Clinically missed cancer: how effectively can radiologists use computer-aided detection?
- PMID: 22358014
- DOI: 10.2214/AJR.11.6423
Clinically missed cancer: how effectively can radiologists use computer-aided detection?
Erratum in
- AJR Am J Roentgenol. 2012 May;198(5):1232
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].
Similar articles
-
1000-Case Reader Study of Radiologists' Performance in Interpretation of Automated Breast Volume Scanner Images with a Computer-Aided Detection System.Ultrasound Med Biol. 2018 Aug;44(8):1694-1702. doi: 10.1016/j.ultrasmedbio.2018.04.020. Epub 2018 May 28. Ultrasound Med Biol. 2018. PMID: 29853222
-
Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection.Radiology. 2001 Apr;219(1):192-202. doi: 10.1148/radiology.219.1.r01ap16192. Radiology. 2001. PMID: 11274556
-
Improvement in sensitivity of screening mammography with computer-aided detection: a multiinstitutional trial.AJR Am J Roentgenol. 2003 Sep;181(3):687-93. doi: 10.2214/ajr.181.3.1810687. AJR Am J Roentgenol. 2003. PMID: 12933460
-
Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography.Health Technol Assess. 2005 Feb;9(6):iii, 1-58. doi: 10.3310/hta9060. Health Technol Assess. 2005. PMID: 15717938 Review.
-
Missed Breast Cancer: What Can We Learn?Curr Probl Diagn Radiol. 2016 Nov-Dec;45(6):402-419. doi: 10.1067/j.cpradiol.2016.03.001. Epub 2016 Mar 9. Curr Probl Diagn Radiol. 2016. PMID: 27079634 Review.
Cited by
-
Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.Front Oncol. 2022 Aug 31;12:980793. doi: 10.3389/fonc.2022.980793. eCollection 2022. Front Oncol. 2022. PMID: 36119479 Free PMC article. Review.
-
An EANM position paper on the application of artificial intelligence in nuclear medicine.Eur J Nucl Med Mol Imaging. 2022 Dec;50(1):61-66. doi: 10.1007/s00259-022-05947-x. Epub 2022 Aug 25. Eur J Nucl Med Mol Imaging. 2022. PMID: 36006443 Free PMC article.
-
Advancing Research on Medical Image Perception by Strengthening Multidisciplinary Collaboration.JNCI Cancer Spectr. 2022 Jan 5;6(1):pkab099. doi: 10.1093/jncics/pkab099. JNCI Cancer Spectr. 2022. PMID: 35699495 Free PMC article.
-
Can artificial intelligence reduce the interval cancer rate in mammography screening?Eur Radiol. 2021 Aug;31(8):5940-5947. doi: 10.1007/s00330-021-07686-3. Epub 2021 Jan 23. Eur Radiol. 2021. PMID: 33486604 Free PMC article.
-
Brain metastasis detection using machine learning: a systematic review and meta-analysis.Neuro Oncol. 2021 Feb 25;23(2):214-225. doi: 10.1093/neuonc/noaa232. Neuro Oncol. 2021. PMID: 33075135 Free PMC article.
Publication types
MeSH terms
Grants and funding
LinkOut - more resources
Full Text Sources
Medical
