Evidence map›Paper›PMID 33083871›Full record

ArticleAbdominal radiology (New York)2021

Differentiation of gastric schwannomas from gastrointestinal stromal tumors by CT using machine learning.

Jian Wang, Zongyu Xie, Xiandi Zhu, Zhongfeng Niu, Hongli Ji, Linyang He, Qiuxiang Hu, Cui Zhang

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Article in Abdominal radiology (New York), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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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

8 authors.

Jian Wang *Department of Radiology, Tongde Hospital of Zhejiang Province, Number 234, Road Gucui, Hangzhou, Zhejiang, China.
Zongyu Xie *Department of Radiology, The First Affliated Hospital of Bengbu Medical College, Bengbu, Anhui, China.
Xiandi ZhuDepartment of Radiology, Tongde Hospital of Zhejiang Province, Number 234, Road Gucui, Hangzhou, Zhejiang, China.
Zhongfeng NiuDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China.
Hongli JiJianpei Technology, Number 198, Road Qidi, Xiaoshan, Hangzhou, Zhejiang, China.
Linyang HeJianpei Technology, Number 198, Road Qidi, Xiaoshan, Hangzhou, Zhejiang, China.
Qiuxiang HuJianpei Technology, Number 198, Road Qidi, Xiaoshan, Hangzhou, Zhejiang, China.
Cui ZhangDepartment of Radiology, Tongde Hospital of Zhejiang Province, Number 234, Road Gucui, Hangzhou, Zhejiang, China. zc553217018@163.com.ORCID 0000-0002-8222-9639

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify schwannomas from gastrointestinal stromal tumors (GISTs) by CT features using Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT).

methodsThis study enrolled 49 patients with schwannomas and 139 with GISTs proven by pathology. CT features with P < 0.1 derived from univariate analysis were inputted to four models. Five machine learning (ML) versions, multivariate analysis, and radiologists' subjective diagnostic performance were compared to evaluate diagnosis performance of all the traditional and advanced methods.

resultsThe CT features with P < 0.1 were as follows: (1) CT attenuation value of unenhancement phase (CTU), (2) portal venous enhancement (CTV), (3) degree of enhancement in the portal venous phase (DEPP), (4) CT attenuation value of portal venous phase minus arterial phase (CTV-CTA), (5) enhanced potentiality (EP), (6) location, (7) contour, (8) growth pattern, (9) necrosis, (10) surface ulceration, (11) enlarged lymph node (LN). LR (M1), RF, DT, and GBDT models contained all of the above 11 variables, while LR (M2) was developed using six most predictive variables derived from (M1). LR (M2) model with AUC of 0.967 in test dataset was thought to be optimal model in differentiating the two tumors. Location in gastric body, exophytic and mixed growth pattern, lack of necrosis and surface ulceration, enlarged lymph nodes, and larger EP were the most important CT features suggestive of schwannomas.

conclusionLR (M2) provided the optimal diagnostic potency among other ML versions, multivariate analysis, and radiologists' performance on differentiation of schwannomas from GISTs.

Indexed as

Gastrointestinal Stromal TumorsNeurilemmomaStomach NeoplasmsHumansMachine LearningRetrospective StudiesTomography, X-Ray ComputedGastrointestinal stromal tumorMachine learningSchwannomaTomography, X-ray computed

Identifiers

PMID33083871

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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.