CT-Based Radiomics Nomogram for Prediction of Progression-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma

Abstract

Purpose We aimed to construct of a nomogram to predict progression-free survival (PFS) in locoregionally advanced nasopharyngeal carcinoma (LA-NPC) with risk stratification using computed tomography (CT) radiomics features and clinical factors. Patients and Methods A total of 311 patients diagnosed with LA-NPC (stage III–IVa) at our hospital between 2010 and 2014 were included. The region of interest (ROI) of the primary nasopharyngeal mass was manually outlined. Independent sample t -test and LASSO-logistic regression were used for selecting the most predictive radiomics features of PFS, and to generate a radiomics signature. A nomogram was built with clinical factors and radiomics features, and the risk stratification model was tested accordingly. Results In total, 20 radiomics features most associated with prognosis were selected. The radiomics nomogram, which integrated the radiomics signature and significant clinical factors, showed excellent performance in predicting PFS, with C-index of 0.873 (95% CI: 0.803~0.943), which was better than that of the clinical nomogram (C-index, 0.729, 95% CI: 0.620~0.838) as well as of the TNM staging system (C-index, 0.689, 95% CI: 0.592–0.787) in validation cohort. The calibration curves and the decision curve analysis (DCA) plot obtained suggested satisfying accuracy and clinical utility of the model. The risk stratification tool was able to predict differences in prognosis of patients in different risk categories ( p <0.001). Conclusion CT-based radiomics features, an in particular, radiomics nomograms, have the potential to become an accurate and reliable tool for assisting with prognosis prediction of LA-NPC.

[Retracted] Prediction of Changes in Tumor Regression during Radiotherapy for Nasopharyngeal Carcinoma by Using the Computed Tomography‐Based Radiomics

“…The results of Peng et al showed that the radiomics model outperformed the EBV DNA model (0.754 vs. 0.675 for the training set and 0.722 vs. 0.671 for the test set) [ 19 ]. The research results of Yan et al showed that the nomogram constructed based on CT (C index, 0.873, 95% confidence interval (CI): 0.803–0.943) was superior to the clinical nomogram in prediction (C index, 0.729, 95% CI: 0.620–0.838) and tumor node metastasis (TNM) staging system (C index, 0.689, 95% CI: 0.592–0.787) [ 20 ]. This is consistent with the results of this work.…”

Nasopharyngeal Carcinoma Radiomic Evaluation with Serial PET/CT: Exploring Features Predictive of Survival in Patients with Long-Term Follow-Up

“…Additionally, 18 F-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) imaging has been used for several years in this disease for staging as well [1][2][3][4][5][6][7]. Most radiomics studies on these tumors have focused on CT and MRI images, including the development of radiomics signatures that outperforms TNM staging when evaluating overall survival [19,[50][51][52], with external validation in a separate study [21]; they are predictors of the tumor's HPV status [53,54]. However, studies evaluating PET radiomics have demonstrated a potential to predict the risk of local failure [55][56][57][58] and distant metastasis [57,58], progression-free [59,60] and overall survival [57][58][59][60].…”

Review of functional magnetic resonance imaging in the assessment of nasopharyngeal carcinoma treatment response

“…With the use of machine learning and deep learning (artificial intelligence), 71 model building with clinical data for local recurrence, 72 distant metastasis, 73 prognosis, 72,74–76 and toxicity prediction 77,78 become implementable. Researchers carried out studies on the risk of local recurrence and prognosis prediction in NPC patients with radiomics features extracted from Computed Tomography (CT) images 79,80 . MR‐based radiomics are also frequently studied nowadays.…”

“…become implementable. Researchers carried out studies on the risk of local recurrence and prognosis prediction in NPC patients with radiomics features extracted from Computed Tomography (CT) images 79,80. MRbased radiomics are also frequently studied nowadays.…”