Ultrasound Findings and O‐RADS Malignancy Risk Stratification of Ovarian Collision Tumors

2021 ◽  
Author(s):  
Yuyang Guo ◽  
Shan Zhou ◽  
Baihua Zhao ◽  
Lieming Wen ◽  
Minghui Liu
Keyword(s):  
Risk Stratification ◽  
Malignancy Risk ◽  
Collision Tumors ◽  
Ultrasound Findings

scholarly journals Performance of current ultrasound-based malignancy risk stratification systems for thyroid nodules in patients with follicular neoplasms

2022 ◽  
Author(s):  
Yinghe Lin ◽  
Shuiqing Lai ◽  
Peiqing Wang ◽  
Jinlian Li ◽  
Zhijiang Chen ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Thyroid Nodules ◽  
Area Under The Curve ◽  
Thyroid Neoplasms ◽  
Malignancy Risk ◽  
Follicular Thyroid Adenoma ◽  
Ultrasound Findings ◽  
Low Efficiency ◽  
Stratification System ◽  
Determining The Indication

Abstract Objectives To investigate the ability of the currently used ultrasound-based malignancy risk stratification systems for thyroid neoplasms (ATA, AACE/ACE/AME, K-TIRADS, EU-TIRADS, ACR-TIRADS and C-TIRADS) in distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA). Additionally, we evaluated the ability of these systems in correctly determining the indication for biopsy. Methods Three hundred twenty-nine follicular neoplasms with definitive postoperative histopathology were included. The nodules were categorized according to each of six stratification systems, based on ultrasound findings. We dichotomized nodules into the positive predictive group of FTC (high and intermediate risk) and negative group of FTC based on the classification results. Missed biopsy was defined as neoplasms that were diagnosed as FTCs but for which biopsy was not indicated based on lesion classification. Unnecessary biopsy was defined as neoplasms that were diagnosed as FTAs but for whom biopsy was considered indicated based on classification. The diagnostic performance and missed and unnecessary biopsy rates were evaluated for each stratification system. Results The area under the curve of each system for distinguishing follicular neoplasms was < 0.700 (range, 0.511–0.611). The missed biopsy rates were 9.0–22.4%. The missed biopsy rates for lesions ≤ 4 cm and lesions sized 2–4 cm were 16.2–35.1% and 0–20.0%, respectively. Unnecessary biopsy rates were 65.3–93.1%. In ≤ 4 cm group, the unnecessary biopsy rates were 62.2–89.7%. Conclusion The malignancy risk stratification systems can select appropriate nodules for biopsy in follicular neoplasms, while they have limitations in distinguishing follicular neoplasms and reducing unnecessary biopsy. Specific stratification systems and recommendations should be established for follicular neoplasms. Key Points • Current ultrasound-based malignancy risk stratification systems of thyroid nodules had low efficiency in the characterization of follicular neoplasms. • The adopted stratification systems showed acceptable performance for selecting FTC for biopsy but unsatisfactory performance for reducing unnecessary biopsy.

scholarly journals European Thyroid Association Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules in Adults: The EU-TIRADS

2017 ◽  
Vol 6 (5) ◽  
pp. 225-237 ◽  
Author(s):  
Gilles Russ ◽  
Steen J. Bonnema ◽  
Murat Faik Erdogan ◽  
Cosimo Durante ◽  
Rose Ngu ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Thyroid Nodules ◽  
Malignancy Risk ◽  
The Eu

Thyroid Nodule Malignancy Risk Stratification Using a Convolutional Neural Network

2020 ◽  
Vol 36 (2) ◽  
pp. 164-172
Author(s):  
Matthew T. Stib ◽  
Ian Pan ◽  
Derek Merck ◽  
William D. Middleton ◽  
Michael D. Beland
Keyword(s):  
Neural Network ◽  
Risk Stratification ◽  
Convolutional Neural Network ◽  
Thyroid Nodule ◽  
Malignancy Risk

Malignancy risk stratification and FNA recommendations for thyroid nodules: A comparison of ACR TI-RADS, AACE/ACE/AME and ATA guidelines

2020 ◽  
Vol 41 (6) ◽  
pp. 102625 ◽  
Author(s):  
Jian-Yun Peng ◽  
Fu-Shun Pan ◽  
Wei Wang ◽  
Zhu Wang ◽  
Quan-Yuan Shan ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Thyroid Nodules ◽  
Malignancy Risk
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