ArticlePolish journal of radiology2023
A radiomic signature based on magnetic resonance imaging to determine adrenal Cushing's syndrome.
Article in Polish journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.
- Pooled it
- AI-enabled precision evaluation of adrenal masses: radiomics, deep learning, and explainable imaging biomarkers.Frontiers in endocrinology · 2026Review
- Patient classification and attribute assessment based on machine learning techniques in the qualification process for surgical treatment of adrenal tumours.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The aim of this study was to develop radiomics signature-based magnetic resonance imaging (MRI) to determine adrenal Cushing's syndrome (ACS) in adrenal incidentalomas (AI). Material and methods: A total of 50 patients with AI were included in this study. The patients were grouped as nonfunctional adrenal incidentaloma (NFAI) and ACS. The lesions were segmented on unenhanced T1-weighted (T1W) in-phase (IP) and opposed-phase (OP) as well as on T2-weighted (T2-W) 3-Tesla MRIs. The LASSO regression model was used for the selection of potential predictors from 111 texture features for each sequence. The radiomics scores were compared between the groups. Results: The median radiomics score in T1W-Op for the NFAI and ACS were -1.17 and -0.17, respectively ( Conclusion: The developed MRI-based radiomic scores can yield high AUCs for prediction of ACS.
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Registered trials
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.