NTCP Modeling for High-Grade Temporal Radionecroses in a Large Cohort of Patients Receiving Pencil Beam Scanning Proton Therapy for Skull Base and Head and Neck Tumors.

Purpose: To develop a normal tissue complication probability model including clinical and dosimetric parameters for high-grade temporal lobe radionecroses (TRN) after pencil beam scanning proton therapy.

Methods And Materials: We included data on 299 patients with skull base and head and neck tumors treated with pencil-beam scan proton therapy, with a total dose of ≥60 Gy (relative biological effectiveness) from May 2004 to November 2018. We considered 9 clinical and 27 dosimetric parameters for the structure-wise modeling of high-grade (grade ≥2) TRN. After eliminating strongly cross-correlated variables, we generated logistic regression models using least absolute shrinkage and selection operator regression. We performed bootstrapping to assess parameter selection robustness and evaluated model performance via cross-correlation by assessing the area under the curve of receiver operating characteristic curves and calibration with a Hosmer-Lemeshow test statistic.

Results: After a median radiologic follow-up of 51.5 months (range, 4-190), 27 patients (9%) developed grade ≥2 TRN. Eleven patients had bitemporal necrosis, resulting in 38 events in 598 temporal lobes for structure-wise analysis. During our bootstrapping analysis, we found that the highest selection frequency was for prescription dose, followed by age, V (%), hypertension, and dose to at least 1 cc (D) (Gy) in the temporal lobe. During our cross-validation, we found that age*prescription-dose*D (Gy)*hypertension was superior in all described test statistics. We built full cohort structure-wise and patient-wise models with maximum area under the curve of receiver operating characteristic curves of 0.79 (structure-wise) and 0.76 (patient-wise).

Conclusions: While developing a logistic regression normal tissue complication probability model to predict grade ≥2 TRN, the best fit was found for the model containing age, prescription dose, D (Gy), and hypertensive blood pressure as risk factors. External validation will be the next step to improve generalizability and potential introduction into clinical routine.