Literature DB >> 23737538

Markers of good performance in mammography depend on number of annual readings.

Mohammad A Rawashdeh1, Warwick B Lee, Roger M Bourne, Elaine A Ryan, Mariusz W Pietrzyk, Warren M Reed, Robert C Heard, Deborah A Black, Patrick C Brennan.   

Abstract

PURPOSE: To explore relationships between reader performance and reader characteristics in mammography for specific radiologist groupings on the basis of annual number of readings.
MATERIALS AND METHODS: The institutional review board approved the study and waived the need for patient consent to use all images. Readers gave informed consent. One hundred sixteen radiologists independently reviewed 60 mammographic cases: 20 cases with cancer and 40 cases with normal findings. Readers located any visualized cancer, and levels of confidence were scored from 1 to 5. A jackknifing free response operating characteristic (JAFROC) method was used, and figures of merit along with sensitivity and specificity were correlated with reader characteristics by using Spearman techniques and standard multiple regressions.
RESULTS: Reader performance was positively correlated with number of years since qualification as a radiologist (P ≤ .01), number of years reading mammograms (P ≤ .03), and number of readings per year (P ≤ .0001). The number of years since qualification as a radiologist (P ≤ .004) and number of years of reading mammograms (P ≤ .002) were negatively related to JAFROC values for radiologists with annual volumes of less than 1000 mammographic readings. For individuals with more than 5000 mammographic readings per year, JAFROC values were positively related to the number of years that the reader was qualified as a radiologist (P ≤ .01), number of years of reading mammograms (P ≤ .002), and number of hours per week of reading mammograms (P ≤ .003). Number of mammographic readings per year was positively related with JAFROC scores for readers with an annual volume between 1000 and 5000 readings (P ≤ .03). Differences in JAFROC scores appear to be more related to specificity than location sensitivity, with the former demonstrating significant relationships with four of the five characteristics analyzed, whereas no relationships were shown for the latter.
CONCLUSION: Radiologists' determinants of performance are associated with annual reading volumes. Ability to recognize normal images is a discriminating factor in individuals with a high volume of mammographic readings. © RSNA, 2013.

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Mesh:

Year:  2013        PMID: 23737538     DOI: 10.1148/radiol.13122581

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  26 in total

1.  Number of mammography cases read per year is a strong predictor of sensitivity.

Authors:  Wasfi I Suleiman; Sarah J Lewis; Dianne Georgian-Smith; Michael G Evanoff; Mark F McEntee
Journal:  J Med Imaging (Bellingham)       Date:  2014-05-07

2.  Radiographers' performance in chest X-ray interpretation: the Nigerian experience.

Authors:  E U Ekpo; N O Egbe; B E Akpan
Journal:  Br J Radiol       Date:  2015-05-12       Impact factor: 3.039

3.  Lack of agreement between radiologists: implications for image-based model observers.

Authors:  Juhun Lee; Robert M Nishikawa; Ingrid Reiser; Margarita L Zuley; John M Boone
Journal:  J Med Imaging (Bellingham)       Date:  2017-05-03

4.  Factors associated with breast screening radiologists' annual mammogram reading volume in Italy.

Authors:  Doralba Morrone; Livia Giordano; Franca Artuso; Daniela Bernardi; Chiara Fedato; Alfonso Frigerio; Daniela Giorgi; Carlo Naldoni; Gianni Saguatti; Daniela Severi; Mario Taffurelli; Daniela Terribile; Leonardo Ventura; Lauro Bucchi
Journal:  Radiol Med       Date:  2016-03-31       Impact factor: 3.469

5.  Performance of 4 years of population-based mammography screening for breast cancer combined with ultrasound in Tyrol / Austria.

Authors:  Sabine Geiger-Gritsch; Martin Daniaux; Wolfgang Buchberger; Rudolf Knapp; Willi Oberaigner
Journal:  Wien Klin Wochenschr       Date:  2017-12-05       Impact factor: 1.704

6.  Comparable prediction of breast cancer risk from a glimpse or a first impression of a mammogram.

Authors:  E M Raat; I Farr; J M Wolfe; K K Evans
Journal:  Cogn Res Princ Implic       Date:  2021-11-06

7.  Mammography self-evaluation online test for screening readers: an Italian Society of Medical Radiology (SIRM) initiative.

Authors:  Beniamino Brancato; Francesca Peruzzi; Calogero Saieva; Simone Schiaffino; Sandra Catarzi; Gabriella Gemma Risso; Andrea Cozzi; Serena Carriero; Massimo Calabrese; Stefania Montemezzi; Chiara Zuiani; Francesco Sardanelli
Journal:  Eur Radiol       Date:  2021-09-04       Impact factor: 5.315

8.  An Investigation into the Consistency in Mammographic Density Identification by Radiologists: Effect of Radiologist Expertise and Mammographic Appearance.

Authors:  Yanpeng Li; Patrick C Brennan; Warwick Lee; Carolyn Nickson; Mariusz W Pietrzyk; Elaine A Ryan
Journal:  J Digit Imaging       Date:  2015-10       Impact factor: 4.056

9.  Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool.

Authors:  Serena Pacilè; January Lopez; Pauline Chone; Thomas Bertinotti; Jean Marie Grouin; Pierre Fillard
Journal:  Radiol Artif Intell       Date:  2020-11-04

10.  Reader characteristics and mammogram features associated with breast imaging reporting scores.

Authors:  Phuong Dung Yun Trieu; Sarah J Lewis; Tong Li; Karen Ho; Kriscia A Tapia; Patrick C Brennan
Journal:  Br J Radiol       Date:  2020-08-05       Impact factor: 3.039

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