Literature DB >> 25868866

An open letter to panels that are deciding guidelines for breast cancer screening.

Daniel B Kopans1.   

Abstract

Panels are presently reviewing breast cancer screening guidelines. It is critical that they understand which publications are scientifically valid, and which analyses are methodologically flawed and not valid. The scientific evidence clearly supports annual mammography screening beginning at the age of 40. The analyses that suggest that screening leads to overdiagnosis of invasive breast cancers are flawed and incorrect. There is little if any overdiagnosis of these cancers. The vast majority of breast cancers occur in women who are not at elevated risk so that excluding them from screening and only screening high risk women will deny the benefits of early detection to most women who develop breast cancer. Guidelines panels should not make decisions that exclude women from screening. Women should be provided with accurate information so that they can make informed decisions and have unimpeded access to screening if that is their preference.

Entities:  

Mesh:

Year:  2015        PMID: 25868866     DOI: 10.1007/s10549-015-3373-8

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  9 in total

1.  Breast cancer screening panels continue to confuse the facts and inject their own biases.

Authors:  D B Kopans
Journal:  Curr Oncol       Date:  2015-10       Impact factor: 3.677

2.  Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning.

Authors:  Hari M Trivedi; Maryam Panahiazar; April Liang; Dmytro Lituiev; Peter Chang; Jae Ho Sohn; Yunn-Yi Chen; Benjamin L Franc; Bonnie Joe; Dexter Hadley
Journal:  J Digit Imaging       Date:  2019-02       Impact factor: 4.056

Review 3.  Systematic Analysis and Critical Appraisal of the Quality of the Scientific Evidence and Conflicts of Interest in Practice Guidelines (2005-2013) for Barrett's Esophagus.

Authors:  Joseph D Feuerstein; Natalia E Castillo; Mona Akbari; Edward Belkin; Jeffrey J Lewandowski; Christine M Hurley; Samuel Lloyd; Daniel A Leffler; Adam S Cheifetz
Journal:  Dig Dis Sci       Date:  2016-06-15       Impact factor: 3.199

4.  An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization.

Authors:  Yiqiu Shen; Nan Wu; Jason Phang; Jungkyu Park; Kangning Liu; Sudarshini Tyagi; Laura Heacock; S Gene Kim; Linda Moy; Kyunghyun Cho; Krzysztof J Geras
Journal:  Med Image Anal       Date:  2020-12-16       Impact factor: 8.545

Review 5.  Implications of Overdiagnosis: Impact on Screening Mammography Practices.

Authors:  Elizabeth Morris; Stephen A Feig; Madeline Drexler; Constance Lehman
Journal:  Popul Health Manag       Date:  2015-09       Impact factor: 2.459

6.  Investigating young women's motivations to engage in early mammography screening in Switzerland: results of a cross-sectional study.

Authors:  Nanon H M Labrie; Ramona Ludolph; Peter J Schulz
Journal:  BMC Cancer       Date:  2017-03-21       Impact factor: 4.430

7.  Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.

Authors:  Nan Wu; Jason Phang; Jungkyu Park; Yiqiu Shen; Zhe Huang; Masha Zorin; Stanislaw Jastrzebski; Thibault Fevry; Joe Katsnelson; Eric Kim; Stacey Wolfson; Ujas Parikh; Sushma Gaddam; Leng Leng Young Lin; Kara Ho; Joshua D Weinstein; Beatriu Reig; Yiming Gao; Hildegard Toth; Kristine Pysarenko; Alana Lewin; Jiyon Lee; Krystal Airola; Eralda Mema; Stephanie Chung; Esther Hwang; Naziya Samreen; S Gene Kim; Laura Heacock; Linda Moy; Kyunghyun Cho; Krzysztof J Geras
Journal:  IEEE Trans Med Imaging       Date:  2019-10-07       Impact factor: 10.048

8.  Population-Attributable Risk Proportion of Clinical Risk Factors for Breast Cancer.

Authors:  Natalie J Engmann; Marzieh K Golmakani; Diana L Miglioretti; Brian L Sprague; Karla Kerlikowske
Journal:  JAMA Oncol       Date:  2017-09-01       Impact factor: 31.777

Review 9.  Empowering study of breast cancer data with application of artificial intelligence technology: promises, challenges, and use cases.

Authors:  Maryam Panahiazar; Nolan Chen; Dmytro Lituiev; Dexter Hadley
Journal:  Clin Exp Metastasis       Date:  2021-10-26       Impact factor: 5.150

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.