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Coordinatized lesion location analysis empowering ROI-based radiomics diagnosis on brain gliomas

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To assess the value of coordinatized lesion location analysis (CLLA), in empowering ROI-based imaging diagnosis of gliomas by improving accuracy and generalization performances.

Methods

In this retrospective study, pre-operative contrasted T1-weighted and T2-weighted MR images were obtained from patients with gliomas from three centers: **ling Hospital, Tiantan Hospital, and the Cancer Genome Atlas Program. Based on CLLA and ROI-based radiomic analyses, a fusion location-radiomics model was constructed to predict tumor grades, isocitrate dehydrogenase (IDH) status, and overall survival (OS). An inter-site cross-validation strategy was used for assessing the performances of the fusion model on accuracy and generalization with the value of area under the curve (AUC) and delta accuracy (ACC) (ACCtesting—ACCtraining). Comparisons of diagnostic performances were performed between the fusion model and the other two models constructed with location and radiomics analysis using DeLong’s test and Wilcoxon signed ranks test.

Results

A total of 679 patients (mean age, 50 years ± 14 [standard deviation]; 388 men) were enrolled. Based on tumor location probabilistic maps, fusion location-radiomics models (averaged AUC values of grade/IDH/OS: 0.756/0.748/0.768) showed the highest accuracy in contrast to radiomics models (0.731/0.686/0.716) and location models (0.706/0.712/0.740). Notably, fusion models ([median Delta ACC: − 0.125, interquartile range: 0.130]) demonstrated improved generalization than that of radiomics model ([− 0.200, 0.195], p = 0.018).

Conclusions

CLLA could empower ROI-based radiomics diagnosis of gliomas by improving the accuracy and generalization of the models.

Clinical relevance statement

This study proposed a coordinatized lesion location analysis for glioma diagnosis, which could improve the performances of the conventional ROI-based radiomics model in accuracy and generalization.

Key Points

• Using coordinatized lesion location analysis, we mapped anatomic distribution patterns of gliomas with specific pathological and clinical features and constructed glioma prediction models.

• We integrated coordinatized lesion location analysis into ROI-based analysis of radiomics to propose new fusion location-radiomics models.

• Fusion location-radiomics models, with the advantages of being less influenced by variabilities, improved accuracy, and generalization performances of ROI-based radiomics models on predicting the diagnosis of gliomas.

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Abbreviations

AUC:

Area under the curve

HGG:

High-grade glioma

IDH:

Isocitrate dehydrogenase

LGG:

Low-grade glioma

OS:

Overall survival

ROI:

Region-of-interest

TCGA:

The Cancer Genome Atlas

WHO:

World Health Organization

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Acknowledgements

Thanks are due to John W Chen from the Massachusetts General Hospital, Harvard Medical School for the revision of our manuscript.

Funding

This study has received funding by grants from the National Key Technology (R&D) Program of the Ministry of Science and Technology (2018YFA0701703 to Zhiqiang Zhang); the National Science and Technology Innovation 2030 − Major program of “Brain Science and Brain-Like Research” (2022ZD0211800 to Zhiqiang Zhang); Xuzhou Medical University Open Fund Project (XYKF202101 to Zhiqiang Zhang); the National Key R&D Program of China (2020AAA0109505 to Guangming Lu); and the National Natural Science Foundation of China (82127806 to Guangming Lu).

Author information

Author notes

  1. **aoxue Liu and Qirui Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Diagnostic Radiology, Affiliated **ling Hospital, Medical School of Nan**g University, 305#, Eastern Zhongshan Rd, Nan**g, 210002, China

    **aoxue Liu, Qirui Zhang, Jianrui Li, Qiang Xu, **an Zhou, Guangming Lu & Zhiqiang Zhang

  2. Department of Radiology, Bei**g Tiantan Hospital, Capital Medical University, Bei**g, 100070, China

    Zhizheng Zhuo, Junjie Li & Yaou Liu

  3. School of Public Health, Shanghai JiaoTong University School of Medicine, Shanghai, 200240, China

    Mengjie Lu

  4. Department of Radiology, The Affiliated Jiangning Hospital of Nan**g Medical University, Nan**g, 211100, China

    Qingqing Zhou

  5. Department of Neurosurgery, Affiliated **ling Hospital, Medical School of Nan**g University, Nan**g, 210002, China

    Hao Pan

  6. Department of Pathology, Affiliated **ling Hospital, Medical School of Nan**g University, Nan**g, 210002, China

    Nan Wu

  7. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200232, China

    Qing Zhou & Feng Shi

  8. State Key Laboratory of Analytical Chemistry for Life Science, Nan**g University, Nan**g, 210093, China

    Guangming Lu & Zhiqiang Zhang

Authors
  1. **aoxue Liu
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Corresponding author

Correspondence to Zhiqiang Zhang.

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Guarantor

The scientific guarantor of this publication is Zhiqiang Zhang from the Department of Diagnostic Radiology, Affiliated **ling Hospital, Medical School of Nan**g University.

Conflict of interest

Qing Zhou and Feng Shi are employees of Shanghai United Imaging Intelligence Co., Ltd. The company has no role in designing and performing the surveillance and analyzing and interpreting the data. The other 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

Mengjie Lu from the School of Public Health, Shanghai Jiaotong University School of Medicine, is one of the authors who has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• cross-sectional study

• multicenter study

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Liu, X., Zhang, Q., Li, J. et al. Coordinatized lesion location analysis empowering ROI-based radiomics diagnosis on brain gliomas. Eur Radiol 33, 8776–8787 (2023). https://doi.org/10.1007/s00330-023-09871-y

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