Background: Intraoperative neurophysiologic monitoring (IOM) has been used clinically since the 1970s and is a reliable tool for detecting impending neurologic compromise. However, there are mixed data as to whether long-term neurologic outcomes are improved with its use. We investigated whether IOM used in conjunction with image guidance produces different patient outcomes than with image guidance alone.
Methods: We reviewed 163 consecutive cases between January 2015 and December 2018 and compared patients undergoing posterior lumbar instrumentation with image guidance using and not using multimodal IOM. Monitored and unmonitored surgeries were performed by the same surgeons, ruling out variability in intersurgeon technique. Surgical and neurologic complication rates were compared between these 2 cohorts.
Results: A total of 163 patients were selected (110 in the nonmonitored cohort vs. 53 in the IOM cohort). Nineteen signal changes were noted. Only 3 of the 19 patients with signal changes had associated neurologic deficits postoperatively (positive predictive value 15.7%). There were 5 neurologic deficits that were observed in the nonmonitored cohort and 8 deficits observed in the monitored cohort. Transient neurologic deficit was significantly higher in the monitored cohort per case (P < 0.0198) and per screw (P < 0.0238); however, there was no difference observed between the 2 cohorts when considering permanent neurologic morbidity per case (P < 0.441) and per screw (P < 0.459).
Conclusions: The addition of IOM to cases using image guidance does not appear to decrease long-term postoperative neurologic morbidity and may have a reduced diagnostic role given availability of intraoperative image-guidance systems.
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http://dx.doi.org/10.1016/j.wneu.2021.05.074 | DOI Listing |
Sci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
View Article and Find Full Text PDFcutaneous melanoma has often unpredictable lymphatic drainage patterns, especially at the level of the trunk, head and neck regions. Sentinel lymph node biopsy (SLNB) is an important prognostic tool that accurately assesses regional lymph node involvement and guides therapeutic decisions. Material and this prospective study involved 104 patients diagnosed with cutaneous melanoma who underwent SLNB using a radioactive tracer.
View Article and Find Full Text PDFCatheter Cardiovasc Interv
December 2024
Cardiac Catheterization Laboratory of the Cardiovascular Institute, Mount Sinai Hospital, New York, New York, USA.
Background: The role of Intravascular ultrasound (IVUS) and optical coherence tomography (OCT) is still unclear in patients with STEMI undergoing PCI in the current second-generation DES era.
Aims: This study aimed to evaluate the trends and outcomes of IVUS-guided PCI in patients with STEMI.
Methods: We used the National Inpatient Sample (NIS) database from 2016 to 2021.
BMC Cancer
December 2024
Department of Surgery, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
Purpose: During breast cancer surgery, the use of dyes such as indigo carmine, methylene blue, or indocyanine green (ICG) for targeting axillary lymph nodes (ALNs) under ultrasound guidance can result in rapid diffusion, complicated tissue differentiation, and disruption of staining. LuminoMark™, a novel ICG-hyaluronic acid mixture, can provide real-time visualization and minimize dye spread, thereby ensuring a clear surgical field. The aim of our study was to evaluate the efficacy of LuminoMark™ for targeting ALNs in patients with breast cancer.
View Article and Find Full Text PDFFront Immunol
December 2024
Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Objective: To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). The objective is to provide guidance for developing scientifically individualized treatment plans, assessing prognosis, and planning preoperative interventions.
Methods: A retrospective analysis was conducted on clinical and imaging data from 270 patients with BC confirmed by surgical pathology at the Third Hospital of Shanxi Medical University between November 2022 and April 2024.
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