Evidence map›Paper›PMID 26683405›Full record

ArticleJournal of the National Cancer Institute2016

Outcomes of Active Surveillance for Ductal Carcinoma in Situ: A Computational Risk Analysis.

Marc D Ryser, Mathias Worni, Elizabeth L Turner, Jeffrey R Marks, Rick Durrett, E Shelley Hwang

Open access · bronzeAbstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Should low-risk DCIS lose the cancer label? An evidence review.Breast cancer research and treatment · 2023
    Review
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Marc D RyserDepartment of Mathematics (MDR, RD) and Duke Global Health Institute (ELT), Duke University, Durham, NC; Division of Advanced Oncologic and GI Surgery (MDR, MW, ESH) and Division of Surgical Sciences (JRM), Department of Surgery, Duke University Medical Center, Durham, NC; Department of Visceral Surgery and Medicine, Inselspital, Berne University Hospital and University of Berne, Berne, Switzerland (MW); Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC (ELT).
Mathias WorniDepartment of Mathematics (MDR, RD) and Duke Global Health Institute (ELT), Duke University, Durham, NC; Division of Advanced Oncologic and GI Surgery (MDR, MW, ESH) and Division of Surgical Sciences (JRM), Department of Surgery, Duke University Medical Center, Durham, NC; Department of Visceral Surgery and Medicine, Inselspital, Berne University Hospital and University of Berne, Berne, Switzerland (MW); Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC (ELT).
Elizabeth L TurnerDepartment of Mathematics (MDR, RD) and Duke Global Health Institute (ELT), Duke University, Durham, NC; Division of Advanced Oncologic and GI Surgery (MDR, MW, ESH) and Division of Surgical Sciences (JRM), Department of Surgery, Duke University Medical Center, Durham, NC; Department of Visceral Surgery and Medicine, Inselspital, Berne University Hospital and University of Berne, Berne, Switzerland (MW); Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC (ELT).
Jeffrey R MarksDepartment of Mathematics (MDR, RD) and Duke Global Health Institute (ELT), Duke University, Durham, NC; Division of Advanced Oncologic and GI Surgery (MDR, MW, ESH) and Division of Surgical Sciences (JRM), Department of Surgery, Duke University Medical Center, Durham, NC; Department of Visceral Surgery and Medicine, Inselspital, Berne University Hospital and University of Berne, Berne, Switzerland (MW); Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC (ELT).
Rick DurrettDepartment of Mathematics (MDR, RD) and Duke Global Health Institute (ELT), Duke University, Durham, NC; Division of Advanced Oncologic and GI Surgery (MDR, MW, ESH) and Division of Surgical Sciences (JRM), Department of Surgery, Duke University Medical Center, Durham, NC; Department of Visceral Surgery and Medicine, Inselspital, Berne University Hospital and University of Berne, Berne, Switzerland (MW); Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC (ELT).
E Shelley HwangDepartment of Mathematics (MDR, RD) and Duke Global Health Institute (ELT), Duke University, Durham, NC; Division of Advanced Oncologic and GI Surgery (MDR, MW, ESH) and Division of Surgical Sciences (JRM), Department of Surgery, Duke University Medical Center, Durham, NC; Department of Visceral Surgery and Medicine, Inselspital, Berne University Hospital and University of Berne, Berne, Switzerland (MW); Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC (ELT). shelley.hwang@duke.edu.
University Hospital of Bern · CHDuke Medical Center · US

Funding

EXPRESSION BASED MARKERS FOR BREAST CANCER DETECTIONU01CA084955 · NCI · DUKE UNIVERSITY · PI MARKS, JEFFREY R. · 1999 to 2015
$6.5M
Mathematical Models of Cancer Initiation, Progression, and Resistance to TherapyR01GM096190 · NIGMS · DUKE UNIVERSITY · PI DURRETT, RICK · 2010 to 2013
$1.4M
NCI NIH HHS U01 CA084955NIGMS NIH HHS R01-GM096190
6 · The paper itself

Abstract

backgroundDuctal carcinoma in situ (DCIS) is a noninvasive breast lesion with uncertain risk for invasive progression. Usual care (UC) for DCIS consists of treatment upon diagnosis, thus potentially overtreating patients with low propensity for progression. One strategy to reduce overtreatment is active surveillance (AS), whereby DCIS is treated only upon detection of invasive disease. Our goal was to perform a quantitative evaluation of outcomes following an AS strategy for DCIS.

methodsAge-stratified, 10-year disease-specific cumulative mortality (DSCM) for AS was calculated using a computational risk projection model based upon published estimates for natural history parameters, and Surveillance, Epidemiology, and End Results data for outcomes. AS projections were compared with the DSCM for patients who received UC. To quantify the propagation of parameter uncertainty, a 95% projection range (PR) was computed, and sensitivity analyses were performed.

resultsUnder the assumption that AS cannot outperform UC, the projected median differences in 10-year DSCM between AS and UC when diagnosed at ages 40, 55, and 70 years were 2.6% (PR = 1.4%-5.1%), 1.5% (PR = 0.5%-3.5%), and 0.6% (PR = 0.0%-2.4), respectively. Corresponding median numbers of patients needed to treat to avert one breast cancer death were 38.3 (PR = 19.7-69.9), 67.3 (PR = 28.7-211.4), and 157.2 (PR = 41.1-3872.8), respectively. Sensitivity analyses showed that the parameter with greatest impact on DSCM was the probability of understaging invasive cancer at diagnosis.

conclusionAS could be a viable management strategy for carefully selected DCIS patients, particularly among older age groups and those with substantial competing mortality risks. The effectiveness of AS could be markedly improved by reducing the rate of understaging.

Indexed as

MammographyPopulation SurveillanceWatchful WaitingAdultAgedCarcinoma, Ductal, BreastCarcinoma, Intraductal, NoninfiltratingDisease ProgressionFemaleHumansMiddle AgedNeoplasm StagingResearch DesignRisk AssessmentRisk FactorsSEER Program

Identifiers

PMID26683405
PMCPMC4849804
OpenAlexW2234559382

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.