Background: The objective of this study was to determine the adherence of radiologists to the guideline of the Society of Radiologists in Ultrasound (SRU)-2010 for the follow-up of ovarian cysts in patients during 2015-2016.
Methods: The patients' data, referring for transvaginal and pelvic ultrasonography, suffering from ovarian cyst were assessed in terms of menopause status, cyst size, and type, as well as follow-ups recommended by radiologist to assess the adherence of reports to SRU-2010.
Results: Three hundred and sixty-four sonography reports were investigated. Seventy-seven percent of the reports had adhered to SRU-2010, 9.9% and 9.1% had under/overmanagement, and 4.1% was incomplete. 94.2% and 5.8% of cases were in pre/postmenopause status, respectively. The highest adherence belonged to cysts in size <1 cm, 1-3 cm, 5-7 cm. The highest adherence, over/undermanagement, and incomplete reports belonged to corpus luteum, hemorrhagic, dermoid cysts, and nodules without flow. The adherence of sonography reports to SRU-2010 for accidental ovarian cysts was 76.9%.
Conclusion: The tendency for overmanagement of simple cysts in premenopausal women and the tendency for undermanagement in simple cysts and in postmenopausal women were higher, respectively. It is expected that more training of the guideline to radiologists will lead to the reduction of unnecessary follow-up, which in turn leads to reduced patient's anxiety and cost of treatment.
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http://dx.doi.org/10.4103/jmu.jmu_137_21 | DOI Listing |
Eur J Trauma Emerg Surg
January 2025
Department of Health Sciences, Norwegian University of Science and Technology (NTNU), Postbox 191, Gjøvik, 2802, Norway.
Purpose: This study aimed to assess adherence to the Scandinavian guidelines, the justification of referrals, and the quality of referrals of patients with mild, minimal, and moderate head injuries in a selection of Norwegian hospitals.
Methods: We collected 283 head CT referrals for head trauma patients at one hospital trust in Norway in 2022. The data included the patients' sex, age, and the referral text.
Radiol Med
January 2025
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Background: Accurate differentiation between benign and malignant pancreatic lesions is critical for effective patient management. This study aimed to develop and validate a novel deep learning network using baseline computed tomography (CT) images to predict the classification of pancreatic lesions.
Methods: This retrospective study included 864 patients (422 men, 442 women) with confirmed histopathological results across three medical centers, forming a training cohort, internal testing cohort, and external validation cohort.
PLoS One
January 2025
Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Objective: This study aimed to introduce and evaluate a novel software-based system, BioTrace, designed for real-time monitoring of thermal ablation tissue damage during image-guided radiofrequency ablation for hepatocellular carcinoma (HCC).
Methods: BioTrace utilizes a proprietary algorithm to analyze the temporo-spatial behavior of thermal gas bubble activity during ablation, as seen in conventional B-mode ultrasound imaging. Its predictive accuracy was assessed by comparing the ablation zones it predicted with those annotated by radiologists using contrast-enhanced computed tomography (CECT) 24 hours post-treatment, considered the gold standard.
Eur Radiol
January 2025
Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists.
Materials And Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLaVA network.
Cancer Manag Res
January 2025
School of Physics, Universiti Sains Malaysia, Penang, 11800 Gelugor, Malaysia.
Introduction: Breast cancer is a significant worldwide health issue, particularly in Jordan, where early detection via mammography is essential for effective disease management. Despite the little radiation risk associated with mammography, it is crucial to monitor radiation exposure to guarantee patient safety. This study intends to assess skin entrance exposure and compute the Mean Glandular Dose (MGD) in mammography units to determine adherence to established criteria and pinpoint areas for enhancement.
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