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  • 1
    Online Resource
    Online Resource
    Using Collaborative Simulation Modeling to Develop a Web-Based Tool to Support Policy-Level Decision Making About Breast Cancer Screening Initiation Age
    SAGE Publications ; 2017
    In:  MDM Policy & Practice Vol. 2, No. 2 ( 2017-07), p. 238146831771798-
    Details
    In: MDM Policy & Practice, SAGE Publications, Vol. 2, No. 2 ( 2017-07), p. 238146831771798-
    Type of Medium: Online Resource
    ISSN: 2381-4683 , 2381-4683
    URL: Article
    DOI: 10.1177/2381468317717982
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2017
    detail.hit.zdb_id: 2861432-X
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  • 2
    Online Resource
    Online Resource
    Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49
    SAGE Publications ; 2018
    In:  Medical Decision Making Vol. 38, No. 1_suppl ( 2018-04), p. 140S-150S
    Details
    In: Medical Decision Making, SAGE Publications, Vol. 38, No. 1_suppl ( 2018-04), p. 140S-150S
    Abstract: Background. The UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results. Methods. Five established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial. Results. The models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial. Conclusions. The models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.
    Type of Medium: Online Resource
    ISSN: 0272-989X , 1552-681X
    URL: Article
    DOI: 10.1177/0272989X17718168
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2018
    detail.hit.zdb_id: 2040405-0
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  • 3
    Online Resource
    Online Resource
    Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches
    SAGE Publications ; 2018
    In:  Medical Decision Making Vol. 38, No. 1_suppl ( 2018-04), p. 126S-139S
    Details
    In: Medical Decision Making, SAGE Publications, Vol. 38, No. 1_suppl ( 2018-04), p. 126S-139S
    Abstract: Background. Ductal carcinoma in situ (DCIS) can be a precursor to invasive breast cancer. Since the advent of screening mammography in the 1980’s, the incidence of DCIS has increased dramatically. The value of screen detection and treatment of DCIS, however, is a matter of controversy, as it is unclear the extent to which detection and treatment of DCIS prevents invasive disease and reduces breast cancer mortality. The aim of this paper is to provide an overview of existing Cancer Intervention and Surveillance Modelling Network (CISNET) modeling approaches for the natural history of DCIS, and to compare these to other modeling approaches reported in the literature. Design. Five of the 6 CISNET models currently include DCIS. Most models assume that some, but not all, lesions progress to invasive cancer. The natural history of DCIS cannot be directly observed and the CISNET models differ in their assumptions and in the data sources used to estimate the DCIS model parameters. Results. These model differences translate into variation in outcomes, such as the amount of overdiagnosis of DCIS, with estimates ranging from 34% to 72% for biennial screening from ages 50 to 74 y. The other models described in the literature also report a large range in outcomes, with progression rates varying from 20% to 91%. Limitations. DCIS grade was not yet included in the CISNET models. Conclusion. In the future, DCIS data by grade from active surveillance trials, the development of predictive markers of progression probability, and evidence from other screening modalities, such as tomosynthesis, may be used to inform and improve the models’ representation of DCIS, and might lead to convergence of the model estimates. Until then, the CISNET model results consistently show a considerable amount of overdiagnosis of DCIS, supporting the safety and value of observational trials for low-risk DCIS.
    Type of Medium: Online Resource
    ISSN: 0272-989X , 1552-681X
    URL: Article
    DOI: 10.1177/0272989X17729358
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2018
    detail.hit.zdb_id: 2040405-0
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  • 4
    Online Resource
    Online Resource
    A Probabilistic Model to Support Radiologists’ Classification Decisions in Mammography Practice
    SAGE Publications ; 2019
    In:  Medical Decision Making Vol. 39, No. 3 ( 2019-04), p. 208-216
    Details
    In: Medical Decision Making, SAGE Publications, Vol. 39, No. 3 ( 2019-04), p. 208-216
    Abstract: We developed a probabilistic model to support the classification decisions made by radiologists in mammography practice. Using the feature observations and Breast Imaging Reporting and Data System (BI-RADS) classifications from radiologists examining diagnostic and screening mammograms, we modeled their decisions to understand their judgments. Our model could help improve the decisions made by radiologists using their own feature observations and classifications while maintaining their observed sensitivities. Based on 112,433 mammographic cases from 36,111 patients and 13 radiologists at 2 separate institutions with a 1.1% prevalence of malignancy, we trained a probabilistic Bayesian network (BN) to estimate the malignancy probabilities of lesions. For each radiologist, we learned an observed probabilistic threshold within the model. We compared the sensitivity and specificity of each radiologist against the BN model using either their observed threshold or the standard 2% threshold recommended by BI-RADS. We found significant variability among the radiologists’ observed thresholds. By applying the observed thresholds, the BN model showed a 0.01% (1 case) increase in false negatives and a 28.9% (3612 cases) reduction in false positives. When using the standard 2% BI-RADS-recommended threshold, there was a 26.7% (47 cases) increase in false negatives and a 47.3% (5911 cases) reduction in false positives. Our results show that we can significantly reduce screening mammography false positives with a minimal increase in false negatives. We find that learning radiologists’ observed thresholds provides valuable information regarding the conservativeness of clinical practice and allows us to quantify the variability in sensitivity across and within institutions. Our model could provide support to radiologists to improve their performance and consistency within mammography practice.
    Type of Medium: Online Resource
    ISSN: 0272-989X , 1552-681X
    URL: Article
    DOI: 10.1177/0272989X19832914
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2040405-0
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  • 5
    Online Resource
    Online Resource
    Developing a Comprehensive Database Management System for Organization and Evaluation of Mammography Datasets
    SAGE Publications ; 2014
    In:  Cancer Informatics Vol. 13s3 ( 2014-01), p. CIN.S14031-
    Details
    In: Cancer Informatics, SAGE Publications, Vol. 13s3 ( 2014-01), p. CIN.S14031-
    Abstract: We aimed to design and develop a comprehensive mammography database system (CMDB) to collect clinical datasets for outcome assessment and development of decision support tools. A Health Insurance Portability and Accountability Act (HIPAA) compliant CMDB was created to store multi-relational datasets of demographic risk factors and mammogram results using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The CMDB collected both biopsy pathology outcomes, in a breast pathology lexicon compiled by extending BI-RADS, and our institutional breast cancer registry. The audit results derived from the CMDB were in accordance with Mammography Quality Standards Act (MQSA) audits and national benchmarks. The CMDB has managed the challenges of multi-level organization demanded by the complexity of mammography practice and lexicon development in pathology. We foresee that the CMDB will be useful for efficient quality assurance audits and development of decision support tools to improve breast cancer diagnosis. Our procedure of developing the CMDB provides a framework to build a detailed data repository for breast imaging quality control and research, which has the potential to augment existing resources.
    Type of Medium: Online Resource
    ISSN: 1176-9351 , 1176-9351
    URL: Article
    DOI: 10.4137/CIN.S14031
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2014
    detail.hit.zdb_id: 2202739-7
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