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Development of a CT radiomics prognostic model for post renal tumor resection overall survival based on transformer enhanced K-means clustering.
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Pericapsular nerve group cryoneurolysis as an option for palliative nonoperative management of hip fracture in a patient with end-stage medical comorbidities.
Can J Anaesth
January 2025
Department of Anesthesiology, Perioperative and Pain Medicine, Alberta Health Services and Cumming School of Medicine, University of Calgary, South Health Campus, 4448 Front St. SE, Calgary, AB, T3M 1M4, Canada.
Purpose: We report the use of a pericapsular nerve group (PENG) cryoneurolysis for longer-term analgesia in a patient with a hip fracture and severe medical comorbidities as an alternative to hip fracture surgery.
Clinical Features: A frail but lucid and fully autonomous 97-yr-old female from an assisted living facility sustained a subcapital fracture of her right proximal femur following a ground level fall. She had significant comorbidities including end-stage respiratory disease.
Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.
J Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
View Article and Find Full Text PDFObjectives: To develop and validate a lesion-based grading system using clinicopathological and MRI features for predicting positive surgical margin (PSM) following robotic-assisted laparoscopic prostatectomy (RALP) among prostate cancer (PCa) patients.
Methods: Consecutive MRI examinations of patients undergoing RALP for PCa were retrospectively collected from two medical institutions. Patients from center 1 undergoing RALP between January 2020 and December 2021 were included in the derivation cohort and those between January 2022 and December 2022 were allocated to the validation cohort.
Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery.
Sci Rep
January 2025
Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China.
With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, surgical decision-making and optimal strategies still lack standardization and personalized customization. Our study aims to devise proper deep learning (DL) models that incorporate key factors influencing surgical outcomes on the coronal plane in AIS patients to facilitate surgical decision-making and predict surgical results for AIS patients.
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