The Reinforced Cementoplasty with Spindles Technique.

Reinforced cementoplasty with spindles is a recently introduced technique that is mainly used for pathological fractures or for bone metastases at risk of fracture in locations with shear stresses. The technique is less challenging to perform than percutaneous screw insertion and does not require equipment sterilization. No general anesthetic is required. A small trocar is all that is needed, and sutures are often unnecessary. Reinforced cementoplasty can therefore be considered as a technical evolution of cementoplasty with the simple addition of material within the trocar. This technique deserves more awareness so that it can be included in interventional radiologists' range of procedures.

Combined vertebroplasty and pedicle screw insertion for vertebral consolidation: feasibility and technical considerations.

PURPOSE: To assess the feasibility and technical accuracy of performing pedicular screw placement combined with vertebroplasty in the radiological setting. METHODS: Patients who underwent combined vertebroplasty and pedicle screw insertion under combined computed tomography and fluoroscopic guidance in 4 interventional radiology centers from 2018 to 2023 were retrospectively assessed. Patient demographics, vertebral lesion type, and procedural data were analyzed. Strict intra-pedicular screw positioning was considered as technical success. Pain score was assessed according to the Visual Analogue Scale before the procedure and in the 1-month follow-up consultation. RESULTS: Fifty-seven patients (38 men and 19 women) with a mean age of 72.8 (SD = 11.4) years underwent a vertebroplasty associated with pedicular screw insertion for the treatment of traumatic fractures (29 patients) and neoplastic disease (28 patients). Screw placement accuracy assessed by post-procedure CT scan was 95.7% (89/93 inserted screws). A total of 93 pedicle screw placements (36 bi-pedicular and 21 unipedicular) in 32 lumbar, 22 thoracic, and 3 cervical levels were analyzed. Mean reported procedure time was 48.8 (SD = 14.7) min and average injected cement volume was 4.4 (SD = 0.9) mL. A mean VAS score decrease of 5 points was observed at 1-month follow-up (7.7, SD = 1.3 versus 2.7, SD = 1.7), p < .001. CONCLUSION: Combining a vertebroplasty and pedicle screw insertion is technically viable in the radiological setting, with a high screw positioning accuracy of 95.7%.

3D MODEL of an anatomically inert human hand: feasibility study.

OBJECTIVES: Surgery for congenital malformation of the hand is complex and protocols are not available. Simulation could help optimize results. The objective of the present study was to design, produce and assess a 3D-printed anatomical support, to improve success in rare and complex surgeries of the hand. MATERIAL AND METHODS: We acquired MRI imaging of the right hand of a 30 year-old subject, then analyzed and split the various skin layers for segmentation. Thus we created the prototype of a healthy hand, using 3D multi-material and silicone printing devices, and drew up a printing protocol suitable for all patients. We printed a base comprising bones, muscles and tendons, with a multi-material 3D printer, then used a 3D silicone printer for skin and subcutaneous fatty cell tissues in a glove-like shape. To evaluate the characteristics of the prototype, we performed a series of dissections on the synthetic hand and on a cadaveric hand in the anatomy lab, comparing realism, ease of handling and the final result of the two supports, and evaluated their respective advantages in surgical and training contexts. A grading form was given to each surgeon to establish a global score. RESULTS: This evaluation highlighted the positive and negative features of the model. The model avoided intrinsic problems of cadavers, such as muscle rigidity or tissue fragility and atrophy, and enables the anatomy of a specific patient to be rigorously respected. On the other hand, vascular and nervous networks, with their potential anatomical variants, are lacking. This preliminary phase highlighted the advantages and inconveniences of the prototype, to optimize the design and printing of future models. It is an indispensable prerequisite before performing studies in eligible pediatric patients with congenital hand malformation. CONCLUSION: The validation of 3D-printed anatomical model of a human hand opens a large field of applications in the area of preoperative surgical planning. The postoperative esthetic and functional benefit of such pre-intervention supports in complex surgery needs assessing.

Artificial Intelligence for Otosclerosis Detection: A Pilot Study.

The gold standard for otosclerosis diagnosis, aside from surgery, is high-resolution temporal bone computed tomography (TBCT), but it can be compromised by the small size of the lesions. Many artificial intelligence (AI) algorithms exist, but they are not yet used in daily practice for otosclerosis diagnosis. The aim was to evaluate the diagnostic performance of AI in the detection of otosclerosis. This case-control study included patients with otosclerosis surgically confirmed (2010-2020) and control patients who underwent TBCT and for whom radiological data were available. The AI algorithm interpreted the TBCT to assign a positive or negative diagnosis of otosclerosis. A double-blind reading was then performed by two trained radiologists, and the diagnostic performances were compared according to the best combination of sensitivity and specificity (Youden index). A total of 274 TBCT were included (174 TBCT cases and 100 TBCT controls). For the AI algorithm, the best combination of sensitivity and specificity was 79% and 98%, with an ideal diagnostic probability value estimated by the Youden index at 59%. For radiological analysis, sensitivity was 84% and specificity 98%. The diagnostic performance of the AI algorithm was comparable to that of a trained radiologist, although the sensitivity at the estimated ideal threshold was lower.

Inter-laboratory reproduction and sensitivity study of a finite element model to quantify human femur failure load: Case of metastases.

INTRODUCTION: Metastases increase the risk of fracture when affecting the femur. Consequently, clinicians need to know if the patient's femur can withstand the stress of daily activities. The current tools used in clinics are not sufficiently precise. A new method, the CT-scan-based finite element analysis, gives good predictive results. However, none of the existing models were tested for reproducibility. This is a critical issue to address in order to apply the technique on a large cohort around the world to help evaluate bone metastatic fracture risk in patients. The aim of this study is then to evaluate 1) the reproducibility 2) the transposition of the reproduced model to another dataset and 3) the global sensitivity of one of the most promising models of the literature (original model). METHODS: The model was reproduced based on the paper describing it and discussion with authors to avoid reproduction errors. The reproducibility was evaluated by comparing the results given in the original model by the original first team (Leuven, Belgium) and the reproduced model made by another team (Lyon, France) on the same dataset of CT-scans of ex vivo femurs. The transposition of the model was evaluated by comparing the results of the reproduced model on two different datasets. The global sensitivity analysis was done by using the Morris method and evaluates the influence of the density calibration coefficient, the segmentation, the orientations and the length of the femur. RESULTS: The original and reproduced models are highly correlated (r2 = 0.95), even though the reproduced model gives systematically higher failure loads. When using the reproduced model on another dataset, predictions are less accurate (r2 with the experimental failure load decreases, errors increase). The global sensitivity analysis showed high influence of the density calibration coefficient (mean variation of failure load of 84 %) and non-negligible influence of the segmentation, orientation and length of the femur (mean variation of failure load between 7 and 10 %). CONCLUSION: This study showed that, although being validated, the reproduced model underperformed when using another dataset. The difference in performance depending on the dataset is commonly the cause of overfitting when creating the model. However, the dataset used in the original paper (Sas et al., 2020a) and the Leuven's dataset gave similar performance, which indicates a lesser probability for the overfitting cause. Also, the model is highly sensitive to density parameters and automation of measurement may minimize the uncertainty on failure load. An uncertainty propagation analysis would give the actual precision of such model and improve our understanding of its behavior and is part of future work.

Finite element models with automatic computed tomography bone segmentation for failure load computation.

Bone segmentation is an important step to perform biomechanical failure load simulations on in-vivo CT data of patients with bone metastasis, as it is a mandatory operation to obtain meshes needed for numerical simulations. Segmentation can be a tedious and time consuming task when done manually, and expert segmentations are subject to intra- and inter-operator variability. Deep learning methods are increasingly employed to automatically carry out image segmentation tasks. These networks usually need to be trained on a large image dataset along with the manual segmentations to maximize generalization to new images, but it is not always possible to have access to a multitude of CT-scans with the associated ground truth. It then becomes necessary to use training techniques to make the best use of the limited available data. In this paper, we propose a dedicated pipeline of preprocessing, deep learning based segmentation method and post-processing for in-vivo human femurs and vertebrae segmentation from CT-scans volumes. 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. We compared the failure load simulation results obtained on femurs and vertebrae using either automatic or manual segmentations and studied the sensitivity of the simulations on small variations of the automatic segmentation. The failure loads obtained using automatic segmentations were comparable to those obtained using manual expert segmentations for all the femurs and vertebrae tested, demonstrating the effectiveness of the automated segmentation approach for failure load simulations.