Publications by authors named "Christine D Berg"

128 Publications

Management of lung cancer screening results based on individual prediction of current and future lung cancer risk.

J Thorac Oncol 2021 Oct 11. Epub 2021 Oct 11.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA. Electronic address:

Objective: We propose a risk-tailored approach for management of lung cancer screening results. This approach incorporates individual risk factors and LDCT image features into calculations of immediate and next-screen (1-year) risks of lung cancer detection, which in turn can recommend short-interval imaging or 1-year or 2-year screening intervals.

Methods: We first extended the "LCRAT+CT" individualized risk calculator to predict lung cancer risk after either a negative or abnormal LDCT screen. To develop the abnormal screens portion, we analyzed 18,129 abnormal LDCTs in the National Lung Screening Trial (NLST), including lung cancers detected immediately (n=649) or at the next screen (n=235). We estimated the potential impact of this approach among NLST participants with any screen result (negative or abnormal).

Results: Applying the draft National Health Service (NHS) England protocol for lung screening to NLST participants referred 76% of participants to a 2-year interval, but delayed diagnosis for 40% of detectable cancers. The LCRAT+CT risk model, with a threshold of <0.95% cumulative lung-cancer risk, would also refer 76% of participants to a 2-year interval, but would delay diagnosis for only 30% of cancers, a 25% reduction versus the NHS protocol. Alternatively, LCRAT+CT, with a threshold of <1.7% cumulative lung-cancer risk, would also delay diagnosis for 40% of cancers, but would refer 85% of participants for a 2-year interval, a 38% further reduction in the number of required 1-year screens beyond the NHS protocol.

Conclusions: Using individualized risk models to determine management in lung cancer screening could substantially reduce the number of screens or increase early detection.
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http://dx.doi.org/10.1016/j.jtho.2021.10.001DOI Listing
October 2021

An efficient randomised trial design for multi-cancer screening blood tests: nested enhanced mortality outcomes of screening trial.

Lancet Oncol 2021 10;22(10):1360-1362

Division of Cancer Prevention, US National Cancer Institute, Bethesda, MD, USA; medical consultant, Bethesda, MD, USA.

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http://dx.doi.org/10.1016/S1470-2045(21)00204-7DOI Listing
October 2021

Using Prediction-Models to Reduce Persistent Racial/Ethnic Disparities in Draft 2020 USPSTF Lung-Cancer Screening Guidelines.

J Natl Cancer Inst 2021 Jan 5. Epub 2021 Jan 5.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA.

We examined whether draft 2020 United States Preventive Services Task Force (USPSTF) lung-cancer screening recommendations "partially ameliorate racial disparities in screening eligibility" compared to 2013 guidelines, as claimed. Using data from the 2015 National Health Interview Survey, USPSTF-2020 increased eligibility by similar proportions for minorities (97.1%) and Whites (78.3%). Contrary to the intent of USPSTF-2020, the relative disparity (differences in percentages of model-estimated gainable life-years from National Lung Screening Trial-like screening by eligible Whites vs minorities) actually increased from USPSTF-2013 to USPSTF-2020 (African Americans: 48.3%-33.4%=15.0% to 64.5%-48.5%=16.0%; Asian Americans: 48.3%-35.6%=12.7% to 64.5%-45.2%=19.3%; Hispanic Americans: 48.3%-24.8%=23.5% to 64.5%-37.0%=27.5%). However, augmenting USPSTF-2020 with high-benefit individuals selected by the Life-Years From Screening with Computed Tomography (LYFS-CT) model nearly eliminated disparities for African Americans (76.8%-75.5%=1.2%), and improved screening efficiency for Asian/Hispanic Americans, although disparities were reduced only slightly (Hispanic Americans) or unchanged (Asian Americans). Draft USPSTF-2020 guidelines increased the number of eligible minorities versus USPSTF-2013 but may inadvertently increase racial/ethnic disparities. LYFS-CT could reduce disparities in screening eligibility by identifying ineligible people with high predicted benefit, regardless of race/ethnicity.
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http://dx.doi.org/10.1093/jnci/djaa211DOI Listing
January 2021

Modeled Reductions in Late-stage Cancer with a Multi-Cancer Early Detection Test.

Cancer Epidemiol Biomarkers Prev 2021 03 16;30(3):460-468. Epub 2020 Dec 16.

National Cancer Institute, Bethesda, California.

Background: Cancer is the second leading cause of death globally, with many cases detected at a late stage when prognosis is poor. New technologies enabling multi-cancer early detection (MCED) may make "universal cancer screening" possible. We extend single-cancer models to understand the potential public health effects of adding a MCED test to usual care.

Methods: We obtained data on stage-specific incidence and survival of all invasive cancers diagnosed in persons aged 50-79 between 2006 and 2015 from the US Surveillance, Epidemiology, and End Results (SEER) program, and combined this with published performance of a MCED test in a state transition model (interception model) to predict diagnostic yield, stage shift, and potential mortality reductions. We model long-term (incident) performance, accou.
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http://dx.doi.org/10.1158/1055-9965.EPI-20-1134DOI Listing
March 2021

Statistical approaches using longitudinal biomarkers for disease early detection: A comparison of methodologies.

Stat Med 2020 12 16;39(29):4405-4420. Epub 2020 Sep 16.

Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.

Early detection of clinical outcomes such as cancer may be predicted using longitudinal biomarker measurements. Tracking longitudinal biomarkers as a way to identify early disease onset may help to reduce mortality from diseases like ovarian cancer that are more treatable if detected early. Two disease risk prediction frameworks, the shared random effects model (SREM) and the pattern mixture model (PMM) could be used to assess longitudinal biomarkers on disease early detection. In this article, we studied the discrimination and calibration performances of SREM and PMM on disease early detection through an application to ovarian cancer, where early detection using the risk of ovarian cancer algorithm (ROCA) has been evaluated. Comparisons of the above three approaches were performed via analyses of the ovarian cancer data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Discrimination was evaluated by the time-dependent receiver operating characteristic curve and its area, while calibration was assessed using calibration plot and the ratio of observed to expected number of diseased subjects. The out-of-sample performances were calculated via using leave-one-out cross-validation, aiming to minimize potential model overfitting. A careful analysis of using the biomarker cancer antigen 125 for ovarian cancer early detection showed significantly improved discrimination performance of PMM as compared with SREM and ROCA, nevertheless all approaches were generally well calibrated. Robustness of all approaches was further investigated in extensive simulation studies. The improved performance of PMM relative to ROCA is in part due to the fact that the biomarker measurements were taken at a yearly interval, which is not frequent enough to reliably estimate the changepoint or the slope after changepoint in cases under ROCA.
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http://dx.doi.org/10.1002/sim.8731DOI Listing
December 2020

Why Oncologists Should Care About Climate Change.

JCO Oncol Pract 2020 12 11;16(12):775-778. Epub 2020 Sep 11.

National Cancer Institute, Bethesda, MD.

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http://dx.doi.org/10.1200/OP.20.00609DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7735036PMC
December 2020

Protocol and Rationale for the International Lung Screening Trial.

Ann Am Thorac Soc 2020 04;17(4):503-512

Department of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada.

: The NLST (National Lung Screening Trial) reported a 20% reduction in lung cancer mortality with low-dose computed tomography screening; however, important questions on how to optimize screening remain, including which selection criteria are most accurate at detecting lung cancers and what nodule management protocol is most efficient. The PLCO (Prostate, Lung, Colorectal and Ovarian) Cancer Screening Trial 6-year and PanCan (Pan-Canadian Early Detection of Lung Cancer) nodule malignancy risk models are two of the better validated risk prediction models for screenee selection and nodule management, respectively. Combined use of these models for participant selection and nodule management could significantly improve screening efficiency.: The ILST (International Lung Screening Trial) is a prospective cohort study with two primary aims: ) Compare the accuracy of the PLCO model against U.S. Preventive Services Task Force (USPSTF) criteria for detecting lung cancers and ) evaluate nodule management efficiency using the PanCan nodule probability calculator-based protocol versus Lung-RADS.: ILST will recruit 4,500 participants who meet USPSTF and/or PLCO risk ≥1.51%/6-year selection criteria. Participants will undergo baseline and 2-year low-dose computed tomography screening. Baseline nodules are managed according to PanCan probability score. Participants will be followed up for a minimum of 5 years. Primary outcomes for aim 1 are the proportion of individuals selected for screening, proportion of lung cancers detected, and positive predictive values of either selection criteria, and outcomes for aim 2 include comparing distributions of individuals and the proportion of lung cancers in each of three management groups: next surveillance scan, early recall scan, or diagnostic evaluation recommended. Statistical powers to detect differences in the four components of primary study aims were ≥82%.: ILST will prospectively evaluate the comparative accuracy and effectiveness of two promising multivariable risk models for screenee selection and nodule management in lung cancer screening.Clinical trial registered with www.clinicaltrials.gov (NCT02871856).
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http://dx.doi.org/10.1513/AnnalsATS.201902-102OCDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175983PMC
April 2020

Risk of Prostate Cancer-related Death Following a Low PSA Level in the PLCO Trial.

Cancer Prev Res (Phila) 2020 04 29;13(4):367-376. Epub 2020 Jan 29.

Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland.

Longer-than-annual screening intervals have been suggested to improve the balance of benefits and harms in prostate cancer screening. Many researchers, societies, and guideline committees have suggested that screening intervals could depend on the prostate-specific antigen (PSA) result. We analyzed data from men ( = 33,897) ages 55-74 years with a baseline PSA test in the intervention arm of the Prostate, Lung, Colorectal and Ovarian Cancer Screening trial (United States, 1993-2001). We estimated 5- and 10-year risks of aggressive cancer (Gleason ≥8 and/or stage III/IV) and 15-year risks of prostate cancer-related mortality for men with baseline PSA ≤ 0.5 ng/mL ( = 4,862), ≤1 ng/mL ( = 15,110), and 1.01-2.5 ng/mL ( = 12,422). A total of 217 men died from prostate cancer through 15 years, although no men with PSA ≤ 1 ng/mL died from prostate cancer within 5 years [95% confidence interval (CI), 0.00%-0.03%]. The 5-year incidence of aggressive disease was low (0.08%; 95% CI, 0.03%-0.12%) for men with PSA ≤ 1 ng/mL, and higher for men with baseline PSA 1.01-2.5 ng/mL (0.51%; 95% CI, 0.38%-0.74%). No men aged ≥65 years with PSA ≤ 0.5 ng/mL died from prostate cancer within 15 years (95% CI, 0.00%-0.32%), and their 10-year incidence of aggressive disease was low (0.25%; 95% CI, 0.00%-0.53%). Compared with white men, black men with PSA ≤ 1 ng/mL had higher 10-year rates of aggressive disease (1.6% vs. 0.4%; < 0.01). Five-year screening intervals may be appropriate for the 45% of men with PSA ≤ 1 ng/mL. Men ages ≥65 years with PSA ≤ 0.5 ng/mL could consider stopping screening. Substantial risk disparities suggest appropriate screening intervals could depend on race/ethnicity.
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http://dx.doi.org/10.1158/1940-6207.CAPR-19-0397DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339970PMC
April 2020

Life-Gained-Based Versus Risk-Based Selection of Smokers for Lung Cancer Screening.

Ann Intern Med 2019 11 22;171(9):623-632. Epub 2019 Oct 22.

National Cancer Institute, Bethesda, Maryland (L.C.C., C.D.B., H.A.K., A.K.C.).

Background: Although risk-based selection of ever-smokers for screening could prevent more lung cancer deaths than screening according to the U.S. Preventive Services Task Force (USPSTF) guidelines, it preferentially selects older ever-smokers with shorter life expectancies due to comorbidities.

Objective: To compare selection of ever-smokers for screening based on gains in life expectancy versus lung cancer risk.

Design: Cohort analyses and model-based projections.

Setting: U.S. population of ever-smokers aged 40 to 84 years.

Participants: 130 964 National Health Interview Survey participants, representing about 60 million U.S. ever-smokers during 1997 to 2015.

Intervention: Annual computed tomography (CT) screening for 3 years versus no screening.

Measurements: Estimated number of lung cancer deaths averted and life-years gained after development of a mortality model.

Results: Using the calibrated and validated mortality model in U.S. ever-smokers aged 40 to 84 years and selecting 8.3 million ever-smokers to match the number selected by the USPSTF criteria in 2013 to 2015, the analysis estimated that life-gained-based selection would increase the total life expectancy from CT screening (633 400 vs. 607 800 years) but prevent fewer lung cancer deaths (52 600 vs. 55 000) compared with risk-based selection. The 1.56 million persons selected by the life-gained-based strategy but not the risk-based strategy were younger (mean age, 59 vs. 75 years) and had fewer comorbidities (mean, 0.75 vs. 3.7).

Limitation: Estimates are model-based and assume implementation of lung cancer screening with short-term effectiveness similar to that from trials.

Conclusion: Life-gained-based selection could maximize the benefits of lung cancer screening in the U.S. population by including ever-smokers who have both high lung cancer risk and long life expectancy.

Primary Funding Source: Intramural Research Program of the National Cancer Institute, National Institutes of Health.
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http://dx.doi.org/10.7326/M19-1263DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7191755PMC
November 2019

Pragmatic randomised clinical trial of proton versus photon therapy for patients with non-metastatic breast cancer: the Radiotherapy Comparative Effectiveness (RadComp) Consortium trial protocol.

BMJ Open 2019 10 15;9(10):e025556. Epub 2019 Oct 15.

Provision Proton Therapy Center, Knoxville, Tennessee, USA.

Introduction: A broad range of stakeholders have called for randomised evidence on the potential clinical benefits and harms of proton therapy, a type of radiation therapy, for patients with breast cancer. Radiation therapy is an important component of curative treatment, reducing cancer recurrence and extending survival. Compared with photon therapy, the international treatment standard, proton therapy reduces incidental radiation to the heart. Our overall objective is to evaluate whether the differences between proton and photon therapy cardiac radiation dose distributions lead to meaningful reductions in cardiac morbidity and mortality after treatment for breast cancer.

Methods: We are conducting a large scale, multicentre pragmatic randomised clinical trial for patients with breast cancer who will be followed longitudinally for cardiovascular morbidity and mortality, health-related quality of life and cancer control outcomes. A total of 1278 patients with non-metastatic breast cancer will be randomly allocated to receive either photon or proton therapy. The primary outcomes are major cardiovascular events, defined as myocardial infarction, coronary revascularisation, cardiovascular death or hospitalisation for unstable angina, heart failure, valvular disease, arrhythmia or pericardial disease. Secondary endpoints are urgent or unanticipated outpatient or emergency room visits for heart failure, arrhythmia, valvular disease or pericardial disease. The Radiotherapy Comparative Effectiveness (RadComp) Clinical Events Centre will conduct centralised, blinded adjudication of primary outcome events.

Ethics And Dissemination: The RadComp trial has been approved by the institutional review boards of all participating sites. Recruitment began in February 2016. Current version of the protocol is A3, dated 08 November 2018. Dissemination plans include presentations at scientific conferences, scientific publications, stakeholder engagement efforts and presentation to the public via lay media outlets.

Trial Registration Number: NCT02603341.
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http://dx.doi.org/10.1136/bmjopen-2018-025556DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797426PMC
October 2019

Improving selection of individuals into lung cancer screening programmes.

Authors:
Christine D Berg

Lancet Oncol 2019 08 26;20(8):1039-1040. Epub 2019 Jun 26.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20850, USA. Electronic address:

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http://dx.doi.org/10.1016/S1470-2045(19)30411-5DOI Listing
August 2019

Contemporary Implications of U.S. Preventive Services Task Force and Risk-Based Guidelines for Lung Cancer Screening Eligibility in the United States.

Ann Intern Med 2019 09 4;171(5):384-386. Epub 2019 Jun 4.

National Cancer Institute, National Institutes of Health, Bethesda, Maryland (R.L., L.C.C., C.D.B., A.K.C., H.A.K.).

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http://dx.doi.org/10.7326/M18-3617DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822170PMC
September 2019

Insights for Management of Ground-Glass Opacities From the National Lung Screening Trial.

J Thorac Oncol 2019 09 22;14(9):1662-1665. Epub 2019 May 22.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland.

Background: In the National Lung Screening Trial (NLST), screen-detected cancers that would not have been identified by the Lung Computed Tomographic Screening Reporting and Data System (Lung-RADS) nodule management guidelines were frequently ground-glass opacities (GGOs). Lung-RADS suggests that GGOs with diameter less than 20 mm return for annual screening, and GGOs greater than or equal to 20 mm receive 6-month follow-up. We examined whether this 20-mm threshold gives consistent management of GGOs compared with solid nodules.

Methods: First, we calculated diameter-specific malignancy probabilities for GGOs and solid nodules in the NLST. Using the solid-nodule malignancy risks as benchmarks, we suggested risk-based management categories for GGOs based on their probability of malignancy. Second, we compared lung-cancer mortality between GGOs and solid nodules in the same risk-based category.

Results: Using the Lung-RADS v1.0 classifications, malignancy probability is higher for GGOs than solid nodules within the same category. A risk-based classification of GGOs would assign annual screening for GGOs 4 to 5 mm (0.4% malignancy risk); 6-month follow-up for GGOs 6 to 7 mm (1.1%), 8 to 14 mm (3.0%), and 15 to 19 mm (5.2%); and 3-month follow-up for greater than or equal to 20 mm (10.9%). This reclassification would have assigned similarly fatal cancers to 3-month follow-up (hazard ratio = 2.0 for lung-cancer death in GGOs versus solid-nodule cancers, 95% confidence interval: 0.4-8.7), but for 6-month follow-up, mortality was lower in GGO cancers (hazard ratio = 0.18, 95% confidence interval: 0.05-0.67).

Conclusions: If Lung-RADS categories for GGOs were based on malignancy probability, then 6- to 19-mm GGOs would receive 6-month follow-up and greater than or equal to 20-mm GGOs would receive 3-month follow-up. Such risk-based management for GGOs could improve the sensitivity of Lung-RADS, especially for large GGO cancers. However, small GGO cancers were less aggressive than their solid-nodule counterparts.
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http://dx.doi.org/10.1016/j.jtho.2019.05.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909540PMC
September 2019

Identification of Candidates for Longer Lung Cancer Screening Intervals Following a Negative Low-Dose Computed Tomography Result.

J Natl Cancer Inst 2019 09;111(9):996-999

Lengthening the annual low-dose computed tomography (CT) screening interval for individuals at lowest risk of lung cancer could reduce harms and improve efficiency. We analyzed 23 328 participants in the National Lung Screening Trial who had a negative CT screen (no ≥4-mm nodules) to develop an individualized model for lung cancer risk after a negative CT. The Lung Cancer Risk Assessment Tool + CT (LCRAT+CT) updates "prescreening risk" (calculated using traditional risk factors) with selected CT features. At the next annual screen following a negative CT, risk of cancer detection was reduced among the 70% of participants with neither CT-detected emphysema nor consolidation (median risk = 0.2%, interquartile range [IQR] = 0.1%-0.3%). However, risk increased for the 30% with CT emphysema (median risk = 0.5%, IQR = 0.3%-0.8%) and the 0.6% with consolidation (median = 1.6%, IQR = 1.0%-2.5%). As one example, a threshold of next-screen risk lower than 0.3% would lengthen the interval for 57.8% of screen-negatives, thus averting 49.8% of next-screen false-positives among screen-negatives but delaying diagnosis for 23.9% of cancers. Our results support that many, but not all, screen-negatives might reasonably lengthen their CT screening interval.
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http://dx.doi.org/10.1093/jnci/djz041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6748798PMC
September 2019

Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening.

Ann Intern Med 2018 07 15;169(1):10-19. Epub 2018 May 15.

National Cancer Institute, Bethesda, Maryland (H.A.K., S.A.K., L.C.P., L.C.C., C.D.B., A.K.C.).

Background: Lung cancer screening guidelines recommend using individualized risk models to refer ever-smokers for screening. However, different models select different screening populations. The performance of each model in selecting ever-smokers for screening is unknown.

Objective: To compare the U.S. screening populations selected by 9 lung cancer risk models (the Bach model; the Spitz model; the Liverpool Lung Project [LLP] model; the LLP Incidence Risk Model [LLPi]; the Hoggart model; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 [PLCOM2012]; the Pittsburgh Predictor; the Lung Cancer Risk Assessment Tool [LCRAT]; and the Lung Cancer Death Risk Assessment Tool [LCDRAT]) and to examine their predictive performance in 2 cohorts.

Design: Population-based prospective studies.

Setting: United States.

Participants: Models selected U.S. screening populations by using data from the National Health Interview Survey from 2010 to 2012. Model performance was evaluated using data from 337 388 ever-smokers in the National Institutes of Health-AARP Diet and Health Study and 72 338 ever-smokers in the CPS-II (Cancer Prevention Study II) Nutrition Survey cohort.

Measurements: Model calibration (ratio of model-predicted to observed cases [expected-observed ratio]) and discrimination (area under the curve [AUC]).

Results: At a 5-year risk threshold of 2.0%, the models chose U.S. screening populations ranging from 7.6 million to 26 million ever-smokers. These disagreements occurred because, in both validation cohorts, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) were well-calibrated (expected-observed ratio range, 0.92 to 1.12) and had higher AUCs (range, 0.75 to 0.79) than 5 models that generally overestimated risk (expected-observed ratio range, 0.83 to 3.69) and had lower AUCs (range, 0.62 to 0.75). The 4 best-performing models also had the highest sensitivity at a fixed specificity (and vice versa) and similar discrimination at a fixed risk threshold. These models showed better agreement on size of the screening population (7.6 million to 10.9 million) and achieved consensus on 73% of persons chosen.

Limitation: No consensus on risk thresholds for screening.

Conclusion: The 9 lung cancer risk models chose widely differing U.S. screening populations. However, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) most accurately predicted risk and performed best in selecting ever-smokers for screening.

Primary Funding Source: Intramural Research Program of the National Institutes of Health/National Cancer Institute.
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http://dx.doi.org/10.7326/M17-2701DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6557386PMC
July 2018

Overall and Multiphasic Findings of the Prostate, Lung, Colorectal and Ovarian (PLCO) Randomized Cancer Screening Trial.

Rev Recent Clin Trials 2018 ;13(4):257-273

National Institutes of Health, Bethesda, MD, United States.

Background: Screening tests are typically evaluated for a single disease, but multiple tests for multiple diseases are performed in practice. The Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial assessed testing for four cancers simultaneously and can be viewed as a multiphasic cancer intervention. This paper presents overall and multiphasic findings of this trial.

Methods: The PLCO trial was a randomized multi-center trial conducted at ten screening centers in the US. Participants were 76,682 men and 78,215 women ages 55 - 74 and free of the target cancers at trial entry. Screening tests were PSA and digital rectal examination for prostate cancer, chest x-ray for lung cancer, flexible sigmoidoscopy for colorectal cancer, CA125 and transvaginal ultrasound for ovarian cancer. Outcomes and harms of screening were assessed including compliance, test results, incidence, mortality, false positives and overdiagnosis.

Results: Screening compliance was 82%, 72,820 (8%) of 906,064 exams were positive, the overall PPV was 4.2% and the cancer detection rate was 3.38/1000. A mortality reduction was observed only for colorectal cancer (RR 0.72, 95% CI 0.61 - 0.85) with no effect on all-cause mortality. Ninety-six percent of positive exams were falsely positive and there was a suggestion of overdiagnosis of prostate and possibly ovarian cancers. Multiphasic testing resulted in 7374 men and 2748 women experiencing multiple false positive results from multiple types of tests.

Conclusion: Multiphasic cancer screening led to reduced mortality for one target cancer and imposed a burden on the health care system that included substantial false positives and likely overdiagnosis.
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http://dx.doi.org/10.2174/1574887113666180409153059DOI Listing
April 2019

Population Testing for High Penetrance Genes: Are We There Yet?

J Natl Cancer Inst 2018 07;110(7):687-689

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD.

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http://dx.doi.org/10.1093/jnci/djx282DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279254PMC
July 2018

Factors Associated With Small Aggressive Non-Small Cell Lung Cancers in the National Lung Screening Trial: A Validation Study.

JNCI Cancer Spectr 2018 Jan 31;2(1):pkx010. Epub 2018 Jan 31.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD.

Background: A small proportion of non-small cell lung cancers (NSCLCs) have been observed to spread to distant lymph nodes (N3) or metastasize (M1) or both, while the primary tumor is small (≤3 cm, T1). These small aggressive NSCLCs (SA-NSLSC) are important as they are clinically significant, may identify unique biologic pathways, and warrant aggressive follow-up and treatment. This study identifies factors associated with SA-NSCLC and attempts to validate a previous finding that women with a family history of lung cancer are at particularly elevated risk of SA-NSCLC.

Methods: This study used a case-case design within the National Cancer Institute's National Lung Screening Trial (NLST) cohort. Case patients and "control" patients were selected based on TNM staging parameters. Case patients (n = 64) had T1 NSCLCs that were N3 or M1 or both, while "control" patients (n = 206) had T2 or T3, N0 to N2, and M0 NSCLCs. Univariate and multivariable logistic regression were used to identify factors associated with SA-NSCLC.

Results: In bootstrap bias-corrected multivariable logistic regression models, small aggressive adenocarcinomas were associated with a positive history of emphysema (odds ratio [OR] = 5.15, 95% confidence interval [CI] = 1.63 to 23.00) and the interaction of female sex and a positive family history of lung cancer (OR = 6.55, 95% CI = 1.06 to 50.80).

Conclusions: Emphysema may play a role in early lung cancer progression. Females with a family history of lung cancer are at increased risk of having small aggressive lung adenocarcinomas. These results validate previous findings and encourage research on the role of female hormones interacting with family history and genetic factors in lung carcinogenesis and progression.
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http://dx.doi.org/10.1093/jncics/pkx010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6649725PMC
January 2018

Preventing Lung Cancer Mortality by Computed Tomography Screening: The Effect of Risk-Based Versus U.S. Preventive Services Task Force Eligibility Criteria, 2005-2015.

Ann Intern Med 2018 02 2;168(3):229-232. Epub 2018 Jan 2.

National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland (L.C.C., H.A.K., A.K.C., C.D.B.).

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http://dx.doi.org/10.7326/M17-2067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6785198PMC
February 2018

Body mass index and breast cancer survival: a Mendelian randomization analysis.

Int J Epidemiol 2017 12;46(6):1814-1822

Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield, UK.

Background: There is increasing evidence that elevated body mass index (BMI) is associated with reduced survival for women with breast cancer. However, the underlying reasons remain unclear. We conducted a Mendelian randomization analysis to investigate a possible causal role of BMI in survival from breast cancer.

Methods: We used individual-level data from six large breast cancer case-cohorts including a total of 36 210 individuals (2475 events) of European ancestry. We created a BMI genetic risk score (GRS) based on genotypes at 94 known BMI-associated genetic variants. Association between the BMI genetic score and breast cancer survival was analysed by Cox regression for each study separately. Study-specific hazard ratios were pooled using fixed-effect meta-analysis.

Results: BMI genetic score was found to be associated with reduced breast cancer-specific survival for estrogen receptor (ER)-positive cases [hazard ratio (HR) = 1.11, per one-unit increment of GRS, 95% confidence interval (CI) 1.01-1.22, P = 0.03). We observed no association for ER-negative cases (HR = 1.00, per one-unit increment of GRS, 95% CI 0.89-1.13, P = 0.95).

Conclusions: Our findings suggest a causal effect of increased BMI on reduced breast cancer survival for ER-positive breast cancer. There is no evidence of a causal effect of higher BMI on survival for ER-negative breast cancer cases.
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http://dx.doi.org/10.1093/ije/dyx131DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5837506PMC
December 2017

The efficacy of prostate-specific antigen screening: Impact of key components in the ERSPC and PLCO trials.

Cancer 2018 03 6;124(6):1197-1206. Epub 2017 Dec 6.

Division of Public Health Sciences, Fred Hutchinson Cancer Research Institute, Seattle, Washington.

Background: The European Randomized Study of Screening for Prostate Cancer (ERSPC) demonstrated that prostate-specific antigen (PSA) screening significantly reduced prostate cancer mortality (rate ratio, 0.79; 95% confidence interval, 0.69-0.91). The US Prostate, Lung, Colorectal, and Ovarian (PLCO) trial indicated no such reduction but had a wide 95% CI (rate ratio for prostate cancer mortality, 1.09; 95% CI, 0.87-1.36). Standard meta-analyses are unable to account for key differences between the trials that can impact the estimated effects of screening and the trials' point estimates.

Methods: The authors calibrated 2 microsimulation models to individual-level incidence and mortality data from 238,936 men participating in the ERSPC and PLCO trials. A cure parameter for the underlying efficacy of screening was estimated by the models separately for each trial. The authors changed step-by-step major known differences in trial settings, including enrollment and attendance patterns, screening intervals, PSA thresholds, biopsy receipt, control arm contamination, and primary treatment, to reflect a more ideal protocol situation and differences between the trials.

Results: Using the cure parameter estimated for the ERSPC, the models projected 19% to 21% and 6% to 8%, respectively, prostate cancer mortality reductions in the ERSPC and PLCO settings. Using this cure parameter, the models projected a reduction of 37% to 43% under annual screening with 100% attendance and biopsy compliance and no contamination. The cure parameter estimated for the PLCO trial was 0.

Conclusions: The observed cancer mortality reduction in screening trials appears to be highly sensitive to trial protocol and practice settings. Accounting for these differences, the efficacy of PSA screening in the PLCO setting is not necessarily inconsistent with ERSPC results. Cancer 2018;124:1197-206. © 2017 American Cancer Society.
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http://dx.doi.org/10.1002/cncr.31178DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5839977PMC
March 2018

Outcomes from ovarian cancer screening in the PLCO trial: Histologic heterogeneity impacts detection, overdiagnosis and survival.

Eur J Cancer 2017 12 21;87:182-188. Epub 2017 Nov 21.

Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

Aim: A mortality benefit from screening for ovarian cancer has never been demonstrated. The aim of this study was to evaluate the screening outcomes for different histologic subtypes of ovarian cancers.

Methods: Women in the screening arm of the Prostate, Lung, Colorectal and Ovarian Screening Trial underwent CA-125 and transvaginal ultrasound annually for 3-5 years. We compared screening test characteristics (including overdiagnosis) and outcomes by tumour type (type II versus other) and study arm (screening versus usual care).

Results: Of 78,215 women randomised, 496 women were diagnosed with ovarian cancer. Of the tumours that were characterised (n = 413; 83%), 74% (n = 305) were type II versus 26% other (n = 108). Among screened patients, 70% of tumours were type II compared to 78% in usual care (p = 0.09). Within the screening arm, 29% of type II tumours were screen detected compared to 54% of the others (p < 0.01). The sensitivity of screening was 65% for type II tumours versus 86% for other types (p = 0.02). 15% of type II screen-detected tumours were stage I/II, compared to 81% of other tumours (p < 0.01). The overdiagnosis rate was lower for type II compared to other tumours (28.2% versus 72.2%; p < 0.01). Ovarian cancer-specific survival was worse for type II tumours compared to others (p < 0.01). Survival was similar for type II (p = 0.74) or other types (p = 0.32) regardless of study arm.

Conclusions: Test characteristics of screening for ovarian cancer differed for type II tumours compared to other ovarian tumours. Type II tumours were less likely to be screen diagnosed, early stage at diagnosis or overdiagnosed.
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http://dx.doi.org/10.1016/j.ejca.2017.10.015DOI Listing
December 2017

Reconciling the Effects of Screening on Prostate Cancer Mortality in the ERSPC and PLCO Trials.

Ann Intern Med 2017 Oct 5;167(7):449-455. Epub 2017 Sep 5.

From University of Michigan, Ann Arbor, Michigan; Fred Hutchinson Cancer Research Center, Seattle, Washington; Erasmus Medical Center, Rotterdam, the Netherlands; National Cancer Institute, Bethesda, Maryland; Queen Mary University of London, London, United Kingdom; Sahlgrenska University Hospital, Göteborg, Sweden; Johns Hopkins Medicine, Baltimore, Maryland; University of Tampere, Tampere, Finland; Washington University School of Medicine, St. Louis, Missouri; University of Colorado, Denver, Colorado; Provinciaal Instituut voor Hygiëne, Antwerp, Belgium; Kantonsspital Aarau, Aarau, Switzerland; Institute for Cancer Prevention, Florence, Italy; Universidad Complutense de Madrid, Parla, Madrid, Spain; and Université de Lille, Lille, France.

Background: The ERSPC (European Randomized Study of Screening for Prostate Cancer) found that screening reduced prostate cancer mortality, but the PLCO (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial) found no reduction.

Objective: To evaluate whether effects of screening on prostate cancer mortality relative to no screening differed between the ERSPC and PLCO.

Design: Cox regression of prostate cancer death in each trial group, adjusted for age and trial. Extended analyses accounted for increased incidence due to screening and diagnostic work-up in each group via mean lead times (MLTs), which were estimated empirically and using analytic or microsimulation models.

Setting: Randomized controlled trials in Europe and the United States.

Participants: Men aged 55 to 69 (ERSPC) or 55 to 74 (PLCO) years at randomization.

Intervention: Prostate cancer screening.

Measurements: Prostate cancer incidence and survival from randomization; prostate cancer incidence in the United States before screening began.

Results: Estimated MLTs were similar in the ERSPC and PLCO intervention groups but were longer in the PLCO control group than the ERSPC control group. Extended analyses found no evidence that effects of screening differed between trials (P = 0.37 to 0.47 [range across MLT estimation approaches]) but strong evidence that benefit increased with MLT (P = 0.0027 to 0.0032). Screening was estimated to confer a 7% to 9% reduction in the risk for prostate cancer death per year of MLT. This translated into estimates of 25% to 31% and 27% to 32% lower risk for prostate cancer death with screening as performed in the ERSPC and PLCO intervention groups, respectively, compared with no screening.

Limitation: The MLT is a simple metric of screening and diagnostic work-up.

Conclusion: After differences in implementation and settings are accounted for, the ERSPC and PLCO provide compatible evidence that screening reduces prostate cancer mortality.

Primary Funding Source: National Cancer Institute.
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http://dx.doi.org/10.7326/M16-2586DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5734053PMC
October 2017

Data sharing in clinical trials: An experience with two large cancer screening trials.

PLoS Med 2017 05 23;14(5):e1002304. Epub 2017 May 23.

Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America.

Paul Pinsky of the US National Cancer Institute and colleagues describe the implementation and outcomes of web-based data sharing from the PLCO and NLST cancer screening trials.
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http://dx.doi.org/10.1371/journal.pmed.1002304DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5441574PMC
May 2017

Extended mortality results for ovarian cancer screening in the PLCO trial with median 15years follow-up.

Gynecol Oncol 2016 Nov 9;143(2):270-275. Epub 2016 Sep 9.

Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, United States.

Background: The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial originally reported no mortality benefit of ovarian cancer screening after a median of 12.4years of follow-up. The UKCTOCS screening trial failed to show a statistically significant mortality reduction in the primary analysis but reported an apparent increased mortality benefit in trial years 7-14 compared to 0-7. Here we report an updated analysis of PLCO with extended mortality follow-up.

Methods: Participants were randomized from 1993 to 2001 at ten U.S. centers to an intervention or usual care arm. Intervention arm women were screened for ovarian cancer with annual trans-vaginal ultrasound (TVU) (4years) and CA-125 (6years), with a fixed cutoff at 35U/mL for CA-125. The original follow-up period was for up to 13years (median follow-up 12.4years); in this analysis follow-up for mortality was extended by up to 6years.

Results: 39,105 (intervention) and 39,111 (usual care) women were randomized, of which 34,253 and 34,304, respectively, had at least one ovary at baseline. Median follow-up was 14.7years in each arm and maximum follow-up 19.2years in each arm. A total of 187 (intervention) and 176 (usual care) deaths from ovarian cancer were observed, for a risk-ratio of 1.06 (95% CI: 0.87-1.30). Risk-ratios were similar for study years 0-7 (RR=1.04), 7-14 (RR=1.06) and 14+ (RR=1.09). The risk ratio for all-cause mortality was 1.01 (95% CI: 0.97-1.05). Ovarian cancer specific survival was not significantly different across trial arms (p=0.16).

Conclusion: Extended follow-up of PLCO indicated no mortality benefit from screening for ovarian cancer with CA-125 and TVU.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5077651PMC
http://dx.doi.org/10.1016/j.ygyno.2016.08.334DOI Listing
November 2016

The Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) Screening Trial Pathology Tissue Resource.

Cancer Epidemiol Biomarkers Prev 2016 12 15;25(12):1635-1642. Epub 2016 Sep 15.

Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland.

Background: Pathology tissue specimens with associated epidemiologic and clinical data are valuable for cancer research. The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial undertook a large-scale effort to create a public resource of pathology tissues from PLCO participants who developed a cancer during the trial.

Methods: Formalin-fixed paraffin-embedded tissue blocks were obtained from pathology laboratories on a loan basis for central processing of tissue microarrays, with additional free-standing tissue cores collected for nucleic acid extraction.

Results: Pathology tissue specimens were obtained for prostate cancer (n = 1,052), lung cancer (n = 434), colorectal cancer (n = 675) and adenoma (n = 658), ovarian cancer and borderline tumors (n = 212), breast cancer (n = 870), and bladder cancer (n = 204). The process of creating this resource was complex, involving multidisciplinary teams with expertise in pathology, epidemiology, information technology, project management, and specialized laboratories.

Conclusions: Creating the PLCO tissue resource required a multistep process, including obtaining medical records and contacting pathology departments where pathology materials were stored after obtaining necessary patient consent and authorization. The potential to link tissue biomarkers to prospectively collected epidemiologic information, screening and clinical data, and matched blood or buccal samples offers valuable opportunities to study etiologic heterogeneity, mechanisms of carcinogenesis, and biomarkers for early detection and prognosis.

Impact: The methods and protocols developed for this effort, and the detailed description of this resource provided here, will be useful for those seeking to use PLCO pathology tissue specimens for their research and may also inform future tissue collection efforts in other settings. Cancer Epidemiol Biomarkers Prev; 25(12); 1635-42. ©2016 AACR.
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http://dx.doi.org/10.1158/1055-9965.EPI-16-0506DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5135604PMC
December 2016

Breast Cancer Screening Interval: Risk Level May Matter.

Authors:
Christine D Berg

Ann Intern Med 2016 11 23;165(10):737-738. Epub 2016 Aug 23.

From Johns Hopkins Medicine, Bethesda, Maryland.

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http://dx.doi.org/10.7326/M16-1791DOI Listing
November 2016

Lung cancer screening makes the GRADE.

Authors:
Christine D Berg

Prev Med 2016 08 7;89:315-316. Epub 2016 Jun 7.

Johns Hopkins Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins School of Medicine, United States.

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http://dx.doi.org/10.1016/j.ypmed.2016.06.006DOI Listing
August 2016

Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States.

JAMA Oncol 2016 Oct;2(10):1295-1302

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles.

Importance: An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention.

Objective: To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors.

Design, Setting, And Participants: Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health Interview Survey, a model for predicting absolute risk of breast cancer was developed combining information on individual level data on epidemiologic risk factors and 24 genotyped SNPs from prospective cohort studies, published estimate of odds ratios for 68 additional SNPs, population incidence rate from the National Cancer Institute-Surveillance, Epidemiology, and End Results Program cancer registry and data on risk factor distribution from nationally representative health survey. The model is used to project the distribution of absolute risk for the population of white women in the United States after adjustment for competing cause of mortality.

Exposures: Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors.

Main Outcomes And Measures: Degree of stratification of absolute risk owing to nonmodifiable (SNPs, family history, height, and some components of menstrual and/or reproductive history) and modifiable factors (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared], menopausal hormone therapy [MHT], alcohol, and smoking).

Results: The average absolute risk for a 30-year-old white woman in the United States developing invasive breast cancer by age 80 years is 11.3%. A model that includes all risk factors provided a range of average absolute risk from 4.4% to 23.5% for women in the bottom and top deciles of the risk distribution, respectively. For women who were at the lowest and highest deciles of nonmodifiable risks, the 5th and 95th percentile range of the risk distribution associated with 4 modifiable factors was 2.9% to 5.0% and 15.5% to 25.0%, respectively. For women in the highest decile of risk owing to nonmodifiable factors, those who had low BMI, did not drink or smoke, and did not use MHT had risks comparable to an average woman in the general population.

Conclusions And Relevance: This model for absolute risk of breast cancer including SNPs can provide stratification for the population of white women in the United States. The model can also identify subsets of the population at an elevated risk that would benefit most from risk-reduction strategies based on altering modifiable factors. The effectiveness of this model for individual risk communication needs further investigation.
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http://dx.doi.org/10.1001/jamaoncol.2016.1025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719876PMC
October 2016
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