Multiscale spatial relationship-based model for predicting bladder wall dose in pelvic radiotherapy.

Abstract

This research aimed to develop a prediction model to assess bladder wall dosimetry during radiotherapy for patients with pelvic tumors, thereby facilitating the refinement and evaluation of radiotherapy treatment plans to mitigate bladder toxicity. Radiotherapy treatment plans of 49 rectal cancer patients and 45 gynecologic cancer patients were collected, and multiple linear regression analyses were used to generate prediction models for bladder wall dose parameters ( , ). These models were based on the multiscale spatial relationship between the planning target volume (PTV) and the bladder or bladder wall. The proportion of bladder or bladder wall volume overlapped by the different distance expansions of the PTV was used as an indicator of the multiscale spatial relationship. The accuracy of these models was verified in a cohort of 12 new patients, with further refinement of radiotherapy treatment plans using the predicted values as optimization parameters. Model accuracy was assessed using root mean square error (RMSE) and mean percentage error (MPE). Models derived from individual disease data outperformed those derived from combined datasets. Predicted bladder wall dose parameters were accurate, with the majority of initial calculated values for new patients falling within the 95% confidence interval of the model predictions. There was a robust correlation between the predicted and actual dose metrics, with a correlation coefficient of 0.943. Using the predicted values to optimize treatment plans significantly reduced bladder wall dose (p 0.001), with bladder wall and decreasing by 2.27±0.80Gy (5.8%±1.8%) and 2.96±2.05 cm3 (7.9%±5.4%), respectively. The formulated prediction model provides a valuable tool for predicting and minimizing bladder wall dose and for optimizing and evaluating radiotherapy treatment plans for pelvic tumor patients. This approach holds promise for reducing bladder toxicity and potentially improving patient outcomes.