Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients
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The aim of this study was to develop a radiomics nomogram by combining the optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical predictors to assess the overall survival of patients with non-small cell lung cancer (NSCLC).
One training cohort of 239 and two validation datasets of 80 and 52 NSCLC patients were enrolled in this study. Nine hundred seventy-five radiomics features were extracted from each patient’s 2D and 3D CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate a radiomics signature. Cox hazard survival analysis and Kaplan-Meier were performed in both cohorts. The radiomics nomogram was developed by integrating the optimized radiomics signature and clinical predictors, its calibration and discrimination were evaluated.
The radiomics signatures were significantly associated with NSCLC patients’ survival time. The signature derived from the combined 2D and 3D features showed a better prognostic performance than those from 2D or 3D alone. Our radiomics nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of patients’ survival compared with clinical predictors alone in the validation cohort. The calibration curve showed predicted survival time was very close to the actual one.
The radiomics signature from the combined 2D and 3D features further improved the predicted accuracy of survival prognosis for the patients with NSCLC. Combination of the optimal radiomics signature and clinical predictors performed better for individualied survival prognosis estimation in patients with NSCLC. These findings might affect trearment strategies and enable a step forward for precise medicine.
• We found both 2D and 3D radiomics signature have favorable prognosis, but 3D signature had a better performance.
• The radiomics signature generated from the combined 2D and 3D features had a better predictive performance than those from 2D or 3D features.
• Integrating the optimal radiomics signature with clinical predictors significantly improved the predictive power in patients’ survival compared with clinical TNM staging alone.
KeywordsNon-small cell lung cancer Radiomics Tomography x-ray computed Nomogram
Analysis of variance
The Harrell concordance index
Lin’ concordance correlation coefficient
Gray-level run length matrix
Least absolute shrinkage and selection operator
Non-small cell lung cancer
This study has received funding by the National Key Research and Development Program of China (Grant No. 2017YFA0205202) and partially funded by the National Natural Science Foundation of China (Grant No. U1401255 and 61672422).
Compliance with ethical standards
The scientific guarantor of this publication is Liyu Huang.
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
Jun Li kindly provided statistical advice for this manuscript.
Written informed consent was not required for this study because the data were obtained from the collection of NSCLC-Radiomics in The Cancer Imaging Archive (TCIA, URL: http://www.cancerimagingarchive.net/).
Institutional Review Board approval was not required because the data were obtained from the collection of NSCLC-Radiomics in The Cancer Imaging Archive (TCIA, URL: http://www.cancerimagingarchive.net/). It is a public database and we added the reference of data in our study.
• diagnostic or prognostic study
• multicenter study
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