Evidence map›Paper›PMID 37020103›Full record

ArticleJournal of endocrinological investigation2023

Radiomics model and clinical scale for the preoperative diagnosis of silent corticotroph adenomas.

H Wang, J Chang, W Zhang, Y Fang, S Li, Y Fan, S Jiang, Y Yao, K Deng, L Lu and 4 more

Abstract read
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In one paragraph

Article in Journal of endocrinological investigation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Radiomics of pituitary adenoma using computer vision: a review.Medical & biological engineering & computing · 2024
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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

14 authors.

H Wang *Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
J Chang *Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
W Zhang *Department of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
Y FangDepartment of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
S LiDepartment of Plastic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China.
Y FanDepartment of Neurosurgery, Beijing Tiantan Hospital, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
S JiangDepartment of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
Y YaoDepartment of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
K DengDepartment of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
L LuDepartment of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China.
X BaoDepartment of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China.
F FengDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China.
R WangDepartment of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China. wangrz@126.com.ORCID http://orcid.org/0000-0003-2080-5474
M FengDepartment of Neurosurgery, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College Hospital, No. 1 Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China. pumchfengming@163.com.ORCID http://orcid.org/0000-0002-1666-2717

Funding

Beijing Municipal Natural Science Foundation M22013CAMS Innovation Fund for Medical Sciences CIFMS 2021-I2M-1-003Key-Area Research and Development Program of Guangdong Province 2021B0101420005National High Level Hospital Clinical Research Funding 2022-PUMCH-C-012National Key R&D Program of China 2021YFE0114300
6 · The paper itself

Abstract

objectiveSilent corticotroph adenomas (SCAs) are a subtype of nonfunctioning pituitary adenomas that exhibit more aggressive behavior. However, rapid and accurate preoperative diagnostic methods are currently lacking.

designThe purpose of this study was to examine the differences between SCA and non-SCA features and to establish radiomics models and a clinical scale for rapid and accurate prediction.

methodsA total of 260 patients (72 SCAs vs. 188 NSCAs) with nonfunctioning adenomas from Peking Union Medical College Hospital were enrolled in the study as the internal dataset. Thirty-five patients (6 SCAs vs. 29 NSCAs) from Fuzhou General Hospital were enrolled as the external dataset. Radiomics models and an SCA scale to preoperatively diagnose SCAs were established based on MR images and clinical features.

resultsThere were more female patients (internal dataset: p < 0.001; external dataset: p = 0.028) and more multiple microcystic changes (internal dataset: p < 0.001; external dataset: p = 0.012) in the SCA group. MRI showed more invasiveness (higher Knosp grades, p ≤ 0.001). The radiomics model achieved AUCs of 0.931 and 0.937 in the internal and external datasets, respectively. The clinical scale achieved an AUC of 0.877 and a sensitivity of 0.952 in the internal dataset and an AUC of 0.899 and a sensitivity of 1.0 in the external dataset.

conclusionsBased on clinical information and imaging characteristics, the constructed radiomics model achieved high preoperative diagnostic ability. The SCA scale achieved the purpose of rapidity and practicality while ensuring sensitivity, which is conducive to simplifying clinical work.

Indexed as

ACTH-Secreting Pituitary AdenomaAdenomaPituitary NeoplasmsFemaleHumansMagnetic Resonance ImagingRetrospective StudiesMachine learningRadiomicsScaleSilent corticotroph adenoma

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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.