Pattern of Thyroid Lesions in Western Region of Saudi Arabia: A Retrospective Analysis and Literature Review
Notice bibliographique
Résumé
BACKGROUND: Ultrasonography (US) is being recognized as a traditional way of the diagnosis of various thyroid disorders, and this will help in detecting the thyroid tumors in early stage. Thyroid nodules are common and usually benign; steps to diagnose malignancy should include a careful clinical evaluation, laboratory tests, a thyroid US exam and a fine-needle aspiration (FNA) biopsy. METHODS: A total of 173 registered cases were used for analysis in this study. Diagnosis was made following US-guided FNA cytology (FNAC) and histopathological diagnosis; clinicopathological and demographic data of all such patients were obtained and analyzed for the present study. For statistical analysis, Statistical Package of Social Sciences v.22 (SPSS) was used. RESULTS: In the current study, 87.3% of patients were female, and 12.7% were male. The mean age of the patients was 43.35 years, 86.4% were Saudi nationals and there was no significant difference between age groups. Overall, the distribution of lesions in all age groups was 41.6% in the right lobe, 9.3% lesions were adenomatous, 71.1% were colloid, and 10.4% were lymphocytic. The final diagnosis of thyroid lesions was confirmed after histopathological examinations. Out of 173 cases, 12.6% (20 cases) of male patients and 87.4% (139 cases) of female patients had benign lesions, respectively. Only one male case was malignant, and seven cases were malignant in female group. Eighty percent of males and 77.7% females have colloid nodules, and 15% of males and 9.3% of females have adenomatous nodules. Four cases were non-diagnostic, one case was atypia in females, and one case was suspicious of malignancy in a male. CONCLUSIONS: Most thyroid lesions in this study population were benign, while papillary carcinoma was the most common malignancy encountered. There was a marked female predominance in all types of thyroid diseases. The most common age group affected is 30 - 39 years. In Saudi Arabia, growing prevalence of thyroid cancer may be due to the increased screening using sensitive imaging in clinical practice, and ultrasonography is the most accurate and cost-effective method for detecting thyroid lesions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».