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http://dx.doi.org/10.2196/46484 | DOI Listing |
Neurophysiol Clin
January 2025
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, PR China. Electronic address:
Objectives: In the present study with a large cohort, we aimed to characterize intracerebral seizure onset patterns (SOP) of mesial temporal lobe epilepsy (mTLE), with or without hippocampal sclerosis (HS) as identified via magnetic resonance imaging (MRI).
Methods: We retrospectively analyzed 255 seizures of 76 consecutive patients with mTLE explored by stereoelectroencephalography (SEEG), including HS-mTLE (n = 52) and non-HS- mTLE (n = 24). Relevant results were obtained by a combination of spectral analysis and manual review.
Surg Endosc
January 2025
Surgery Department, Meander Medical Centre, Maatweg, Amersfoort, 3818 TZ, Utrecht, The Netherlands.
Background: Specific pelvic bone dimensions have been identified as predictors of total mesorectal excision (TME) difficulty and outcomes. However, manual measurement of these dimensions (pelvimetry) is labor intensive and thus, anatomic criteria are not included in the pre-operative difficulty assessment. In this work, we propose an automated workflow for pelvimetry based on pre-operative magnetic resonance imaging (MRI) volumes.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Urology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China (B.Z., F.M., X.S., S.L., Q.W.); Department of Urology, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, Guangdong 510080, China (Q.W.). Electronic address:
Rationale And Objectives: To develop an automatic deep-radiomics framework that diagnoses and stratifies prostate cancer in patients with prostate-specific antigen (PSA) levels between 4 and 10 ng/mL.
Materials And Methods: A total of 1124 patients with histological results and PSA levels between 4 and 10 ng/mL were enrolled from one public dataset and two local institutions. An nnUNet was trained for prostate masks, and a feature extraction module identified suspicious lesion masks.
Lung Cancer
December 2024
Università Vita-Salute San Raffaele, Milan, Italy; Department of Medical Oncology, IRCCS Ospedale San Raffaele, Milan, Italy.
Background: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to technical limits and to explainability issues in this specific setting. Response to standard first-line immunotherapy (ICI) in metastatic Non-Small-Cell Lung Cancer (NSCLC) is an interesting population for machine learning (ML), since up to 30% of patients do not benefit.
View Article and Find Full Text PDFSci Adv
January 2025
Department of Anesthesiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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