Author: Sixue Dong, Chaosu Hu, Weigang Hu, Xiaomin Ou, Jiazhou Wang, Zhen Zhang 👨🔬
Affiliation: Fudan University Shanghai Cancer Center 🌍
Purpose:
This study aimed to predict the PFS of the patients who were diagnosed with hypopharyngeal cancer and received postoperative chemoradiotherapy by using multi-omics which integrating clinical factors, dosimetric and radiomic features.
Methods:
This study retrospectively collected the pretreatment MRI of 88 hypopharyngeal cancer patients with postoperative chemoradiotherapy, including 56 cases from one center and 32 cases from another center, and the GTV were countered for all cases. A Python-based library, pyradiomics was used to extract the radiomics features from each GTV. LASSO regression was used to identify the most important features for classifier establishment. Complete radiotherapy data are retained for 48 patients among them, and the PTV were countered for radiotherapy planning. The dose distribution features extracted by using pyradiomics and the dosimetric parameters were combined with the radiomics features to establish the classifiers. The probabilities of positive sample calculated from the best classifier, the radiomics and multi-omics signatures were obtained for establish the Cox models.
Results:
The ensemble learning (EL) model was selected as the superior model with the higher area under the AUC values than other classifier during the radiomics-only analysis, and the EL model with stacking technique showed the best performance, yielding AUC values of 0.93, 0.79, and 0.78 for the training, testing, and external validation cohorts, respectively. Furthermore, the multi-omics analysis integrating radiomics and dosiomics improved the effectiveness of the EL model with AUC values of 0.98 and 0.88 for the training and testing cohorts, respectively. Furthermore, the C-index of the Cox models resulted in a 0.099 improvement in the testing cohort when employing the multi-omics signature versus the radiomics signature.
Conclusion:
Regarding the patients with hypopharyngeal cancer receiving postoperative chemoradiotherapy, the multi-omics-based prognostic prediction could achieve a more robust predictive capability than the radiomics-only study. This approach warrants further validation through prospective studies.