Pregnancy Outcomes After a Breast Cancer Diagnosis: A Systematic Review and Meta-analysis

2018 ◽  
Vol 18 (1) ◽  
pp. e79-e88 ◽  
Author(s):  
Brigitte Gerstl ◽  
Elizabeth Sullivan ◽  
Angela Ives ◽  
Christobel Saunders ◽  
Handan Wand ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Cancer Diagnosis ◽  
Pregnancy Outcomes ◽  
Meta Analysis ◽  
Breast Cancer Diagnosis

scholarly journals Weight Gain After Breast Cancer Diagnosis and All-Cause Mortality: Systematic Review and Meta-Analysis

2015 ◽  
Vol 107 (12) ◽  
pp. djv275 ◽  
Author(s):  
Mary C. Playdon ◽  
Michael B. Bracken ◽  
Tara B. Sanft ◽  
Jennifer A. Ligibel ◽  
Maura Harrigan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Weight Gain ◽  
Cancer Diagnosis ◽  
Meta Analysis ◽  
Breast Cancer Diagnosis ◽  
All Cause Mortality

scholarly journals Long term absence of invasive breast cancer diagnosis in 2,402,672 pre and postmenopausal women: A systematic review and meta-analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0237925
Author(s):  
Winnifred Cutler ◽  
James Kolter ◽  
Catherine Chambliss ◽  
Heather O’Neill ◽  
Hugo M. Montesinos-Yufa
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Postmenopausal Women ◽  
Cancer Diagnosis ◽  
Invasive Breast Cancer ◽  
Meta Analysis ◽  
Breast Cancer Diagnosis

The mental health impacts of receiving a breast cancer diagnosis: A meta-analysis

2021 ◽  
Author(s):  
Justine Fortin ◽  
Mélissandre Leblanc ◽  
Guillaume Elgbeili ◽  
Matthew J. Cordova ◽  
Marie-France Marin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Cancer Diagnosis ◽  
Meta Analysis ◽  
Breast Cancer Diagnosis ◽  
Health Impacts

Breast Cancer Diagnosis by Scintimammography: A Meta-analysis and Review of the Literature

2003 ◽  
Vol 80 (1) ◽  
pp. 115-126 ◽  
Author(s):  
Moishe Liberman ◽  
Fotini Sampalis ◽  
David S. Mulder ◽  
John S. Sampalis
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Meta Analysis ◽  
Breast Cancer Diagnosis ◽  
Review Of The Literature

scholarly journals Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review

BMJ Open ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. e019264 ◽  
Author(s):  
Iosief Abraha ◽  
Alessandro Montedori ◽  
Diego Serraino ◽  
Massimiliano Orso ◽  
Gianni Giovannini ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Cancer Diagnosis ◽  
Invasive Breast Cancer ◽  
Primary Breast Cancer ◽  
Breast Cancer Diagnosis ◽  
Administrative Databases ◽  
Cochrane Library ◽  
Icd 10 ◽  
Incident Cases

ObjectiveTo define the accuracy of administrative datasets to identify primary diagnoses of breast cancer based on the International Classification of Diseases (ICD) 9th or 10th revision codes.DesignSystematic review.Data sources: MEDLINE, EMBASE, Web of Science and the Cochrane Library (April 2017).Eligibility criteriaThe inclusion criteria were: (a) the presence of a reference standard; (b) the presence of at least one accuracy test measure (eg, sensitivity) and (c) the use of an administrative database.Data extractionEligible studies were selected and data extracted independently by two reviewers; quality was assessed using the Standards for Reporting of Diagnostic accuracy criteria.Data analysisExtracted data were synthesised using a narrative approach.ResultsFrom 2929 records screened 21 studies were included (data collection period between 1977 and 2011). Eighteen studies evaluated ICD-9 codes (11 of which assessed both invasive breast cancer (code 174.x) and carcinoma in situ (ICD-9 233.0)); three studies evaluated invasive breast cancer-related ICD-10 codes. All studies except one considered incident cases.The initial algorithm results were: sensitivity ≥80% in 11 of 17 studies (range 57%–99%); positive predictive value was ≥83% in 14 of 19 studies (range 15%–98%) and specificity ≥98% in 8 studies. The combination of the breast cancer diagnosis with surgical procedures, chemoradiation or radiation therapy, outpatient data or physician claim may enhance the accuracy of the algorithms in some but not all circumstances. Accuracy for breast cancer based on outpatient or physician’s data only or breast cancer diagnosis in secondary position diagnosis resulted low.ConclusionBased on the retrieved evidence, administrative databases can be employed to identify primary breast cancer. The best algorithm suggested is ICD-9 or ICD-10 codes located in primary position.Trial registration numberCRD42015026881.

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