Transvaginal ultrasonography (USG) is most commonly used before surgery to accurately diagnose benign and malignant ovarian masses for effective treatment, avoid unnecessary interventions, improve the prognosis of patients, and preserve fertility in patients with benign tumors. Therefore, the objective of the present systematic review was to assess the diagnostic efficacy of ultrasound-based International Ovarian Tumor Analysis (IOTA) Simple Rules (SR) and Assessment of Different NEoplasias in the adneXa (ADNEX) model in predicting malignancy among women with adnexal masses. A systematic literature search was carried out on electronic databases consisting of Science Direct, PubMed, and Google Scholar. The keywords utilized to perform the literature search and include relevant articles consisted of "Diagnostic Efficacy", AND "Ultrasound-Based International Ovarian Tumor Analysis Simple Rules", AND "International Ovarian Tumor Analysis ADNEX Model", AND "Adnexal masses", AND "Ovarian tumors". Based on the selection criteria, a total of five studies were included. The study concluded that both the models showed high diagnostic efficacy for malignancy prediction; however, in comparison to the IOTA SR, the IOTA ADNEX model demonstrated good diagnostic efficacy.
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http://dx.doi.org/10.7759/cureus.67365 | DOI Listing |
BMC Cancer
January 2025
Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
Background: To date, there remains a paucity of comparative investigations pertaining to preoperative immunochemotherapy and conventional chemotherapy in the context of limited-stage small-cell lung cancer (LS-SCLC) patients. This study conducted a comprehensive comparative assessment concerning the safety and efficacy profiles of preoperative immunochemotherapy and chemotherapy in individuals diagnosed with stage I-IIIB SCLC.
Methods: This investigation collected 53 consecutive patients diagnosed with LS-SCLC spanning stage I to IIIB who underwent preoperative immunochemotherapy or conventional chemotherapy at our hospital from January 2019 to July 2021.
BMC Neurol
January 2025
Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, School of Medicine, College of Medicine, National Sun Yat-Sen University, No. 123 Ta-Pei Road, Niao-Sung Dist, Kaohsiung, 83305, Taiwan.
Background And Purpose: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a 3D convolutional neural network and a 3D Transformer-based model for white matter hyperintensities segmentation, focusing on their efficacy with limited datasets and similar computational resources.
Materials And Methods: We implemented a convolution-based model (3D ResNet-50 U-Net with spatial and channel squeeze & excitation) and a Transformer-based model (3D Swin Transformer with a convolutional stem).
Eat Weight Disord
January 2025
Department of Gastrointestinal Surgery, The First Affiliated Hospital, Wenzhou Medical University, Nanbaixiang Street, Wenzhou, 325035, Zhejiang, China.
Purpose: The weight-adjusted waist index (WWI) is a novel anthropometric measure. WWI is linked to reduced muscle mass and strength; however, its efficacy for assessing sarcopenia and predicting adverse outcomes has yet to be validated. This study compared and examined the relationship between sarcopenia and WWI across different diagnostic criteria and aimed to evaluate its potential as a predictor of sarcopenia and all-cause mortality.
View Article and Find Full Text PDFPharmacoeconomics
January 2025
Sheffield Centre for Health and Related Research (SCHARR), School of Medicine and Population Health, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield, UK.
Background: Testing high-risk populations for non-visible haematuria may enable earlier detection of bladder cancer, potentially decreasing mortality. This research aimed to assess the cost-effectiveness of urine dipstick screening for bladder cancer in high-risk populations in England.
Methods: A microsimulation model developed in R software was calibrated to national incidence data by age, sex and stage, and validated against mortality data.
J Imaging Inform Med
January 2025
College of Computer, Chongqing University, No. 55 Daxuecheng South Rd, Shapingba, 401331, Chongqing, China.
Convolutional neural networks (CNNs) have become indispensable to medical image diagnosis research, enabling the automated differentiation of diseased images from extensive medical image datasets. Due to their efficacy, these methods raise significant privacy concerns regarding patient images and diagnostic models. To address these issues, some researchers have explored privacy-preserving medical image diagnosis schemes using fully homomorphic encryption (FHE).
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