Evidence map›Paper›PMID 39821022›Full record

ReviewAdvances in experimental medicine and biology2025

Models for Studying Ductal Carcinoma In Situ Progression.

Isabella Nair, Fariba Behbod

Abstract readReview
PubMed Publisher
In one paragraph

Review in Advances in experimental medicine and biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Isabella NairDepartment of General Surgery, University of Missouri - Kansas City, Kansas City, MO, USA.
Fariba BehbodDepartment of Pathology and Laboratory Medicine, MS 3045, The University of Kansas Medical Center, Kansas City, KS, USA. fbehbod@kumc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An estimated 55,720 new cases of ductal carcinoma in situ (DCIS) will be diagnosed in 2023 in the USA alone because of the increased use of screening mammography. The treatment goal in DCIS is early detection and treatment with the hope of preventing progression into invasive disease. Previous studies show progression into invasive cancer as well as reduction in mortality from treatment is not as high as previously thought. So, are we overdiagnosing and over-treating DCIS? An understanding of the natural progression of DCIS is paramount to address this. The purpose of this chapter is to describe various models that have been developed to simulate the processes involved in DCIS to invasive ductal carcinoma (IDC) transition. While each model possesses a unique set of strengths and weaknesses, they have collectively contributed to the current understanding of the molecular and cellular mechanisms underlying this transition. Even though much has been learned, continued advancement of the current models to best match the composition of DCIS epithelial and stromal microenvironment including the extracellular matrix (ECM), stromal cell types, and immune microenvironment will be essential. These advances will undoubtedly pave the way toward a full understanding of mechanisms associated with progression and in predicting when a DCIS lesion remains indolent and when triggers tip in the balance toward progression to malignancy.

Indexed as

Breast NeoplasmsCarcinoma, Ductal, BreastCarcinoma, Intraductal, NoninfiltratingModels, BiologicalDisease ProgressionFemaleHumansTumor MicroenvironmentAnimal modelsBreast cancerDuctal carcinoma in situInvasive ductal carcinomaMouse-INtraDuctal (MIND) modelNonmalignant breast cancersPrecancer biology

Identifiers

What OpenQuestion holds

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Registered trials

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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.