CISNET-DFCI (Dana-Farber)

Cancer Intervention and Surveillance Modeling Network, Dana-Farber Cancer Institute

Basic Model Attributes
Cancer site	Breast
Host institution	Dana-Farber Cancer Institute
Purpose	The purpose of the model is to predict the mortality associated with female breast cancer. The predictions may be by chronological year and/or age. Mortality may change by advances in treatment and/or changing dissemination of screening. The model incorporates the possibility that these latter two factors will change by chronological time and age. The model is general and enables the prediction of changes in mortality if technical advances are made by radiology or the discovery of other disease markers.
Contact	Sandra Lee (sjlee@jimmy.harvard.edu)
Profile	CISNET_ModelProfile_BREAST_DF_001_07232013_57416.pdf

Purpose: The main purpose of the CISNET-DFCI model, which was developed at Dana-Farber Cancer Institute, is to predict the mortality associated with female breast cancer in the presence of screening and treatment in the US population. The predictions may be by chronological year and/or age. Mortality may change by advances in treatment and/or changing dissemination of screening. The model incorporates the possibility that these latter two factors will change by chronological time and age. The model is general and enables the prediction of changes in mortality if technical advances are made by radiology or the discovery of other disease markers.

Overview: CISNET-DFCI is a stochastic model that depicts the early detection process of screening. The model has been developed for the screening of chronic diseases, but applied mainly to early detection of invasive breast cancer. This analytic approach was applied to estimate the impact of mammography screening and treatment on breast cancer incidence and mortality. A series of equations was derived to project the age-specific incidence of breast cancer and the probability of breast cancer deaths (mortality) in the presence of screening. Other outcomes associated with screening, such as life-years gained, quality adjusted life years (QALYs), cost-effectiveness, overdiagnosis, and false positive findings, are also generated. In generating outcomes, Mathcad and Matlab software packages are utilized to process the probability formulations.

Natural History: For invasive breast cancer, the model characterizes the natural history of breast cancer by four health states: i) S₀ is a disease-free state (disease –free or disease cannot be detected by any screening modality); ii) S_p is a pre-clinical state (disease can be diagnosed by a screening test); iii) S_c is a clinical state (symptomatic disease); and iv) S_d is a disease-specific death state. There are two main model assumptions: i) invasive breast cancer is progressive and described by the transitions S₀ to S_p to S_c and some eventually progress to S_d; ii) the mortality benefit from screening is attributable to a stage shift in diagnosis. The main goal of screening is to diagnose individuals in S_p where subjects have an early-stage disease with no symptoms. The stage shift implies that the subjects are diagnosed earlier (in S_p) before symptoms surface (in S_c). CISNET-DFCI mathematically derives a distribution of the lead time in the presence of screening and adjusts the lead time bias in mortality modeling. The lead time distribution is also used to quantify the probability of overdiagnosis for screen-detected cases. The model assumptions have been validated by projecting the results from randomized screening trials and comparing with published results.

Screening and Treatment: Any specific screening patterns or combination of screening patterns as used in the US population, for example, can be applied to specific birth cohorts. The mortality benefit of the mammography screening is obtained by finding cases in an earlier stage. This is addressed through a stage shift in the model. Treatment benefits captured as hazard reductions are applied to the baseline (in the absence of screening and treatment) underlying breast cancer survival data. Screening dissemination patterns, treatment dissemination patterns, stage shift and hazard reductions from treatment are provided as common input parameters.

Model Updates: CISNET-DFCI incorporated a plausible ductal carcinoma in situ (DCIS) model in 2014. Figure 1 displays the natural history of breast cancer including DCIS. The natural history model now includes preclinical undetectable DCIS (Sdu), preclinical screen-detectable DCIS (Sdp) and clinical DCIS (S_dc) states. The updated model allows DCIS in the preclinical screen-detectable DCIS state (S_dp) to regress to S_du, or to transition to S_dc, or S_p.

Figure 1 CISNET-DFCI Model: Natural History of Breast Cancer

Other updates include: i) incorporation of molecular subtypes of estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status, ER/HER2-specific natural history parameters; ii) ER/HER2-specific baseline underlying survival; and iii) ER/HER2-specific treatment efficacy up to the year 2015.

References

Lee S, Li X, Huang H, Zelen M. The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update. Med Decis Making. 2018 Apr;38(1_suppl):44S-53S. [Abstract]
Lee S, Zelen M. A stochastic model for predicting the mortality of breast cancer. J Natl Cancer Inst Monogr 2006;(36):79-86. [Abstract]
Lee S, Zelen M. Mortality modeling of early detection programs. Biometrics. June 1, 2008 [Abstract]
Lee S, Zelen M. Modelling the early detection of breast cancer. Ann Oncol, Aug. 1, 2003 [Abstract]
Lee S, Huang H, Zelen M, Early detection of disease and scheduling of screening examinations, Stat Methods Med Res, Dec. 1, 2004 [Abstract]

Tip: Hover your cursor over the dashed attribute links below for more information. View the details of this model in a grid with other breast models.

Detailed Package Attributes
Attribute Category	Attribute
Approach
Primary Purpose	Screening evaluation, Population trends,
Features
Intervention	Screening, Treatment,
Natural History	Metastases,
Construction
Approach	Analytic,
Methods	State Transition,
Unit of Analysis	Person, Population,
Data Source	BCSC, Two County Study,
Census
Cancer Registry	SEER,
Linked
Clinical Trial	HIP,
Survey
Meta Analysis	EBCTCG,
Assumptions
Benefit Factors
Screening	Stage Shift, Temporal Trends (Age), Lead Time,
Treatment	Estrogen receptor (ER) Status, Temporal Trends (Calendar year, Age, Birth cohort), Modality,
Vaccination
Inputs	Incidence, Stage Distribution,
Screening	Attendance, Dissemination, Effect (Screening effect (stage shift) used with corresponding screening patterns),
Diagnosis
Precancer
Treatment	Dissemination, Efficacy, Effect (Benefit of each RX used in population added),
Precancer
Survival	Relative (Our model uses the disease specific survival by age and stage in the absence of screening and treatment as an input.),
Mortality	Other cause,
Risk Factor	Natural History,
Vaccination
Outputs	Incidence,
Disease
Prevalence
Treatment	Effect (Benefit of treatment effect at population level assessed),
Precancer
Screening	Effect (Screening effect at population level assessed by age, chronological year),
Risk Factor
Outcomes	Survival, Life years, QALY, Cause-specific Mortality, All-cause Mortality,
Screening	False Positives, True Positives, Biopsies performed, Unnecessary biopsies, Overdiagnoses, History,
Treatment	Overtreatment,
Implementation
Development
Tested Platforms	Windows, Solaris,
Language	Mathcad,

2024

Trentham-Dietz A, with the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Working Group Steering Committee, Weighing the Benefits and Risks of Mammography Screening Strategies., Radiology, Oct. 1, 2024 [Abstract]
Trentham-Dietz A, Chapman CH, Jayasekera J, Lowry KP, Heckman-Stoddard BM, Hampton JM, Caswell-Jin JL, Gangnon RE, Lu Y, Huang H, et al., Collaborative Modeling to Compare Different Breast Cancer Screening Strategies: A Decision Analysis for the US Preventive Services Task Force., JAMA, April 30, 2024 [Abstract]
Trentham-Dietz A, Chapman CH, Jayasekera J, Lowry KP, Heckman-Stoddard B, Hampton JM, Caswell-Jin J, Lu Y, Gangnon RE, Sun L, et al., , , April 1, 2024 [Abstract]
Caswell-Jin JL, Sun LP, Munoz D, Lu Y, Li Y, Huang H, Hampton JM, Song J, Jayasekera J, Schechter C, et al., Analysis of Breast Cancer Mortality in the US-1975 to 2019., JAMA, Jan. 16, 2024 [Abstract]

2023

Mandelblatt JS, Schechter CB, Stout NK, Huang H, Stein S, Hunter Chapman C, Trentham-Dietz A, Jayasekera J, Gangnon RE, Hampton JM, et al., Population simulation modeling of disparities in US breast cancer mortality., J Natl Cancer Inst Monogr, Nov. 8, 2023 [Abstract]

2021

Alagoz O, Lowry KP, Kurian AW, Mandelblatt JS, Ergun MA, Huang H, Lee SJ, Schechter CB, Tosteson ANA, Miglioretti DL, et al., Impact of the COVID-19 Pandemic on Breast Cancer Mortality in the US: Estimates From Collaborative Simulation Modeling., J Natl Cancer Inst, July 14, 2021 [Abstract]
Trentham-Dietz A, Alagoz O, Chapman C, Huang X, Jayasekera J, van Ravesteyn NT, Lee SJ, Schechter CB, Yeh JM, Plevritis SK, et al., Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts., PLoS Comput Biol, June 17, 2021 [Abstract]

2020

Chootipongchaivat S, van Ravesteyn NT, Li X, Huang H, Weedon-Fekjær H, Ryser MD, Weaver DL, Burnside ES, Heckman-Stoddard BM, de Koning HJ, et al., Modeling the natural history of ductal carcinoma in situ based on population data., Breast Cancer Res, May 27, 2020 [Abstract]

2019

Lowry KP, Trentham-Dietz A, Schechter CB, Alagoz O, Barlow WE, Burnside ES, Conant EF, Hampton JM, Huang H, Kerlikowske K, Lee SJ, et al., Long-term Outcomes and Cost-effectiveness of Breast Cancer Screening with Digital Breast Tomosynthesis in the United States, J Natl Cancer Inst, Sept. 10, 2019 [Abstract]
Ryser MD, Weaver DL, Zhao F, Worni M, Grimm LJ, Gulati R, Etzioni R, Hyslop T, Lee SJ, Hwang ES, Cancer Outcomes in DCIS Patients Without Locoregional Treatment., J Natl Cancer Inst, Sept. 1, 2019 [Abstract]

2018

van Ravesteyn NT, van den Broek JJ, Li X, Weedon-Fekjær H, Schechter CB, Alagoz O, Huang X, Weaver DL, Burnside ES, Punglia RS, de Koning HJ, et al., Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches, Med Decis Making, April 1, 2018 [Abstract]
Lee SJ, Li X, Huang H, Zelen M, The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update, Med Decis Making, April 1, 2018 [Abstract]
Mandelblatt JS, Near AM, Miglioretti DL, Munoz D, Sprague BL, Trentham-Dietz A, Gangnon R, Kurian AW, Weedon-Fekjaer H, Cronin KA, Plevritis SK, et al., Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling, Med Decis Making, April 1, 2018 [Abstract]
van den Broek JJ, van Ravesteyn NT, Mandelblatt JS, Huang H, Ergun MA, Burnside ES, Xu C, Li Y, Alagoz O, Lee SJ, Stout NK, et al., Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49, Med Decis Making, April 1, 2018 [Abstract]
van den Broek JJ, van Ravesteyn NT, Mandelblatt JS, Cevik M, Schechter CB, Lee SJ, Huang H, Li Y, Munoz DF, Plevritis SK, de Koning HJ, et al., Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology, Med Decis Making, April 1, 2018 [Abstract]
Alagoz O, Berry DA, de Koning HJ, Feuer EJ, Lee SJ, Plevritis SK, Schechter CB, Stout NK, Trentham-Dietz A, Mandelblatt JS, Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models, Med Decis Making, April 1, 2018 [Abstract]
Plevritis SK, Munoz D, Kurian AW, Stout NK, Alagoz O, Near AM, Lee SJ, van den Broek JJ, Huang X, Schechter CB, Sprague BL, et al., Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012, JAMA, Jan. 9, 2018 [Abstract]

2017

Burnside ES, Lee SJ, Bennette C, Near AM, Alagoz O, Huang H, van den Broek JJ, Kim JY, Ergun MA, van Ravesteyn NT, Stout NK, et al., Using Collaborative Simulation Modeling to Develop a Web-Based Tool to Support Policy-Level Decision Making About Breast Cancer Screening Initiation Age, MDM Policy Pract, July 1, 2017 [Abstract]

2016

Mandelblatt JS, Stout NK, Schechter CB, van den Broek JJ, Miglioretti DL, Krapcho M, Trentham-Dietz A, Munoz D, Lee SJ, Berry DA, van Ravesteyn NT, et al., Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies, Ann Intern Med, Feb. 16, 2016 [Abstract]

2015

Gangnon RE, Sprague BL, Stout NK, Alagoz O, Weedon-Fekjær H, Holford TR, Trentham-Dietz A, The contribution of mammography screening to breast cancer incidence trends in the United States: an updated age-period-cohort model, Cancer Epidemiol Biomarkers Prev, June 1, 2015 [Abstract]

2014

Munoz D, Near AM, van Ravesteyn NT, Lee SJ, Schechter CB, Alagoz O, Berry DA, Burnside ES, Chang Y, Chisholm G, de Koning HJ, et al., Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality, J Natl Cancer Inst, Nov. 1, 2014 [Abstract]
Weedon-Fekjær H, Romundstad PR, Vatten LJ, Modern mammography screening and breast cancer mortality: population study, BMJ, June 17, 2014 [Abstract]
Stout NK, Lee SJ, Schechter CB, Kerlikowske K, Alagoz O, Berry D, Buist DS, Cevik M, Chisholm G, de Koning HJ, Huang H, et al., Benefits, harms, and costs for breast cancer screening after US implementation of digital mammography, J Natl Cancer Inst, June 1, 2014 [Abstract]

2012

van Ravesteyn NT, Miglioretti DL, Stout NK, Lee SJ, Schechter CB, Buist DS, Huang H, Heijnsdijk EA, Trentham-Dietz A, Alagoz O, Near AM, et al., Tipping the balance of benefits and harms to favor screening mammography starting at age 40 years: a comparative modeling study of risk, Ann Intern Med, May 1, 2012 [Abstract]
Weedon-Fekjær H, Bakken K, Vatten LJ, Tretli S, Understanding recent trends in incidence of invasive breast cancer in Norway: age-period-cohort analysis based on registry data on mammography screening and hormone treatment use, BMJ, Jan. 30, 2012 [Abstract]

2011

Mandelblatt JS, Cronin KA, Berry DA, Chang Y, de Koning HJ, Lee SJ, Plevritis SK, Schechter CB, Stout NK, van Ravesteyn NT, Zelen M, et al., Modeling the impact of population screening on breast cancer mortality in the United States, Breast, Oct. 1, 2011 [Abstract]

2010

Weedon-Fekjaer H, Tretli S, Aalen OO, Estimating screening test sensitivity and tumour progression using tumour size and time since previous screening, Stat Methods Med Res, Oct. 1, 2010 [Abstract]
Kalager M, Zelen M, Langmark F, Adami HO, Effect of screening mammography on breast-cancer mortality in Norway, N Engl J Med, Sept. 23, 2010 [Abstract]

2009

Mandelblatt JS, Cronin KA, Bailey S, Berry DA, de Koning HJ, Draisma G, Huang H, Lee SJ, Munsell M, Plevritis SK, Ravdin P, et al., Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms, Ann Intern Med, Nov. 17, 2009 [Abstract]
Rue M, Vilaprinyo E, Lee S, Martinez-Alonso M, Carles MD, Marcos-Gragera R, Pla R, Espinas JA, Effectiveness of early detection on breast cancer mortality reduction in Catalonia (Spain), BMC Cancer, Sept. 15, 2009 [Abstract]

2008

Lee SJ, Zelen M, Mortality modeling of early detection programs, Biometrics, June 1, 2008 [Abstract]

2006

Lee S, Zelen M, A stochastic model for predicting the mortality of breast cancer, J Natl Cancer Inst Monogr, Jan. 1, 2006 [Abstract]
Cronin KA, Feuer EJ, Clarke LD, Plevritis SK, Impact of adjuvant therapy and mammography on U.S. mortality from 1975 to 2000: comparison of mortality results from the cisnet breast cancer base case analysis, J Natl Cancer Inst Monogr, Jan. 1, 2006 [Abstract]

2005

Zelen M, Risks of cancer and families, J Natl Cancer Inst, Nov. 2, 2005 [Abstract]
Berry DA, Cronin KA, Plevritis SK, Fryback DG, Clarke L, Zelen M, Mandelblatt JS, Yakovlev AY, Habbema JD, Feuer EJ, Effect of screening and adjuvant therapy on mortality from breast cancer, N Engl J Med, Oct. 27, 2005 [Abstract]
Shen Y, Zelen M, Robust modeling in screening studies: estimation of sensitivity and preclinical sojourn time distribution, Biostatistics, Oct. 1, 2005 [Abstract]

2004

Lee S, Huang H, Zelen M, Early detection of disease and scheduling of screening examinations, Stat Methods Med Res, Dec. 1, 2004 [Abstract]
Hu P, Zelen M, Planning of randomized early detection trials, Stat Methods Med Res, Dec. 1, 2004 [Abstract]
Zelen M, Forward and backward recurrence times and length biased sampling: age specific models, Lifetime Data Anal, Dec. 1, 2004 [Abstract]
Davidov O, Zelen M, Overdiagnosis in early detection programs, Biostatistics, Oct. 1, 2004 [Abstract]

2003

Lee SJ, Zelen M, Modelling the early detection of breast cancer, Ann Oncol, Aug. 1, 2003 [Abstract]
Davidov O, Zelen M, The theory of case-control studies for early detection programs, Biostatistics, July 1, 2003 [Abstract]

2002

Hu P, Zelen M, Experimental design issues for the early detection of disease: novel designs, Biostatistics, Sept. 1, 2002 [Abstract]
Parmigiani G, Skates S, Zelen M, Modeling and optimization in early detection programs with a single exam, Biometrics, March 1, 2002 [Abstract]
Zelen M, Lee SJ, Models and the early detection of disease: methodological considerations, Cancer Treat Res, Jan. 1, 2002 [Abstract]
Lee SJ, Zelen M, Statistical models for screening: planning public health programs, Cancer Treat Res, Jan. 1, 2002 [Abstract]

2001

Shen Y, Wu D, Zelen M, Testing the independence of two diagnostic tests, Biometrics, Dec. 1, 2001 [Abstract]
Shen Y, Zelen M, Screening sensitivity and sojourn time from breast cancer early detection clinical trials: mammograms and physical examinations, J Clin Oncol, Aug. 1, 2001 [Abstract]