Abstract
Purpose
To develop a predictive model by 18F-FDG PET/CT radiomic features and to validate the predictive value of the model for distinguishing solitary lung adenocarcinoma from tuberculosis.
Methods
A total of 235 18F-FDG PET/CT patients with pathologically or follow-up confirmed lung adenocarcinoma (n = 131) or tuberculosis (n = 104) were retrospectively and randomly divided into a training (n = 163) and validation (n = 72) cohort. Based on the Transparent Reporting of Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), this work was belonged to TRIPOD type 2a study. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) algorithm were used to select the optimal predictors from 92 radiomic features that were extracted from PET/CT, and the optimal predictors were used to build the radiomic model in the training cohort. The meaningful clinical variables comprised the clinical model, and the combination of the radiomic model and clinical model was a complex model. The performances of the models were assessed by the area under the receiver operating characteristic curve (AUC) in the training and validation cohorts.
Results
In the training cohort, 9 radiomic features were selected as optimal predictors to build the radiomic model. The AUC of the radiomic model was significantly higher than that of the clinical model in the training cohort (0.861 versus 0.686, p < 0.01), and this was similar in the validation cohort (0.889 versus 0.644, p < 0.01). The AUC of the radiomic model was slightly lower than that of the complex model in the training cohort (0.861 versus 0.884, p > 0.05) and validation cohort (0.889 versus 0.909, p > 0.05), but there was no significant difference.
Conclusion
18F-FDG PET/CT radiomic features have a significant value in differentiating solitary lung adenocarcinoma from tuberculosis.
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Acknowledgements
The authors would like to acknowledge all the coworkers who participated in this study.
Funding
This study was supported by the Foundation of Science and Technology Department of Hebei Province, China (grant number 15277776D).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xinming Zhao, Yujing Hu, Jianyuan Zhang, Jingya Han and Meng Dai. The first draft of the manuscript was written by Yujing Hu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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All procedures performed in the study and involving human participants were carried out in accordance with the ethical standards of the institutional and/or national research committee and with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. The study was approved by the Institutional Review Board of the Fourth Hospital of Hebei Medical University.
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This retrospective analysis was approved by the Institutional Review Board of the Fourth Hospital of Hebei Medical University, and the requirement of informed consent was waived.
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Hu, Y., Zhao, X., Zhang, J. et al. Value of 18F-FDG PET/CT radiomic features to distinguish solitary lung adenocarcinoma from tuberculosis. Eur J Nucl Med Mol Imaging 48, 231–240 (2021). https://doi.org/10.1007/s00259-020-04924-6
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DOI: https://doi.org/10.1007/s00259-020-04924-6