Evidence map›Paper›PMID 39867764›Full record

ArticlePakistan journal of medical sciences2025

Prediction of axillary lymph node metastasis in breast cancer patients based on ultrasonograhic-clinicopathologic features.

Tuba Laiq, Zubia Masood, Hiba Siddiqui, Maliha Javed, M Jawaid A Mallick

Abstract read
In one paragraph

Article in Pakistan journal of medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Tuba LaiqTuba Laiq, MBBS Post graduate trainee, Department of Oncology, Dr. Ziauddin Hospital, Karachi, Pakistan.
Zubia MasoodZubia Masood, FCPS, MHPE Associate Professor of Surgery Baqai Medical University, Consultant Surgeon, Dr. Ziauddin Hospital, Karachi, Pakistan.
Hiba SiddiquiHiba Siddiqui, MBBS Post graduate trainee, Department of Oncology, Dr. Ziauddin Hospital, Karachi, Pakistan.
Maliha JavedMaliha Javaid, MBBS Resident medical officer, Department of Oncology, Dr. Ziauddin Hospital, Karachi, Pakistan.
M Jawaid A MallickM. Jawaid A. Mallick, MD Consultant Oncologist, Head of Department of Oncology, Dr. Ziauddin Hospital, Karachi, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background & Objective: Determination of axillary lymph-node status plays a pivotal role in decision making for breast cancer treatment. Biopsy is the current standard of care but hold risks of complications as well. We aimed to find out the correlation of sonographic features of lymph node and histo-pathological findings, to predict axillary lymph-node metastasis in breast cancer patients. Method: This retrospective observational study included 176 breast cancer patients at a private tertiary care hospital from January 2019 to December 2023. The study calculated sensitivity, specificity and accuracy of ultrasound (US) in identifying ALN metastasis. Also, binary logistic regression analysis was used to demonstrate the association between suspicious findings on axillary US with pathology report. Patients who never had undergone axillary surgery or with insufficient data, were excluded from our study. Results: In our study Axillary US was found to be 84.2% sensitive, 48.1% specific, and 67.6% accurate in identifying nodal metastases. In this context, ALN metastases was strongly and independently correlated with cortical thickness > 3 mm and the absence of a fatty hilum (P <.05). Conclusion: Ultrasound was found to be highly sensitive but not specific in predicting metastatic lymph nodes in patients with breast cancer.

Indexed as

Axillary nodesBreast cancerMetastasisSensitivitySpecificityUltrasound

Identifiers

PMID39867764
PMCPMC11755317

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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.