A prediction model for xerostomia in locoregionally advanced nasopharyngeal carcinoma patients receiving radical radiotherapy

Abstract Background This study was to evaluate the predictors of xerostomia and Grade 3 xerostomia in locoregionally advanced nasopharyngeal carcinoma (NPC) patients receiving radical radiotherapy and establish prediction models for xerostomia and Grade 3 xerostomia based on the predictors. Methods...

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Main Authors: Minying Li (Author), Jingjing Zhang (Author), Yawen Zha (Author), Yani Li (Author), Bingshuang Hu (Author), Siming Zheng (Author), Jiaxiong Zhou (Author)
Format: Book
Published: BMC, 2022-06-01T00:00:00Z.
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100 1 0 |a Minying Li  |e author 
700 1 0 |a Jingjing Zhang  |e author 
700 1 0 |a Yawen Zha  |e author 
700 1 0 |a Yani Li  |e author 
700 1 0 |a Bingshuang Hu  |e author 
700 1 0 |a Siming Zheng  |e author 
700 1 0 |a Jiaxiong Zhou  |e author 
245 0 0 |a A prediction model for xerostomia in locoregionally advanced nasopharyngeal carcinoma patients receiving radical radiotherapy 
260 |b BMC,   |c 2022-06-01T00:00:00Z. 
500 |a 10.1186/s12903-022-02269-0 
500 |a 1472-6831 
520 |a Abstract Background This study was to evaluate the predictors of xerostomia and Grade 3 xerostomia in locoregionally advanced nasopharyngeal carcinoma (NPC) patients receiving radical radiotherapy and establish prediction models for xerostomia and Grade 3 xerostomia based on the predictors. Methods Totally, 365 patients with locoregionally advanced NPC who underwent radical radiotherapy were randomly divided into the training set (n = 255) and the testing set (n = 110) at a ratio of 7:3. All variables were included in the least absolute shrinkage and selection operator regression to screen out the potential predictors for xerostomia as well as the Grade 3 xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The random forest (RF), a decision tree classifier (DTC), and extreme-gradient boosting (XGB) models were constructed. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC) and accuracy were analyzed to evaluate the predictive performance of the models. Results In the RF model for predicting xerostomia, the sensitivity was 1.000 (95%CI 1.000-1.000), the PPV was 0.990 (95%CI 0.975-1.000), the NPV was 1.000 (95%CI 1.000-1.000), the AUC was 0.999 (95%CI 0.997-1.000) and the accuracy was 0.992 (95%CI 0.981-1.000) in the training set. The sensitivity was 0.933 (95%CI 0.880-0.985), the PPV was 0.933 (95%CI 0.880-0.985), and the AUC was 0.915 (95%CI 0.860-0.970) in the testing set. Hypertension, age, total radiotherapy dose, dose at 50% of the left parotid volume, mean dose to right parotid gland, mean dose to oral cavity, and course of induction chemotherapy were important variables associated with the risk of xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy. The AUC of DTC model for predicting xerostomia was 0.769 (95%CI 0.666-0.872) in the testing set. The AUC of the XGB model for predicting xerostomia was 0.834 (0.753-0.916) in the testing set. The RF model showed the good predictive ability with the AUC of 0.986 (95%CI 0.972-1.000) in the training set, and 0.766 (95%CI 0.626-0.905) in the testing set for identifying patients who at high risk of Grade 3 xerostomia in those with high risk of xerostomia. Conclusions An RF model for predicting xerostomia in locoregionally advanced NPC patients receiving radical radiotherapy and an RF model for predicting Grade 3 xerostomia in those with high risk of xerostomia showed good predictive ability. 
546 |a EN 
690 |a Xerostomia 
690 |a Nasopharyngeal carcinoma 
690 |a Prediction 
690 |a Dentistry 
690 |a RK1-715 
655 7 |a article  |2 local 
786 0 |n BMC Oral Health, Vol 22, Iss 1, Pp 1-13 (2022) 
787 0 |n https://doi.org/10.1186/s12903-022-02269-0 
787 0 |n https://doaj.org/toc/1472-6831 
856 4 1 |u https://doaj.org/article/949769f1a3fc40a79f746fbd8e17b35b  |z Connect to this object online.