A Patient-Specific Fracture Risk Assessment Tool for Femoral Bone Metastases: Using the Bone Strength (BOS) Score in Clinical Practice

Using a statistical shape model to estimate the knee landmarks for aligning femurs for femoral finite element models

BACKGROUND AND OBJECTIVE

The BOne Strength (BOS) score is a CT-based tool to assess fracture risk for patients with femoral bone metastases using finite element (FE) models. Until now, the knee joint center (KJC) and centers of the condyles (CoCs) were needed to create the FE model, hence BOS scores of incompletely scanned femurs could not be calculated. In this study, a statistical shape model (SSM) was used to align FE models of femurs with a removed knee anatomy. The aim was to determine the effect of using an SSM with different proximal femur fractions on KJC and CoC locations, and on the BOS score.

METHODS

QCT scans of 117 femurs were used to generate patient-specific FE models of the proximal femur. These models were aligned using the knee joint center (KJC), center of condyles (CoC) and femoral head center. The femurs were artificially shortened by removing 30 %, 50 % or 70 % of the femur. A recently developed SSM was used to reconstruct the distal femur. For each of the femur fractions, the difference between the original and SSM-reconstructed KJC and CoC were determined and the BOS scores were calculated.

RESULTS

Although the individual differences between the original and SSM-reconstructed KJC and CoC location could be large, the effect on the individual BOS scores was limited. The SSM-reconstructed BOS scores were highly correlated to the original BOS scores.

CONCLUSION

Using SSM to align femurs with a removed knee anatomy resulted in varying estimation of knee anatomy between patients but relatively accurate BOS scores.

The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis

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 popular deep learning model U-Net known as nnU-Net was used to automatically segment metastatic lesions within the femur. For these models with segmented lesions (Finite Element with Segmented Lesion: FE-with-SL), the calcium equivalent density within the metastatic lesions was set to zero after being segmented by the neural network, simulating absence of load-bearing capacity of these lesions. The models (either with or without automatically segmented lesions) were loaded incrementally in axial direction until failure was simulated. Dice coefficient was used to evaluate the similarity of the manual and automatic segmentation. Mean calcium equivalent density values within the automatically segmented lesions were calculated. Failure loads and patterns were determined. Furthermore, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for both groups by comparing the predictions to the occurrence or absence of actual fracture within the patient cohorts. The automatic segmentation algorithm performed in a none-robust manner. Dice coefficients describing the similarity between consented manual and automatic segmentations were relatively low (mean 0.45 ± standard deviation 0.33, median 0.54). Failure load difference between the FE-no-SL and FE-with-SL groups varied from 0 % to 48 % (mean 6.6 %). Correlation analysis of failure loads between the two groups showed a strong relationship (R2 > 0.9). From the 50 cases, four cases showed clear deviations for which models with automatic lesion segmentation (FE-with-SL) showed considerably lower failure loads. In the whole database including osteolytic and mixed lesions, sensitivity and NPV remained the same, but specificity and PPV decreased from 94 % to 83 %, and from 78 % to 54 % respectively from FE-no-SL to FE-with-SL. This study indicates that the nnU-Net yielded none-robust outcomes in femoral lesion segmentation and that other segmentation algorithms should be considered. However, the difference in failure pattern and failure load between FE models with automatically segmented osteolytic and mixed lesions were relatively small in most cases with a few exceptions. On the other hand, the accuracy of fracture risk assessment using the BOS score was lower compared to the FE-no-SL. In conclusion, this study showed that automatic lesion segmentation is a none-solved issue and therefore, quantifying lesion characteristics and the subsequent effect on the fracture risk using deep learning will remain challenging.