Rationale and Objectives
This study evaluates to what extent technologists’ experience, training, or practice in mammography are associated with screening mammography positioning quality.
Materials and Methods
Positioning quality of a random sample of 1278 mammograms drawn from the 394,190 screening examinations performed in 2004–2005 in the Breast Cancer Screening Program of Quebec (Canada) was evaluated by an expert radiologist. Information on technologists’ experience, training, and practice was obtained by mailed questionnaire. Multivariable Poisson regression models with robust estimation of variance were used to assess the association of technologists’ characteristics with higher positioning quality.
Results
Of 254 randomly selected technologists, 220 (86.6%) completed the questionnaire. Participating technologists did 89.2% of available sampled mammograms (1088 of 1220), of which 45.9% were of higher positioning quality. Technologists who, in addition to mandatory training, followed at least 15 hours of hands-on training in positioning performed higher positioning quality (adjusted ratio = 1.3, 95%CI = 1.1–1.5) than technologists with no such additional training. Technologists providing at least 15 hours of continued medical education also performed higher positioning quality (adjusted ratio = 1.3, 95%CI = 1.1–1.5) than those who provided less than 15 hours of continued medical education. Being involved in film development and proportion of mammograms performed that are screening compared to diagnostic were also associated with positioning quality, although the latter association was less clear.
Conclusions
Extra hands-on training in positioning could further improve screening mammography positioning quality in the screening program because many technologists did not have such additional training.
Introduction
Mammography quality is believed to influence sensitivity and specificity of breast cancer screening. One study suggested that lower quality of positioning may reduce screening sensitivity . Other studies have suggested that poor mammography quality, including poor positioning, is associated with missed cancers or later stage at diagnosis . Positioning is the aspect of mammography quality that is most frequently suboptimal . This finding was also observed in the Quebec Breast Cancer Screening Program .
Mammography technologists play a central role in the achievement of high-quality mammograms as they are responsible for positioning of the breasts. However, how technologists’ characteristics influence mammography quality is understudied. Only two studies concerning the association between technologists’ characteristics and mammography quality were identified. New technologists were found to perform better positioning quality than experienced technologists in one recent European study . In another study conducted in the Chicago area, facilities relying only on technologists dedicated to mammography were not found to perform higher quality mammograms than facilities relying on technologists with a mixed practice . These studies each analyzed only one technologists’ characteristic. To our knowledge, no study has examined the association of a wide range of technologists’ characteristics such as experience, training, and practice, with mammography quality.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Materials and Methods
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Women Characteristics
Get Radiology Tree app to read full this article<
Technologists’ Characteristics
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Mammography Positioning Quality
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Statistical Analyses
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Results
Get Radiology Tree app to read full this article<
Table 1
Characteristics of the Eligible Population, Sample, and Sample Available for Analyses †
Population Sample Sample Available for Analyses_N_ = 394,190N = 1,278N = 1,088 No. (%) No. (%) No. (%) Women characteristics Age, y, mean (SD) 58.5 (5.5) 58.5 (5.5) 58.6 (5.5) Breast density ≥50% 136,017 (34.5) 430 (33.6) 367 (33.7) Body mass index (kg/m 2 ), \* mean (SD) 26.7 (5.2) 26.5 (5.2) 26.5 (5.1) Parity (at least one child) 328,507 (83.3) 1,063 (83.2) 915 (84.1) Menopausal 341,278 (86.6) 1,115 (87.2) 949 (87.2) Indication of breast pain 27,868 (7.1) 82 (6.4) 67 (6.2) Previous breast aspiration or biopsy 42,958 (10.9) 118 (9.2) 94 (8.6) Screening history Initial mammogram in the program without prior mammograms 28,043 (7.1) 105 (8.2) 85 (7.8) Initial mammogram in the program but at least one prior mammogram 76,973 (19.5) 246 (19.2) 202 (18.6) Subsequent mammogram in the program 289,174 (73.4) 927 (72.5) 801 (73.6) Technologists average yearly volume of screening mammograms (PQDCS), mean (SD) 898.1 (646.5) 747.0 (563.4) 769.3 (579.5) Private facility 253,898 (64.4) 786 (61.5) 666 (61.2)
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Table 2
Technologists’ Experience, Training, and Practice Characteristics, and Positioning Quality
Technologists \* Mammograms Higher Positioning Quality No. No. % Adj. Ratio † (95%CI) Total 233 1088 45.9 Model 1: experience ‡ Mammography experience (y) <5 22 98 45.9 1.0 5–9 46 224 41.1 0.9 (0.7–1.2) 10–19 88 407 45.7 1.0 (0.8–1.2) ≥20 71 337 48.1 0.9 (0.7–1.2) Missing 6 22 63.6 P value P = 0.96 Average yearly TOTAL mammography volume (2004–2005) <1000 77 322 41.9 1.0 1000–<2000 60 257 42.0 0.9 (0.8–1.1) 2000–<3000 50 247 48.6 1.0 (0.9–1.3) ≥3000 43 245 51.8 1.1 (0.9–1.3) Missing 3 17 52.9 P value P = 0.38 Proportion of mammograms that are screening (2004–2005), % >0–≤25 9 42 31.0 1.0 >25–≤50 65 304 45.7 1.4 (0.8–2.4) >50–≤75 99 452 49.1 1.6 (1.0–2.8) >75–100 44 228 40.8 1.3 (0.7–2.2) Missing 16 62 48.4 P value P = 0.03 Model 2: training § Continued medical education followed (h) <15 10 41 43.9 1.0 15 98 477 49.7 1.1 (0.8–1.5) 16–30 96 449 43.6 1.0 (0.7–1.4) >30 27 116 39.7 0.9 (0.6–1.3) Missing 2 5 40.0 P value P = 0.11 Continued medical education given (h) <15 210 967 45.0 1.0 ≥15 23 121 52.9 1.2 (1.0–1.5) P value P = 0.04 Trained others for positioning No 176 843 45.6 1.0 Yes 55 240 47.1 1.0 (0.9–1.2) Missing 2 5 40.0 P value P = 0.86 Additional hands-on training (h) in positioning 0 98 517 42.4 1.0 <15 74 310 46.4 1.1 (0.9–1.3) ≥15 59 256 52.3 1.3 (1.1–1.4) Missing 2 5 40.0 P value P = 0.006 Model 3: practice ¶ Responsible for film development No 52 249 41.4 1.0 Yes 181 839 47.2 1.2 (1.0–1.4) P value P = 0.04 Responsible for film quality No 9 33 36.4 1.0 Yes 224 1055 46.2 1.1 (0.7–1.7) P value P = 0.76 Supervision of technologists No 181 856 45.2 1.0 Yes 49 224 48.7 1.1 (0.9–1.3) Missing 3 8 37.5 P value P = 0.50 Responsible for quality control at facility No 174 842 45.6 1.0 Yes 59 246 46.7 1.0 (0.9–1.2) P value P = 0.73 Receives feedback for rejected images for technical reasons No 90 456 46.7 1.0 Yes 142 629 45.3 1.0 (0.8–1.1) Missing 1 3 33.3 P value P = 0.74 Average duration of a mammogram (min) ≤5 21 118 44.9 1.0 >5–10 161 749 47.0 1.0 (0.8–1.3) >10 50 218 43.1 1.0 (0.7–1.3) Missing 1 3 0.0 P value P = 0.82
Adj, adjusted; CI, confidence interval.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Table 3
Selected Technologists’ Characteristics and Positioning Quality
Technologists \* Mammograms \* Higher Positioning Quality No. No. % Adj. Ratio †,‡ (95%CI) Proportion of mammograms that are screening (2004–2005), % >0–≤25 9 42 31.0 1.0 >25–≤50 65 304 45.7 1.4 (0.8–2.4) >50–≤75 99 452 49.1 1.5 (0.9–2.6) >75–100 44 228 40.8 1.2 (0.7–2.1) P value 0.03 Continued medical education followed (h) <15 10 41 43.9 1.0 15 98 477 49.7 1.0 (0.7–1.5) 16–30 96 449 43.6 0.9 (0.6–1.3) >30 27 116 39.7 0.8 (0.5–1.3) P value 0.13 Continued medical education given (h) <15 210 967 45.0 1.0 ≥15 23 121 52.9 1.3 (1.1–1.5) P value 0.005 Additional hands-on training (h) in positioning 0 98 517 42.4 1.0 <15 74 310 46.4 1.1 (1.0–1.3) ≥15 59 256 52.3 1.3 (1.1–1.5) P value 0.01 Responsible for film development No 52 249 41.4 1.0 Yes 181 839 47.2 1.2 (1.0–1.4) P value 0.03
Adj, Adjusted; CI, confidence interval.
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Discussion
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
Acknowledgments
Get Radiology Tree app to read full this article<
Get Radiology Tree app to read full this article<
References
1. Taplin S.H., Rutter C.M., Finder C., et. al.: Screening mammography: clinical image quality and the risk of interval breast cancer. AJR Am J Roentgenol 2002; 178: pp. 797-803.
2. Birdwell R.L., Ikeda D.M., O’Shaughnessy K.F., et. al.: Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection. Radiology 2001; 219: pp. 192-202.
3. Rauscher G.H., Conant E.F., Khan J.A., et. al.: Mammogram image quality as a potential contributor to disparities in breast cancer stage at diagnosis: an observational study. BMC Cancer 2013; 13: pp. 208.
4. Hofvind S., Vee B., Sorum R., et. al.: Quality assurance of mammograms in the Norwegian Breast Cancer Screening Program. Eur J Radiogr 2009; 1: pp. 22-29.
5. Bassett L.W., Farria D.M., Bansal S., et. al.: Reasons for failure of a mammography unit at clinical image review in the American College of Radiology Mammography Accreditation Program. Radiology 2000; 215: pp. 698-702.
6. Souza D.E., Sabino S.M., Silva T.B., et. al.: Implementation of a clinical quality control program in a mammography screening service of Brazil. Anticancer Res 2014; 34: pp. 5057-5065.
7. Gwak Y.J., Kim H.J., Kwak J.Y., et. al.: Clinical image evaluation of film mammograms in Korea: comparison with the ACR standard. Korean J Radiol 2013; 14: pp. 701-710.
8. Guertin M.H., Theberge I., Dufresne M.P., et. al.: Clinical image quality in daily practice of breast cancer mammography screening. Can Assoc Radiol J 2014; 65: pp. 199-206.
9. van Landsveld-Verhoeven C., den Heeten G.J., Timmers J., et. al.: Mammographic positioning quality of newly trained versus experienced radiographers in the Dutch breast cancer screening programme. Eur Radiol 2015; 25: pp. 3322-3327.
10. Henderson L.M., Benefield T., Marsh M.W., et. al.: The influence of mammographic technologists on radiologists’ ability to interpret screening mammograms in community practice. Acad Radiol 2014; 22: pp. 278-289.
11. Henderson L.M., Marsh M.W., Benefield T., et. al.: Characterizing the mammography technologist workforce in North Carolina. J Am Coll Radiol 2015; 12: pp. 1419-1426.
12. American College of Radiology : Mammography quality control manual 1999.1999.American College of RadiologyReston, VA
13. Yelland L.N., Salter A.B., Ryan P.: Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data. Am J Epidemiol 2011; 174: pp. 984-992.
14. Zou G.: A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol 2004; 159: pp. 702-706.
15. Zou G.Y., Donner A.: Extension of the modified Poisson regression model to prospective studies with correlated binary data. Stat Methods Med Res 2013; 22: pp. 661-670.
16. Miglioretti D.L., Haneuse S.J., Anderson M.L.: Statistical approaches for modeling radiologists’ interpretive performance. Acad Radiol 2009; 16: pp. 227-238.
17. Food and Drug Administration : Compliance Guidance: The Mammography Quality Standards Act Final Regulations: Preparing For MQSA Inspections.2001.
18. Bloom B.S.: Effects of continuing medical education on improving physician clinical care and patient health: a review of systematic reviews. Int J Technol Assess Health Care 2005; 21: pp. 380-385.
19. Davis D., O’Brien M.A., Freemantle N., et. al.: Impact of formal continuing medical education: do conferences, workshops, rounds, and other traditional continuing education activities change physician behavior or health care outcomes?. JAMA 1999; 282: pp. 867-874.
20. Forsetlund L., Bjorndal A., Rashidian A., et. al.: Continuing education meetings and workshops: effects on professional practice and health care outcomes. Cochrane Database Syst Rev 2009; CD003030
21. Bassett L.W., Hirbawi I.A., DeBruhl N., et. al.: Mammographic positioning: evaluation from the view box. Radiology 1993; 188: pp. 803-806.
22. Eklund G.W., Cardenosa G.: The art of mammographic positioning. Radiol Clin North Am 1992; 30: pp. 21-53.
23. Bentley K., Poulos A., Rickard M.: Mammography image quality: analysis of evaluation criteria using pectoral muscle presentation. Radiography 2008; 14: pp. 189-194.
24. Peart O.: Positioning challenges in mammography. Radiol Technol 2014; 85: pp. 417M-439M.
25. Destounis S., Newell M., Pinsky R.: Breast imaging and intervention in the overweight and obese patient. AJR Am J Roentgenol 2011; 196: pp. 296-302.
26. Bassett L.W., Hoyt A.C., Oshiro T.: Digital mammography: clinical image evaluation. Radiol Clin North Am 2010; 48: pp. 903-915.
27. Moreira C., Svoboda K., Poulos A., et. al.: Comparison of the validity and reliability of two image classification systems for the assessment of mammogram quality. J Med Screen 2005; 12: pp. 38-42.
28. Greenland S., Rothman K.J., Lash T.L.: Validity in epidemiologic studies.Rothman K.J.Greenland S.Lash T.L.Modern epidemiology.2008.Lippincott Williams & WilkinsPhiladelphia, PA: