Dosimetry of Submandibular Glands on Xerostomia for Nasopharyngeal Carcinoma

Abstract

Purpose: To investigate dosimetry of submandibular glands on xerostomia after intensity-modulated radiotherapy for nasopharyngeal carcinoma (NPC).

Methods: From September 2015 to March 2016, 195 NPC patients were investigated. Xerostomia was evaluated at 12 months after treatment via the RTOG/EORTC system. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for grades 2-3 xerostomia. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis.

Results: The V30 of the parotid glands was selected based on the least absolute shrinkage and selection operator regression. The nomogram displayed good discrimination with a C-index of 0.698 (95% confidence interval [CI]: 0.626-0.771) and good calibration (model 1). Addition of the dosimetric parameters including the mean dose to the submandibular glands, V50 of the submandibular glands, and volume of the submandibular glands to the model 1 failed to show incremental prognostic value (model 2). The model 2 showed a C-index of 0.704 (95% CI: 0.632-0.776). Decision curve analysis demonstrated that the model 1 was clinically useful when intervention was decided at the possibility threshold of > 20%. Within this range, net benefit was comparable between the model 1 and model 2.

Conclusion: PGv30 was a major predictive factor of grades 2-3 xerostomia for NPC. In contrast, the mean dose to the submandibular glands, V50 of the submandibular glands, and volume of the submandibular glands were not independent predictive factors.

Keywords: NPC; intensity-modulated radiotherapy; nasopharyngeal carcinoma; submandibular glands; xerostomia.

Figure 1

Texture feature selection using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. (A) Tuning parameter (λ) selection in the LASSO model used 10-fold cross-validation via minimum criteria. The area under the receiver operating characteristic (AUC) curve was plotted versus log (λ). Dotted vertical lines were drawn at the optimal values by using the minimum criteria and the 1 standard error of the minimum criteria (the 1-SE criteria). (B) LASSO coefficient profiles of the 13 texture features. A coefficient profile plot was produced against the log(lambda) sequence. Vertical line was drawn at the value selected using 10-fold cross-validation, where optimal lambda resulted in 1 nonzero coefficient.

Figure 2

Nomogram of grades 2–3 xerostomia at 12 months after treatment (model 1). The nomogram was developed based on the result of the least absolute shrinkage and selection operator (LASSO) binary logistic regression model.

Figure 3

The Calibration curve of the nomogram for predicting grade 2–3 xerostomia at 12 months after treatment (model 1). The y-axis represents the actual grades 2–3 xerostomia rate. The x-axis represents the predicted grades 2–3 xerostomia risk. The diagonal line represents a perfect prediction by an ideal model. The red solid line represents the performance of the nomogram, of which a closer fit to the diagonal line represents a better prediction.

Figure 4

Nomogram of grades 2–3 xerostomia at 12 months after treatment (model 2). The nomogram was conducted with the addition of SMGmean, SMGv50, and SMGvolume to the model 1.

Figure 5

The Calibration curve of the nomogram for predicting grades 2–3 xerostomia at 12 months after treatment (model 2). The y-axis represents the actual grades 2–3 xerostomia rate. The x-axis represents the predicted grades 2–3 xerostomia risk. The diagonal line represents a perfect prediction by an ideal model. The red solid line represents the performance of the nomogram, of which a closer fit to the diagonal line represents a better prediction.

Figure 6

Decision curve analysis for the model 1 and the model 2. The y-axis measures the net benefit. The black dotted line represents the model 1. The red dotted line represents the model 2. The grey line represents the assumption that all patients have grades 2–3 xerostomia. Thin black solid line represents the assumption that no patients have grades 2–3 xerostomia. The net benefit was calculated by subtracting the proportion of all patients who are false positive from the proportion who are true positive, weighting by the relative harm of forgoing treatment compared with the negative consequences of an unnecessary treatment. The decision curve showed that if the threshold probability of a patient or doctor is > 20%, using the model 1 in the current study to predict grades 2–3 xerostomia adds more benefit than the treat-all-patients scheme or the treat-none scheme. Within this range, net benefit was comparable between the model 1 and model 2.