| Title: |
The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis. |
| Authors: |
Ataei, A.; Eggermont, F.E.; Verdonschot, N.J.; Lessmann, N.; Tanck, E.J.M. |
| Source: |
Bone, 179, pp. 116987 |
| Publication Year: |
2024 |
| Collection: |
Radboud University: DSpace |
| Subject Terms: |
Orthopaedics - Radboud University Medical Center |
| Description: |
Contains fulltext : 301223.pdf (Publisher’s version ) (Open Access) ; Bone ranks as the third most frequent tissue affected by cancer metastases, following the lung and liver. Bone metastases are often painful and may result in pathological fracture, which is a major cause of morbidity and mortality in cancer patients. To quantify fracture risk, finite element (FE) analysis has shown to be a promising tool, but metastatic lesions are typically not specifically segmented and therefore their mechanical properties may not be represented adequately. Deep learning methods potentially provide the opportunity to automatically segment these lesions and change the mechanical properties more adequately. In this study, our primary focus was to gain insight into the performance of an automatic segmentation algorithm for femoral metastatic lesions using deep learning methods and the subsequent effects on FE outcomes. The aims were to determine the similarity between manual segmentation and automatic segmentation; the differences in predicted failure load between FE models with automatically segmented osteolytic and mixed lesions and the models with CT-based lesion values (the gold standard); and the effect on the BOne Strength (BOS) score (failure load adjusted for body weight) and subsequent fracture risk assessments. From two patient cohorts, a total number of 50 femurs with osteolytic and mixed metastatic lesions were included in this study. The femurs were segmented from CT images and transferred into FE meshes. The material behavior was implemented as non-linear isotropic. These FE models were considered as gold standard (Finite Element no Segmented Lesion: FE-no-SL), whereby the local calcium equivalent density of both femur and metastatic lesion was extracted from CT-values. Lesions in the femur were manually segmented by two biomechanical experts after which final lesion segmentation for each femur was obtained based on consensus of opinions between two observers. Subsequently, a self-configuring variant of the ... |
| Document Type: |
article in journal/newspaper |
| Language: |
unknown |
| Relation: |
https://repository.ubn.ru.nl//bitstream/handle/2066/301223/301223.pdf; https://hdl.handle.net/2066/301223 |
| DOI: |
10.1016/j.bone.2023.116987 |
| Availability: |
https://hdl.handle.net/2066/301223; https://repository.ubn.ru.nl//bitstream/handle/2066/301223/301223.pdf; https://doi.org/10.1016/j.bone.2023.116987 |
| Accession Number: |
edsbas.73985054 |
| Database: |
BASE |