Comparison of single reading with double reading of mammograms, and change in effectiveness with experience

1995 ◽  
Vol 68 (813) ◽  
pp. 958-962 ◽  
Author(s):  
R M L Warren ◽  
W Duffy
Keyword(s):  
Double Reading ◽  
Single Reading

scholarly journals Independent Double Reading of Screening Mammograms

1995 ◽  
Vol 2 (2) ◽  
pp. 99-101 ◽  
Author(s):  
Stefano Ciatto ◽  
Marco Rosselli Del Turco ◽  
Doralba Morrone ◽  
Sandra Catarzi ◽  
Daniela Ambrogetti ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Detection Rate ◽  
Recall Rate ◽  
Cancer Detection Rate ◽  
Diagnostic Assessment ◽  
Double Reading ◽  
Single Reading ◽  
The Mean ◽  
Screening Mammograms ◽  
The Cost

Objective — To evaluate the cost effectiveness of independent double reading of screening mammograms. Setting — Prospective study of 18817 women undergoing first or repeat screening in a population based programme in the Florence district. Methods — Mammograms were independently double read by experienced radiologists. Subjects with mammographic abnormalities reported by at least one reader were recalled for diagnostic assessment. The mean increase in recall rate, cancer detection rate, and screening costs attributable to double reading was calculated. Results — Eleven of 125 cancers were detected by only one reader. The mean increase in cancer detection rate attributable to double reading compared with single reading was 4·6% (95% confidence interval (CI) 1·1 to 8·9). From a total of 748 cases referred for diagnostic assessment, 196 subjects were referred by one reader only. The mean increase in referral rate attributable to double reading compared with single reading was 15·1% (CI 12·3 to 17·8). Double reading caused a marked increase in the cost for each woman screened −8·5% at the first screening and 6·2% at repeat screening and a more limited increase in the cost for each cancer detected −3·5% at the first screening and 2·7% at repeat screening. Cancers detected by only one screener were at an earlier stage than those detected by both screeners (P = 0·6, not significant). Conclusions — Independent double reading results in only a modest increase in the detection of cancers and therefore may not be cost effective.

scholarly journals Mammographic Features of Breast Cancers at Single Reading with Computer-aided Detection and at Double Reading in a Large Multicenter Prospective Trial of Computer-aided Detection: CADET II

Radiology ◽  
2010 ◽  
Vol 256 (2) ◽  
pp. 379-386 ◽  
Author(s):  
Jonathan J. James ◽  
Fiona J. Gilbert ◽  
Matthew G. Wallis ◽  
Maureen G. C. Gillan ◽  
Susan M. Astley ◽  
...  
Keyword(s):  
Prospective Trial ◽  
Breast Cancers ◽  
Computer Aided Detection ◽  
Double Reading ◽  
Single Reading ◽  
Computer Aided

Retrospective comparison between single reading plus an artificial intelligence algorithm and two-view digital tomosynthesis with double reading in breast screening

2021 ◽  
pp. 096914132098419
Author(s):  
Axel Graewingholt ◽  
Stephen Duffy
Keyword(s):  
Artificial Intelligence ◽  
Detection Rate ◽  
Sensitivity Threshold ◽  
Lower Sensitivity ◽  
Digital Tomosynthesis ◽  
Tumour Type ◽  
Double Reading ◽  
Experienced Radiologist ◽  
Single Reading ◽  
Higher Sensitivity

Objective To examine the breast cancer detection rate by single reading of an experienced radiologist supported by an artificial intelligence (AI) system, and compare with two-dimensional full-field digital mammography (2D-FFDM) double reading. Materials and methods Images (3D-tomosynthesis) of 161 biopsy-proven cancers were re-read by the AI algorithm and compared to the results of first human reader, second human reader and consensus following double reading in screening. Detection was assessed in subgroups by tumour type, breast density and grade, and at two operating points, referred to as a lower and a higher sensitivity threshold. Results The AI algorithm method gave similar results to double-reading 2D-FFDM, and the detection rate was significantly higher compared to single-reading 2D-FFDM. At the lower sensitivity threshold, the algorithm was significantly more sensitive than reader A (97.5% vs. 89.4%, p = 0.02), non-significantly more sensitive than reader B (97.5% vs. 94.4%, p = 0.2) and non-significantly less sensitive than the consensus from double reading (97.5% vs. 99.4%, p = 0.2). At the higher sensitivity threshold, the algorithm was significantly more sensitive than reader A (99.4% vs. 89.4%, p < 0.001) and reader B (99.4% vs. 94.4%, p = 0.02) and identical to the consensus sensitivity (99.7% in both cases, p = 1.0). There were no significant differences in the detection capability of the AI system by tumour type, grading and density. Conclusion In this proof of principle study, we show that sensitivity using single reading with a suitable AI algorithm is non-inferior to that of standard of care using 2D mammography with double reading, when tomosynthesis is the primary screening examination.

scholarly journals Double reading in breast cancer screening: considerations for policy-making

2020 ◽  
Vol 93 (1106) ◽  
pp. 20190610
Author(s):  
Sian Taylor-Phillips ◽  
Chris Stinton
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Imaging Techniques ◽  
Three Dimensional ◽  
Screen Mammography ◽  
Historical Evidence ◽  
Double Reading ◽  
Single Reading ◽  
Exact Version

In this article, we explore the evidence around the relative benefits and harms of breast cancer screening using a single radiologist to examine each female’s mammograms for signs of cancer (single reading), or two radiologists (double reading). First, we briefly explore the historical evidence using film-screen mammography, before providing an in-depth description of evidence using digital mammography. We classify studies according to which exact version of double reading they use, because the evidence suggests that effectiveness of double reading is contingent on whether the two radiologists are blinded to one another’s decisions, and how the decisions of the two radiologists are integrated. Finally, we explore the implications for future mammography, including using artificial intelligence as the second reader, and applications to more complex three-dimensional imaging techniques such as tomosynthesis.

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