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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



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.


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.


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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Area under the curve


Non-small cell lung cancer


Pleural invasion


Lymphovascular invasion


Computed tomography


Regions of interest






First Affiliated Hospital of Gannon Medical University


Gray level co-occurrence matrix


Gray level run length matrix


Gray level size zone matrix


Neighboring gray level difference matrix


Gray level dependence matrix


Correlation coefficient


Multivariate minimum medundancy maximum relevance


Embedded least absolute shrinkage and selection operator


Support vector machine


K-nearest neighbors


Random forests


Naive Bayes classifier


Logistic regression


Multilayer perceptron


Linear discriminant analysis


Receiver operating characteristic


Shapley additive explanation.


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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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Authors and Affiliations



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

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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).

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