NTCP modelling for high-grade temporal radionecrosis in a large cohort of patients receiving pencil beam scanning proton therapy for skull base and head and neck tumors.

Schröder, C; Köthe, A; De Angelis, C; Basler, L; Fattori, G; Safai, S; Leiser, D; Lomax, A J; Weber, D C (2022). NTCP modelling for high-grade temporal radionecrosis in a large cohort of patients receiving pencil beam scanning proton therapy for skull base and head and neck tumors. International journal of radiation oncology, biology, physics, 113(2), pp. 448-455. Elsevier 10.1016/j.ijrobp.2022.01.047

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PURPOSE/OBJECTIVES

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

MATERIALS/METHODS

Data of 299 patients with skull base and Head and Neck tumors treated with PBS PT with a total dose of ≥60 GyRBE from 05/2004-11/2018 were included. Patients with a ≥ grade (G) 2 TRN (CTCAE v5.0 criteria) were considered as having a high-grade TRN. Nine clinical and 27 dosimetric parameters were considered for structure-wise modelling. After elimination of strongly cross-correlated variables, logistic regression models were generated using penalized LASSO regression. Bootstrapping was performed to assess parameter selection robustness. Model performance was evaluated via cross-correlation by assessing the area under the curve of receiver operating characteristic curves (AUC-ROC) and calibration with a Hosmer-Lemeshow test statistic.

RESULTS

After a median radiological follow-up of 51.5 months (range, 4-190), 27 (9%) patients developed a ≥ G2 TRN. Eleven patients had bitemporal necrosis, resulting in 38 events in 598 temporal lobes for structure-wise analysis. During Bootstrapping analysis, the highest selection frequency was found for prescription dose (PD), followed by Age, V40Gy[%], Hypertension (HBP) and D1cc[Gy]. During cross validation Age*PD* D1cc[Gy]*HBP was superior in all described test statistics. Full cohort structure wise and patient wise models were built with a maximum AUC-ROC of 0.79 (structure-wise) and 0.76 (patient-wise).

CONCLUSION

While developing a logistic regression NTCP model to predict ≥ G2 TRN, the best fit was found for the model containing Age, PD, D1cc[Gy] and HBP as risk factors. External validation will be the next step to improve generalizability and potential introduction into clinical routine.

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