Evidence map›Paper›PMID 32851505›Full record

ArticlePituitary2020

Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas.

Yae Won Park, Yunjun Kang, Sung Soo Ahn, Cheol Ryong Ku, Eui Hyun Kim, Se Hoon Kim, Eun Jig Lee, Sun Ho Kim, Seung-Koo Lee

Abstract read
PubMed Publisher
In one paragraph

Article in Pituitary, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
–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

28 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Radiomics of pituitary adenoma using computer vision: a review.Medical & biological engineering & computing · 2024
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  6. Pituitary adenoma imaging as a determinant of acromegaly diagnosis and outcomes.The Journal of clinical endocrinology and metabolism · 2026
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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

9 authors.

Yae Won ParkDepartment of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
Yunjun KangIntegrated Science and Engineering Division, Underwood International College, Yonsei University, Seoul, Korea.
Sung Soo AhnDepartment of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
Cheol Ryong KuPituitary Tumor Center, Severance Hospital, Seoul, Korea.
Eui Hyun KimPituitary Tumor Center, Severance Hospital, Seoul, Korea. euihyunkim@yuhs.ac.ORCID http://orcid.org/0000-0002-2523-7122
Se Hoon KimDepartment of Pathology, Yonsei University College of Medicine, Seoul, Korea.
Eun Jig LeePituitary Tumor Center, Severance Hospital, Seoul, Korea.
Sun Ho KimDepartment of Neurosurgery, Ewha Womans University College of Medicine, Seoul, Korea.
Seung-Koo LeeDepartment of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.

Funding

Korea Basic Science Institute 2020R1I1A1A0107164811
6 · The paper itself

Abstract

purposeTo investigate whether radiomic features from magnetic resonance image (MRI) can predict the granulation pattern of growth hormone (GH)-secreting pituitary adenoma patients.

methodsSixty-nine pathologically proven acromegaly patients (densely granulated [DG] = 50, sparsely granulated [SG] = 19) were included. Radiomic features (n = 214) were extracted from contrast-enhancing and total tumor portions from T2-weighted (T2) MRIs. Imaging features were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression model with fivefold cross-validation. Diagnostic performance for predicting granulation pattern was compared with that for qualitative T2 signal intensity assessment and T2 relative signal intensity (rSI) using the area under the receiver operating characteristics curve (AUC).

resultsFour significant radiomic features from the contrast-enhancing tumor (1 from shape, 1 from first order feature, and 2 from second order features) were selected by LASSO for model construction. The radiomics model showed an AUC, accuracy, sensitivity, and specificity of 0.834 (95% confidence interval [CI] 0.738-0.930), 73.7%, 74.0%, and 73.9%, respectively. The radiomics model showed significantly better performance than the model using qualitative T2 signal intensity assessment (AUC 0.597 [95% CI 0.447-0.747], P = 0.009) and T2 rSI (AUC 0.647 [95% CI 0.523-0.759], P = 0.037).

conclusionRadiomic features may be useful biomarkers to differentiate granulation pattern of GH-secreting pituitary adenoma patients, and showed better performance than qualitative assessment or rSI evaluation.

Indexed as

AdultFemaleGrowth Hormone-Secreting Pituitary AdenomaHumansInsulin-Like Growth Factor ILogistic ModelsMagnetic Resonance ImagingMaleMiddle AgedPituitary NeoplasmsRetrospective StudiesROC CurveInsulin-Like Growth Factor IAcromegalyGranulation patternGrowth hormone-secreting pituitary adenomaMagnetic resonance imagingPituitary neoplasmsRadiomics

Identifiers

PMID32851505

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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.