Preoperative planning is important for accurate intraoperative execution in many surgical fields. Planning for distal femoral osteotomies (DFOs) and proximal tibial osteotomies (PTOs) consists of choosing the level of the osteotomy, measuring the angle of the osteotomy based on hip-knee-ankle alignment, and choosing a proper osteotomy wedge size. Medical imaging IT solutions company Sectra has implemented a new osteotomy tool in their radiographic system that is simpler than the accepted standard of modified center of rotation of angulation (mCORA) technique, yet unvalidated. In this study, we aim to compare the Sectra osteotomy tool versus the mCORA technique to measure the osteotomy angles as well as wedge sizes in both DFOs and PTOs to validate this new tool.We enrolled = 30 consecutive patients with DFOs and = 30 PTOs from the last year. The Pearson correlation coefficient (PCC) along with descriptive statistics was used to evaluate for similarity between the two techniques. We also compared interobserver and intraobserver reliability using intraclass correlation coefficients (ICC).The PCC for osteotomy angles in DFOs and PTOs were both 0.998 ( < 0.001 for both). For wedge sizes, the PCC in DFOs was 0.993 and 0.980 in PTOs ( < 0.001 for both). ICCs were high for both interobserver measurements in osteotomy angles and wedge sizes (range: 0.989-0.999) as well as intraobserver measurements (0.994-0.999).The Sectra osteotomy tool is a validated tool for preoperative measurements of DFOs and PTOs. It is reliable and simpler than the current practice of the mCORA technique. We suggest future studies to analyze this Sectra osteotomy tool in other settings as to incorporate it into widespread clinical use.
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http://dx.doi.org/10.1055/s-0040-1710372 | DOI Listing |
Eur Radiol
January 2025
Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Objectives: We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification.
Methods: The MRI features were extracted for molecular subgroups by a novel multi-parameter convolutional neural network (CNN) called Bi-ResNet-MB. Then, MR features were used to establish a prognosis model based on XGBoost.
Spine (Phila Pa 1976)
January 2025
Department of Orthopedics, Brown University, Providence, RI.
Study Design: Retrospective cohort study.
Objective: Evaluate the utility of Delirium Risk Assessment Score (DRAS), Delirium Risk Assessment Tool (DRAT), and Delirium Elderly At-Risk (DEAR) in patients undergoing posterior lumbar interbody fusions.
Background: Surgical interventions can place patients at risk for postoperative delirium (POD), an acute and often severe cognitive impairment associated with poor outcomes.
Surg Innov
January 2025
Paediatrics & Child Health, Aga Khan University Hospital, Karachi, Pakistan.
Insights Imaging
January 2025
Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Objective: To assess the utility of clinical and MRI features in distinguishing ovarian clear cell carcinoma (CCC) from adnexal masses with ovarian-adnexal reporting and data system (O-RADS) MRI scores of 4-5.
Methods: This retrospective study included 850 patients with indeterminate adnexal masses on ultrasound. Two radiologists evaluated all preoperative MRIs using the O-RADS MRI risk stratification system.
Eur Radiol
January 2025
Department of Ultrasound, Chengdu Second People's Hospital, Chengdu, China.
Objectives: This study aimed to develop a multimodal radiopathomics model utilising preoperative ultrasound (US) and fine-needle aspiration cytology (FNAC) to predict large-number cervical lymph node metastasis (CLNM) in patients with clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC).
Materials And Methods: This multicentre retrospective study included patients with PTC between October 2017 and June 2024 across seven institutions. Patients were categorised based on the presence or absence of large-number CLNM in training, validation, and external testing cohorts.
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