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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

As 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.

Methods

A 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).

Results

Macroscopic 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.

Conclusions

A 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.

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Abbreviations

AFMF:

Attention-based feature map fusion block

AUC:

Area under curve

CR:

Complete response

CT:

Computed tomography

HRLGG:

High-risk low-grade glioma

KM:

Kaplan and Meier

MFR:

Multimodal fusion radiomics

MGMT:

Methylguanine-DNA-methyltransferase

MR:

Minor response

MRI:

Magnetic resonance imaging

PD:

Progression disease

PFS:

Progression-free survival

PR:

Partial response

RANO:

Response Assessment in Neuro-Oncology

ROI:

Region of interest

SD:

Stable disease

TMZ:

Temozolomide

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Funding

This study has received funding by the National Natural Science Foundation of China Youth Fund (No. 62001119), the Major research plan of the National Natural Science Foundation (No. 91959127) and the Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01) and ZJLab.

Author information

Author notes

  1. Guoqing Wu and Zhifeng Shi contributed equally to the manuscript.

Authors and Affiliations

  1. School of Information Science and Technology, Fudan University, Shanghai, China

    Guoqing Wu, Xuan Xie, Yuanyuan Wang & Jinhua Yu

  2. Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, China

    Guoqing Wu, Xuan Xie, Yuanyuan Wang & Jinhua Yu

  3. Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China

    Zhifeng Shi, Zeyang Li, Qisheng Tang & Jinsong Wu

  4. National Center for Neurological Disorders, Shanghai, China

    Zhifeng Shi, Zeyang Li, Qisheng Tang & Jinsong Wu

  5. Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China

    Zhifeng Shi, Zeyang Li, Qisheng Tang & Jinsong Wu

  6. Neurosurgical Institute of Fudan University, Shanghai, China

    Zhifeng Shi, Zeyang Li, Qisheng Tang & Jinsong Wu

  7. Department of Pathology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China

    Jingjing Zhu

  8. Department of Radiology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China

    Zhong Yang

  9. The AI Lab of Huashan Hospital, Fudan University, Shanghai, China

    Jinhua Yu

Authors
  1. Guoqing Wu
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  10. Jinhua Yu
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Corresponding authors

Correspondence to Jinsong Wu or Jinhua Yu.

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Guarantor

Jinhua Yu, School of Information Science and Technology, Fudan University, No. 220 Handan Rd, 200433, Shanghai, China.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Jinhua Yu has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional review board approval was obtained. The study was approved by the Ethics Committee of the Huashan Hospital and the Shanghai International Hospital.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

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Wu, G., Shi, Z., Li, Z. et al. Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model. Eur Radiol 34, 2861–2872 (2024). https://doi.org/10.1007/s00330-023-10378-9

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  • DOI: https://doi.org/10.1007/s00330-023-10378-9

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