Thickness and design features of clinical cranial implants-what should automated methods strive to replicate?

PURPOSE: New deep learning and statistical shape modelling approaches aim to automate the design process for patient-specific cranial implants, as highlighted by the MICCAI AutoImplant Challenges. To ensure applicability, it is important to determine if the training data used in developing these algorithms represent the geometry of implants designed for clinical use. METHODS: Calavera Surgical Design provided a dataset of 206 post-craniectomy skull geometries and their clinically used implants. The MUG500+ dataset includes 29 post-craniectomy skull geometries and implants designed for automating design. For both implant and skull shapes, the inner and outer cortical surfaces were segmented, and the thickness between them was measured. For the implants, a 'rim' was defined that transitions from the repaired defect to the surrounding skull. For unilateral defect cases, skull implants were mirrored to the contra-lateral side and thickness differences were quantified. RESULTS: The average thickness of the clinically used implants was 6.0 ± 0.5 mm, which approximates the thickness on the contra-lateral side of the skull (relative difference of -0.3 ± 1.4 mm). The average thickness of the MUG500+ implants was 2.9 ± 1.0 mm, significantly thinner than the intact skull thickness (relative difference of 2.9 ± 1.2 mm). Rim transitions in the clinical implants (average width of 8.3 ± 3.4 mm) were used to cap and create a smooth boundary with the skull. CONCLUSIONS: For implant modelers or manufacturers, this shape analysis quantified differences of cranial implants (thickness, rim width, surface area, and volume) to help guide future automated design algorithms. After skull completion, a thicker implant can be more versatile for cases involving muscle hollowing or thin skulls, and wider rims can smooth over the defect margins to provide more stability. For clinicians, the differing measurements and implant designs can help inform the options available for their patient specific treatment.

Treatment affects load to failure and microdamage accumulation in healthy and osteolytic rat vertebrae.

Bone turnover and microdamage are impacted by the presence of skeletal metastases which can contribute to increased fracture risk. Treatments for metastatic disease may further impact bone quality. This exploratory study aimed to establish an initial understanding of microdamage accumulation and load to failure in healthy and osteolytic rat vertebrae following focal and systemic cancer treatment (docetaxel (DTX), stereotactic body radiotherapy (SBRT), or zoledronic acid (ZA)). Osteolytic spine metastases were developed in 6-week-old athymic female rats via intracardiac injection of HeLa human cervical cancer cells (day 0). Additional rats served as healthy controls. Rats were either untreated, received SBRT to the T10-L6 vertebrae on day 14 (15 Gy, two fractions), DTX on day 7 or 14, or ZA on day 7. Rats were euthanized on day 21. Tumor burden was assessed with bioluminescence images acquired on day 14 and 21, histology of the excised T11 and L5 vertebrae, and ex-vivo µCT images of the T13-L4. Microstructural parameters (bone volume/total volume, trabecular number, spacing, thickness, and bone mineral density) were measured from L2 vertebrae. Load to failure was measured with axial compressive loading of the L1-L3 motion segments. Microdamage accumulation was labeled in T13 vertebrae with BaSO4 staining and was visualized with high resolution µCT imaging. Microdamage volume fraction was defined as the ratio of BaSO4 to bone volume. DTX administered on day 7 reduced tumor growth significantly (p < 0.05). Microdamage accumulation was found to be increased by the presence of metastases but was reduced by all treatments with ZA showing the largest improvement in HeLa cell injected rats. Load to failure was decreased in untreated and SBRT HeLa cell injected rats compared to healthy controls (p < 0.01). There was a moderate negative correlation between load to failure and microdamage volume fraction in vertebrae from rats injected with HeLa cells (R = -0.35, p = 0.031). Strong correlations were also found between microstructural parameters and load to failure and microdamage accumulation. Several factors, including the presence of osteolytic lesions and use of cancer therapies, influence microdamage accumulation and load to failure in rat vertebrae. Understanding the impact of these treatments on fracture risk of metastatic vertebrae is important to improve management of patients with spinal metastases.

Artificial Intelligence-Based Modeling Can Predict Face Shape Based on Underlying Craniomaxillofacial Bone.

Reconstructing facial deformities is often challenging due to the complex 3-dimensional (3D) anatomy of the craniomaxillofacial skeleton and overlying soft tissue structures. Bilateral injuries cannot benefit from mirroring techniques and as such preinjury information (eg, 2D pictures or 3D imaging) may be utilized to determine or estimate the desired 3D face shape. When patient-specific information is not available, other options such as statistical shape models may be employed; however, these models require registration to a consistent orientation which may be challenging. Artificial intelligence (AI) has been used to identify facial features and generate highly realistic simulated faces. As such, it was hypothesized that AI can be used to predict 3D face shape by learning its relationship with the underlying bone surface anatomy in a subject-specific manner. An automated image processing and AI modeling workflow using a modified 3D UNet was generated to estimate 3D face shape using the underlying bone geometry and additional metadata (eg, body mass index and age) obtained from 5 publicly available computed tomography imaging datasets. Visually, the trained models provided a reasonable prediction of the contour and geometry of the facial tissues. The pipeline achieved a validation dice=0.89 when trained on the combined 5 datasets, with the highest dice=0.925 achieved with the single HNSCC dataset. Estimated predefect facial geometry may ultimately be used to aid preoperative craniomaxillofacial surgical planning, providing geometries for intraoperative templates, guides, navigation, molds, and forming tools. Automated face shape prediction may additionally be useful in forensic studies to aid in the identification of unknown skull remains.

Accuracy of Orbital Shape Reconstruction-Comparative Analysis of Errors in Implant Shape Versus Implant Positioning: A Cadaveric Study.

INTRODUCTION: Orbital blowout fractures are commonly reconstructed with implants shaped to repair orbital cavity defects, restore ocular position and projection, and correct diplopia. Orbital implant shaping has traditionally been performed manually by surgeons, with more recent use of computer-assisted design (CAD). Accuracy of implant placement is also key to reconstruction. This study compares the placement accuracy of orbital implants, testing the hypothesis that CAD-shaped implants indexed to patient anatomy will better restore orbit geometry compared with manually shaped implants and manually placed implants. METHODS: The placement accuracy of orbital implants was assessed within a cadaveric blowout fracture model (3 skulls, 6 orbits) via 3-dimensional CT analysis. Defects were repaired with 4 different techniques: manually placed-manually shaped composite (titanium-reinforced porous polyethylene), manually placed CAD composite, indexed placed CAD composite, and indexed placed CAD titanium mesh. RESULTS: Implant placement accuracy differed significantly with the implant preparation method ( P =0.01). Indexing significantly improved the placement accuracy ( P =0.002). Indexed placed titanium mesh CAD implants (1.42±0.33 mm) were positioned significantly closer to the intact surface versus manually placed-manually shaped composite implants (2.12±0.39 mm). DISCUSSION: Computer-assisted design implants indexed to patient geometry yielded average errors below the acceptable threshold (2 mm) for enophthalmos and diplopia. This study highlights the importance of adequately indexing CAD-designed implants to patient geometry to ensure accurate orbital reconstructions.

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

Development and validation of a radiofrequency ablation treatment planning system for vertebral metastases.

PURPOSE: Bone-targeted radiofrequency ablation (RFA) is widely used in the treatment of vertebral metastases. While radiation therapy utilizes established treatment planning systems (TPS) based on multimodal imaging to optimize treatment volumes, current RFA of vertebral metastases has been limited to qualitative image-based assessment of tumour location to direct probe selection and access. This study aimed to design, develop and evaluate a computational patient-specific RFA TPS for vertebral metastases. METHODS: A TPS was developed on the open-source 3D slicer platform, including procedural setup, dose calculation (based on finite element modelling), and analysis/visualization modules. Usability testing was carried out by 7 clinicians involved in the treatment of vertebral metastases on retrospective clinical imaging data using a simplified dose calculation engine. In vivo evaluation was performed in a preclinical porcine model (n = 6 vertebrae). RESULTS: Dose analysis was successfully performed, with generation and display of thermal dose volumes, thermal damage, dose volume histograms and isodose contours. Usability testing showed an overall positive response to the TPS as beneficial to safe and effective RFA. The in vivo porcine study showed good agreement between the manually segmented thermally damaged volumes vs. the damage volumes identified from the TPS (Dice Similarity Coefficient = 0.71 ± 0.03, Hausdorff distance = 1.2 ± 0.1 mm). CONCLUSION: A TPS specifically dedicated to RFA in the bony spine could help account for tissue heterogeneities in both thermal and electrical properties. A TPS would enable visualization of damage volumes in 2D and 3D, assisting clinicians in decisions about potential safety and effectiveness prior to performing RFA in the metastatic spine.

Are We Overoperating on Isolated Orbital Floor Fractures?

BACKGROUND: Orbital floor fracture defect size and inferior rectus (IR) rounding index are currently accepted indications for surgery to prevent late enophthalmos. The authors analyzed the positive predictive value (PPV) of these indications. METHODS: Twenty-eight patients with orbital floor fractures presenting without enophthalmos underwent Hertel exophthalmometry at presentation and at weeks 1, 2, 3, 6, 13, 26, and 52 or more after injury. Orbital defect size and IR rounding index were measured from computed tomographic scans, and PPVs of defects of 1.5 to 2 cm 2 or larger and IR rounding index of 1 or higher for enophthalmos (≥2 mm) were calculated. RESULTS: Nineteen patients had isolated orbital floor fractures (group A), three had noncontinuous orbital floor and medial wall fractures (group B), and six had continuous orbital floor with medial wall fractures (group C). Mean follow-up time was 440 days. Of all patients, 20 had a defect size of 1.5 cm 2 or larger, 12 had a defect of 2.0 cm 2 or larger, and 13 had an IR rounding index of 1 or higher. Of the 28 patients, only one from group A and two from group C developed enophthalmos of 2 mm only. The PPVs of orbital floor defect size of 1.5 cm 2 or larger and 2 cm 2 or larger (groups A and B only) for late enophthalmos were 6.7% and 0%, respectively. The PPV of IR rounding index of 1 or higher for late enophthalmos (all groups) was 0%. CONCLUSIONS: For patients with orbital floor fractures presenting without enophthalmos, defects of 1.5 cm 2 or larger and 2 cm 2 or larger, and IR rounding index of 1 or higher, are weakly predictive of late enophthalmos. Furthermore, patients who do not develop enophthalmos within 3 weeks of injury are unlikely to develop significant (>2 mm) late enophthalmos. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.

Sarcopenia in Men With Bone-Predominant Metastatic Castration-Resistant Prostate Cancer Undergoing Ra-223 Therapy.

INTRODUCTION: Osteosarcopenia is the progressive loss of musculoskeletal structure and functionality, contributing to disability and mortality. Despite complex interactions between bone and muscle, osteosarcopenia prevention and treatment in men with metastatic castration-resistant prostate cancer (mCRPC) focuses predominantly on bone health. It is unknown whether Radium-223 (Ra-223) therapy affects sarcopenia. METHODS: We identified 52 patients with mCRPC who had received Ra-223 and had a baseline plus ≥1 follow-up abdominopelvic CT scan. The total contour area (TCA) and averaged Hounsfield units (HU) of the left and right psoas muscles were obtained at the inferior L3 endplate, and the psoas muscle index (PMI) was calculated therefrom. Intrapatient musculoskeletal changes were analyzed across various time points. RESULTS: TCA and PMI gradually declined over the study period (P = .002, P = .003, respectively), but Ra-223 therapy did not accelerate sarcopenia, nor the decline of HU compared to the pre-Ra-223 period. The median overall survival of patients with baseline sarcopenia was numerically worse (14.93 vs. 23.23 months, HR 0.612, P = .198). CONCLUSIONS: Ra-223 does not accelerate sarcopenia. Thus, worsening muscle parameters in men with mCRPC undergoing Ra-223 therapy are attributable to other factors. Further research is needed to determine whether baseline sarcopenia predicts poor overall survival in such patients.

Open-source pipeline for automatic segmentation and microstructural analysis of murine knee subchondral bone.

µCT images are commonly analysed to assess changes in bone density and microstructure in preclinical murine models. Several platforms provide automated analysis of bone microstructural parameters from volumetric regions of interest (ROI). However, segmentation of the regions of subchondral bone to create the volumetric ROIs remains a manual and time-consuming task. This study aimed to develop an automated end-to-end pipeline, combining segmentation and microstructural analysis, to evaluate subchondral bone in the mouse proximal knee. METHODS: A segmented dataset of µCT scans from 62 knees (healthy and arthritic) from 10-week male C57BL/6 mice was used to train a U-Net type architecture to automate segmentation of the subchondral trabecular bone. These segmentations were used in tandem with the original scans as input for microstructural analysis along with thresholded trabecular bone. Manually and U-Net segmented ROIs were fed into two available pipelines for microstructural analysis: the ITKBoneMorphometry library and CTan (SKYSCAN). Outcome parameters were compared between pipelines, including: bone volume (BV), total volume (TV), BV/TV, trabecular number (TbN), trabecular thickness (TbTh), trabecular separation (TbSp), and bone surface density (BSBV). RESULTS: There was good agreement for all bone measures comparing the manual and U-Net pipelines utilizing ITK (R = 0.88-0.98) and CTAn (R = 0.91-0.98). ITK and CTAn showed good agreement for BV, TV, BV/TV, TbTh and BSBV (R = 0.9-0.98). However, limited agreement was seen between TbN (R = 0.73) and TbSb (R = 0.59) due to methodological differences in how spacing is evaluated. Microstructural parameters generated from manual and automatic segmentations showed high correlation across all measures. Using the CTAn pipeline yielded strong R2 values (0.83-0.96) and very strong agreement based on ICC (0.90-0.98). The ITK pipeline yielded similarly high R2 values (0.91-0.96, except for TbN (0.77)), and ICC values (0.88-0.98). The automated segmentations yield lower average values for BV, TV and BV/TV (ranging from 14 % to 6.3 %), but differences were not found to be influenced by the mean ROI values. CONCLUSIONS: This integrated pipeline seamlessly automated both segmentation and quantification of the proximal tibia subchondral bone microstructure. This automated pipeline allows the analysis of large volumes of data, and its open-source nature may enable the standardization of microstructural analysis of trabecular bone across different research groups.

Multi-Modal Deep Learning for Assessing Surgeon Technical Skill.

This paper introduces a new dataset of a surgical knot-tying task, and a multi-modal deep learning model that achieves comparable performance to expert human raters on this skill assessment task. Seventy-two surgical trainees and faculty were recruited for the knot-tying task, and were recorded using video, kinematic, and image data. Three expert human raters conducted the skills assessment using the Objective Structured Assessment of Technical Skill (OSATS) Global Rating Scale (GRS). We also designed and developed three deep learning models: a ResNet-based image model, a ResNet-LSTM kinematic model, and a multi-modal model leveraging the image and time-series kinematic data. All three models demonstrate performance comparable to the expert human raters on most GRS domains. The multi-modal model demonstrates the best overall performance, as measured using the mean squared error (MSE) and intraclass correlation coefficient (ICC). This work is significant since it demonstrates that multi-modal deep learning has the potential to replicate human raters on a challenging human-performed knot-tying task. The study demonstrates an algorithm with state-of-the-art performance in surgical skill assessment. As objective assessment of technical skill continues to be a growing, but resource-heavy, element of surgical education, this study is an important step towards automated surgical skill assessment, ultimately leading to reduced burden on training faculty and institutes.

Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation.

BACKGROUND: Physiotherapy is a critical element in the successful conservative management of low back pain (LBP). A gold standard for quantitatively measuring physiotherapy participation is crucial to understanding physiotherapy adherence in managing recovery from LBP. OBJECTIVE: This study aimed to develop and evaluate a system with wearable inertial sensors to objectively detect the performance of unsupervised exercises for LBP comprising movement in multiple planes and sitting postures. METHODS: A quantitative classification design was used within a machine learning framework to detect exercise performance and posture in a cohort of healthy participants. A set of 8 inertial sensors were placed on the participants, and data were acquired as they performed 7 McKenzie low back exercises and 3 sitting posture positions. Engineered time series features were extracted from the data and used to train 9 models by using a 6-fold cross-validation approach, from which the best 2 models were selected for further study. In addition, a convolutional neural network was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed the most to the models. Finally, a subset of sensor locations and channels was included in a hyperparameter grid search to identify the optimal sensor configuration and best performing algorithms for exercise and posture classification. The final models were evaluated using the F1 score in a 10-fold cross-validation approach. RESULTS: In total, 19 healthy adults with no history of LBP each completed at least one full session of exercises and postures. Random forest and XGBoost (extreme gradient boosting) models performed the best out of the initial set of 9 engineered feature models. The optimal hardware configuration was identified as a 3-sensor setup-lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XGBoost model achieved the highest exercise (F1 score: mean 0.94, SD 0.03) and posture (F1 score: mean 0.90, SD 0.11) classification scores. The convolutional neural network achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F1 score: mean 0.94, SD 0.02) and the accelerometer channel alone for posture classification (F1 score: mean 0.88, SD 0.07). CONCLUSIONS: This study demonstrates the potential of a 3-sensor lower body wearable solution (eg, smart pants) that can identify exercises in multiple planes and proper sitting postures, which is suitable for the treatment of LBP. This technology has the potential to improve the effectiveness of LBP rehabilitation by facilitating quantitative feedback, early problem diagnosis, and possible remote monitoring.

Bullseye EVD: preclinical evaluation of an intra-procedural system to confirm external ventricular drainage catheter positioning.

PURPOSE: External ventricular drainage (EVD) is a life-saving procedure indicated for elevated intracranial pressure. A catheter is inserted into the ventricles to drain cerebrospinal fluid and release the pressure on the brain. However, the standard freehand EVD technique results in catheter malpositioning in up to 60.1% of procedures. This proof-of-concept study aimed to evaluate the registration accuracy of a novel image-based verification system "Bullseye EVD" in a preclinical cadaveric model of catheter placement. METHODS: Experimentation was performed on both sides of 3 cadaveric heads (n = 6). After a pre-interventional CT scan, a guidewire simulating the EVD catheter was inserted as in a clinical EVD procedure. 3D structured light images (Einscan, Shining 3D, China) were acquired of an optical tracker placed over the guidewire on the surface of the scalp, along with three distinct cranial regions (scalp, face, and ear). A computer vision algorithm was employed to determine the guidewire position based on the pre-interventional CT scan and the intra-procedural optical imaging. A post-interventional CT scan was used to validate the performance of the Bullseye optical imaging system in terms of trajectory and offset errors. RESULTS: Optical images which combined facial features and exposed scalp within the surgical field resulted in the lowest trajectory and offset errors of 1.28° ± 0.38° and 0.33 ± 0.19 mm, respectively. Mean duration of the optical imaging procedure was 128 ± 35 s. CONCLUSIONS: The Bullseye EVD system presents an accurate patient-specific method to verify freehand EVD positioning. Use of facial features was critical to registration accuracy. Workflow automation and development of a user interface must be considered for future clinical evaluation.

Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning.

Access to healthcare, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure participation. The aim of this study was to develop and evaluate the potential for performing automatic, unsupervised video-based monitoring of at-home low-back and shoulder physiotherapy exercises using a mobile phone camera. Joint locations were extracted from the videos of healthy subjects performing low-back and shoulder physiotherapy exercises using an open source pose detection framework. A convolutional neural network was trained to classify physiotherapy exercises based on the segments of keypoint time series data. The model's performance as a function of input keypoint combinations was studied in addition to its robustness to variation in the camera angle. The CNN model achieved optimal performance using a total of 12 pose estimation landmarks from the upper and lower body (low-back exercise classification: 0.995 ± 0.009; shoulder exercise classification: 0.963 ± 0.020). Training the CNN on a variety of angles was found to be effective in making the model robust to variations in video filming angle. This study demonstrates the feasibility of using a smartphone camera and a supervised machine learning model to effectively classify at-home physiotherapy participation and could provide a low-cost, scalable method for tracking adherence to physical therapy exercise programs in a variety of settings.

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design.

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi.

Spatial correspondence of spinal cord white matter tracts using diffusion tensor imaging, fibre tractography, and atlas-based segmentation.

PURPOSE: Neuroimaging provides great utility in complex spinal surgeries, particularly when anatomical geometry is distorted by pathology (tumour, degeneration, etc.). Spinal cord MRI diffusion tractography can be used to generate streamlines; however, it is unclear how well they correspond with white matter tract locations along the cord microstructure. The goal of this work was to evaluate the spatial correspondence of DTI tractography with anatomical MRI in healthy anatomy (where anatomical locations can be well defined in T1-weighted images). METHODS: Ten healthy volunteers were scanned on a 3T system. T1-weighted (1 × 1 × 1 mm) and diffusion-weighted images (EPI readout, 2 × 2 × 2 mm, 30 gradient directions) were acquired and subsequently registered (Spinal Cord Toolbox (SCT)). Atlas-based (SCT) anatomic label maps of the left and right lateral corticospinal tracts were identified for each vertebral region (C2-C6) from T1 images. Tractography streamlines were generated with a customized approach, enabling seeding of specific spinal tract regions corresponding to individual vertebral levels. Spatial correspondence of generated fibre streamlines with anatomic tract segmentations was compared in unseeded regions of interest (ROIs). RESULTS: Spatial correspondence of the lateral corticospinal tract streamlines was good over a single vertebral ROI (Dice's similarity coefficient (DSC) = 0.75 ± 0.08, Hausdorff distance = 1.08 ± 0.17 mm). Over larger ROI, fair agreement between tractography and anatomical labels was achieved (two levels: DSC = 0.67 ± 0.13, three levels: DSC = 0.52 ± 0.19). CONCLUSION: DTI tractography produced good spatial correspondence with anatomic white matter tracts, superior to the agreement between multiple manual tract segmentations (DSC ~ 0.5). This supports further development of spinal cord tractography for computer-assisted neurosurgery.

Biomechanical Properties of Metastatically Involved Osteolytic Bone.

PURPOSE OF REVIEW: Skeletal metastasis involves the uncoupling of physiologic bone remodeling resulting in abnormal bone turnover and radical changes in bony architecture, density, and quality. Bone strength assessment and fracture risk prediction are critical in clinical treatment decision-making. This review focuses on bone tissue and structural mechanisms altered by osteolytic metastasis and the resulting changes to its material and mechanical behavior. RECENT FINDINGS: Both organic and mineral phases of bone tissue are altered by osteolytic metastatic disease, with diminished bone quality evident at multiple length-scales. The mechanical performance of bone with osteolytic lesions is influenced by a combination of tissue-level and structural changes. This review considers the effects of osteolytic metastasis on bone biomechanics demonstrating its negative impact at tissue and structural levels. Future studies need to assess the cumulative impact of cancer treatments on metastatically involved bone quality, and its utility in directing multimodal treatment planning.

Relationships between inhalable and total hexavalent chromium exposures in steel passivation, welding and electroplating operations of Ontario.

The main aim of the study was to assess the relationship between inhalable hexavalent chromium and "total" hexavalent chromium. Air sampling was conducted at steel passivation operation of a steel manufacturer at a stainless steel welding operation and at two hard chrome electroplaters. Air samples were taken side-by-side for "total" dust using closed-face 37-mm diameter cassette samplers and for inhalable dust using Institute of Occupational Medicine inhalable samplers. A total of 40 pairs of total and inhalable dust samples were collected and later analyzed. The range of "total" dust and inhalable dust concentrations in µg/m3 measured were 30-410 and 0.02 to 740 respectively for steel passivation; 260 to 1520 and 477 to 6970 for welding; and 0.01 to 1500 and 204 to 2130 for electroplaters. The range of "total" dust hexavalent chromium and inhalable dust hexavalent chromium concentrations in µg/m3 were 0.02-89 and 0.02 to 150 respectively for steel making; 4.1 to 4.9 and 2.2 to 6.9 for welding and 0.01 to 9.3 and 0.01 to 21 for electroplaters. A linear relationship between inhalable hexavalent chromium and "total" hexavalent chromium was found with a slope of 1.4 (CI:1.3, 1.5) and 0 offset. A ratio of 1.4 can thus be used as a conversion factor to convert previous data of hexavalent chromium taken on "total" dust basis to inhalable hexavalent chromium concentrations.

CT based quantitative measures of the stability of fractured metastatically involved vertebrae treated with spine stereotactic body radiotherapy.

Mechanical instability secondary to vertebral metastases can lead to pathologic vertebral compression fracture (VCF) mechanical pain, neurological compromise, and the need for surgical stabilization. Stereotactic body radiation therapy (SBRT) as a treatment for spinal metastases is effective for pain and local tumor control, it has been associated with an increased risk of VCF. This study quantified computed tomography (CT) based stability measures in metastatic vertebrae with VCF treated with spine SBRT. It was hypothesized that semi-automated quantification of VCF based on CT metrics would be related to clinical outcomes. 128 SBRT treated spinal metastases patients were identified from a prospective database. Of these, 18 vertebral segments were identified with a VCF post-SBRT. A semi-automated system for quantifying VCF was developed based on CT imaging before and after SBRT. The system identified and segmented SBRT treated vertebral bodies, calculated stability metrics at single time points and changes over time. In the vertebrae that developed a new (n = 7) or progressive (n = 11) VCF following SBRT, the median time to VCF/VCF progression was 1.74 months (range 0.53-7.79 months). Fractured thoracolumbar vertebrae that went on to be stabilized (cemented and/or instrumented), had greater fractured vertebral body volume progression over time (12%) compared to those not stabilized (0.4%, p < 0.05). Neither the spinal instability neoplastic score (SINS) or any single timepoint stability metrics in post-hoc analyses correlated with future stabilization. This pilot study presents a quantitative semi-automated method assessing fractured thoracolumbar vertebrae based on CT. Increased fractured vertebral body volume progression post-SBRT was shown to predict those patients who were subsequently stabilized, motivating study of methods that assess temporal radiological changes toward augmenting existing clinical management in the metastatic spine.

Adherence Tracking With Smart Watches for Shoulder Physiotherapy in Rotator Cuff Pathology: Protocol for a Longitudinal Cohort Study.

BACKGROUND: Physiotherapy is essential for the successful rehabilitation of common shoulder injuries and following shoulder surgery. Patients may receive some training and supervision for shoulder physiotherapy through private pay or private insurance, but they are typically responsible for performing most of their physiotherapy independently at home. It is unknown how often patients perform their home exercises and if these exercises are performed correctly without supervision. There are no established tools for measuring this. It is, therefore, unclear if the full benefit of shoulder physiotherapy treatments is being realized. OBJECTIVE: The proposed research will (1) validate a smartwatch and machine learning (ML) approach for evaluating adherence to shoulder exercise participation and technique in a clinical patient population with rotator cuff pathology; (2) quantify the rate of home physiotherapy adherence, determine the effects of adherence on recovery, and identify barriers to successful adherence; and (3) develop and pilot test an ethically conscious adherence-driven rehabilitation program that individualizes patient care based on their capacity to effectively participate in their home physiotherapy. METHODS: This research will be conducted in 2 phases. The first phase is a prospective longitudinal cohort study, involving 120 patients undergoing physiotherapy for rotator cuff pathology. Patients will be issued a smartwatch that will record 9-axis inertial sensor data while they perform physiotherapy exercises both in the clinic and in the home setting. The data collected in the clinic under supervision will be used to train and validate our ML algorithms that classify shoulder physiotherapy exercise. The validated algorithms will then be used to assess home physiotherapy adherence from the inertial data collected at home. Validated outcome measures, including the Disabilities of the Arm, Shoulder, and Hand questionnaire; Numeric Pain Rating Scale; range of motion; shoulder strength; and work status, will be collected pretreatment, monthly through treatment, and at a final follow-up of 12 months. We will then relate improvement in patient outcomes to measured physiotherapy adherence and patient baseline variables in univariate and multivariate analyses. The second phase of this research will involve the evaluation of a novel rehabilitation program in a cohort of 20 patients. The program will promote patient physiotherapy engagement via the developed technology and support adherence-driven care decisions. RESULTS: As of December 2019, 71 patients were screened for enrollment in the noninterventional validation phase of this study; 65 patients met the inclusion and exclusion criteria. Of these, 46 patients consented and 19 declined to participate in the study. Only 2 patients de-enrolled from the study and data collection is ongoing for the remaining 44. CONCLUSIONS: This study will provide new and important insights into shoulder physiotherapy adherence, the relationship between adherence and recovery, barriers to better adherence, and methods for addressing them. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17841.

Elevated Microdamage Spatially Correlates with Stress in Metastatic Vertebrae.

Metastasis of cancer to the spine impacts bone quality. This study aims to characterize vertebral microdamage secondary to metastatic disease considering the pattern of damage and its relationship to stress and strain under load. Osteolytic and mixed osteolytic/osteoblastic vertebral metastases were produced in athymic rats via HeLa cervical or canine Ace-1 prostate cancer cell inoculation, respectively. After 21 days, excised motion segments (T12-L2) were µCT scanned, stained with BaSO4 and re-imaged. T13-L2 motion segments were loaded in axial compression to induce microdamage, re-stained and re-imaged. L1 (loaded) and T12 (unloaded) vertebrae were fixed, sample blocks cut, polished and BSE imaged. µFE models were generated of all L1 vertebrae with displacement boundary conditions applied based on the loaded µCT images. µCT stereological analysis, BSE analysis and µFE derived von Mises stress and principal strains were quantitatively compared (ANOVA), spatial correlations determined and patterns of microdamage assessed qualitatively. BaSO4 identified microdamage was found to be spatially correlated with regions of high stress in µFEA. Load-induced microdamage was shown to be elevated in the presence of osteolytic and mixed metastatic disease, with diffuse, crossed hatched areas of microdamage present in addition to linear microdamage and microfractures in metastatic tissue, suggesting diminished bone quality.