Evidence map›Paper›PMID 38405203›Full record

ArticleNeuro-oncology advances

Identifying IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: Manual recognition followed by artificial intelligence recognition.

Lei He, Hong Zhang, Tianshi Li, Jianing Yang, Yanpeng Zhou, Jiaxiang Wang, Tuerhong Saidaer, Xiaoyan Bai, Xing Liu, Yinyan Wang and 1 more

Open access · goldAbstract read
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Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.8field-weighted citation impact, top 17% of its field
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

4 citing papers in PubMed, 4 citations in OpenAlex.

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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

11 authors at 1 institution in 1 country.

Lei HeDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0001-8098-2849
Hong ZhangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Tianshi LiDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Jianing YangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Yanpeng ZhouDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Jiaxiang WangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Tuerhong SaidaerDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Xiaoyan BaiDepartment of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Xing LiuDepartment of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Yinyan WangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.
Lei WangDepartment of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0002-6916-704X
Capital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The T2-FLAIR mismatch sign (T2FM) has nearly 100% specificity for predicting IDH-mutant and 1p/19q noncodeleted astrocytomas (astrocytomas). However, only 18.2%-56.0% of astrocytomas demonstrate a positive T2FM. Methods must be considered for distinguishing astrocytomas from negative T2FM gliomas. In this study, positive T2FM gliomas were manually distinguished from nonenhancing gliomas, and then a support vector machine (SVM) classification model was used to distinguish astrocytomas from negative T2FM gliomas. Methods: Nonenhancing gliomas (regardless of pathological type or grade) diagnosed between January 2022 and October 2022 ( Results: The specificity of T2FM for predicting astrocytomas was 100% in both the training and validation sets, while the sensitivity was 42.75% and 67.22%, respectively. Using a classification model of SVM based on radiomics features, among negative T2FM gliomas, the accuracy was above 85% when the prediction score was greater than 0.70 in identifying astrocytomas and above 95% when the prediction score was less than 0.30 in identifying nonastrocytomas. Conclusions: Manual screening of positive T2FM gliomas, followed by the SVM classification model to differentiate astrocytomas from negative T2FM gliomas, may be a more effective method for identifying astrocytomas in nonenhancing gliomas.

Indexed as

astrocytomamachine learningMRInonenhancing gliomaT2-FLAIR mismatch

Identifiers

PMID38405203
PMCPMC10894653
OpenAlexW4391463600

What OpenQuestion holds

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Registered trials

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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.