Objective: The objective of the present study was to determine the malignancy risk for nodules categorised as atypia or follicular lesions of undetermined significance atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) and to investigate the predictors of malignancy.
Methods: All nodules diagnosed as AUS/FLUS on fine needle aspiration (FNAs) performed between January 2011 and December 2015 were retrospectively reviewed. Clinical data, ultrasonographic features, follow-up data and the final pathological results were recorded. After further exclusion, only nodules that underwent surgical excision were included in the final analysis. The malignancy rate and the range of malignancy rates were calculated. Clinical and ultrasound features were examined to determine the predictors of malignancy.
Results: During the study period, FNA was performed on 9938 nodules, and 1019 (10.2%) nodules were diagnosed as AUS/FLUS. After further exclusion, 976 nodules were evaluated. After the initial diagnosis of AUS/FLUS, 139 (14.2%) patients underwent surgery, 518 (53.1%) had repeated FNAs. A total of 305 (31%) had undergone surgical excision at different time points. For surgically confirmed nodules, the malignancy rate after the initial FNA was 34.5% (the lower and upper thresholds for the malignancy rate were 19.3% and 66.3%, respectively), and 37.9% after the repeated FNA. No ultrasound feature was determined as a predictor, whereas age (>55 years) was a predictor for malignancy.
Conclusions: The overall malignancy rate for nodules diagnosed as AUS/FLUS and the malignancy rate for nodules that underwent repeated FNA after AUS/FLUS were higher than the expected malignancy rates of the National Cancer Institute. It is, therefore, suggested that the current recommendations should be reconsidered.
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http://dx.doi.org/10.1111/cyt.12438 | DOI Listing |
J Transl Med
January 2025
Scientia Clinical Research and Prince of Wales Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia.
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Sci Rep
January 2025
Department of Clinical Laboratory, Taizhou Central Hospital (Taizhou University Hospital), No. 999 Donghai Avenue, Taizhou City, 318000, Zhejiang Province, China.
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January 2025
Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
Genome-wide association studies (GWAS) have detected several susceptibility variants for urinary bladder cancer, but how gene regulation affects disease development remains unclear. To extend GWAS findings, we conducted a transcriptome-wide association study (TWAS) using PrediXcan to predict gene expression levels in whole blood using genome-wide genotype data for 6180 bladder cancer cases and 5699 controls included in the database of Genotypes and Phenotypes (dbGaP). Logistic regression was used to estimate adjusted gene-level odds ratios (OR) per 1-standard deviation higher expression with 95% confidence intervals (CI) for bladder cancer risk.
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January 2025
Department of Radiobiology and Diagnostic Onco-Cytogenetics, Centre of Radiotherapy, National Institute of Oncology, 1122, Ráth György utca 7-9, Budapest, Hungary.
Due to the better survival of patients with tumorous diseases, it is increasingly important to predict the side effects of radiotherapy, for which the Radiation-Induced Lymphocyte Apoptosis (RILA) method is proving to be effective in multicentric studies. Prostate cancer is the leading cause of cancer-related deaths among men worldwide, which is usually treated with radiotherapy. We recruited 49 patients with localized prostate cancer and performed RILA measurements before radiotherapy.
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January 2025
Department of Computer Engineering, Inha University, Incheon, Republic of Korea.
The most prevalent form of malignant tumors that originate in the brain are known as gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients' overall survival rate. Therefore, we have introduced a deep learning approach that employs a combination of MRI scans to accurately segment brain tumors and predict survival in patients with gliomas.
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