Literature DB >> 22358014

Clinically missed cancer: how effectively can radiologists use computer-aided detection?

Robert M Nishikawa1, Robert A Schmidt, Michael N Linver, Alexandra V Edwards, John Papaioannou, Margaret A Stull.   

Abstract

OBJECTIVE: The purpose of this study was to determine the effectiveness with which radiologists can use computer-aided detection (CADe) to detect cancer missed at screening.
MATERIALS AND METHODS: An observer study was performed to measure the ability of radiologists to detect breast cancer on mammograms with and without CADe. The images in the study were from 300 analog mammographic examinations. In 234 cases the mammograms were read clinically as normal and free of cancer for at least 2 subsequent years. In the other 66 cases, cancers were missed clinically. In 256 cases, current and previous mammograms were available. Eight radiologists read the dataset and recorded a BI-RADS assessment, the location of the lesion, and their level of confidence that the patient should be recalled for diagnostic workup for each suspicious lesion. Jackknife alternative free-response receiver operating characteristic analysis was used.
RESULTS: The jackknife alternative free-response receiver operating characteristic figure of merit was 0.641 without aid and 0.659 with aid (p = 0.06; 95% CI, -0.001 to 0.036). The sensitivity increased 9.9% (95% CI, 3.4-19%) and the callback rate 12.1% (95% CI, 7.3-20%) with CADe. Both increases were statistically significant (p < 0.001). Radiologists on average ignored 71% of correct computer prompts.
CONCLUSION: Use of CADe can increase radiologist sensitivity 10% with a comparable increase in recall rate. There is potential for CADe to have a bigger clinical impact because radiologists failed to recognize a correct computer prompt in 71% of missed cancer cases [corrected].

Entities:  

Mesh:

Year:  2012        PMID: 22358014     DOI: 10.2214/AJR.11.6423

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  15 in total

Review 1.  Physician centred imaging interpretation is dying out - why should I be a nuclear medicine physician?

Authors:  Roland Hustinx
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-06-07       Impact factor: 9.236

2.  Analog Computer-Aided Detection (CAD) information can be more effective than binary marks.

Authors:  Corbin A Cunningham; Trafton Drew; Jeremy M Wolfe
Journal:  Atten Percept Psychophys       Date:  2017-02       Impact factor: 2.199

3.  Is there a safety-net effect with computer-aided detection?

Authors:  Ethan Du-Crow; Susan M Astley; Johan Hulleman
Journal:  J Med Imaging (Bellingham)       Date:  2019-12-26

4.  Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment.

Authors:  Victoria L Mango; Mary Sun; Ralph T Wynn; Richard Ha
Journal:  AJR Am J Roentgenol       Date:  2020-04-22       Impact factor: 3.959

5.  Importance of Better Human-Computer Interaction in the Era of Deep Learning: Mammography Computer-Aided Diagnosis as a Use Case.

Authors:  Robert M Nishikawa; Kyongtae T Bae
Journal:  J Am Coll Radiol       Date:  2017-10-31       Impact factor: 5.532

Review 6.  Endoscopic ultrasonography for surveillance of individuals at high risk for pancreatic cancer.

Authors:  Gabriele Lami; Maria Rosa Biagini; Andrea Galli
Journal:  World J Gastrointest Endosc       Date:  2014-07-16

7.  Impact of computer-aided detection systems on radiologist accuracy with digital mammography.

Authors:  Elodia B Cole; Zheng Zhang; Helga S Marques; R Edward Hendrick; Martin J Yaffe; Etta D Pisano
Journal:  AJR Am J Roentgenol       Date:  2014-10       Impact factor: 3.959

8.  Can artificial intelligence reduce the interval cancer rate in mammography screening?

Authors:  Kristina Lång; Solveig Hofvind; Alejandro Rodríguez-Ruiz; Ingvar Andersson
Journal:  Eur Radiol       Date:  2021-01-23       Impact factor: 5.315

9.  Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography.

Authors:  Matthias Hammon; Peter Dankerl; Alexey Tsymbal; Michael Wels; Michael Kelm; Matthias May; Michael Suehling; Michael Uder; Alexander Cavallaro
Journal:  Eur Radiol       Date:  2013-02-09       Impact factor: 5.315

10.  Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: a diagnostic test.

Authors:  Maoling Zhu; Can Xu; Jianguo Yu; Yijun Wu; Chunguang Li; Minmin Zhang; Zhendong Jin; Zhaoshen Li
Journal:  PLoS One       Date:  2013-05-21       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.