Context: The chance that a thyroid nodule is malignant is higher when there is a history of childhood radiation exposure.
Objective: The objective of the study was to determine how the size of a thyroid nodule, the number of nodules, and the distribution of nodules influence the risk of cancer in irradiated patients.
Patients: From a cohort of 4296 radiation-exposed people, we studied the 1059 that underwent thyroid surgery. DESIGN AND OUTCOMES: We studied the association between the size, number, distribution, and rank order of thyroid nodules and the chance of malignancy.
Results: There were 612 malignant nodules in 358 patients and 2037 benign ones in 930 patients. There was no change in the risk that a nodule was malignant with increasing size (odds ratio 0.91/cm, P = 0.11) among the 1709 nodules that were 0.5 cm or greater. A solitary nodule had a similar likelihood of being malignant as a nodule that was one of several (18.8 vs. 17.3%), whereas patients with multiple nodules were more likely to have thyroid cancer than those with solitary nodules [30.7 vs. 18.7%; risk ratio 1.64 (1.27-2.13)]. Aspirating only the largest nodule would have missed 111 of the cancers (42%), whereas aspirating the two largest nodules would have missed 45 of the cases (17%), although none would have been 10 mm or greater.
Conclusions: In radiation-exposed patients, the following conclusions were made: 1) the likelihood that a nodule is malignant is independent of nodule number and size; 2) the likelihood of cancer is increased if more than one nodule is present; 3) evaluating the two largest nodules by fine-needle aspiration would have resulted in a significant number of cases being missed but none with large cancers; and 4) more than half of the patients with thyroid cancer had multifocal tumors.
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http://dx.doi.org/10.1210/jc.2008-0055 | DOI Listing |
J Med Case Rep
January 2025
Department of Clinical Medicine, Jining Medical University, Jining, China.
Background: Superficial acral fibromyxoma is a noncancerous, benign tumor of soft tissue with an unidentified origin. Occurrences of abnormalities on the palm are less frequently documented.
Case Report Presentation: A 47-year-old East Asian woman presented with a palm tumor on her left knuckle that had been present for 4 months.
Endocrine
January 2025
Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, China.
Purpose: To evaluate the diagnostic value of different subtypes of non-punctate echogenic foci in thyroid malignancy.
Methods: Retrospective research of 342 thyroid nodules with calcification was performed. The echogenic foci were divided into punctate echogenic foci (type I) and non-punctate echogenic foci (type II), and type II were further divided into four subtypes: macrocalcification (type IIa), continuous peripheral calcification (type IIb), discontinuous peripheral calcification (type IIc) and isolated calcification (type IId).
Diagn Cytopathol
January 2025
Servizio di Endocrinologia e Diabetologia, Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland.
The measurement of Calcitonin (Ctn) in fine-needle aspiration (FNA) washout fluids (FNA-Ctn) has demonstrated excellent sensitivity, significantly higher than FNA cytology, in detecting medullary thyroid carcinoma (MTC). However, the absence of a fixed cutoff value for FNA-Ctn poses a limitation. This study aimed to investigate whether the sensitivity of FNA-Ctn in detecting MTC varies with different cutoffs reported in the literature.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia.
Computer-aided diagnostic systems have achieved remarkable success in the medical field, particularly in diagnosing malignant tumors, and have done so at a rapid pace. However, the generalizability of the results remains a challenge for researchers and decreases the credibility of these models, which represents a point of criticism by physicians and specialists, especially given the sensitivity of the field. This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability.
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