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Artificial intelligence models assisting physicians in quantifying pancreatic necrosis in acute pancreatitis.
Quant Imaging Med Surg
January 2025
Department of Intensive Care Unit, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Background: Acute pancreatitis (AP) is a potentially life-threatening condition characterized by inflammation of the pancreas, which can lead to complications such as pancreatic necrosis. The modified computed tomography severity index (MCTSI) is a widely used tool for assessing the severity of AP, particularly the extent of pancreatic necrosis. The accurate and timely assessment of the necrosis volume is crucial in guiding treatment decisions and improving patient outcomes.
View Article and Find Full Text PDFAutomated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT.
Radiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
View Article and Find Full Text PDFComparison of 1D and 3D volume measurement techniques in NF2-associated vestibular schwannoma monitoring.
Sci Rep
January 2025
Department of Neurosurgery, University Hospital Tübingen, Tübingen, Germany.
To compare 1D (linear) tumor volume calculations and classification systems with 3D-segmented volumetric analysis (SVA), focusing specifically on their effectiveness in the evaluation and management of NF2-associated vestibular schwannomas (VS). VS were clinically followed every 6 months with cranial, thin-sliced (< 3 mm) MRI. We retrospectively reviewed and used T1-weighted post-contrast enhanced (gadolinium) images for both SVA and linear measurements.
View Article and Find Full Text PDFOnline correction of intrafraction motion during volumetric modulated arc therapy for prostate radiotherapy using fiducial-based kV imaging: A cohort study quantifying the frequency of shifts and analysis of men at highest risk.
J Appl Clin Med Phys
January 2025
Department of Radiation and Cellular Oncology, University of Chicago Medical Center, Chicago, Illinois, USA.
Background: Various methods exist to correct for intrafraction motion (IFM) of the prostate during radiotherapy. We sought to characterize setup corrections in our practice informed by the TrueBeam Advanced imaging package, and analyze factors associated with IFM.
Methods: 132 men received radiation therapy for prostate cancer with a volumetric modulated arc therapy technique.
Prognostic value of manual versus automatic methods for assessing extents of resection and residual tumor volume in glioblastoma.
J Neurosurg
January 2025
1Department of Neurosurgery, St. Olav's University Hospital, Trondheim, Norway.
Objective: The extent of resection (EOR) and postoperative residual tumor (RT) volume are prognostic factors in glioblastoma. Calculations of EOR and RT rely on accurate tumor segmentations. Raidionics is an open-access software that enables automatic segmentation of preoperative and early postoperative glioblastoma using pretrained deep learning models.
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