A multivariable normal tissue complication probability model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma patients in the modern radiotherapy era

Radiation-induced hypothyroidism (RHT) is a common long-term complication for nasopharyngeal carcinoma (NPC) survivors. A model using clinical and dosimetric factors for predicting risk of RHT could suggest a proper dose-volume parameters for the treatment planning in an individual level. We aim to develop a multivariable normal tissue complication probability (NTCP) model for RHT in NPC patients after intensity-modulated radiotherapy or volumetric modulated arc therapy. The model was developed using retrospective clinical data and dose-volume data of the thyroid and pituitary gland based on a standard backward stepwise multivariable logistic regression analysis and was then internally validated using 10-fold cross-validation. The final NTCP model consisted of age, pretreatment thyroid-stimulating hormone and mean thyroid dose. The model performance was good with an area under the receiver operating characteristic curve of 0.749 on an internal (200 patients) and 0.812 on an external (25 patients) validation. The mean thyroid dose at ≤45 Gy was suggested for treatment plan, owing to an RHT incidence of 2% versus 61% in the >45 Gy group.

Predictive Model and Precaution for Oral Mucositis During Chemo-Radiotherapy in Nasopharyngeal Carcinoma Patients

PURPOSE

To explore risk factors for severe acute oral mucositis of nasopharyngeal carcinoma (NPC) patients receiving chemo-radiotherapy, build predictive models and determine preventive measures.

METHODS AND MATERIALS

Two hundred and seventy NPC patients receiving radical chemo-radiotherapy were included. Oral mucosa structure was contoured by oral cavity contour (OCC) and mucosa surface contour (MSC) methods. Oral mucositis during treatment was prospectively evaluated and divided into severe mucositis group (grade ≥ 3) and non-severe mucositis group (grade < 3) according to RTOG Acute Reaction Scoring System. Nineteen clinical features and nineteen dosimetric parameters were included in analysis, least absolute shrinkage and selection operator (LASSO) logistic regression model was used to construct a risk score (RS) system.

RESULTS

Two predictive models were built based on the two delineation methods. MSC based model is more simplified one, it includes body mass index (BMI) classification before radiation, retropharyngeal lymph node (RLN) area irradiation status and MSC V55%, RS = -1.480 + (0.021 × BMI classification before RT) + (0.126 × RLN irradiation) + (0.052 × MSC V55%). The cut-off of MSC based RS is -1.011, with an area under curve (AUC) of 0.737 (95%CI: 0.672-0.801), a specificity of 0.595 and a sensitivity of 0.786. OCC based model involved more variables, RS= -4.805+ (0.152 × BMI classification before RT) + (0.080 × RT Technique) + (0.097 × Concurrent Nimotuzumab) + (0.163 × RLN irradiation) + (0.028 × OCC V15%) + (0.120 × OCC V60%). The cut-off of OCC based RS is -0.950, with an AUC of 0.767 (95%CI: 0.702-0.831), a specificity of 0.602 and a sensitivity of 0.819. Analysis in testing set shown higher AUC of MSC based model than that of OCC based model (AUC: 0.782 vs 0.553). Analysis in entire set shown AUC in these two method-based models were close (AUC: 0.744 vs 0.717).

CONCLUSION

We constructed two risk score predictive models for severe oral mucositis based on clinical features and dosimetric parameters of nasopharyngeal carcinoma patients receiving chemo-radiotherapy. These models might help to discriminate high risk population in clinical practice that susceptible to severe oral mucositis and individualize treatment plan to prevent it.