Evidence map›Paper›PMID 40586837›Full record

ArticleHormones (Athens, Greece)2025

A novel approach for classifying patients with adrenal tumors based on decision support systems and artificial intelligence.

Dimitrios A Binas, Grigoria Betsi, Theodore Economopoulos, Chrysoula Mytareli, Charis Bourgioti, Paraskevi Xekouki, Anna Angelousi, George K Matsopoulos

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Article in Hormones (Athens, Greece), 2025. 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
–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

2 citing papers in PubMed.

  1. Article
  2. A longitudinal study of dual-energy X-ray absorptiometry status in menopausal women with adrenal tumors.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie
    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

8 authors.

Dimitrios A BinasBiomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technological University of Athens, Athens, Greece. d.binas@biomed.ntua.gr.ORCID http://orcid.org/0009-0004-2233-0024
Grigoria BetsiEndocrinology and Diabetes Clinic, University of Crete School of Medicine, University General Hospital of Heraklion Crete, Iraklio, Greece.
Theodore EconomopoulosBiomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technological University of Athens, Athens, Greece.
Chrysoula MytareliFirst Department of Internal Medicine, Center of Excellence for Rare Endocrine Diseases, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece.
Charis BourgiotiDepartment of Radiology, School of Medicine National, Kapodistrian University of Athens, Aretaieion Hospital, Athens, Greece.
Paraskevi XekoukiEndocrinology and Diabetes Clinic, University of Crete School of Medicine, University General Hospital of Heraklion Crete, Iraklio, Greece.
Anna AngelousiFirst Department of Internal Medicine, Center of Excellence for Rare Endocrine Diseases, National and Kapodistrian University of Athens, Laikon General Hospital, Athens, Greece.
George K MatsopoulosBiomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technological University of Athens, Athens, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsAdrenal incidentalomas (AIs) encompass a wide range of clinical entities, from incidental benign neoplasms that need to be monitored to aggressive malignancies requiring urgent medical intervention and treatment. The incidence of adrenal tumors is steadily rising, reflecting a growing trend in their prevalence and highlighting the necessity for heightened awareness and advanced diagnostic strategies to address this escalating health concern. This retrospective study was undertaken in order to explore the possibility of developing a decision support system for classifying adrenal tumors as benign and malignant or suspicious for malignancy.

methodsA powerful combination of 12 clinical, biochemical, epidemiological, and radiological features of 256 patients with adrenal tumors was analyzed to make predictions and various machine learning models were trained and compared to identify the model that consistently achieves the highest accuracy.

resultsAn average accuracy of over 91% and a balanced accuracy surpassing 94% was achieved based on a small amount of data and a small number of patients, without using imaging radiomics.

conclusionThe present study provided a decision-making process in healthcare systems permitting reliable automated classification of adrenal tumors. This is a promising candidate for integration into an initial screening tool for adrenal cancer, offering clinicians a valuable resource for early detection and intervention.

Indexed as

Adrenal Gland NeoplasmsArtificial IntelligenceDecision Support Systems, ClinicalAdultAgedFemaleHumansMachine LearningMaleMiddle AgedRetrospective StudiesAdrenal incidentalomasBenign adrenal tumorsClassifying patients with features suspicious for malignant adrenal tumorsDecision support systemMachine learning

Identifiers

PMID40586837

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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.