Development and validation of a model for temporal lobe necrosis for nasopharyngeal carcinoma patients with intensity modulated radiation therapy

Jiazhou Wang†,
Yibing Miao†,
Xiaomin Ou,
Xiaoshen Wang,
Xiayun He,
Chunying Shen,
Hongmei Ying,
Weigang Hu and
Chaosu HuEmail author

†Contributed equally

Radiation Oncology201914:42

https://doi.org/10.1186/s13014-019-1250-z

Received: 22 November 2018
Accepted: 4 March 2019
Published: 12 March 2019

Abstract

Purpose

To develop and validate a quantitative complication model of temporal lobe necrosis (TLN). To analyze the effect of clinical and dosimetric factors on TLN.

Patients and methods

In this study the prediction model was developed in a training cohort that consisted of 256 nasopharyngeal carcinoma (NPC) patients from January 2009 to December 2009. Dosimetric and clinical factors were extracted for model building. Dosimetric factors including the maximum dose, minimum dose, mean dose, dose covering specific volume and dose of percentage volume. Clinical factors include age, gender, T/N-stage, overall stage, diabetes and hypertension. LASSO (least absolute shrinkage and selection operator) regression model was used for feature selection, and prediction model building. A testing cohort containing 493 consecutive patients from January 2010 to December 2010 was used for model validation. The performance of the prediction model was assessed with respect to its calibration, discrimination.

Results

The prediction model, which consisted of two dosimetric features (D0.5cc and D10), is significantly associated with LN status (P < .001 for both training and testing cohorts). None of clinical factors show direct prediction value. The model shows good discrimination, with a C-index of 0.685 (95% CI: 0.6048–0.765) on testing set, and a consistent trend in calibration on testing set.

Conclusion

This study presents a prediction model can be conveniently used to facilitate the individualized prediction of TLN in patients with NPC. Clinical factors have no direct impact on TLN.