Purpose: Long-term evaluation of the combination of two needle aspiration techniques (NAT) (fine-needle aspiration [FNA] and aspiration needle biopsy [ANB]) in performing an efficient preoperative selection of palpable thyroid nodules.

Patients And Methods: Eight years of extensive use of surgery for the detection of thyroid cancer was compared with 12 years of preoperative selection of by NAT.

Results: A total of 1,140 operations were performed from 1972 to 1979, and 35 malignant nodules were discovered (3.1%). Five thousand four hundred three patients were examined by NAT from 1980 to 1992; 483 (9%) underwent surgery and 158 malignant nodules were excised. The number of malignant nodules identified by NAT was 166 (eight were not excised) (3.1% of the total population examined). The principal clinical and pathologic features were similar in both groups. ANB yielded a definite benign diagnosis in 88 patients with inadequate FNA findings, it correctly identified four malignant nodules diagnosed as benign by FNA, it showed a macrofollicular component in 115 nodules diagnosed by FNA as microfollicular nodules, and it significantly changed the predictive value of 79 suspicions FNA diagnoses.

Conclusion: Introduction of NAT reduced the number of operations for palpable thyroid nodules from 143 to 40 per year and increased from four to 13 the number of malignant nodules excised without any change in the overall incidence of malignant nodules. The combination of ANB to FNA significantly contributed to the high and efficient preoperative patient selection, principally by reducing the number of indeterminate or suspicious, as well as false-negative, preoperative FNA diagnoses.

Download full-text PDF

Source
http://dx.doi.org/10.1200/JCO.1996.14.5.1704DOI Listing

Publication Analysis

Top Keywords

malignant nodules
24
preoperative selection
12
nodules
10
needle aspiration
8
aspiration techniques
8
thyroid nodules
8
efficient preoperative
8
palpable thyroid
8
nodules excised
8
number malignant
8

Similar Publications

Purpose: Ovarian-Adnexal Reporting and Data System (O-RADS) US provides a standardized lexicon for ovarian and adnexal lesions, facilitating risk stratification based on morphological features for malignancy assessment, which is essential for proper management. However, systematic determination of inter-reader reliability in O-RADS US categorization remains unexplored. This study aimed to systematically determine the inter-reader reliability of O-RADS US categorization and identify the factors that affect it.

View Article and Find Full Text PDF

Multifaceted pulmonary manifestations of amyloidosis: state-of-the-art update.

Expert Rev Respir Med

January 2025

Division of Pulmonary & Critical Care Medicine, Mayo Clinic, Rochester MN, USA.

Introduction: Amyloidosis, a polymeric deposition disease classified according to protein subtype, may have varied pulmonary manifestations. Its anatomic-radiologic phenotypes include nodular, cystic, alveolar-septal, and tracheobronchial forms. Clinical presentation may range from asymptomatic parenchymal nodules to respiratory failure from diffuse parenchymal infiltration or diaphragmatic deposition.

View Article and Find Full Text PDF

Metallomic Classification of Pulmonary Nodules Using Blood by Deep-Learning-Boosted Synchrotron Radiation X-ray Fluorescence.

Environ Health (Wash)

January 2025

CAS-HKU Joint Laboratory of Metallomics on Health and Environment, & CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, & Beijing Metallomics Facility, & National Consortium for Excellence in Metallomics, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China.

Ambient air pollution is an important contributor to increasing cases of lung cancer, which is a malignant cancer with the highest mortality among all cancers. It primarily manifests in the form of pulmonary nodules, but not all will develop into lung cancer. Therefore, it is highly desired to distinguish between benign and malignant pulmonary nodules for the early prevention and treatment of lung cancer.

View Article and Find Full Text PDF

Background: The accuracy of intraoperative rapid frozen pathology is suboptimal, and the assessment of invasiveness in malignant pulmonary nodules significantly influences surgical resection strategies. Predicting the invasiveness of lung adenocarcinoma based on preoperative imaging is a clinical challenge, and there are no established standards for the optimal threshold value using the threshold segmentation method to predict the invasiveness of stage T1 lung adenocarcinoma. This study aimed to explore the efficacy of three-dimensional solid component volume (3D SCV) [calculated by artificial intelligence (AI) threshold segmentation method] in predicting the aggressiveness of T1 lung adenocarcinoma and to determine its optimal threshold and cut-off point.

View Article and Find Full Text PDF

Diagnostic value of greyscale ultrasound combined with superb microvascular imaging in thyroid nodules: a systematic review and meta-analysis.

Quant Imaging Med Surg

January 2025

Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Background: Superb microvascular imaging (SMI) is an advanced form of Doppler flow imaging which has advantages in tiny vessels and low-speed flow. This study aimed to evaluate the diagnostic performance of combining greyscale ultrasound (US) with SMI in differentiating between benign and malignant thyroid nodules.

Methods: A search was conducted in PubMed, Embase, Cochrane Library, Scopus, and Web of Science for relevant studies published till 25 October 2023 that investigated the combined use of greyscale US and SMI to differentiate between benign and malignant thyroid nodules.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!