Evidence map›Paper›PMID 38900321›Full record

ArticleAbdominal radiology (New York)2024

A head-to-head comparison of computed tomography- and magnetic resonance imaging-based radiomics in assessing pediatric peripheral neuroblastic tumor cell behavior.

Haoru Wang, Xin Chen, Ling He, Hao Ding, Mingye Xie, Jinhua Cai

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
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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

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

  1. Radiomics-Based Machine Learning for DeterminingTechnology in cancer research & treatment
    Pooled it
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4 · The record

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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

6 authors.

Haoru WangDepartment of Radiology, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.ORCID 0000-0003-2497-0872
Xin ChenDepartment of Radiology, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.ORCID 0000-0001-6640-5287
Ling HeDepartment of Radiology, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.ORCID 0000-0003-2702-2579
Hao DingDepartment of Radiology, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.
Mingye XieDepartment of Radiology, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.ORCID 0009-0009-1049-5258
Jinhua CaiDepartment of Radiology, Children's Hospital of Chongqing Medical University, No. 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China. cai_jinhua@126.com.ORCID 0000-0001-7971-8936

Funding

Natural Science Foundation of Chongqing Municipality (CSTB)2023NSCQ-BHX0127
6 · The paper itself

Abstract

purposeTo compare the performance of radiomics from contrast-enhanced computed tomography (CECT) and non-contrast magnetic resonance imaging (MRI) in assessing cellular behavior in pediatric peripheral neuroblastic tumors (PNTs). MATERIALS AND

methodsA retrospective analysis of 81 PNT patients who underwent venous phase CECT, T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI) scans was conducted. The patients were classified into neuroblastoma and ganglioneuroblastoma/ganglioneuroma based on their pathological subtypes. Additionally, they were categorized into favorable histology and unfavorable histology according to the International Neuroblastoma Pathology Classification (INPC). Tumor regions of interest were segmented on CECT, axial T1WI, and axial T2WI images, and radiomics models were developed based on the selected radiomics features. Following five-fold cross-validation, the performance of the radiomics models derived from CECT and MRI was compared using the area under the receiver operating characteristic curve (AUC) and accuracy.

resultsFor discriminating pathological subtypes, the AUC for CECT radiomics models ranged from 0.765 to 0.870, with an accuracy range of 0.728 to 0.815. In contrast, the AUC for MRI radiomics models ranged from 0.549 to 0.748, with an accuracy range of 0.531 to 0.778. Regarding the discrimination of INPC subgroups, the AUC for CECT radiomics models ranged from 0.503 to 0.759, with an accuracy range of 0.432 to 0.741. Meanwhile, the AUC for MRI radiomics models ranged from 0.512 to 0.739, with an accuracy range of 0.605 to 0.815.

conclusionsCECT radiomics outperforms non-contrast MRI radiomics in evaluating pathological subtypes. When assessing INPC subgroups, CECT radiomics demonstrates comparability with non-contrast MRI radiomics.

Indexed as

Contrast MediaMagnetic Resonance ImagingNeuroblastomaTomography, X-Ray ComputedAdolescentChildChild, PreschoolFemaleGanglioneuroblastomaHumansInfantMaleRadiomicsRetrospective StudiesContrast MediaChildrenComputed tomographyGanglioneuroblastomaGanglioneuromaMagnetic resonance imagingNeuroblastomaPeripheral neuroblastic tumorsRadiomics

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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.