scholarly journals How to Optimize Population Screening Programs for Breast Cancer Using Mathematical Models

10.5772/31047 ◽  
2012 ◽  
Author(s):  
Montserrat Rue ◽  
Misericordia Carles ◽  
Ester Vilaprinyo ◽  
Roger Pla ◽  
Montserrat Martinez-Alonso ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mathematical Models ◽  
Population Screening ◽  
Screening Programs

Promoting Participation in a Population Screening Program for Breast and Cervical Cancer: A Randomized Trial of Different Invitation Strategies

1998 ◽  
Vol 84 (3) ◽  
pp. 348-353 ◽  
Author(s):  
Nereo Segnan ◽  
Carlo Seriore ◽  
Livia Giordano ◽  
Antonio Ponti ◽  
Guglielmo Ronco
Keyword(s):  
Breast Cancer ◽  
Cervical Cancer ◽  
Cancer Screening ◽  
Screening Program ◽  
Block Design ◽  
Population Screening ◽  
Screening Programs ◽  
Group A ◽  
Group B ◽  
Group D

Aims and background Attendance level has been identified as a major determinant of cost-effectiveness of organized screening programs. We tested the effectiveness of 4 different invitation systems in the context of an organized population screening program for cervical and breast cancer. Methods Women eligible for invitation - 8385 for cervical and 8069 for breast cancer screening - listed in the rosters of 43 and 105 general practitioners (GP), respectively, who had accepted to collaborate in the program, were randomized to 4 invitation groups: Group A - letter signed by the GP, with a prefixed appointment; Group B - open-ended invitation, signed by the GP, prompting women to contact the screening center to arrange an appointment; Group C - letter (same as for group A), signed by the program coordinator, with a prefixed appointment; Group D - extended letter (highlighting the benefits of early cancer detection) signed by the GP, with a prefixed appointment. Assignment to the interventions was based on a randomized block design (block=GP). Results Assuming Group A as the reference, the overall compliance with cervical cancer screening was reduced by 39% in Group B (RR=0.61; 95% CI, 0.56-0.68) and by 14% in Group C (RR=0.86; 95% CI, 0.78-0.93); no difference was observed for Group D (RR=1.03; 95% CI, 0.95-1.1). The response pattern was similar for breast screening (Group B: RR=0.71; 95% CI, 0.65-0.76; Group C: RR=0.87; 95% CI, 0.81-0.94; Group D: RR=1.01; 95% CI, 0.94-1.08). Conclusions Personal invitation letters signed by the woman's GP, with preallocated appointments, induce a significant increase in compliance with screening. Efficiency can be ensured through the adoption of overbooking, provided that attendance levels are regularly monitored.

Cancer screening. Current state of the problem

2020 ◽  
pp. 4-12
Author(s):  
Marina Kochiyeva
Keyword(s):  
Breast Cancer ◽  
Cervical Cancer ◽  
Colon Cancer ◽  
Cancer Screening ◽  
Target Population ◽  
Evidence Based ◽  
Screening Programs ◽  
Current State ◽  
Methodological Approaches ◽  
Based Medicine

Data on modern methodological approaches that are used in screening for cancer are summarized. General principles of organizing screening studies are examined from the perspective of evidence-based medicine, target population, research methods, and effectiveness of the implemented screening programs for breast cancer, cervical cancer, and colon cancer are determined.

scholarly journals Psychological predictors of intention and avoidance of attending organized mammography screening in Norway: applying the Extended Parallel Process Model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Anna Ivanova ◽  
Ingela Lundin Kvalem
Keyword(s):  
Breast Cancer ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Mammography Screening ◽  
Psychological Factors ◽  
Process Model ◽  
Parallel Process ◽  
Extended Parallel Process Model ◽  
Screening Programs ◽  
Response Efficacy

Abstract Background Mammography screening is the main method for early detection of breast cancer in Norway. Few studies have focused on psychological determinants of both attendance and non-attendance of publicly available mammography screening programs. The aim of the current study, guided by the Extended Parallel Process Model, was to examine how psychological factors influence defensive avoidance of breast cancer screening and intention to attend mammography. Methods Cross-sectional survey data from a community sample of women living in Norway aged ≥ 18 (N = 270), and without a history of breast cancer, was collected from September 2018 to June 2019 and used to investigate the relationships between the Extended Parallel Process Model (EPPM) constructs and two outcomes: defensive avoidance of breast cancer screening and intention to attend mammography within the next two years. After adjusting for confounding factors, the hierarchical multiple linear regression analyses was conducted to assess the ability of the independent variables based on the EPPM to predict the two outcome variables. Significance level was chosen at p < 0.05. Results Multivariate analyses showed that defensive avoidance of breast cancer screening was predicted by lower perceived susceptibility to breast cancer (β =  − 0.22, p = 0.001), lower response efficacy of mammography screening (β =  − 0.33, p = 0.001), higher breast cancer fear (β = 0.15, p = 0.014), and checking breasts for lumps (β =  − 0.23, p = 0.001). Intention to attend mammography within the next two years was predicted by higher response efficacy of mammography screening (β = 0.13, p = 0.032), having a lower educational level (β =  − 0.10, p = 0.041), and regular previous mammography attendance compared to never attending (β = 0.49, p = 0.001). Conclusions The study revealed that defensive avoidance of breast cancer screening and intention to attend mammography were not predicted by the same pattern of psychological factors. Our findings suggest future health promotion campaigns need to focus not only on the psychological factors that encourage women’s decision to attend the screening, but also to counter factors that contribute to women’s decision to avoid it.

scholarly journals Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

2021 ◽  
Author(s):  
Suzanne L. van Winkel ◽  
Alejandro Rodríguez-Ruiz ◽  
Linda Appelman ◽  
Albert Gubern-Mérida ◽  
Nico Karssemeijer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Detection ◽  
Support System ◽  
Reading Time ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Screening Programs ◽  
The Impact

Abstract Objectives Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist. Methods A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance. Results On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115). Conclusions Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. Key Points • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.

scholarly journals Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

Cancers ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 636 ◽  
Author(s):  
Regina Padmanabhan ◽  
Hadeel Shafeeq Kheraldine ◽  
Nader Meskin ◽  
Semir Vranic ◽  
Ala-Eddin Al Moustafa
Keyword(s):  
Breast Cancer ◽  
Mathematical Models ◽  
Immune Checkpoint ◽  
Checkpoint Inhibitors ◽  
Breast Cancers ◽  
Treatment Protocols ◽  
Cancer Subtypes ◽  
Cancer Management ◽  
Her2 Breast Cancer ◽  
Programmed Death

Breast cancer is one of the major causes of mortality in women worldwide. The most aggressive breast cancer subtypes are human epidermal growth factor receptor-positive (HER2+) and triple-negative breast cancers. Therapies targeting HER2 receptors have significantly improved HER2+ breast cancer patient outcomes. However, several recent studies have pointed out the deficiency of existing treatment protocols in combatting disease relapse and improving response rates to treatment. Overriding the inherent actions of the immune system to detect and annihilate cancer via the immune checkpoint pathways is one of the important hallmarks of cancer. Thus, restoration of these pathways by various means of immunomodulation has shown beneficial effects in the management of various types of cancers, including breast. We herein review the recent progress in the management of HER2+ breast cancer via HER2-targeted therapies, and its association with the programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) axis. In order to link research in the areas of medicine and mathematics and point out specific opportunities for providing efficient theoretical analysis related to HER2+ breast cancer management, we also review mathematical models pertaining to the dynamics of HER2+ breast cancer and immune checkpoint inhibitors.

scholarly journals Positive predictive value highlights four novel candidates for actionable genetic screening from analysis of 220,000 clinicogenomic records

2021 ◽  
Author(s):  
Kelly M. Schiabor Barrett ◽  
Alexandre Bolze ◽  
Yunyun Ni ◽  
Simon White ◽  
Magnus Isaksson ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Genetic Screening ◽  
Predictive Value ◽  
Rare Variants ◽  
Large Population ◽  
Population Screening ◽  
Cystic Kidney Disease ◽  
Cystic Kidney ◽  
Screening Programs ◽  
Thalassemia Minor

Abstract Purpose To identify conditions that are candidates for population genetic screening based on population prevalence, penetrance of rare variants, and actionability. Methods We analyzed exome and medical record data from >220,000 participants across two large population health cohorts with different demographics. We performed a gene-based collapsing analysis of rare variants to identify genes significantly associated with disease status. Results We identify 74 statistically significant gene–disease associations across 27 genes. Seven of these conditions have a positive predictive value (PPV) of at least 30% in both cohorts. Three are already used in population screening programs (BRCA1, BRCA2, LDLR), and we also identify four new candidates for population screening: GCK with diabetes mellitus, HBB with β-thalassemia minor and intermedia, PKD1 with cystic kidney disease, and MIP with cataracts. Importantly, the associations are actionable in that early genetic screening of each of these conditions is expected to improve outcomes. Conclusion We identify seven genetic conditions where rare variation appears appropriate to assess in population screening, four of which are not yet used in screening programs. The addition of GCK, HBB, PKD1, and MIP rare variants into genetic screening programs would reach an additional 0.21% of participants with actionable disease risk, depending on the population.

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