Evidence map›Paper›PMID 38802716›Full record

ArticleAnnals of surgical oncology2024

A Comparison of Near-Infrared Autofluorescence Findings in Benign Versus Malignant Adrenal Tumors.

Panagiotis Bletsis, Ege Akgun, Gustavo Romero-Velez, Anibal La Riva, Eren Berber

Abstract readComparative Study
In one paragraph

Article in Annals of surgical oncology, 2024. 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

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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

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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

5 authors.

Panagiotis Bletsis *Department of Endocrine Surgery, Metabolism and Endocrinology Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
Ege Akgun *Department of Endocrine Surgery, Metabolism and Endocrinology Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
Gustavo Romero-VelezDepartment of Endocrine Surgery, Metabolism and Endocrinology Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
Anibal La RivaDepartment of Endocrine Surgery, Metabolism and Endocrinology Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
Eren BerberDepartment of Endocrine Surgery, Metabolism and Endocrinology Institute, Cleveland Clinic Foundation, Cleveland, OH, USA. berbere@ccf.org.ORCID http://orcid.org/0000-0002-1964-9286

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany adrenal tumors are deemed radiologically indeterminate and surgically removed. Adrenal tissue, like parathyroid glands, exhibits near-infrared autofluorescence (NIRAF) properties. This study was designed to investigate the potential of NIRAF to differentiate benign versus malignant adrenal tumors.

methodsPatients undergoing adrenalectomy between October 2021 and May 2023 were prospectively studied. Adrenalectomy specimens were inspected with NIRAF imaging. Specimen autofluorescence (AF) characteristics were recorded. Comparisons were made between different tumor types and a logistic regression model was constructed to differentiate benign versus malignant tumors. A receiver operating characteristic curve was used to identify an optimal AF threshold differentiating benign versus malignant tumors.

resultsA total of 108 adrenal specimens were examined: adrenocortical adenomas/other benign lesions (n = 72), pheochromocytomas (n = 18), adrenocortical neoplasms of uncertain behavior (n = 4), and malignant tumors (n = 14). A significant difference in normalized AF intensity was identified when comparing adrenocortical adenomas (3.08 times background) with pheochromocytomas (1.95, p = 0.001) and malignant tumors (1.11, p < 0.0001). The Area Under the Curve differentiating benign vs malignant tumors was 0.87, with an optimal normalized AF threshold at 1.93.

conclusionsDifferent adrenal pathologies exhibit diverse AF properties. These findings suggest a potential intraoperative utility of NIRAF in predicting benign versus malignant nature for radiologically indeterminate adrenal tumors.

Indexed as

Adrenal Gland NeoplasmsOptical ImagingPheochromocytomaAdrenal Cortex NeoplasmsAdrenalectomyAdrenocortical AdenomaAdultAgedDiagnosis, DifferentialFemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisProspective Studies

Identifiers

PMID38802716
PMCPMC11236849

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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.