Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026
The smoothing factor in breast intensity-modulated radiotherapy: dosimetric impact and a predictive model of plan acceptability.
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Ungvári Tamás, Szabó Döme, Gáldi Ádám, Bassirani Amir Hossein, Dankovics Zsófia, Kiss Balázs, Olajos Judit, Major Tibor, Tőkési Károly
Аннотация
BACKGROUND: The smoothing factor is a key planning parameter in intensity-modulated radiotherapy that affects both plan quality and deliverability. This study investigates the dosimetric impact of SF in breast radiotherapy and introduces a logistic regression-based model to predict plan acceptability.
MATERIALS AND METHODS: IMRT plans were generated for 21 breast cancer patients using SF values from 10 to 300 (126 plans total). Dosimetric parameters included monitor units, ipsilateral lung dose (V17 Gy), planning target volume coverage, and complexity (modulation complexity score). A logistic regression model was trained using six key features to classify plan quality.
RESULTS: Plans with SF values between 100 and 150 provided the best compromise between monitor units, PTV coverage, and modulation complexity. The predictive model performed well (AUC = 0.88), with conformity index, maximum dose, and modulation complexity score emerging as the most influential predictors. It enables automated classification and supports smoothing factor selection.
CONCLUSIONS: The smoothing factor strongly influences the performance of intensity-modulated radiotherapy plans. Our predictive model provides a robust tool for plan evaluation, supporting clinical decision-making and optimisation of smoothing factor in breast radiotherapy.
MATERIALS AND METHODS: IMRT plans were generated for 21 breast cancer patients using SF values from 10 to 300 (126 plans total). Dosimetric parameters included monitor units, ipsilateral lung dose (V17 Gy), planning target volume coverage, and complexity (modulation complexity score). A logistic regression model was trained using six key features to classify plan quality.
RESULTS: Plans with SF values between 100 and 150 provided the best compromise between monitor units, PTV coverage, and modulation complexity. The predictive model performed well (AUC = 0.88), with conformity index, maximum dose, and modulation complexity score emerging as the most influential predictors. It enables automated classification and supports smoothing factor selection.
CONCLUSIONS: The smoothing factor strongly influences the performance of intensity-modulated radiotherapy plans. Our predictive model provides a robust tool for plan evaluation, supporting clinical decision-making and optimisation of smoothing factor in breast radiotherapy.