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Radiomics under 2D regions, 3D regions, and peritumoral regions reveal tumor heterogeneity in non-small cell lung cancer: a multicenter study

  • Chest Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

Lung cancer has significant genetic and phenotypic heterogeneity, leading to poor prognosis. Radiomic features have emerged as promising predictors of the tumor phenotype. However, the role of underlying information surrounding the cancer remains unclear.

Materials and methods

We conducted a retrospective study of 508 patients with NSCLC from three institutions. Radiomics models were built using features from six tumor regions and seven classifiers to predict three prognostically significant tumor phenotypes. The models were evaluated and interpreted by the mean area under the receiver operating characteristic curve (AUC) under nested cross-validation and Shapley values. The best-performing predictive models corresponding to six tumor regions and three tumor phenotypes were identified for further comparative analysis. In addition, we designed five experiments with different voxel spacing to assess the sensitivity of the experimental results to the spatial resolution of the voxels.

Results

Our results demonstrated that models based on 2D, 3D, and peritumoral region features yielded mean AUCs and 95% confidence intervals of 0.759 and [0.747–0.771] for lymphovascular invasion, 0.889 and [0.882–0.896] for pleural invasion, and 0.839 and [0.829–0.849] for T-staging in the testing cohort, which was significantly higher than all other models. Similar results were obtained for the model combining the three regional features at five voxel spacings.

Conclusion

Our study revealed the predictive role of the developed methods with multi-regional features for the preoperative assessment of prognostic factors in NSCLC. The analysis of different voxel spacing and model interpretability strengthens the experimental findings and contributes to understanding the biological significance of the radiological phenotype.

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Abbreviations

AUC:

Area under the curve

NSCLC:

Non-small cell lung cancer

PI:

Pleural invasion

LVI:

Lymphovascular invasion

CT:

Computed tomography

ROI:

Regions of interest

2D:

Two-dimensional

3D:

Three-dimensional

FAHGMU:

First Affiliated Hospital of Gannon Medical University

GLCM:

Gray level co-occurrence matrix

GLRLM:

Gray level run length matrix

GLSZM:

Gray level size zone matrix

NGTDM:

Neighboring gray level difference matrix

GLDM:

Gray level dependence matrix

CC:

Correlation coefficient

mRMR:

Multivariate minimum medundancy maximum relevance

LASSO:

Embedded least absolute shrinkage and selection operator

SVM:

Support vector machine

kNN:

K-nearest neighbors

RF:

Random forests

NB:

Naive Bayes classifier

LR:

Logistic regression

MLP:

Multilayer perceptron

LDA:

Linear discriminant analysis

ROC:

Receiver operating characteristic

SHAP:

Shapley additive explanation.

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Acknowledgements

This work was supported by the Overseas Joint Training Program and the Innovative Research Grant Program (Grant No. 2022GDJC-D20) for Postgraduates of Guangzhou University, as well as by the National Natural Science Foundation of China (Grant No. 61971118) and the Natural Science Foundation of Guangdong (Grant No. 2022A1515010102).

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Not applicable.

Author information

Author notes

  1. Xingping Zhang and Guijuan Zhang contributed equally to this work.

Authors and Affiliations

  1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China

    Xingping Zhang, Xiaoxia Yin, Hong Yang & Yanchun Zhang

  2. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia

    Xingping Zhang, Jiao Yin, Hua Wang & Yanchun Zhang

  3. Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000, China

    Xingping Zhang & Yanchun Zhang

  4. Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, China

    Guijuan Zhang

  5. Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, China

    Xingting Qiu

  6. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189, China

    Wenjun Tan

  7. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Liefa Liao

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  1. Xingping Zhang
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Contributions

XZ, HW, and YZ designed and wrote the study, and reviewed and edited the final manuscript. XZ, GZ, and XQ participated in data collection, tumor segmentation, statistical analysis, and clinical review of this work. YJ, WT, XY, HY, and LL contributed to the expert review of the manuscript.

Corresponding authors

Correspondence to Hua Wang or Yanchun Zhang.

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Conflict of interest

All authors declare no financial or non-financial competing interests.

Ethics approval and consent to participate

The institutional review boards of the First Affiliated Hospital of Gannon Medical University approved this retrospective study and waived the requirement for informed consent (No. LLSC-2023149).

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Zhang, X., Zhang, G., Qiu, X. et al. Radiomics under 2D regions, 3D regions, and peritumoral regions reveal tumor heterogeneity in non-small cell lung cancer: a multicenter study. Radiol med 128, 1079–1092 (2023). https://doi.org/10.1007/s11547-023-01676-9

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