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Positive Lymph Node Status Before and After Neoadjuvant Chemoradiotherapy Improves Prediction of Disease-Free Survival in Esophageal Squamous Cell Carcinoma Patients.
Ann Surg Oncol
January 2025
Departments of Thoracic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.
Background: This study proposes a modified lymph node (LN) staging category (BALN) on the basis of the number of positive LNs before (prepN) and after (ypN) neoadjuvant chemoradiotherapy (nCRT) to improve prognostic stratification in esophageal squamous cell carcinoma (ESCC).
Patients And Methods: A total of 381 patients with ESCC who underwent nCRT at three medical centers were retrospectively enrolled. The ypN categories were scored according to the eighth edition of the American Joint Committee of Cancer (AJCC) staging manual.
Video Laryngoscopy for Endotracheal Intubation: A Consideration for Manual In-Line Stabilization Without Cervical Collar Versus Full Immobilization.
Ther Clin Risk Manag
January 2025
Department of Emergency Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Introduction: Traumatic patients with cervical spine motion restriction have difficulty with endotracheal intubation (ETI) due to the limitations of neck movement and mouth opening. Nevertheless, the removal of the cervical collar for ETI in a prehospital setting may lead to a deterioration in neurological outcomes. This study compares the success rate of ETI utilizing a video laryngoscope (VL) on a manikin, contrasting manual in-line stabilization (MILS) without a cervical hard collar against full immobilization.
View Article and Find Full Text PDFFully automated segmentation and classification of renal tumors on CT scans via machine learning.
BMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
Use of ChatGPT Large Language Models to Extract Details of Recommendations for Additional Imaging From Free-Text Impressions of Radiology Reports.
AJR Am J Roentgenol
January 2025
Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Boston, MA 02120 Phone: 617-525-9702.
Automated extraction of actionable details of recommendations for additional imaging (RAIs) from radiology reports could facilitate tracking and timely completion of clinically necessary RAIs and thereby potentially reduce diagnostic delays. To assess the performance of large-language models (LLMs) in extracting actionable details of RAIs from radiology reports. This retrospective single-center study evaluated reports of diagnostic radiology examinations performed across modalities and care settings within five subspecialties (abdominal imaging, musculoskeletal imaging, neuroradiology, nuclear medicine, thoracic imaging) in August 2023.
View Article and Find Full Text PDFAims: The current American Joint Committee on Cancer (AJCC) staging manual and the College of American Pathologists (CAP) colorectal carcinoma (CRC) protocol specify use of a four-tiered grading system (i.e. grades 1-4; well-differentiated-undifferentiated) for CRC, based on percentage of gland formation.
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