PURPOSE: To estimate the extent of variability in screening mammography cancer detection rates and its effect on a hypothetical clinical trial of a new screening modality used to measure changes in cancer detection rate. MATERIALS AND METHODS: Each registry and the statistical coordinating center received institutional review board approval along with approval for consenting processes or a waiver of consent to enroll participants, link data, and perform analytic studies. This study was HIPAA compliant. The authors estimated the distribution of individual radiologists' breast cancer detection rates for 2,289,132 screening mammograms (9030 cancers) read by 510 radiologists in the United States who participated in the Breast Cancer Surveillance Consortium from 1996 through 2002. They then computed the distributions of breast cancer detection rates expected from a trial of screening mammography and multiple radiologists, as well as similar distributions for a hypothetical new modality that depicts one additional cancer per reader per 1000 screening examinations. Statistical power was calculated. RESULTS: The mean screening mammography cancer detection rate for individual radiologists was 3.91 cancers (standard deviation, 1.93; range, 0.25-13.75) per 1000 examinations. To achieve 80% power to detect a hypothetical increase of one additional cancer detected per reader per 1000 screening examinations, a trial in which a new modality was compared with standard mammography would require at least 25 radiologists each reading the images of at least 8000 screening examinations or 91 radiologists each reading the images of 1000-2000 examinations. CONCLUSION: The low breast cancer prevalence in an average-risk screening population and the large interradiologist variability in the observed cancer detection rate suggest that for new technologies to demonstrate significant improvement in cancer detection rate in a clinical trial, very large samples of both radiologists and patients will be required.
PURPOSE: To estimate the extent of variability in screening mammography cancer detection rates and its effect on a hypothetical clinical trial of a new screening modality used to measure changes in cancer detection rate. MATERIALS AND METHODS: Each registry and the statistical coordinating center received institutional review board approval along with approval for consenting processes or a waiver of consent to enroll participants, link data, and perform analytic studies. This study was HIPAA compliant. The authors estimated the distribution of individual radiologists' breast cancer detection rates for 2,289,132 screening mammograms (9030 cancers) read by 510 radiologists in the United States who participated in the Breast Cancer Surveillance Consortium from 1996 through 2002. They then computed the distributions of breast cancer detection rates expected from a trial of screening mammography and multiple radiologists, as well as similar distributions for a hypothetical new modality that depicts one additional cancer per reader per 1000 screening examinations. Statistical power was calculated. RESULTS: The mean screening mammography cancer detection rate for individual radiologists was 3.91 cancers (standard deviation, 1.93; range, 0.25-13.75) per 1000 examinations. To achieve 80% power to detect a hypothetical increase of one additional cancer detected per reader per 1000 screening examinations, a trial in which a new modality was compared with standard mammography would require at least 25 radiologists each reading the images of at least 8000 screening examinations or 91 radiologists each reading the images of 1000-2000 examinations. CONCLUSION: The low breast cancer prevalence in an average-risk screening population and the large interradiologist variability in the observed cancer detection rate suggest that for new technologies to demonstrate significant improvement in cancer detection rate in a clinical trial, very large samples of both radiologists and patients will be required.
Authors: Joann G Elmore; Erin J Aiello Bowles; Berta Geller; Natalia Vukshich Oster; Patricia A Carney; Diana L Miglioretti; Diana S M Buist; Karla Kerlikowske; Edward A Sickles; Tracy Onega; Robert D Rosenberg; Bonnie C Yankaskas Journal: Acad Radiol Date: 2010-06 Impact factor: 3.173
Authors: Brandon D Gallas; Heang-Ping Chan; Carl J D'Orsi; Lori E Dodd; Maryellen L Giger; David Gur; Elizabeth A Krupinski; Charles E Metz; Kyle J Myers; Nancy A Obuchowski; Berkman Sahiner; Alicia Y Toledano; Margarita L Zuley Journal: Acad Radiol Date: 2012-02-03 Impact factor: 3.173
Authors: Robert M Nishikawa; Suddhasatta Acharyya; Constantine Gatsonis; Etta D Pisano; Elodia B Cole; Helga S Marques; Carl J D'Orsi; Dione M Farria; Kalpana M Kanal; Mary C Mahoney; Murray Rebner; Melinda J Staiger Journal: Radiology Date: 2009-04 Impact factor: 11.105
Authors: Joshua J Fenton; Linn Abraham; Stephen H Taplin; Berta M Geller; Patricia A Carney; Carl D'Orsi; Joann G Elmore; William E Barlow Journal: J Natl Cancer Inst Date: 2011-07-27 Impact factor: 13.506
Authors: Joshua J Fenton; Guibo Xing; Joann G Elmore; Heejung Bang; Steven L Chen; Karen K Lindfors; Laura-Mae Baldwin Journal: Ann Intern Med Date: 2013-04-16 Impact factor: 25.391
Authors: Elizabeth S Burnside; Yunzhi Lin; Alejandro Munoz del Rio; Perry J Pickhardt; Yirong Wu; Roberta M Strigel; Mai A Elezaby; Eve A Kerr; Diana L Miglioretti Journal: PLoS One Date: 2014-02-21 Impact factor: 3.240
Authors: Michael Phillips; J David Beatty; Renee N Cataneo; Jan Huston; Peter D Kaplan; Roy I Lalisang; Philippe Lambin; Marc B I Lobbes; Mayur Mundada; Nadine Pappas; Urvish Patel Journal: PLoS One Date: 2014-03-05 Impact factor: 3.240