Yusufaly, Tahir

Abstract Standard of care RPT may result in dose-limiting side effects of the salivary glands include sialadenitis and xerostomia that are inconsistent with outcomes from external beam for equivalent ADs. This work develops a parametrizable ‘Macro-to-Micro’ (M2μ) model that demonstrates the consideration of small-scale dosimetry in comparison to conventional methods that typically assume uniform voxel or organ-level uptake. Anatomical features of the salivary gland are represented by annular structures (a centralized branching network of ducts, including excretory, lobar, interlobular) and small-scale voxels (intralobular ducts and acinar cells) with dimensions set to reproduce ex-vivo murine histopathology measurements and scaled up in size for extrapolation to humans. Simulations were performed by scoring and recording S-value histograms to the target ductal and acinar cells, for two beta emitters (177Lu, 131I) and one alpha emitter (225Ac). Four idealized activity distributions were created to assign activity to surface and volumes of the annuli, and GEANT4 v11 was used for radiation transport calculations. Comparisons against whole gland uniform spherical self-dose S-value calculations were conducted to validate the radiation transport and to highlight differences between M2μ and conventional dosimetry approaches. Analyses for both models showed greater S-value variation in comparison to the homogeneously distributed activity S-value calculation. The most notable result was that the calculated S-values differed between different branches, depending on the geometric size of the annuli. For the surrounding acinar cells, S-values from ductal cells decreased as a function of distance from the branching structures. High variability of S-values, depending on ductal cell dimensions as well as within the acinar cell distribution of the salivary gland highlights the potential clinical utility of small-scale approaches to RPT salivary gland dosimetry, contingent on clinical translation and validation. To this end, future work should further refine the model and incorporate small-scale activity distributions from pre-clinical and translational studies.

Salivary glands are common organs at risk in both head and neck external beam radiotherapy (EBRT) and radiopharmaceutical therapy (RPT), but incidences of xerostomia in RPT are inconsistent with the EBRT Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) limits. In EBRT, salivary glands are usually assumed to be parallel organs, with QUANTEC guidelines based on Dmean, but this is known to be a gross over-simplification of the full complexity of the underlying functional organization. The goal of this work is to combine machine learning of EBRT dose–outcome data with stylized small-scale RPT dosimetry to discover more reliable normal tissue complication probability (NTCP) models of xerostomia across both modalities. A retrospective cohort of 211 EBRT patients was analyzed using a custom-designed in-house machine learning workflow. From this, a hierarchy of three models of increasing complexity was trained, evaluated for performance and generalization, and coupled with stylized small-scale salivary gland dosimetry to assess the influence of model complexity on the predicted NTCP for plausible patterns of RPT dose nonuniformity. The three models in the hierarchy (A, B, C), in increasing order of complexity, associate xerostomia with the following: the mean dose to the whole contralateral parotid (model A), the mean dose to a ductally localized region (model B) and a serial interaction dose term between two ductal sub-compartments (model C). While the difference between the three models for EBRT p-values and AUCs is rather marginal, for physiologically driven ductal dose distributions in RPT, the predicted reduction in TD50 can be as large as a factor of 10. These results provide hints towards a plausible reconciliation of the observed inconsistency of xerostomia in RPT with EBRT dose limits.