Accurate diagnosis of thyroid tumors is challenging. A particular problem is distinguishing between follicular thyroid carcinoma (FTC) and benign follicular thyroid adenoma (FTA), where histology of fine-needle aspirates is not conclusive. It is often necessary to remove healthy thyroid to rule out carcinoma. In order to find markers to improve diagnosis, we quantified gene transcript expression from FTC, FTA, and normal thyroid, revealing 73 differentially expressed transcripts (P < or = 0.0001). Using an independent set of 23 FTCs, FTAs, and matched normal thyroids, 17 genes with large expression differences were tested by real-time RT-PCR. Four genes (DDIT3, ARG2, ITM1, and C1orf24) differed between the two classes FTC and FTA, and a linear combination of expression levels distinguished FTC from FTA with an estimated predictive accuracy of 0.83. Furthermore, immunohistochemistry for DDIT3 and ARG2 showed consistent staining for carcinoma in an independent set 59 follicular tumors (estimated concordance, 0.76; 95% confidence interval, [0.59, 0.93]). A simple test based on a combination of these markers might improve preoperative diagnosis of thyroid nodules, allowing better treatment decisions and reducing long-term health costs.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC385398PMC
http://dx.doi.org/10.1172/JCI19617DOI Listing

Publication Analysis

Top Keywords

ftc fta
12
thyroid carcinoma
8
diagnosis thyroid
8
follicular thyroid
8
markers improve
8
independent set
8
ddit3 arg2
8
thyroid
7
preoperative diagnostic
4
diagnostic test
4

Similar Publications

Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm.

J Ultrasound Med

November 2024

Department of Ultrasound, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.

Article Synopsis
  • This study aimed to assess the effectiveness of using radiomics from ultrasound images to distinguish between follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA), as well as create a noninvasive preoperative prediction tool for these conditions.
  • The researchers analyzed data from 389 patients who had undergone surgery for FTC or FTA, developing machine learning models and comparing their performance using metrics like the area under the receiver operating characteristic curve (AUC).
  • The results indicated that the radiomics model using random forest provided good discrimination between FTC and FTA, and a combined model, which included both radiomics features and clinical data, showed even better performance and clinical utility for preoperative identification.
View Article and Find Full Text PDF
Article Synopsis
  • Follicular thyroid carcinoma (FTC) is a common thyroid cancer that's tricky to tell apart from follicular thyroid adenoma (FTA), especially using preoperative ultrasound.
  • This study looked at 96 patients with FTC or FTA to determine which ultrasound features are most indicative of FTC.
  • Key findings show that characteristics like mixed vascularization and central stellate scarring are significant indicators of FTC, improving diagnostic accuracy when combined with chronic lymphocytic thyroiditis.
View Article and Find Full Text PDF

Purposes: To provide novel aspects for the preoperative diagnosis and appropriate differentiation strategies for follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA).

Methods: Among 25,765 cases, a total of 326 patients with follicular thyroid neoplasms between 2013 and 2019 were enrolled. Patient demographics, perioperative parameters, surgical profiles and oncologic outcomes were collected and analyzed.

View Article and Find Full Text PDF

Diagnostic value of an interpretable machine learning model based on clinical ultrasound features for follicular thyroid carcinoma.

Quant Imaging Med Surg

September 2024

Department of Pediatrics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China.

Article Synopsis
  • Follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) are difficult to differentiate due to similar clinical and ultrasound features, prompting a study to create a machine learning model to improve FTC diagnosis.
  • The study analyzed data from 780 patients across two hospitals to develop and validate various ML algorithms, focusing on ultrasound characteristics and clinical parameters to identify FTC more accurately.
  • Key factors influencing FTC diagnosis included age, echogenicity, certain antibody levels, and various ultrasound characteristics, which were assessed through multiple diagnostic performance metrics to find the best predictive model.
View Article and Find Full Text PDF

RAS p.Q61R is the most prevalent hot-spot mutation in RAS and RAS-like mutated thyroid nodules. A few studies evaluated RAS p.

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!