Morphological-Dosimetric Model for Adaptive Radiotherapy Triggering In Bladder Cancer Radiotherapy
Abstract
Purpose
To analyze the morphological and dosimetric variations in bladder patients during radiotherapy, and establish a dose-anatomy adaptive radiotherapy(ART) triggering model, providing a basis for individualized ART in clinical practice.
Methods
A retrospective study of 29 bladder patients receiving radiotherapy was prescribed with 12-25 Gy /7-12 fractions. Image guidance was performed prior to treatment using Fan Beam CT(FBCT) after bladder filling. Morphological and dosimetric parameters of the initial plan (Plan0), plans calculated on FBCTs(PlanF), and ART plans (PlanA) were collected for each fraction.GTV D98% and D95% were used as ART trigger. Logistic regression was employed to establish the relationship between the triggers and the morphological characteristics of the targets and organs-at-risk (OARs).
Results
Results of 29 patients (226 fractions) revealed that the morphological changes of GTV(the centroid shift, Dice,HD95) and bladder(Dice,HD95) were significantly associated with off-target events (GTV exceeding the irradiation field) (P<0.001).Off-targets occurred at a higher rate (52.78%) in elongated bladders than in oval and spherical bladders. The Off-targets incidence reached 50% or higher when bladder volume changes were ≤−20% or within the 50%–70% range. The PlanA showed significantly higher GTV dose (D98%, D95%) than the Plan0 and PlanF (P<0.001), and delivered lower dose to the rectum V10Gy, bladder(V15Gy and V10Gy), and colons Dmax than PlanF (P<0.001). Fractions were divided into four groups ( non-trigger ART, D98%-triggered only, D95%-triggered only, and dual-triggered) using ART triggers of D98% ≥ 95% and D95% ≥ 98%. A logistic regression-derived predictive model combined morphological and dosimetric indicators achieved favorable balance in overall predictive accuracy with an AUC of 0.869.
Conclusion
The morphological changes of GTV and bladder are key anatomical factors influencing off-target events. ART demonstrated significant improvements in target coverage and OARs protection. The logistic regression model based GTV D98% and D95% thresholds can accurately predicts ART triggering.