Evidence map›Paper›PMID 31788377›Full record

ArticleCureus2019

Mammographic Criteria for Determining the Diagnostic Accuracy of Microcalcifications in the Detection of Malignant Breast Lesions.

Qurat Hadi, Imrana Masroor, Zainab Hussain

Open access · diamondAbstract read
In one paragraph

Article in Cureus, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 21% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
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

3 authors at 2 institutions in 1 country.

Qurat HadiRadiology, Dow Institute of Radiology (OJHA Campus), Dow University of Health Sciences, Karachi, PAK.
Imrana MasroorRadiology, Aga Khan University Hospital, Karachi, PAK.
Zainab HussainRadiology, Aga Khan University Hospital, Karachi, PAK.
Aga Khan University Hospital · PKDow University of Health Sciences · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Breast cancer is a progressive disease, with conditions secondary to primary breast cancer being among the more common causes of malignancy-related deaths in women. Early diagnosis can halt disease progression and significantly improve patient's survival. Microcalcifications detected on mammograms may be an indicator of breast cancer. This study assessed the diagnostic accuracy of microcalcifications seen on mammograms for the detection of malignant breast disease when compared with histopathology. Materials and methods This study enrolled 144 women referred to the Radiology Department of Aga Khan University Hospital in Karachi, Pakistan, for mammograms and who were found to have suspicious microcalcifications, for which they underwent subsequent biopsy with histopathology over one year. The accuracy of microcalcifications, along with their sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV), were assessed relative to histopathology results. Results Compared with histopathology results, microcalcifications had a sensitivity of 88%, and specificity of 62.8%, a PPV of 55.7%, and an NPV of 90.8%. The overall accuracy of microcalcifications was 71.5%. Conclusions The presence of microcalcifications on mammograms may predict breast malignancy. Studies with larger numbers of patients are required to determine whether microcalcifications have higher specificity and PPV relative to breast histopathology.

Indexed as

biradsbreast cancerfine branching microcalcificationspleomorphic microcalcifications

Identifiers

PMID31788377
PMCPMC6857828
OpenAlexW2980326814

What OpenQuestion holds

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LicenceCC BY
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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.