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Nelson HD, Tyne K, Naik A, et al. Screening for Breast Cancer: Systematic Evidence Review Update for the US Preventive Services Task Force [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2009 Nov. (Evidence Syntheses, No. 74.)

Cover of Screening for Breast Cancer

Screening for Breast Cancer: Systematic Evidence Review Update for the US Preventive Services Task Force [Internet].

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Appendix BDetailed Methods

Appendix B1. Literature Search Strategies

Screening

Database: EBM Reviews - Cochrane Central Register of Controlled Trials
Search Strategy:
1((Breast$ or mammary) adj3 (Neoplas$ or tumor$ or cancer$ or carcinom$)).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
2(screen$ or (routine$ adj3 (test$ or check$ or diagnos$ or detect$))).mp.
3((clinical$ or physical$) adj3 (exam$ or detect$ or diagnos$)).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
42 or 3
51 and 4
Database: EBM Reviews - Cochrane Database of Systematic Reviews
Search Strategy:
1((Breast$ or mammary) adj3 (Neoplas$ or tumor$ or cancer$ or carcinom$)).mp. [mp=title, abstract, full text, keywords, caption text]
2(screen$ or (routine$ adj3 (test$ or check$ or diagnos$ or detect$))).mp.
3((clinical$ or physical$) adj3 (exam$ or detect$ or diagnos$)).mp. [mp=title, abstract, full text, keywords, caption text]
42 or 3
51 and 4
6((Breast$ or mammary) adj3 (Neoplas$ or tumor$ or cancer$ or carcinom$)).kw.
71 not 6
84 and 6
Database: Ovid MEDLINE(R)
Search Strategy:
1exp Breast Neoplasms/
2exp neoplasms/di
3exp breast/
42 and 3
51 or 4
6exp mass screening/
7(screen$ or (routine$ adj3 (test$ or check$ or diagnos$ or detect$))).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
86 or 7
95 and 8
10exp Physical Examination/
11exp Breast/
12exp Breast Neoplasms/
1311 or 12
1410 and 13
15exp Mammography/
169 and 14
179 and 15
18exp Mortality/
19mo.fs.
2018 or 19
2116 and 20
2217 and 20
2321 or 22
24limit 23 to (humans and english language)
25limit 24 to (guideline or meta analysis or randomized controlled trial)
26(random$ or rct).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
2724 and 26
28(meta-analy$ or metaanaly$ or (systematic$ adj10 review$)).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
2924 and 28
3025 or 27 or 29
3124 not 30

Digital Mammography

Database: EBM Reviews - Cochrane Central Register of Controlled Trials
Search Strategy:
1((digital$ or computer$) adj7 mammogra$).mp.
2from 1 keep 1–37
Database: EBM Reviews - Cochrane Database of Systematic Reviews
Search Strategy:
1((digital$ or computer$) adj7 mammogra$).mp.
2from 1 keep 1
Database: Ovid MEDLINE(R)
Search Strategy:
1exp Breast Neoplasms/
2exp neoplasms/di
3exp breast/
42 and 3
51 or 4
6exp mass screening/
7(screen$ or (routine$ adj3 (test$ or check$ or diagnos$ or detect$))).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
86 or 7
95 and 8
10exp Physical Examination/
11exp Breast/
12exp Breast Neoplasms/
1311 or 12
1410 and 13
15exp Mammography/
169 and 14
179 and 15
1816 or 17
19(digital$ adj7 mammogra$).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
20exp Image Processing, Computer-Assisted/
21exp Mammography/
2220 and 21
2319 or 22
248 and 23
25limit 24 to english language
26from 25 keep 1–395

MRI

Database: EBM Reviews - Cochrane Central Register of Controlled Trials
Search Strategy:
1((Breast$ or mammary) adj3 (Neoplas$ or tumor$ or cancer$ or carcinom$)).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
2(mri or magnetic resonance imag$).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
31 and 2
4from 3 keep 1–29
Database: EBM Reviews - Cochrane Database of Systematic Reviews
Search Strategy:
1((Breast$ or mammary) adj3 (Neoplas$ or tumor$ or cancer$ or carcinom$)).mp. [mp=title, abstract, full text, keywords, caption text]
2(mri or magnetic resonance imag$).mp. [mp=title, abstract, full text, keywords, caption text]
31 and 2
4from 3 keep 1–9
Database: Ovid MEDLINE(R)
Search Strategy:
1exp Breast Neoplasms/
2exp neoplasms/di
3exp breast/
42 and 3
51 or 4
6exp mass screening/
7(screen$ or (routine$ adj3 (test$ or check$ or diagnos$ or detect$))).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
86 or 7
95 and 8
10exp Physical Examination/
11exp Breast/
12exp Breast Neoplasms/
1311 or 12
1410 and 13
15exp Mammography/
169 and 14
179 and 15
1816 or 17
19exp Magnetic Resonance Imaging/
205 and 19
218 and 20
22from 21 keep 1–232

DCIS

Database: Ovid MEDLINE(R)
Search Strategy:
1exp Carcinoma, Intraductal, Noninfiltrating/
2exp Breast Neoplasms/
31 and 2
4overdiagno$.mp. [mp=title, original title, abstract, name of substance word, subject heading word]
5over-diagno$.mp. [mp=title, original title, abstract, name of substance word, subject heading word]
6(overtreat$ or over-treat$).mp.
7exp Diagnostic Errors/
8exp Mass Screening/
9exp mammography/
108 or 9
113 and 7 and 10
124 or 5 or 6
133 and 12
14from 13 keep 1–22

Adverse Effects

Database: EBM Reviews - Cochrane Central Register of Controlled Trials
Search Strategy:
1exp mammography/
2mammogra$.mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
3exp physical examination/
4((physical$ or clinical$ or manual$) adj3 exam$).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
5exp mass screening/
6screen$.mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
71 or 2 or 3 or 4 or 5 or 6
8exp breast/
9exp breast diseases/di, ep
10(breast$ or mammar$).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
118 or 9 or 10
127 and 11
13((advers$ adj3 effect$) or harm$ or contraindicat$).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
14ae.fs.
1513 or 14
1612 and 15
17exp Mammography/ae, ct [Adverse Effects, Contraindications]
18exp Physical Examination/ae, ct
19exp Mass Screening/ae, ct [Adverse Effects, Contraindications]
2017 or 18 or 19
2111 and 20
22exp Diagnostic Errors/
23(overtest$ or overdiagnos$ or over-test$ or over-diagnos$).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
24(false$ adj2 (result$ or positiv$ or negativ$)).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
25(observer$ adj3 bias$).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
26(diagnos$ adj3 (error$ or mistak$ or incorrect$)).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
2722 or 23 or 24 or 25 or 26
2812 and 27
29exp “Wounds and Injuries”/ci, et [Chemically Induced, Etiology]
30exp Stress, Psychological/
31exp Prejudice/
32exp Stereotyping/
33(anxiet$ or anxious$ or fear$ or discriminat$ or unfair$ or prejudic$ or stigma$ or stereotyp$).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
3429 or 30 or 31 or 32 or 33
3512 and 34
3616 or 21 or 28 or 35
37from 36 keep 1–240
Database: Ovid MEDLINE(R)
Search Strategy:
1exp mammography/
2exp physical examination/
3exp mass screening/
41 or 2 or 3
5exp breast/
6exp breast diseases/di, ep
75 or 6
84 and 7
9exp Mammography/ae, ct [Adverse Effects, Contraindications]
10exp Physical Examination/ae, ct
11exp Mass Screening/ae, ct [Adverse Effects, Contraindications]
129 or 10 or 11
137 and 12
14exp Diagnostic Errors/
15(overtest$ or overdiagnos$ or over-test$ or over-diagnos$).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
1614 or 15
178 and 16
18exp “Wounds and Injuries”/ci, et [Chemically Induced, Etiology]
19exp Stress, Psychological/
20exp Prejudice/
21exp Stereotyping/
2218 or 19 or 20 or 21
238 and 22
2413 or 17 or 23
25limit 24 to english language
26limit 25 to (meta analysis or randomized controlled trial)
27exp Evaluation Studies/
28Comparative Study.pt.
29exp Epidemiologic Studies/
3027 or 28 or 29
3125 and 30
3226 or 31
33from 32 keep 1–319
Database: Ovid MEDLINE(R)
Search Strategy:
1exp mammography/
2exp physical examination/
3exp mass screening/
41 or 2 or 3
5exp breast/
6exp breast diseases/di, ep
75 or 6
84 and 7
9exp Mammography/ae, ct [Adverse Effects, Contraindications]
10exp Physical Examination/ae, ct
11exp Mass Screening/ae, ct [Adverse Effects, Contraindications]
129 or 10 or 11
137 and 12
14exp Diagnostic Errors/
15(overtest$ or overdiagnos$ or over-test$ or over-diagnos$).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
16misdiagnos$.mp. [mp=title, original title, abstract, name of substance word, subject heading word]
17(false$ adj (positiv$ or negativ$)).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
18((incorrect$ or false$ or wrong$ or bias$ or mistake$ or error$ or erroneous$) adj3 (result$ or finding$ or test$ or diagnos$)).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
19((inappropriat$ or unnecess$ or unneed$) adj3 (treat$ or surg$ or therap$ or regimen$)).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
20(observ$ adj3 bias$).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
2114 or 15 or 16 or 17 or 18 or 19 or 20
228 and 21
23exp “Wounds and Injuries”/ci, et [Chemically Induced, Etiology]
24exp Stress, Psychological/
25exp Prejudice/
26exp Stereotyping/
2723 or 24 or 25 or 26
288 and 27
2913 or 22 or 28
30limit 29 to english language
31limit 30 to (meta analysis or randomized controlled trial)
32exp Evaluation Studies/
33Comparative Study.pt.
34exp Epidemiologic Studies/
3532 or 33 or 34
3630 and 35
3731 or 36
38limit 30 to yr= “2000 – 2007”
39from 38 keep 1–391

Cost

Database: EBM Reviews - Cochrane Central Register of Controlled Trials
Search Strategy:
1((Breast$ or mammary) adj3 (Neoplas$ or tumor$ or cancer$ or carcinom$)).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
2(screen$ or (routine$ adj3 (test$ or check$ or diagnos$ or detect$))).mp.
3((clinical$ or physical$) adj3 (exam$ or detect$ or diagnos$)).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
4(cost or costs or costing or economic$ or financial$).mp. [mp=title, original title, abstract, mesh headings, heading words, keyword]
51 and (2 or 3) and 4
6from 5 keep 1–86
Database: EBM Reviews - Cochrane Database of Systematic Reviews
Search Strategy:
1((Breast$ or mammary) adj3 (Neoplas$ or tumor$ or cancer$ or carcinom$)).mp. [mp=title, abstract, full text, keywords, caption text]
2(screen$ or (routine$ adj3 (test$ or check$ or diagnos$ or detect$))).mp.
3((clinical$ or physical$) adj3 (exam$ or detect$ or diagnos$)).mp. [mp=title, abstract, full text, keywords, caption text]
4(cost or costs or costing or economic$ or financial$).mp. [mp=title, abstract, full text, keywords, caption text]
51 and (2 or 3) and 4
6from 5 keep 1–97
Database: Ovid MEDLINE(R)
Search Strategy:
1exp Breast Neoplasms/
2exp neoplasms/di
3exp breast/
42 and 3
51 or 4
6exp mass screening/
7(screen$ or (routine$ adj3 (test$ or check$ or diagnos$ or detect$))).mp. [mp=title, original title, abstract, name of substance word, subject heading word]
86 or 7
95 and 8
10exp Physical Examination/
11exp Breast/
12exp Breast Neoplasms/
1311 or 12
1410 and 13
15exp Mammography/
169 and 14
179 and 15
1816 or 17
19exp “Costs and Cost Analysis”/
2018 and 19
21limit 20 to english language
22from 21 keep 1–376

Appendix B2. Search Results by Key Question

Image appendices.app2fu1

Appendix B3. List of Excluded Studies

Wrong population, including high risk

  1. Berg WA, Blume JD, Cormack JB, et al. Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. JAMA. 2008;299(18):2151–2163. [PMC free article: PMC2718688] [PubMed: 18477782]
  2. Boetes C. The evaluation of women with familial risk of breast cancer. J Exp Clin Cancer Res. 2002;21( 3 Suppl):97–101. [PubMed: 12585662]
  3. Bordas P, Jonsson H, Nystrom L, et al. Early breast cancer deaths in women aged 40–74 years diagnosed during the first 5 years of organised mammography service screening in north Sweden. Breast. 2004;13(4):276–283. [PubMed: 15325661]
  4. Buseman S, Mouchawar J, Calonge N, et al. Mammography screening matters for young women with breast carcinoma: evidence of downstaging among 42–49-year-old women with a history of previous mammography screening. Cancer. 2003;97(2):352–358. [PubMed: 12518359]
  5. Claus EB, Stowe M, Carter D, et al. The risk of a contralateral breast cancer among women diagnosed with ductal and lobular breast carcinoma in situ: data from the Connecticut Tumor Registry. Breast. 2003;12(6):451–456. [PubMed: 14659121]
  6. Gilbert FJ. Should we use MRI to screen women at high-risk of breast cancer? Cancer Imaging. 2005;5(1):32–38. [PMC free article: PMC1665222] [PubMed: 16154817]
  7. Joensuu H, Lehtimaki T, Holli K, et al. Risk for distant recurrence of breast cancer detected by mammography screening or other methods. JAMA. 2004;292(9):1064–1073. [PubMed: 15339900]
  8. Lalonde L, David J, Trop I. Magnetic resonance imaging of the breast: current indications. Can Assoc Radiol J. 2005;56(5):301–308. [PubMed: 16579024]
  9. Lash TL, Fox MP, Buist DSM, et al. Mammography surveillance and mortality in older breast cancer survivors. J Clin Oncol. 2007;25(21):30001–30006. [PubMed: 17548838]
  10. Leach MO, Boggis CRM, Dixon AK, et al. Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: a prospective multicentre cohort study (MARIBS) Lancet. 2005;365(9473):1769–1778. [PubMed: 15910949]
  11. Lehman CD, Gatsonis C, Kuhl CK, et al. MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer. New Eng J Med. 2007;356(13):1295–1303. [PubMed: 17392300]
  12. MARIBS Study Group. Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: A prospective multicentre cohort study. Lancet. 2005;365(9473):1769–1778. [PubMed: 15910949]
  13. Narod SA, Lubinski J, Ghadirian P, et al. Screening mammography and risk of breast cancer in BRCA1 and BRCA2 mutation carriers: a case-control study. Lancet Oncol. 2006;7(5):402–406. [PubMed: 16648044]
  14. Nelson HD, Huffman LH, Fu R, et al. Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: Systematic evidence review for the U.S. Preventive Services Task Force. Ann Intern Med. 2005;143(5):362–379. [PubMed: 16144895]
  15. Rijnsburger AJ, Essink-Bot ML, van Dooren S, et al. Impact of screening for breast cancer in high-risk women on health-related quality of life. Br J Cancer. 2004;91(1):69–76. [PMC free article: PMC2364757] [PubMed: 15199386]
  16. Sim LSJ, Hendriks JHCL, Fook-Chong SMC. Breast ultrasound in women with familial risk of breast cancer. Ann Acad Med Singapore. 2004;33(5):600–606. [PubMed: 15531956]
  17. Tilanus-Linthorst MM, Obdeijn IM, Bartels KC, et al. First experiences in screening women at high risk for breast cancer with MR imaging. Breast Cancer Res Treat. 2000;63(1):53–60. [PubMed: 11079159]
  18. Walter LC, Eng C, et al. Screening mammography for frail older women: what are the burdens? J Gen Intern Med. 2001;16(11):779–84. [PMC free article: PMC1495292] [PubMed: 11722693]
  19. Warnberg F, Casalini P, Nordgren H, et al. Ductal carcinoma in situ of the breast: a new phenotype classification system and its relation to prognosis. Breast Cancer Res Treat. 2002;73(3):215–221. [PubMed: 12160327]
  20. Warren R. Screening women at high risk of breast cancer on the basis of evidence. Eur J Radiol. 2001;39(1):50–59. [PubMed: 11439231]

Wrong intervention

  1. Belkic K. Current dilemmas and future perspectives for breast cancer screening with a focus on optimization of magnetic resonance spectroscopic imaging by advances in signal processing. Isr Med Assoc J. 2004;6(10):610–618. [PubMed: 15473589]
  2. Lindfors KK, O’Connor J, Parker RA. False-positive screening mammograms: effect of immediate versus later work-up on patient stress. Radiology. 2001;218(1):247–253. [PubMed: 11152810]
  3. Warren R, Allgood P, Hunnam G, et al. An audit of assessment procedures in women who develop breast cancer after a negative result. J Med Screen. 2004;11(4):180–186. [PubMed: 15624238]
  4. Wirfalt E, Vessby B, Mattisson I, et al. No relations between breast cancer risk and fatty acids of erythrocyte membranes in postmenopausal women of the Malmo Diet Cancer cohort (Sweden) Eur J Clin Nutr. 2004;58(5):761–770. [PubMed: 15116079]

Wrong outcome

  1. Anderson TJ, Waller M, Ellis IO, et al. Influence of annual mammography from age 40 on breast cancer pathology. Human Pathol. 2004;35(10):1252–1259. [PubMed: 15492993]
  2. Bartella L, Liberman L, Morris EA, et al. Nonpalpable mammographically occult invasive breast cancers detected by MRI. Am J Roentgenol. 2006;186(3):865–870. [PubMed: 16498122]
  3. Birdwell RL, Bandodkar P, Ikeda DM. Computer-aided detection with screening mammography in a university hospital setting. Radiology. 2005;236(2):451–457. [PubMed: 16040901]
  4. Burani R, Caimi F, Maggioni C, et al. Quality assessment of the mammographic screening programme in the Azienda Sanitaria locale Provincia Milano 1 -- analysis of interval cancers and discussion of possible causes of diagnostic error. Radiol Med (Torino) 2005;109(3):260–267. [PubMed: 15775895]
  5. Cupples TE, Cunningham JE, Reynolds JC. Impact of computer-aided detection in a regional screening mammography program. Am J Roentgenol. 2005;185(4):944–950. [PubMed: 16177413]
  6. Elmore JG, Nakano CY, Koepsell TD, et al. International variation in screening mammography interpretations in community-based programs. J Natl Cancer Inst. 2003;95(18):1384–1393. [PMC free article: PMC3146363] [PubMed: 13130114]
  7. Evans AJ, Kutt E, Record C, et al. Radiological findings of screen-detected cancers in a multi-centre randomized, controlled trial of mammographic screening in women from age 40 to 48 years. Clin Radiol. 2006;61(9):784–788. [PubMed: 16905387]
  8. Evans AJ, Pinder SE, Ellis IO, et al. Screen detected ductal carcinoma in situ (DCIS): overdiagnosis or an obligate precursor of invasive disease? J Med Screen. 2001;8(3):149–151. [PubMed: 11678555]
  9. Feigin KN, Keating DM, Telford PM, et al. Clinical breast examination in a comprehensive breast cancer screening program: contribution and cost. Radiology. 2006;240(3):650–655. [PubMed: 16926322]
  10. Fitzgibbon ML, Gapstur SM, Knight SJ. Mujeres felices por ser saludables: a breast cancer risk reduction program for Latino women. Prev Med. 2003;36(5):536–546. [PubMed: 12689798]
  11. Flegg KM, Rowling YJ. Clinical breast examination. Aust Fam Physician. 2000;29(4):343–346. [PubMed: 10800219]
  12. Galinsky D, Kisselgoff D, Sella T, et al. Effect of breast magnetic resonance imaging on the clinical management of breast cancer. Isr Med Assoc J. 2005;7(11):700–703. [PubMed: 16308991]
  13. Giorgi Rossi P, Camilloni L, Mantellini P, et al. Breast cancer diagnostic methods: screen-detected and clinical cases. Tumori. 2007;93(5):452–460. [PubMed: 18038877]
  14. Goscin CP, Berman CG, Clark RA. Magnetic resonance imaging of the breast. Cancer Control. 2001;8(5):399–406. [PubMed: 11579335]
  15. Gur D, Wallace LP, Klym AH, et al. Trends in recall, biopsy, and positive biopsy rates for screening mammography in an academic practice. Radiology. 2005;235(2):396–401. [PubMed: 15770039]
  16. Hadi N, Sadeghi-Hassanabadi A, Talei AR, et al. Assessment of a breast cancer screening programme in Shiraz, Islamic Republic of Iran. East Mediterr Health J. 2002;8(2–3):386–392. [PubMed: 15339128]
  17. Kessar P, Perry N, Vinnicombe SJ, et al. How significant is detection of ductal carcinoma in situ in a breast screening programme? Clin Radiol. 2002;57(9):807–814. [PubMed: 12384106]
  18. Khoo LA, Taylor P, Given-Wilson RM. Computer-aided detection in the United Kingdom National Breast Screening Programme: Prospective study. Radiology. 2005;237(2):444–449. [PubMed: 16244252]
  19. Kim do Y, Moon WK, Cho N, et al. MRI of the breast for the detection and assessment of the size of ductal carcinoma in situ. Korean J Radiol. 2007;8(1):32–39. [PMC free article: PMC2626696] [PubMed: 17277561]
  20. Kinkel K, Vlastos G. MR imaging: breast cancer staging and screening. Semin Surg Oncol. 2001;20(3):187–196. [PubMed: 11523103]
  21. Kolb TM, Lichy J, Newhouse JH. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: An analysis of 27,825 patient evaluations. Radiology. 2002;225(1):165–175. [PubMed: 12355001]
  22. Law J, Faulkner K. Concerning the relationship between benefit and radiation risk, and cancers detected and induced, in a breast screening programme. Br J Radiol. 2002;75(896):678–684. [PubMed: 12153942]
  23. Leach MO, Eeles RA, Turnbull LW, et al. The UK national study of magnetic resonance imaging as a method of screening for breast cancer (MARIBS) J Exp Clin Cancer Res. 2002;21(3 Suppl):107–114. [PubMed: 12585664]
  24. Menell JH, Morris EA, Dershaw DD, et al. Determination of the presence and extent of pure ductal carcinoma in situ by mammography and magnetic resonance imaging. Breast J. 2005;11(6):382–390. [PubMed: 16297080]
  25. Morris EA. Screening for breast cancer with MRI. Semin Ultrasound CT MR. 2003;24(1):45–54. [PubMed: 12708644]
  26. Moss S, Thomas I, Evans A, et al. Randomised controlled trial of mammographic screening in women from age 40: results of screening in the first 10 years. Br J Cancer. 2005;92(5):949–954. [PMC free article: PMC2361899] [PubMed: 15726102]
  27. Oestreicher N, White E, Lehman CD, et al. Predictors of sensitivity of clinical breast examination (CBE) Breast Cancer Res Treat. 2002;76(1):73–81. [PubMed: 12408378]
  28. Olsen AH, Jensen A, Njor SH, et al. Breast cancer incidence after the start of mammography screening in Denmark. Br J Cancer. 2003;88(3):362–365. [PMC free article: PMC2747539] [PubMed: 12569377]
  29. Ostbye T, Greenberg GN, Taylor DH Jr, et al. Screening mammography and Pap tests among older American women 1996–2000: results from the Health and Retirement Study (HRS) and Asset and Health Dynamics Among the Oldest Old (AHEAD) Ann Fam Med. 2003;1(4):209–217. [PMC free article: PMC1466605] [PubMed: 15055410]
  30. Paajanen H, Kyhala L, Varjo R, et al. Effect of screening mammography on the surgery of breast cancer in Finland: a population-based analysis during the years 1985–2004. Am Surg. 2006;72(2):167–171. [PubMed: 16536250]
  31. Park BW, Kim SI, Kim MH, et al. Clinical breast examination for screening of asymptomatic women: the importance of clinical breast examination for breast cancer detection. Yonsei Med J. 2000;41(3):312–318. [PubMed: 10957884]
  32. Pisano ED, Gatsonis C, Hendrick E, et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med. 2005;353(17):1773–1783. [PubMed: 16169887]
  33. Smith-Bindman R, Chu PW, Miglioretti DL, et al. Comparison of screening mammography in the United States and the United Kingdom. JAMA. 2003;290(16):2129–2137. [PubMed: 14570948]
  34. Warren R, Duffy S. Interval cancers as an indicator of performance in breast screening. Breast Cancer. 2000;7(1):9–18. [PubMed: 11029765]
  35. Warren RML, Crawley A. Is breast MRI ever useful in a mammographic screening programme? Clin Radiol. 2002;57(12):1090–1097. [PubMed: 12475534]
  36. Warren RML, Pointon L, Caines R, et al. What is the recall rate of breast MRI when used for screening asymptomatic women at high risk? Magn Reson Imaging. 2002;20(7):557–565. [PubMed: 12413602]
  37. Weaver DL, Rosenberg RD, Barlow WE, et al. Pathologic findings from the Breast Cancer Surveillance Consortium: population-based outcomes in women undergoing biopsy after screening mammography. Cancer. 2006;106(4):732–742. [PubMed: 16411214]
  38. Zabicki K, Colbert JA, Dominguez FJ, et al. Breast cancer diagnosis in women <= 40 versus 50 to 60 years: Increasing size and stage disparity compared with older women over time. Ann Surg Oncol. 2006;13(8):1072–1077. [PubMed: 16865599]
  39. Zotov V, Shyyan R, Program PBCA. Introduction of breast cancer screening in Chernihiv Oblast in the Ukraine: report of a PATH Breast Cancer Assistance Program experience. Breast J. 2003;9( Suppl 2):S75–80. [PubMed: 12713500]

Wrong study design or no original data for meta-analysis

  1. Aberle DR, Chiles C, Gatsonis C, et al. Imaging and cancer: research strategy of the American College of Radiology Imaging Network. Radiology. 2005;235(3):741–751. [PubMed: 15914473]
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  3. Anttila A, Koskela J, Hakama M. Programme sensitivity and effectiveness of mammography service screening in Helsinki, Finland. J Med Screen. 2002;9(4):153–158. [PubMed: 12518004]
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Does not address a Key Question

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Wrong age

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Does not break out data by age for meta-analysis

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Contextual only

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Non-English language

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Covered by included papers or previous USPSTF report

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Appendix B4. U.S. Preventive Services Task Force Quality Rating Methodology for Randomized Controlled Trials and Observational Studies1

Randomized Controlled Trials (RCTs) and Cohort Studies

Criteria

  • Initial assembly of comparable groups: RCTs—adequate randomization, including concealment and whether potential confounders were distributed equally among groups; cohort studies—consideration of potential confounders with either restriction or measurement for adjustment in the analysis; consideration of inception cohorts
  • Maintenance of comparable groups (includes attrition, cross-overs, adherence, contamination)
  • Important differential loss to follow-up or overall high loss to follow-up
  • Measurements: equal, reliable, and valid (includes masking of outcome assessment)
  • Clear definition of interventions
  • Important outcomes considered
  • Analysis: adjustment for potential confounders for cohort studies, or intension-to-treat analysis for RCTs

Definition of ratings based on above criteria

Good: Meets all criteria: Comparable groups are assembled initially and maintained throughout the study (follow-up at least 80 percent); reliable and valid measurement instruments are used and applied equally to the groups; interventions are spelled out clearly; important outcomes are considered; and appropriate attention to confounders in analysis.

Fair: Studies will be graded “fair” if any or all of the following problems occur, without the important limitations noted in the “poor” category below: Generally comparable groups are assembled initially but some question remains whether some (although not major) differences occurred in follow-up; measurement instruments are acceptable (although not the best) and generally applied equally; some but not all important outcomes are considered; and some but not all potential confounders are accounted for.

Poor: Studies will be graded “poor” if any of the following major limitations exists: Groups assembled initially are not close to being comparable or maintained throughout the study; unreliable or invalid measurement instruments are used or not applied at all equally among groups (including not masking outcome assessment); and key confounders are given little or no attention.

Case Control Studies

Criteria

  • Accurate ascertainment of cases
  • Nonbiased selection of cases/controls with exclusion criteria applied equally to both
  • Response rate
  • Diagnostic testing procedures applied equally to each group
  • Measurement of exposure accurate and applied equally to each group
  • Appropriate attention to potential confounding variable

Definition of ratings based on criteria above

Good: Appropriate ascertainment of cases and nonbiased selection of case and control participants; exclusion criteria applied equally to cases and controls; response rate equal to or greater than 80 percent; diagnostic procedures and measurements accurate and applied equally to cases and controls; and appropriate attention to confounding variables.

Fair: Recent, relevant, without major apparent selection or diagnostic work-up bias but with response rate less than 80 percent or attention to some but not all important confounding variables.

Poor: Major selection or diagnostic work-up biases, response rates less than 50 percent, or inattention to confounding variables.

REFERENCE
1.
Harris RP, Helfand M, Woolf SH, et al. Current Methods of the U.S. Preventive Services Task Force: a review of the process. Am J Prev Med. 2001;20(3Suppl):21–35. [PubMed: 11306229]

Appendix B5. Quality Rating Methodology for Systematic Reviews1–3

  1. Comprehensiveness of sources/search strategy used:
    1. Were search terms reported?
    2. Was the search comprehensive (Medline, search reference lists and/or experts)?
    3. Were the search terms applicable?
  2. Standard appraisal of included studies:
    1. Were inclusion/exclusion criteria reported?
    2. Are criteria valid?
  3. Quality/validity assessment:
    1. Were criteria for validity/quality assessment explicit and applied to all studies?
    2. Were quality criteria appropriate (e.g. criteria appropriate for study design)?
  4. Analysis/synthesis:
    1. Were methods used to combine studies reported?
    2. Were studies that were combined similar to one another (e.g. appropriate to combine, similar patient populations etc)?
  5. Validity of conclusions:
    1. Were conclusions supported by the data?
  6. Recency and relevance:
    1. Is the study recent and relevant to scope?

REFERENCES

1.
Harris RP, Helfand M, Woolf SH, et al. Current methods of the U.S. Preventive Services Task Force: a review of the process. Am J Prev Med. 2001;20(3Suppl):21–35. [PubMed: 11306229]
2.
National Institute for Health and Clinical Excellence. The Guidelines Manual . London: Institute for Health and Clinical Excellence; 2006.
3.
Oxman AD, Guyatt GH. Validation of an index of the quality of review articles. J Clin Epidemiol. 1991;44:1271–8. [PubMed: 1834807]

Appendix B6. Details of the Meta-analysis

The meta-analysis is an update of the previous 2002 meta-analysis that includes results from published trials of mammography screening for women age 39–49 years reporting reduction in breast cancer mortality. With the addition of only 1 new data point, the meta-analysis for the update was less extensive than the 2002 meta-analysis. We did not update the model for relative risk and length of follow-up (the two-level hierarchical model). We conducted similar updates for other age groups for context.

As with the original 2002 meta-analysis, we estimated the model by using a Bayesian data analytic framework but this time using the BRugs package in R.1,2 BRugs is an R interface to OpenBUGS, the successor to WinBUGS. The R code to create the dataset is below.

# R code to create dataset

study <- c(‘Age’, ‘CNBSS-1’, ‘HIP’, ‘Gothenburg’, ‘Stockholm’, ‘Malmo’, ‘Kopparberg’, ‘Ostergotland’)

y.int <- c(105, 105, 64, 34, 34, 53, 22, 31)

n.int <- c(53884, 25214, 13740, 11724, 14303, 13568, 9582, 10285)

py.int <- c(578390, 282606, 192360, NA, 203000, 184000, 124566, 172000)

y.cntl <- c(251, 108, 82, 59, 13, 66, 16, 30)

n.cntl <- c(106956, 25216, 13740, 14217, 8021, 12279, 5031, 10459)

py.cntl <- c(1149380, 282575, 192360, NA, 117000, 160000, 65403, 176000)

n <- 10000

rate.int <- n * y.int/n.int

rate.cntl <- n * y.cntl/n.cntl

rr <- rate.int/rate.cntl

rd <- rate.int-rate.cntl

nns <- 1/((y.cntl/n.cntl) - (y.int/n.int))

dataset <- data.frame(

study,

y.int, n.int, py.int, rate.int,

y.cntl, n.cntl, py.cntl, rate.cntl,

rr, rd, nns

)

# Save dataset for BRugs to use

dataset.bugs <- cbind(y.int, n.int, y.cntl, n.cntl)

colnames(dataset.bugs) <- c(“y.int”, “n.int”, “y.cntl”, “n.cntl”)

bugsData(data.frame(dataset.bugs), fileName=“dataset.bugs”, digits = 5)

constants <- cbind(nrow(dataset.bugs))

colnames(constants) <- c(“n”)

bugsData(data.frame(constants), fileName=“constants.bugs”, digits = 1)

The model assumes that the number of deaths from each study come from a binomial distribution with the probability parameter of α for the control group and α + β for the screening group. A random component, σ zi, is added to both probability parameters to allow for the random effect of the study i. Noninformative prior probability distributions were used.

# BUGS model

# This model is saved in a text file named “model.bugs”

model;

{

for(i in 1 : n) {

z[i] ~ dnorm(0, 1)

logit(p.int[i]) <- alpha + beta + sigma * z[i]

logit(p.cntl[i]) <- alpha + sigma * z[i]

y.int[i] ~ dbin(p.int[i], n.int[i])

y.cntl[i] ~ dbin(p.cntl[i], n.cntl[i])

}

alpha ~ dnorm(−5.0, 1.0E-1)

beta ~ dnorm(0.0, 1.0E-1)

sigma ~ dnorm(0.5, 1.0E-1) I(0,)

}

Four separate Markov chains with overdispersed initial values were used for estimation. A burn-in of 10,000 draws was used to initialize the chains and was checked for convergence.

# Check the model and load the dataset

modelCheck(“model.bugs”)

modelData(“constants.bugs”)

modelData(“dataset.bugs”)

# Compile the model with 4 MCMC chains

modelCompile(numChains=4)

# Generate overdispersed initial values

modelGenInits()

# Keep MCMC samples of parameters alpha, beta, and sigma

samplesSet(“alpha”)

samplesSet(“beta”)

samplesSet(“sigma”)

# Thin samples so only 1000 draws are left

samplesSetThin(10000/(1000/getNumChains()))

# Generate 10,000 burn-in draws

modelUpdate(10000)

samplesHistory(“*”, thin=samplesGetThin())

The convergence of the parameter estimation was assessed and deemed adequate from the 10,000 burn-in draws. Next, we generated 100,000 draws from the four chains. These draws were thinned to yield a sample of 1,000 uncorrelated estimates from the posterior distributions.

# Clear samples from the previous burn-in

samplesClear(“*”)

# Keep MCMC samples of parameters alpha, beta, and sigma

samplesSet(“alpha”)

samplesSet(“beta”)

samplesSet(“sigma”)

# Thin samples so only 1000 draws are left

samplesSetThin(100000/(1000/getNumChains()))

modelUpdate(100000)

samplesHistory(“*”, thin=samplesGetThin())

# Check correlation of the thinned samples

for (i in 1:getNumChains()) {

samplesAutoC(“*”, i, thin=samplesGetThin())

}

# Check the probability distribution of the parameters

samplesDensity(“*”, thin=samplesGetThin())

# Output sample estimates to an R object

brugs.nodes <- samplesHistory(“*”, thin=samplesGetThin(), plot=FALSE)

After the model was estimated and the samples were thinned, sample rates per 10,000 women screened with mammography and control participants were calculated from the estimates of alpha and beta. Sample relative risk, risk difference, and number needed to invite to screening were calculated from the sample rates.

# Assign parameter samples to separate R vectors

alpha <- as.vector(brugs.nodes$alpha)

beta <- as.vector(brugs.nodes$beta)

sigma <- as.vector(brugs.nodes$sigma)

# Rate calculations

# Note: this produces 1000 samples for each rate, RR, RD, and NNS

n <- 10000

rate1 <- n * exp(alpha+beta)/(1+exp(alpha+beta))

rate2 <- n * exp(alpha)/(1+exp(alpha))

rr <- rate1/rate2

rd <- rate1 - rate2

nns <- n/(rate2 - rate1)

From the 1,000 thinned posterior samples, point estimates (mean) and 95% credible intervals (2.5 and 97.5 percentiles) for relative risk, risk difference, and number needed to invite to screening were calculated.

# Define R function; it will be used a number of times

brugs.nodesummary <- function(x, name) {

Samples <- length(x)

Mean <- mean(x)

SD <- sd(x)

MCMC.error <- sd(x)/sqrt(length(x))

Median <- median(x)

P.025 <- quantile(x, prob=c(0.025))

P.975 <- quantile(x, prob=c(0.975))

nodesummary <- data.frame(cbind(Samples, Mean, Median, P.025, P.975, SD, MCMC.error))

rownames(nodesummary) <- name

colnames(nodesummary) <- c(“Samples”, “Mean”, “Median”, “P.025”, “P.975”, “SD”, “MCMC.error”)

data.frame(nodesummary)

}

# Call defined function brugs.nodesummary

print(brugs.nodesummary(alpha, “alpha”))

print(brugs.nodesummary(beta, “beta”))

print(brugs.nodesummary(sigma, “sigma”))

print(brugs.nodesummary(rate1, “rate1”))

print(brugs.nodesummary(rate2, “rate2”))

print(brugs.nodesummary(rr, “rr”))

print(brugs.nodesummary(rd, “rd”))

print(brugs.nodesummary(nns, “nns”))

The pooled number needed to invite to screening could be misleading if the baseline risk of mortality is appreciably varied between studies. 3 One recommendation to accommodate this is to apply the pooled relative risk estimate to a range of control rates and then calculate number needed to invite to screening. The pooled rate of mortality among the control groups of our studies was estimated to be 35.5 deaths per 10,000 women (95% CrI, 25.1–48.3). The range of mortality rates among the control groups was 16.2 to 59.7 per 10,000 women. Applying the pooled relative risk estimate of 0.85 to the high end of the mortality rate range (59.7) yields a number needed to invite to screening estimate of 1,116 per 10,000 women. Applying the pooled relative risk estimate of 0.85 to the low end of the mortality rate range (16.2) yields a number needed to invite to screening estimate of 4,115 per 10,000 women. This range 1,116 to 4,115 per 10,000 women is within the 95% CrI we report for number needed to invite to screening that we estimated from the posterior distributions of our mortality rate estimates. Alternatively, the bounds of our 95% CrI to number needed to invite to screening correspond to a range of control group mortality rates of 10.5 to 71.8 per 10,000 women, a range beyond that seen in the studies included in our analysis.

REFERENCES

1.
R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2006.
2.
Thomas A, O’Hara B, Ligges U, Sturtz S. Making BUGS Open. R News. 2006;6(1):12–17.
3.
Smeeth L, Haines A, Ebrahim S. Numbers needed to treat derived from meta-analyses – sometimes informative, usually misleading. BMJ. 1999;318(7197):1548–1551. [PMC free article: PMC1115910] [PubMed: 10356018]

Appendix B7. Breast Cancer Surveillance Consortium Methods

Breast Cancer Surveillance Consortium

In 1994 the National Cancer Institute (NCI) established the Breast Cancer Surveillance Consortium (BCSC) to study breast cancer screening practices in the United States, with the recognition that results from controlled clinical trials of mammography may differ from the results of community screening practices. Each of the Consortium’s seven research sites collects population-based screening and diagnostic mammography data and links it to state cancer registries. Sites include the Carolina Mammography Registry (North Carolina), Group Health Cooperative (Seattle), New Hampshire Mammography Network, San Francisco Mammography Registry, Vermont Breast Cancer Surveillance System, Colorado Mammography Project, and New Mexico Mammography Project. In five of the states, mammography data is also linked to pathology registries, which include benign as well as malignant outcomes. A comparison of women represented in the BCSC against 2000 Census data shows that Consortium sites are located in counties that contain slightly more than 5% of the U.S. population, and represent the population in important sociodemographic respects.1

Currently, the Consortium’s database contains information on 6,000,000 mammography examinations, 2,017,869 women, and 74,000 breast cancer cases.2 Detailed information on the distribution of key variables, mammographic data, characteristics of cases, and screening performance, among others, are detailed on the BCSC website: http://breastscreening.cancer.gov/data/

BCSC data include screening as well as diagnostic mammography. Screening mammography examinations are those designated as such by the ordering provider or radiologist, and not performed within 9 months of a previous one. Diagnostic mammography examinations are those indicated as such when ordered, or by the radiologist, or those performed for a woman reporting breast symptoms. Mammography information includes breast density, BI-RADS score, and recommendations for further imaging or work-up. In addition, prior to each mammography examination, a woman fills out a questionnaire which includes demographic as well as previous mammography information. Each mammography examination is given an initial BI-RADs score which categorizes it as “positive” or “negative.” In our analysis, an initial score of 0, 4, 5, or 3 with immediate work-up is considered positive, whereas a score of 1, 2 or 3 with short-term interval work-up (3–6 months) is negative. Additional imaging, such as such as magnification, ultrasound, compression or repeat views, or a diagnostic procedure is linked to screening mammography if done within 60 days of mammography.

In this report, we included BCSC data from 2000–2005 to examine the 1) frequency of additional imaging and biopsy procedures resulting from positive screening mammography, 3) potential adverse effects of mammography screening, and 4) relative incidence of DCIS and invasive cancers detected by mammography screening. Information for women under age 40 years or who have a history of breast augmentation or previous breast cancer diagnosis has been excluded.

REFERENCES

1.
National Cancer Institute. Breast Cancer Surveillance Consortium: Evaluating Screening Performance in Practice. NIH Publication No. 04–5490. Bethesda, MD: National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services; Apr, 2004. Available at: http:​//breastscreening​.cancer.gov/espp.pdf.
2.
National Cancer Institute. Breast Cancer Surveillance Consortium Fact Sheet . Bethesda, MD: National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services; Apr, 2007. Available at: http:​//breastscreening​.cancer.gov/about/BCSC_fact_sheet.pdf.

Appendix B8. Expert Reviewers of the Draft Report

Helen J. Barr, MD

Director, Division of Mammography Quality and Radiation Programs, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Rockville, Maryland

Nancy N. Baxter, MD, PhD, FRCSC, FACRS

Division of General Surgery, University of Toronto St. Michael’s Hospital, Toronto, Canada

Donald A. Berry, PhD

Head, Division of Quantitative Sciences, Professor and Frank T. McGraw Memorial Chair for Cancer Research, Chairman, Department of Biostatistics, University of Texas, MD Anderson Cancer Center

Stephen W. Duffy, BSc, MSc, CStat

Centre for Epidemiology, Mathematics and Statistics, Wolfson Institute of Preventive Medicine, London, United Kingdom

Suzanne W. Fletcher, MD

Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, United States

Ronald G. Kaczmarek, MD, MPH

Center for Devices and Radiological Health, Food and Drug Administration, Rockville, Maryland

Linda S. Kinsinger, MD, MPH

Director of the National Center for Prevention, Department of Veterans Affairs, Austin, Texas

Barnett S. Kramer, MD

Director, Office of Disease Prevention, National Institutes of Health

Herschel W. Lawson, MD, FACOG

Senior Medical Advisor, Division of Cancer Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia

Anthony B. Miller, MD

Professor Emeritus and Head, Division of Clinical Epidemiology, German Cancer Research Centre, Heidelberg, Germany

Jacqueline W. Miller, MD

National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia

Eugenio Paci, MD

Unit of Clinical and Descriptive Epidemiology, Centre for Study and Prevention of Cancer, Florence, Italy

Philip C. Prorok, PhD

Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland

Lisa C. Richardson, MDnnn

Medical Officer, Centers for Disease Control and Prevention, Atlanta, Georgia

Robert C. Smith, MD, PhD

United States Food and Drug Administration

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