Background: Human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) have better prognosis and treatment response compared to HPV-negative OPSCC. This study aims to noninvasively predict HPV status of OPSCC using clinical and/or radiological variables.
Methods: Seventy-seven magnetic resonance radiomic features were extracted from T1-weighted postcontrast images of the primary tumor of 153 patients. Logistic regression models were created to predict HPV status, determined with immunohistochemistry, based on clinical variables, radiomic features, and its combination. Model performance was evaluated using area under the curve (AUC).
Results: Model performance showed AUCs of 0.794, 0.764, and 0.871 for the clinical, radiomic, and combined models, respectively. Smoking, higher T-classification (T3 and T4), larger, less round, and heterogeneous tumors were associated with HPV-negative tumors.
Conclusion: Models based on clinical variables and/or radiomic tumor features can predict HPV status in OPSCC patients with good performance and can be considered when HPV testing is not available.
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http://dx.doi.org/10.1002/hed.26505 | DOI Listing |
World J Surg Oncol
January 2025
Department of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang, China.
Objective: This study aimed to evaluate and compare the clinicopathologic features of primary fallopian tubal carcinoma (PFTC) and high-grade serous ovarian cancer (HGSOC) and explore the prognostic factors of these two malignant tumors.
Methods: Fifty-seven patients diagnosed with PFTC from 2006 to 2015 and 60 patients diagnosed with HGSOC from 2014 to 2015 with complete prognostic information were identified at Women's Hospital of Zhejiang University. The clinicopathological and surgical data were collected, and the survival of the patients was followed for 5 years after surgery.
Arthritis Res Ther
January 2025
Department of Biomedical Sciences, Humanitas University, Via R Levi Montalcini 4, Pieve Emanuele, Milan, 20090, Italy.
Background: There is still a significant proportion of patients with rheumatoid arthritis (RA) in whom multiple therapeutic lines are ineffective. These cases are defined by the EULAR criteria as Difficult-to-Treat RA (D2T-RA) for which there is limited knowledge of predisposing factors.
Objective: To identify the clinical features associated with D2T-RA in real-life practice.
Diabetol Metab Syndr
January 2025
Department of Clinical Nutrition, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
Background: The potential therapeutic role of magnesium (Mg) in type 2 diabetes mellitus (T2DM) remains insufficiently studied despite its known involvement in critical processes like lipid metabolism and insulin sensitivity. This study examines the impact of Mg-focused nutritional education on lipid profile parameters, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in T2DM patients.
Methods: Thirty participants with T2DM were recruited for this within-subject experimental study.
Diabetol Metab Syndr
January 2025
First Central Clinical Medical Institute, Tianjin Medical University, Tianjin, China.
Background: To identify the relationship between BMI or lipid metabolism and diabetic neuropathy using a Mendelian randomization (MR) study.
Methods: Body constitution-related phenotypes, namely BMI (kg/m), total cholesterol (TC), and triglyceride (TG), were investigated in this study. Despite the disparate origins of these data, all were accessible through the IEU OPEN GWAS database ( https://gwas.
Biomark Res
January 2025
Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Background: Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.
Methods: A cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio).
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