
1Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
Purpose:
Most of the patients who died of breast cancer have developed bone metastases. To understand the pathogenesis of bone metastases and to analyze treatment response of different bone remodeling therapies, preclinical animal models are examined. In breast cancer, bone metastases are often bone destructive. To assess treatment response of bone remodeling therapies, the volumes of these lesions have to be determined during the therapy process. The manual delineation of missing structures, especially if large parts are missing, is very time-consuming and not reproducible. Reproducibility is highly important to have comparable results during the therapy process. Therefore, a computerized approach is needed. Also for the preclinical research, a reproducible measurement of the lesions is essential. Here, the authors present an automated segmentation method for the measurement of missing bone mass in a preclinical rat model with bone metastases in the hind leg bones based on 3D CT scans.
Methods:
The affected bone structure is compared to a healthy model. Since in this preclinical rat trial the metastasis only occurs on the right hind legs, which is assured by using vessel clips, the authors use the left body side as a healthy model. The left femur is segmented with a statistical shape model which is initialised using the automatically segmented medullary cavity. The left tibia and fibula are segmented using volume growing starting at the tibia medullary cavity and stopping at the femur boundary. Masked images of both segmentations are mirrored along the median plane and transferred manually to the position of the affected bone by rigid registration. Affected bone and healthy model are compared based on their gray values. If the gray value of a voxel indicates bone mass in the healthy model and no bone in the affected bone, this voxel is considered to be osteolytic.
Results:
The lesion segmentations complete the missing bone structures in a reasonable way. The mean ratio vr∕vm of the reconstructed bone volume vr and the healthy model bone volume vm is 1.07, which indicates a good reconstruction of the modified bone.
Conclusions:
The qualitative and quantitative comparison of manual and semi-automated segmentation results have shown that comparing a modified bone structure with a healthy model can be used to identify and measure missing bone mass in a reproducible way.
Insights
An automated method accurately measures missing bone mass in rats with breast cancer bone metastases. This reproducible technique compares affected bone to a healthy model, aiding preclinical research and therapy assessment.
Area of Science:
- Biomedical Engineering
- Preclinical Research
- Medical Imaging
Background:
- Breast cancer bone metastases are often destructive, necessitating accurate measurement for treatment assessment.
- Manual measurement of bone lesions in preclinical models is time-consuming and lacks reproducibility.
- Automated methods are crucial for reliable quantification of bone loss in metastatic disease.
Purpose of the Study:
- To develop and validate an automated segmentation method for measuring missing bone mass in a preclinical rat model of breast cancer bone metastases.
- To establish a reproducible technique for assessing bone lesion volumes in hind leg bones using 3D CT scans.
- To enable accurate monitoring of treatment response for bone remodeling therapies.
Main Methods:
- Utilized a preclinical rat model with induced bone metastases in the right hind leg.
- Employed the contralateral (left) hind leg as a healthy bone model.
- Segmented healthy bone using statistical shape and volume growing models, then mirrored and registered to the affected side for comparison.
- Identified osteolytic lesions by comparing gray values between healthy and affected bone models.
Main Results:
- The automated segmentation method successfully reconstructed missing bone structures.
- A mean ratio of reconstructed bone volume to healthy model bone volume (vr/vm) of 1.07 indicated good reconstruction accuracy.
- The method demonstrated reproducibility in identifying and measuring bone loss.
Conclusions:
- Comparing a modified bone structure to a healthy contralateral model is a reproducible approach for quantifying missing bone mass.
- The developed automated segmentation method provides a reliable tool for preclinical research on bone metastases.
- This technique supports reproducible assessment of bone remodeling therapies in metastatic breast cancer models.
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