Evidence map›Paper›PMID 30616515›Full record

ArticleBMC neurology2019

Noninvasive molecular diagnosis of craniopharyngioma with MRI-based radiomics approach.

Xi Chen, Yusheng Tong, Zhifeng Shi, Hong Chen, Zhong Yang, Yuanyuan Wang, Liang Chen, Jinhua Yu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 2 pooled it
4.8field-weighted citation impact, top 4% 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

35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 57 citations in OpenAlex.

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  18. Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 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

8 authors at 2 institutions in 1 country.

Xi ChenDepartment of Electronic Engineering, Fudan University, Shanghai, China.
Yusheng TongDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Zhifeng ShiDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
Hong ChenDepartment of Pathology, Huashan Hospital, Fudan University, Shanghai, China.
Zhong YangDepartment of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Yuanyuan WangDepartment of Electronic Engineering, Fudan University, Shanghai, China.
Liang ChenDepartment of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China. hs_chenliang@fudan.edu.cn.
Jinhua YuDepartment of Electronic Engineering, Fudan University, Shanghai, China. jhyu@fudan.edu.cn.
Fudan University · CNHuashan Hospital · CN

Funding

National Natural Science Foundation of China the National Basic Research Program of China (2015CB755500)
6 · The paper itself

Abstract

backgroundFrequent somatic mutations of BRAF and CTNNB1 were identified in both histological subtypes of craniopharyngioma (adamantinomatous and papillary) which shed light on target therapy to cure this oncogenic disease. The aim of this study was to investigate the noninvasive MRI-based radiomics diagnosis to detect BRAF and CTNNB1 mutations in craniopharyngioma patients.

methodsForty-four patients pathologically diagnosed as adamantinomatous craniopharyngioma (ACP) or papillary craniopharyngioma (PCP) were retrospectively studied. High-throughput features were extracted from manually segmented tumors in MR images of each case. The modifications-robustness in region of interests and Random Forest-based feature selection methods were adopted to select the most significant features. Random forest classifier with 10-fold cross-validation was applied to build our radiomics model.

resultsFour features were selected to make pathological diagnosis between ACP and PCP with area under the receiver operating characteristic curve (AUC) of 0.89, accurancy (ACC) of 0.86, sensitivity (SENS) of 0.89 and specificity (SPEC) of 0.85. The other two features were applied to estimate BRAF V600E mutation with AUC of 0.91, ACC of 0.93, SENS of 0.83 and SPEC of 0.97. Accurate predication of CTNNB1 mutation by three selected features was realized with AUC of 0.93, ACC of 0.86, SENS of 0.86 and SPEC of 0.86.

conclusionsWe developed a reliable MRI-based radiomics approach to perform pathological and molecular diagnosis in craniopharyngioma patients with considerably accurate prediction, which could offer potential guidance for clinical decision-making.

Indexed as

AdolescentAdultAgedbeta CateninChildChild, PreschoolCraniopharyngiomaHumansMagnetic Resonance ImagingMiddle AgedMutationPituitary NeoplasmsRetrospective StudiesROC CurveSensitivity and SpecificityYoung Adultbeta CateninCTNNB1 protein, humanCraniopharyngiomaMachine learningMolecular diagnosisNon-invasivenessRadiomics approach

Identifiers

PMID30616515
PMCPMC6322318
OpenAlexW2943141591

What OpenQuestion holds

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LicenceCC BY
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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.