The theory of periodic screening I: Lead time and proportion detected

1976 ◽  
Vol 8 (1) ◽  
pp. 127-143 ◽  
Author(s):  
Philip C. Prorok
Keyword(s):  
Chronic Disease ◽  
Natural History ◽  
Time Distribution ◽  
Lead Time ◽  
Screening Program ◽  
Clinical State ◽  
Recurrence Time ◽  
Sojourn Time Distribution ◽  
History Of ◽  
Forward Recurrence Time

A stochastic model for a periodic screening program is presented in which the natural history of a chronic disease is assumed to follow a progressive path from a preclinical state to a clinical state. The sampling of preclinical state sojourn times by screening examinations generates bounds on the preclinical state recurrence times. The distribution of the bounded forward recurrence time is derived and used to obtain the distribution and mean of the lead time, and relationships for calculating the proportion of preclinical cases detected. These expressions are derived in terms of the preclinical state sojourn-time distribution and adjustible parameters important in the design of a periodic screening program.

The theory of periodic screening I: Lead time and proportion detected

1976 ◽  
Vol 8 (01) ◽  
pp. 127-143 ◽  
Author(s):  
Philip C. Prorok
Keyword(s):  
Chronic Disease ◽  
Natural History ◽  
Time Distribution ◽  
Lead Time ◽  
Screening Program ◽  
Clinical State ◽  
Recurrence Time ◽  
Sojourn Time Distribution ◽  
History Of ◽  
Forward Recurrence Time

A stochastic model for a periodic screening program is presented in which the natural history of a chronic disease is assumed to follow a progressive path from a preclinical state to a clinical state. The sampling of preclinical state sojourn times by screening examinations generates bounds on the preclinical state recurrence times. The distribution of the bounded forward recurrence time is derived and used to obtain the distribution and mean of the lead time, and relationships for calculating the proportion of preclinical cases detected. These expressions are derived in terms of the preclinical state sojourn-time distribution and adjustible parameters important in the design of a periodic screening program.

Nationwide analysis: Changes in the natural history of low risk localized prostate cancer.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 12-12
Author(s):  
John Thomas Helgstrand ◽  
Nina Klemann ◽  
Birgitte Grønkaer Toft ◽  
Ben Vainer ◽  
Martin Andreas Røder ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Natural History ◽  
Lead Time ◽  
Specific Antigen ◽  
Tumor Burden ◽  
Low Risk ◽  
Stage Migration ◽  
History Of ◽  
The Difference ◽  
The Impact

12 Background: Increased use of prostate-specific antigen (PSA) has introduced an increase in PCa incidence and a lead time and stage migration at diagnosis, altering the natural history of PCa. Contemporary PCa patients are likely younger and have smaller tumor burden at diagnosis. We investigated if changes in the PCa landscape have altered the course of low-risk localized PCa. Methods: In the Danish Prostate Cancer Registry (DaPCaR), patients diagnosed from 1995 to 2011 with localized (T1-2, N0/X, M0) PCa with Gleason score ≤ 6 were identified. Patients were stratified into three periods of diagnosis; 1995-2000 (period 1), 2001-2005 (period 2) and 2006-2011 (period 3). Competing risk analysis treating PCa and other-cause death as competing events was performed. Results: Of the 5,660 patients identified, 35.9% had undergone radical prostatectomy (RP). From period 1 to period 3, the median age at diagnosis decreased from 72.2 to 66.0 years and the median PSA decreased from 16.2 to 8.6 ng/mL. From period 1 to period 3, the 5-year risk of PCa-death decreased from 14.3% (95% CI: 12.1-16.4%) to 1.3% (95% CI: 0.83-1.7%), p < .0.0001 and the risk of other cause death decreased from 18.1% (95% CI: 15.8-20.5%) to 7.2% (95% CI: 6.2-8.2), p = 0.0001. In patients undergoing RP, the 5-year risk of PCa-death decreased from 0.67% (95% CI: 0.67-2.0%) for patients diagnosed in period 1 to 0.45% (95% CI: 0.0055-0.84), for patients diagnosed in period 3, p = 0.92. For patients not undergoing RP, the 5-year risk of PCa death decreased from 16.6% (95% CI: 14.1-19.1) to 2.0% (95% CI: 1.2-2.7%), p < 0.0001. Conclusions: In a nationwide cohort of patients with low risk localized PCa, the 5-year risk of PCa-death significantly decreased when comparing patients diagnosed during 2006-2011 to those diagnosed during 1995-2000. This was mainly driven by patients not undergoing RP. In the most recently diagnosed group, the difference in 5-year PCa-death between patients undergoing RP and not undergoing RP was small (0.45% vs. 2.0%). Our data demonstrate that the impact of PSA induced lead-time and stage migration has diminished the absolute effect of RP on the risk of 5-year PCa-death because contemporary low-risk localized patients have a significantly better prognosis.

Estimating Age-Specific Mean Sojourn Time of Breast Cancer and Sensitivity of Mammographic Screening by Breast Density among Korean Women

2021 ◽  
Author(s):  
Eunji Choi ◽  
Mina Suh ◽  
So-Youn Jung ◽  
Kyu-Won Jung ◽  
Sohee Park ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Natural History ◽  
Older Women ◽  
Breast Density ◽  
Transition Rate ◽  
Mammographic Screening ◽  
Dense Breast ◽  
Clinical State ◽  
Korean Women ◽  
History Of

Abstract Background: High breast cancer incidence among women in forties are specific to Asian, implicating dense breast. This study examined the natural history of breast cancer progression among Korean women according to the levels of breast density.Methods: We applied a three-state Markov model to fit the natural history of breast cancer to data in the Korean National Cancer Screening Program. Diagnosis of breast cancer was ascertained by linkage to the Korean Central Cancer Registry. Disease progression rates (i.e., transition rates from healthy to preclinical state, and from preclinical to clinical state) were estimated across levels of breast density determined by the Breast Imaging, Reporting and Data System (BI-RADS). Preclinical incidence of breast cancer, mean sojourn time (MST) and mammographic screening sensitivity were simultaneously generated in the model.Results: Overall prevalence of dense breast among Korean women was 53.9%, which declined with age. Transition rate from healthy to preclinical state, indicating the preclinical incidence of breast cancer, was estimated to be higher among women aged 40-49 years (0.0019, 95% CI; 0.0017-0.0021) and women aged 50-59 years (0.0020, 95% CI; 0.0017-0.0022), than older women aged 60-69 years (0.0014, 95% CI; 0.0012-0.0017). Transition rate from preclinical to clinical state was also fastest among younger age groups, which directly translated to the shortest MSTs, estimated as 1.98 (95% CI; 1.67-2.33), 2.49 (95% CI; 1.92-3.22) and 3.07 (95% CI; 2.11-4.46) years for women in forties, fifties and sixties, respectively. The sensitivity of the mammographic screening was higher among older women (0.70, 95% CI; 0.62-0.77) than women in fifties (0.65, 95% CI; 0.62-0.77) and women in forties (0.61, 95% CI; 0.54-0.61). Having dense breasts increased the likelihood of the preclinical cancer risk (1.96 to 2.35 times) and decreased the duration of MST (1.53 to 2.02 times).Conclusions: Korean women showed 1.5 to 2 times higher prevalence of dense breast tissues, compared to Western women. This study estimated Korean-specific parameters for the natural history of breast cancer that would be utilized for establishing optimal screening strategies in countries with higher dense breast prevalence.

Some non-stationary point processes with stationary forward recurrence time distribution

1975 ◽  
Vol 12 (1) ◽  
pp. 167-169 ◽  
Author(s):  
Mats Rudemo
Keyword(s):  
Stationary Point ◽  
Time Distribution ◽  
Point Processes ◽  
Recurrence Time ◽  
Renewal Processes ◽  
Forward Recurrence Time ◽  
Stationary Point Processes

Examples are given of point processes that are non-stationary but have stationary forward recurrence time distributions. They are obtained by modification of stationary Poisson and renewal processes.

scholarly journals Symptom lead time distribution in lung cancer: natural history and prospects for early diagnosis

2014 ◽  
Vol 43 (6) ◽  
pp. 1865-1873 ◽  
Author(s):  
Anthony E Ades ◽  
Mousumi Biswas ◽  
Nicky J Welton ◽  
William Hamilton
Keyword(s):  
Lung Cancer ◽  
Natural History ◽  
Early Diagnosis ◽  
Time Distribution ◽  
Lead Time

Some non-stationary point processes with stationary forward recurrence time distribution

1975 ◽  
Vol 12 (01) ◽  
pp. 167-169
Author(s):  
Mats Rudemo
Keyword(s):  
Stationary Point ◽  
Time Distribution ◽  
Point Processes ◽  
Recurrence Time ◽  
Renewal Processes ◽  
Forward Recurrence Time ◽  
Stationary Point Processes

Examples are given of point processes that are non-stationary but have stationary forward recurrence time distributions. They are obtained by modification of stationary Poisson and renewal processes.

scholarly journals The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update

2018 ◽  
Vol 38 (1_suppl) ◽  
pp. 44S-53S ◽  
Author(s):  
Sandra J. Lee ◽  
Xiaoxue Li ◽  
Hui Huang ◽  
Marvin Zelen
Keyword(s):  
Breast Cancer ◽  
Natural History ◽  
Early Detection ◽  
Invasive Breast Cancer ◽  
Mammography Screening ◽  
Lead Time ◽  
Clinical State ◽  
Model Parameters ◽  
Screening Strategies ◽  
The Impact

Background. We present updated features to a model developed by Dana-Farber investigators within the Cancer Intervention and Surveillance Modeling Network (CISNET). The initial model was developed to evaluate the impact of mammography screening strategies. Methods. This major update includes the incorporation of ductal carcinoma in situ (DCIS) as part of the natural history of breast cancer. The updated model allows DCIS in the pre-clinical state to regress to undetectable early-stage DCIS, or to transition to invasive breast cancer, or to clinical DCIS. We summarize model assumptions for DCIS natural history and model parameters. Another new development is the derivation of analytical expressions for overdiagnosis. Overdiagnosis refers to mammographic identification of breast cancer that would never have resulted in disease symptoms in the patient’s remaining lifetime (i.e., lead time longer than residual survival time). This is an inevitable consequence of early detection. Our model uniquely assesses overdiagnosis using an analytical formulation. We derive the lead time distribution resulting from the early detection of invasive breast cancer and DCIS, and formulate the analytical expression for overdiagnosis. Results. This formulation was applied to assess overdiagnosis from mammography screening. Other model updates involve implementing common model input parameters with updated treatment dissemination and effectiveness, and improved mammography performance. Lastly, the model was expanded to incorporate subgroups by breast density and molecular subtypes. Conclusions. The incorporation of DCIS and subgroups and the derivation of an overdiagnosis estimation procedure improve the model for evaluating mammography screening programs.

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