Evidence map›Paper›PMID 33933557›Full record

ReviewBiochimica et biophysica acta. Reviews on cancer2021

Improving DCIS diagnosis and predictive outcome by applying artificial intelligence.

Mary-Kate Hayward, Valerie M Weaver

Open access · greenAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biochimica et biophysica acta. Reviews on cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 28% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. The promise of artificial intelligence in diagnostic breast pathology.Virchows Archiv : an international journal of pathology · 2026
    Article
  3. Article
  4. Review
  5. Review
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 at 1 institution in 1 country.

Mary-Kate HaywardCenter for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, California, USA.
Valerie M WeaverCenter for Bioengineering and Tissue Regeneration, Department of Surgery, University of California, San Francisco, California, USA; Department of Bioengineering and Therapeutic Sciences, and UCSF Helen Diller Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA; Department of Radiation Oncology, Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, California, USA. Electronic address: valerie.weaver@ucsf.edu.
University of California, San Francisco · US

Funding

Tissue mechanics reprograms the tissue to malignancy and metastasisR35CA242447 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI VALERIE MARIE WEAVER · 2020 to 2026
$6.6M
A biophysical stromal basis for radiation sensitivity in ductal carcinoma in situR01CA174929 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PARK, CATHERINE C, WEAVER, VALERIE MARIE · 2014 to 2018
$3.1M
Tension-Stat3-miR-mediated metastasisR01CA192914 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SEEWALDT, VICTORIA L., WEAVER, VALERIE MARIE · 2015 to 2019
$2.9M
Tissue Tension, RANK and Breast Cancer RiskR01CA222508 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HWANG, E.SHELLEY, WEAVER, VALERIE MARIE · 2018 to 2022
$2.8M
NCI NIH HHS R01 CA174929NCI NIH HHS R01 CA192914NCI NIH HHS R01 CA222508NCI NIH HHS R35 CA242447
6 · The paper itself

Abstract

Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all DCIS will progress to invasion. Current histopathological classification systems are unable to predict which cases will or will not progress, and therefore many women with DCIS may be overtreated. Artificial intelligence (AI) image-based analysis methods have potential to identify and analyze novel features that may facilitate tumor identification, prediction of disease outcome and response to treatment. Indeed, these methods prove promising for accurately identifying DCIS lesions, and show potential clinical utility in the therapeutic stratification of DCIS patients. Here, we review how AI techniques in histopathology may aid diagnosis and clinical decisions in regards to DCIS, and how such techniques could be incorporated into clinical practice.

Indexed as

Artificial IntelligenceDiagnosis, Computer-AssistedImage Interpretation, Computer-AssistedMicroscopyBiopsyBreast NeoplasmsCarcinoma, Intraductal, NoninfiltratingFemaleHumansPredictive Value of TestsPrognosisReproducibility of ResultsBreast cancerDCISImage analysisPathology

Identifiers

PMID33933557
PMCPMC10217774
OpenAlexW3159960413

What OpenQuestion holds

Textmetadata
LicenceTDM
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.