Evidence map›Paper›PMID 37889272›Full record

ArticleEuropean radiology2024

Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model.

Guoqing Wu, Zhifeng Shi, Zeyang Li, Xuan Xie, Qisheng Tang, Jingjing Zhu, Zhong Yang, Yuanyuan Wang, Jinsong Wu, Jinhua Yu

Abstract read
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Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

10 authors.

Guoqing Wu *School of Information Science and Technology, Fudan University, Shanghai, China.
Zhifeng Shi *Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Zeyang LiDepartment of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Xuan XieSchool of Information Science and Technology, Fudan University, Shanghai, China.
Qisheng TangDepartment of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Jingjing ZhuDepartment of Pathology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Zhong YangDepartment of Radiology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Yuanyuan WangSchool of Information Science and Technology, Fudan University, Shanghai, China.
Jinsong WuDepartment of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China. wujinsong@huashan.org.cn.
Jinhua YuSchool of Information Science and Technology, Fudan University, Shanghai, China. jhyu@fudan.edu.cn.ORCID http://orcid.org/0000-0002-0654-6034

Funding

National Major Science and Technology Projects of China 2018SHZDZX01National Natural Science Foundation of China 62001119National Natural Science Foundation of China 91959127
6 · The paper itself

Abstract

objectivesAs a few types of glioma, young high-risk low-grade gliomas (HRLGGs) have higher requirements for postoperative quality of life. Although adjuvant chemotherapy with delayed radiotherapy is the first treatment strategy for HRLGGs, not all HRLGGs benefit from it. Accurate assessment of chemosensitivity in HRLGGs is vital for making treatment choices. This study developed a multimodal fusion radiomics (MFR) model to support radiochemotherapy decision-making for HRLGGs.

methodsA MFR model combining macroscopic MRI and microscopic pathological images was proposed. Multiscale features including macroscopic tumor structure and microscopic histological layer and nuclear information were grabbed by unique paradigm, respectively. Then, these features were adaptively incorporated into the MFR model through attention mechanism to predict the chemosensitivity of temozolomide (TMZ) by means of objective response rate and progression free survival (PFS).

resultsMacroscopic tumor texture complexity and microscopic nuclear size showed significant statistical differences (p < 0.001) between sensitivity and insensitivity groups. The MFR model achieved stable prediction results, with an area under the curve of 0.950 (95% CI: 0.942-0.958), sensitivity of 0.833 (95% CI: 0.780-0.848), specificity of 0.929 (95% CI: 0.914-0.936), positive predictive value of 0.833 (95% CI: 0.811-0.860), and negative predictive value of 0.929 (95% CI: 0.914-0.934). The predictive efficacy of MFR was significantly higher than that of the reported molecular markers (p < 0.001). MFR was also demonstrated to be a predictor of PFS.

conclusionsA MFR model including radiomics and pathological features predicts accurately the response postoperative TMZ treatment. CLINICAL RELEVANCE STATEMENT: Our MFR model could identify young high-risk low-grade glioma patients who can have the most benefit from postoperative upfront temozolomide (TMZ) treatment. KEY POINTS: • Multimodal radiomics is proposed to support the radiochemotherapy of glioma. • Some macro and micro image markers related to tumor chemotherapy sensitivity are revealed. • The proposed model surpasses reported molecular markers, with a promising area under the curve (AUC) of 0.95.

Indexed as

Brain NeoplasmsChemoradiotherapyGliomaMagnetic Resonance ImagingTemozolomideAdolescentAdultAntineoplastic Agents, AlkylatingClinical Decision-MakingFemaleHumansMaleNeoplasm GradingRadiomicsRetrospective StudiesYoung AdultAntineoplastic Agents, AlkylatingTemozolomideHematoxylin and eosin-stained slidesMagnetic resonance imagingMultimodal radiomicsRadiochemotherapyYoung high-risk low-grade glioma patients

Identifiers

PMID37889272

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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.