Literature DB >> 26025577

Application of Computer-Aided Diagnosis to the Sonographic Evaluation of Cervical Lymph Nodes.

Junhua Zhang1, Yuanyuan Wang2, Bo Yu3, Xinling Shi4, Yufeng Zhang4.   

Abstract

We initiated an observer study to evaluate a computerized system developed in our previous study for automatic extraction of 10 features and estimation of the malignancy probability of cervical nodes in sonograms. In the present study, five expert radiologists and five resident radiologists interpreted the sonograms of 178 nodes. The malignancy rating and patient management recommendation (biopsy or follow-up) were made without and then with the computer aid. Under these two reading conditions, the performances of radiologists and agreement among a group of radiologists were evaluated by using the receiver operating characteristic (ROC) analysis and the κ statistic, respectively. With the computer aid, the performances of radiologists improved significantly, as indicated by the increase in the area under the ROC curve (Az) from 0.843 to 0.896 (p = 0.031) and from 0.705 to 0.822 (p < 0.001), for the expert and resident groups, respectively. Agreement among all 10 radiologists improved from slight to moderate as indicated by an increase in the κ value from 0.195 to 0.421 (p < 0.001). The average performance of residents with aid (Az = 0.822) was close to that of experts without aid (Az = 0.843). Results indicate that computer-aided diagnosis is useful to improve radiologist performance (especially that of inexperienced radiologists) in the ultrasonographic evaluation of cervical nodes and to reduce variability among radiologists.
© The Author(s) 2015.

Entities:  

Keywords:  cervical lymph node; computer-aided diagnosis (CAD); observer study; sonography

Mesh:

Year:  2015        PMID: 26025577     DOI: 10.1177/0161734615589080

Source DB:  PubMed          Journal:  Ultrason Imaging        ISSN: 0161-7346            Impact factor:   1.578


  4 in total

Review 1.  Artificial intelligence in radiology.

Authors:  Ahmed Hosny; Chintan Parmar; John Quackenbush; Lawrence H Schwartz; Hugo J W L Aerts
Journal:  Nat Rev Cancer       Date:  2018-08       Impact factor: 60.716

2.  Artificial intelligence for ultrasonography: unique opportunities and challenges.

Authors:  Seong Ho Park
Journal:  Ultrasonography       Date:  2020-11-03

3.  Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.

Authors:  Baptiste Vasey; Stephan Ursprung; Benjamin Beddoe; Elliott H Taylor; Neale Marlow; Nicole Bilbro; Peter Watkinson; Peter McCulloch
Journal:  JAMA Netw Open       Date:  2021-03-01

4.  Differentiation between metastatic and tumour-free cervical lymph nodes in patients with papillary thyroid carcinoma by grey-scale sonographic texture analysis.

Authors:  Ali Abbasian Ardakani; Alireza Rasekhi; Afshin Mohammadi; Ebrahim Motevalian; Bahareh Khalili Najafabad
Journal:  Pol J Radiol       Date:  2018-02-04
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.