AIMC Topic: Retrospective Studies

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Deep learning reconstruction with single-energy metal artifact reduction in pelvic computed tomography for patients with metal hip prostheses.

Japanese journal of radiology
PURPOSE: The aim of this study was to assess the impact of the deep learning reconstruction (DLR) with single-energy metal artifact reduction (SEMAR) (DLR-S) technique in pelvic helical computed tomography (CT) images for patients with metal hip pros...

The association of radiologic body composition parameters with clinical outcomes in level-1 trauma patients.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
PURPOSE: The present study aims to assess whether CT-derived muscle mass, muscle density, and visceral fat mass are associated with in-hospital complications and clinical outcome in level-1 trauma patients.

A Novel Mini-Invasive Technique of Arthroscopic-Assisted Reduction and Robot-Assisted Fixation for Trans-Scaphoid Perilunate Fracture Dislocations.

Orthopaedic surgery
OBJECTIVE: Perilunate injuries are rare but devastating carpal injuries. The treatment of perilunate injuries remains challenging and contentious. This study aims to describe a novel mini-invasive surgical technique of arthroscopic-assisted reduction...

Deep learning analysis of endometrial histology as a promising tool to predict the chance of pregnancy after frozen embryo transfers.

Journal of assisted reproduction and genetics
PURPOSE: Endometrial histology on hematoxylin and eosin (H&E)-stained preparations provides information associated with receptivity. However, traditional histological examination by Noyes' dating method is of limited value as it is prone to subjectiv...

Robot-assisted sacrohysteropexy vs robot-assisted sacrocolpopexy in women with primary advanced apical prolapse: A retrospective cohort study.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: This study aimed to evaluate the anatomic and clinical outcomes of robot-assisted sacrohysteropexy (RASH) against robot-assisted sacrocolpopexy (RASC) for the treatment of primary advanced apical prolapse.

Development and External Validation of a Machine Learning Model for Prediction of Lymph Node Metastasis in Patients with Prostate Cancer.

European urology oncology
BACKGROUND: Pelvic lymph node dissection (PLND) is the gold standard for diagnosis of lymph node involvement (LNI) in patients with prostate cancer. The Roach formula, Memorial Sloan Kettering Cancer Center (MSKCC) calculator, and Briganti 2012 nomog...

Predicting survival after radiosurgery in patients with lung cancer brain metastases using deep learning of radiomics and EGFR status.

Physical and engineering sciences in medicine
The early prediction of overall survival (OS) in patients with lung cancer brain metastases (BMs) after Gamma Knife radiosurgery (GKRS) can facilitate patient management and outcome improvement. However, the disease progression is influenced by multi...

Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image.

Abdominal radiology (New York)
OBJECTIVES: Patients with T4 obstructive colorectal cancer (OCC) have a high mortality rate. Therefore, an accurate distinction between T4 and T1-T3 (NT4) in OCC is an important part of preoperative evaluation, especially in the emergency setting. Th...

An easy-to-use artificial intelligence preoperative lymph node metastasis predictor (LN-MASTER) in rectal cancer based on a privacy-preserving computing platform: multicenter retrospective cohort study.

International journal of surgery (London, England)
BACKGROUND: Although the surgical treatment strategy for rectal cancer (RC) is usually based on the preoperative diagnosis of lymph node metastasis (LNM), the accurate diagnosis of LNM has been a clinical challenge. In this study, we developed machin...