Evidence map›Paper›PMID 41737063›Full record

ArticleCureus2026

Analysis of Galectin-3 in Differentiating Non-malignant and Malignant Nodular Thyroid Lesions.

Suganya J, Keerthikasri Es, Sowmya Srinivasan

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

3 authors.

Suganya JPathology, Mahatma Gandhi Medical College and Research Institute, Sri Balaji Vidyapeeth (SBV) University, Puducherry, IND.
Keerthikasri EsPathology, Mahatma Gandhi Medical College and Research Institute, Sri Balaji Vidyapeeth (SBV) University, Puducherry, IND.
Sowmya SrinivasanPathology, Mahatma Gandhi Medical College and Research Institute, Sri Balaji Vidyapeeth (SBV) University, Puducherry, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Accurate differentiation of benign and malignant thyroid nodules is critical for appropriate management. Fine-needle aspiration cytology (FNAC), although valuable, has limitations, especially in follicular-patterned neoplasms. Galectin-3, a beta-galactoside-binding lectin involved in cell proliferation and apoptosis, has been proposed as a diagnostic immunohistochemical marker. This study evaluates the utility of galectin-3 in distinguishing malignant from non-malignant nodular thyroid lesions. Methods A prospective observational study was conducted on 56 female patients undergoing thyroidectomy. Histopathological evaluation categorized cases into the benign (n=25), borderline (n=6), and malignant (n=25) groups. Immunohistochemical staining for galectin-3 was performed on paraffin-embedded tissue sections. Positivity was defined by cytoplasmic and/or nuclear staining. Results Galectin-3 expression was observed in 66.7% (14/21) of malignant lesions, predominantly in classic papillary thyroid carcinoma. None of the benign or borderline cases exhibited galectin-3 positivity. Notably, other papillary carcinoma variants were also negative. Statistical analysis showed a significant correlation between galectin-3 expression and malignancy (p < 0.001). Conclusion Galectin-3 is a highly specific immunohistochemical marker for malignant thyroid lesions, particularly classic papillary thyroid carcinoma. Its absence in benign and borderline lesions highlights its diagnostic value. Incorporating galectin-3 into routine histopathological evaluation can improve diagnostic accuracy, especially in indeterminate or follicular-patterned thyroid neoplasms.

Indexed as

biomarkerdiagnosisgalectin-3immunohistochemistryimmunosurveillancetargeted therapythyroid cancer

Identifiers

PMID41737063
PMCPMC12926785

What Socratic holds

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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 Socratic graph.