Prognostic and predictive value of radiomics features at MRI in nasopharyngeal carcinoma

Abstract

Purpose To explore the value of MRI-based radiomics features in predicting risk in disease progression for nasopharyngeal carcinoma (NPC). Methods 199 patients confirmed with NPC were retrospectively included and then divided into training and validation set using a hold-out validation (159: 40). Discriminative radiomic features were selected with a Wilcoxon signed-rank test from tumors and normal masticatory muscles of 37 NPC patients. LASSO Cox regression and Pearson correlation analysis were applied to further confirm the differential expression of the radiomic features in the training set. Using the multiple Cox regression model, we built a radiomic feature-based classifier, Rad-Score. The prognostic and predictive performance of Rad-Score was validated in the validation cohort and illustrated in all included 199 patients. Results We identified 1832 differentially expressed radiomic features between tumors and normal tissue. Rad-Score was built based on one radiomic feature: CET1-w_wavelet.LLH_GLDM_Dependence-Entropy. Rad-Score showed a satisfactory performance to predict disease progression in NPC with an area under the curve (AUC) of 0.604, 0.732, 0.626 in the training, validation, and the combined cohort (all 199 patients included) respectively. Rad-Score improved risk stratification, and disease progression-free survival was significantly different between these groups in every cohort of patients (p = 0.044 or p < 0.01). Combining radiomics and clinical features, higher AUC was achieved of the prediction of 3-year disease progression-free survival (PFS) (AUC, 0.78) and 5-year disease PFS (AUC, 0.73), although there was no statistical difference. Conclusion The radiomics classifier, Rad-Score, was proven useful for pretreatment prognosis prediction and showed potential in risk stratification for NPC.

Adjuvant chemotherapy or no adjuvant chemotherapy? A prediction model for the risk stratification of recurrence or metastasis of nasopharyngeal carcinoma combining MRI radiomics with clinical factors

“…I compared the predictive performance to previous studies on the same data. It was found that the AUC values of our model were higher in both the training and validation cohort ( S2 Fig ) [ 34 – 36 ], but these results contain more features and sequences. In our study, the CRM showed higher predictive performance than CM, in both the training and the validation cohorts.…”

MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma

“…Therefore, radiomics labelling can be a useful supplement to clinical features in terms of prognostic value, which can explain the prognostic prediction performance of the radiomics model in the present study is better than that of the clinical model. The potential clinical value of predictive models based on radiomics in predicting PFS in NPC patients has been previously emphasized [ 21 , 34 ]. However, previous reports mostly used the Cox model to predict the prognosis of NPC.…”

“…However, previous reports mostly used the Cox model to predict the prognosis of NPC. Different studies included different stages and treatment methods for NPC patients, resulting in different clinical and radiomics features, thereby increasing the study heterogeneity and affecting the prediction performance [ 21 – 23 ]. A study [ 35 ] constructed a Cox proportional hazard regression model to predict the PFS of NPC patients.…”

“…The traditional Cox risk regression model is the most commonly used one for predicting the efficacy and prognosis of NPC, but it is unstable in diagnostic efficiency, and no standardized guideline is available. Thus, it remains controversial in the prognostic prediction of NPC [21][22][23].…”