Fracture Risk Evaluation of Bone Metastases: A Burning Issue

Major progress has been achieved to treat cancer patients and survival has improved considerably, even for stage-IV bone metastatic patients. Locomotive health has become a crucial issue for patient autonomy and quality of life. The centerpiece of the reflection lies in the fracture risk evaluation of bone metastasis to guide physician decision regarding physical activity, antiresorptive agent prescription, and local intervention by radiotherapy, surgery, and interventional radiology. A key mandatory step, since bone metastases may be asymptomatic and disseminated throughout the skeleton, is to identify the bone metastasis location by cartography, especially within weight-bearing bones. For every location, the fracture risk evaluation relies on qualitative approaches using imagery and scores such as Mirels and spinal instability neoplastic score (SINS). This approach, however, has important limitations and there is a need to develop new tools for bone metastatic and myeloma fracture risk evaluation. Personalized numerical simulation qCT-based imaging constitutes one of these emerging tools to assess bone tumoral strength and estimate the femoral and vertebral fracture risk. The next generation of numerical simulation and artificial intelligence will take into account multiple loadings to integrate movement and obtain conditions even closer to real-life, in order to guide patient rehabilitation and activity within a personalized-medicine approach

Bone antiresorptive agents in the treatment of bone metastases associated with solid tumours or multiple myeloma.

Skeletal lesions contribute substantially to morbidity and mortality in patients with cancer. The disease manifestation course during metastatic bone disease is driven by tumour cells in the bone marrow, which alter the functions of bone-resorbing (osteoclasts) and bone-forming (osteoblasts) cells, promoting skeletal destruction. Successful therapeutic strategies for the treatment of metastatic bone disease include bisphosphonates and denosumab that inhibit osteoclast-mediated bone resorption. Inhibitors of cathepsin K, Src and activin A are under clinical investigation as potential anti-osteolytics. In this review, we describe current knowledge and future directions of antiresorptive therapies that may reduce or prevent destructive bone lesions from solid tumours and multiple myeloma

Multi-frequency shear modulus measurements discriminate tumorous from healthy tissues

As far as their mechanical properties are concerned, cancerous lesions can be confused with healthy surrounding tissues in elastography protocols if only the magnitude of moduli is considered. We show that the frequency dependence of the tissue's mechanical properties allows for discriminating the tumor from other tissues, obtaining a good contrast even when healthy and tumor tissues have shear moduli of comparable magnitude. We measured the shear modulus G*(?) of xenograft subcutaneous tumors developed in mice using breast human cancer cells, compared with that of fat, skin and muscle harvested from the same mice. As the absolute shear modulus |G*(?)| of tumors increases by 42% (from 5.2 to 7.4kPa) between 0.25 and 63Hz, it varies over the same frequency range by 77% (from 0.53 to 0.94kPa) for the fat, by 103% (from 3.4 to 6.9kPa) for the skin and by 120% (from 4.4 to 9.7kPa) for the muscle. These measurements fit well to the fractional model G*(?)=K(i?)n, yielding a coefficient K and a power-law exponent n for each sample. Tumor, skin and muscle have comparable K parameter values, that of fat being significantly lower; the p-values given by a Mann-Whitney test are above 0.14 when comparing tumor, skin and muscle between themselves, but below 0.001 when comparing fat with tumor, skin or muscle. With regards the n parameter, tumor and fat are comparable, with p-values above 0.43, whereas tumor differs from both skin and muscle, with p-values below 0.001. Tumor tissues thus significantly differs from fat, skin and muscle on account of either the K or the n parameter, i.e. of either the magnitude or the frequency-dependence of the shear modulus

Fracture Risk Evaluation of Bone Metastases: A Burning Issue

Major progress has been achieved to treat cancer patients and survival has improved considerably, even for stage-IV bone metastatic patients. Locomotive health has become a crucial issue for patient autonomy and quality of life. The centerpiece of the reflection lies in the fracture risk evaluation of bone metastasis to guide physician decision regarding physical activity, antiresorptive agent prescription, and local intervention by radiotherapy, surgery, and interventional radiology. A key mandatory step, since bone metastases may be asymptomatic and disseminated throughout the skeleton, is to identify the bone metastasis location by cartography, especially within weight-bearing bones. For every location, the fracture risk evaluation relies on qualitative approaches using imagery and scores such as Mirels and spinal instability neoplastic score (SINS). This approach, however, has important limitations and there is a need to develop new tools for bone metastatic and myeloma fracture risk evaluation. Personalized numerical simulation qCT-based imaging constitutes one of these emerging tools to assess bone tumoral strength and estimate the femoral and vertebral fracture risk. The next generation of numerical simulation and artificial intelligence will take into account multiple loadings to integrate movement and obtain conditions even closer to real-life, in order to guide patient rehabilitation and activity within a personalized-medicine approach

Impact of Bone Metastases on Patients with Renal Cell Carcinoma or Melanoma Treated with Combotherapy Ipilimumab Plus Nivolumab

(1) Background: Ipilimumab plus nivolumab (combo-ICI) improves overall survival (OS) in patients with advanced renal cell carcinoma (RCC) or melanoma. The impact of bone metastases (BM) on survival outcomes of combo-ICI-treated patients is unknown. (2) Methods: This single-center retrospective observational study involved 36 combo-ICI-treated patients with advanced RCC and 35 with melanoma. Clinical and laboratory data preceding the initiation of combo-ICI were collected. Univariate and multivariate Cox proportional hazard models were used to assess the effect of BM on overall survival (OS) and progression-free survival (PFS). (3) Results: zNine RCC and 11 melanoma patients had baseline BM. In unadjusted analysis, baseline BM was associated with a poorer OS in the RCC cohort. Baseline BM did not have any impact on survival outcomes in melanoma patients. After adjustment on baseline performance status and on neutrophil-to-lymphocyte ratio (NLR), the impact of BM was no longer significant, but a NLR ≥ 3 was significantly associated with a poorer OS in the RCC cohort. (4) Conclusions: The presence of baseline BM seems to be associated with worse outcomes in RCC combo-ICI-treated patients, while its effect might not be independent from the inflammatory state (approximated by the NLR). BM seems to have no impact on the outcomes of melanoma combo-ICI-treated patients

Influence of specific metastasis mechanical properties on the predicted failure load of metastasised femur

International audienc

Deep learning for bone segmentation: towards automating failure load simulations

In this paper, we propose a dedicated pipeline of pre-processing, deep learning-based segmentation and post-processing for human femurs and vertebras segmentation of CT-scans volumes. For both tasks, we experimented with three U-Net architectures and showed that out-of-the-box models enable automatic and high-quality volume segmentation, if carefully trained

Deep learning for bone segmentation: towards automating failure load simulations

In this paper, we propose a dedicated pipeline of pre-processing, deep learning-based segmentation and post-processing for human femurs and vertebras segmentation of CT-scans volumes. For both tasks, we experimented with three U-Net architectures and showed that out-of-the-box models enable automatic and high-quality volume segmentation, if carefully trained

Influence of specific metastasis mechanical properties on the predicted failure load of metastasised femur

International audienc