Modelling dose and dose-averaged linear energy transfer to predict high-grade temporal lobe necrosis following...

In skull-base proton therapy (PT), severe toxicity outcomes such as temporal lobe necrosis (TLN) may be associated with inadequate management of the actual radiobiological effectiveness (RBE) of proton beams. Combining dose-averaged linear energy transfer (LET_d) and dose may be crucial in treatment plan optimization and evaluation.

To gather evidence on the combined effect of LET_d and dose on voxel- and structure-wise levels in determining a high-grade TLN (CTCAE v.5 grade ≥G2, G2-TLN) in skull-base tumors treated with PT; a dose-LET_d-volume histogram (DLVH)-based model was built to predict G2-TLN, combined with an analysis of the Dose-LET_d voxel-based distributions.

We retrospectively analyzed data of skull-base chordomas (61) and chondrosarcomas (18), treated at CNAO National Center for Oncological Hadrontherapy (Pavia, Italy) with PT between September 2011 and July 2020, with a prescription dose of 74 and 70 Gy(RBE), respectively. RBE was set to a constant value of 1.1, while TL dose was constrained to D2cc < 71 Gy(RBE) in the optimization process. Only patients experiencing a G2-TLN were included in the voxel-wise analysis and statistically significant differences between dose-matched LET_d distributions within necrotic-brain areas (necrosis contoured at onset) and healthy-brain voxels were explored through association tests and mixed-effects logistic regression models within dose bins. On a structure-wise level, G2-TLN association with clinical and DLVH variables was analyzed for each temporal lobe to implement a pre-processing pipeline. Hence, bootstrap enhanced Elastic-Net regularized logistic regression, followed by the area under the receiver operating characteristic curve (AUROC), was used to select the final model. Performance and calibration were evaluated with cross-validation AUROC and Hosmer-Lemeshow test, respectively.

With a median follow-up of 49.7 months, 13 patients experienced G2-TLN (17 TL) after a median time of 21.5 months. Seven out of 13 G2-TLN patients reported a statistically significant difference between LET_d in necrotic- and healthy-brain voxels after dose-matching, with 71.4% of them presenting higher LET_d values in necrotic-brain voxels. LET_d showed significant associations with the onset of G2-TLN in dose bins 35-40 Gy(RBE) (Odds Ratio, OR: 7.4, 95%CI: 3.5-26.5, p < 0.001) and 50-55 Gy(RBE) (OR: 2.4, 95%CI: 1.5-4.0, p < 0.001). Regarding structure-wise modelling, the final logistic regression model was built using two DLVH variables, V_(dose,LETd) (V_(68,0) and V_(19,4.6)), which showed an independent association with G2-TLN and proved the highest AUROC. Good performance and calibration were reported by the cross-validation AUROC (0.89, 95%CI: 0.78-0.95) and Hosmer-Lemeshow test (p = 1).

Voxels receiving higher LET_d in the low and medium dose ranges (35-40 and 50-55 Gy(RBE)) were associated with high-grade necrosis on a voxel-wise level. The volumes of TL receiving high doses (68 Gy(RBE)) or high LET_d (4.6 keV/µm) with doses higher than 19 Gy(RBE) were the major predictors of G2-TLN. Further evaluation with a larger sample size and an external validation cohort is necessary to assess our findings and validate the presented model.