Journal of applied clinical medical physics · 2025
Modeling dosimetric benefits from daily adaptive RT for gynecological cancer patients with and without knowledge-based dose prediction.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ghimire Rupesh, Moore Lance, Branco Daniela, Rash Dominique L, Mayadev Jyoti S, Ray Xenia
Аннотация
PURPOSE: Daily online adaptive radiotherapy (ART) improves dose metrics for gynecological cancer patients, but the on-treatment process is resource-intensive requiring longer appointments and additional time from the entire adaptive team. To optimize resource allocation, we propose a model to identify high-priority patients.
METHODS: For 49 retrospective cervical and endometrial cancer patients, we calculated two initial plans: the treated standard-of-care (InitialSOC) and a reduced margin initial plan (InitialART) for adapting with the Ethos treatment planning system. Daily doses corresponding to standard and reduced margins (DailySOC and DailyART) were determined by re-segmenting the anatomy based on the treatment CBCT and calculating dose on a synthetic CT. These initial and daily doses were used to estimate the ART benefit ( Δ D a i l y ${{\Delta}}Daily$ = DailySOC-DailyART) versus initial plan differences ( Δ I n i t i a l ${{\Delta}}Initial$ = InitialSOC-InitialART) via multivariate linear regression. Dosimetric benefits were modeled with initial plan differences ( Δ I n i t i a l ${{\Delta}}Initial$ ) of B o w e l V 40 G y $Bowel\ {{V}_{40Gy}}$ (cc), B l a d d e r D 50 % $Bladder\ {{D}_{50{\mathrm{\% }}}}$ (Gy), and R e c t u m D 50 % $Rectum\ {{D}_{50{\mathrm{\% }}}}$ (Gy). Anatomy (intact uterus or post-hysterectomy), DoseType (simultaneous integrated boost [SIB] vs. single dose), and/or prescription value. To establish a logistic model, we classified the top 10% in each metric as high-benefit patients. We then built a logistic model to predict these patients from the previous predictors. Leave-one-out validation and ROC analysis were used to evaluate the accuracy. To improve the clinical efficiency of this predictive process, we also created knowledge-based plans for the ΔInitial plans ( Δ I n i t i a l R P ${{\Delta}}Initia{{l}_{RP}}$ ) and repeated the analysis.
RESULTS: In both Δ I n i t i a l O r i g ${{\Delta}}Initia{{l}_{Orig}}$ and Δ I n i t i a l R P ${{\Delta}}Initia{{l}_{RP}}$ our multivariate analysis showed low R2 values 0.34-0.52 versus 0.14-0.38. The most significant predictor in each multivariate model was the corresponding ∆Initial metric (e.g., Δ I n i t i a l ${{\Delta}}Initial$ Bowel (V40 Gy), p < 1e-05). In the logistic model, the metrics with the strongest correlation to the high-benefit patients were B o w e l V 40 G y $Bowel\ {{V}_{40Gy}}$ (cc), B l a d d e r D 50 % $Bladder\ {{D}_{50{\mathrm{\% }}}}$ (Gy), D o s e T y p e $DoseType$ , and S I B D o s e $SIBDose$ prescription. The models for original and knowledge-based plans had an AUC of 0.85 versus 0.78. The sensitivity and specificity were 0.92/0.72 and 0.69/0.80, respectively.
CONCLUSION: This methodology will allow clinics to prioritize patients for resource-intensive daily online ART.
METHODS: For 49 retrospective cervical and endometrial cancer patients, we calculated two initial plans: the treated standard-of-care (InitialSOC) and a reduced margin initial plan (InitialART) for adapting with the Ethos treatment planning system. Daily doses corresponding to standard and reduced margins (DailySOC and DailyART) were determined by re-segmenting the anatomy based on the treatment CBCT and calculating dose on a synthetic CT. These initial and daily doses were used to estimate the ART benefit ( Δ D a i l y ${{\Delta}}Daily$ = DailySOC-DailyART) versus initial plan differences ( Δ I n i t i a l ${{\Delta}}Initial$ = InitialSOC-InitialART) via multivariate linear regression. Dosimetric benefits were modeled with initial plan differences ( Δ I n i t i a l ${{\Delta}}Initial$ ) of B o w e l V 40 G y $Bowel\ {{V}_{40Gy}}$ (cc), B l a d d e r D 50 % $Bladder\ {{D}_{50{\mathrm{\% }}}}$ (Gy), and R e c t u m D 50 % $Rectum\ {{D}_{50{\mathrm{\% }}}}$ (Gy). Anatomy (intact uterus or post-hysterectomy), DoseType (simultaneous integrated boost [SIB] vs. single dose), and/or prescription value. To establish a logistic model, we classified the top 10% in each metric as high-benefit patients. We then built a logistic model to predict these patients from the previous predictors. Leave-one-out validation and ROC analysis were used to evaluate the accuracy. To improve the clinical efficiency of this predictive process, we also created knowledge-based plans for the ΔInitial plans ( Δ I n i t i a l R P ${{\Delta}}Initia{{l}_{RP}}$ ) and repeated the analysis.
RESULTS: In both Δ I n i t i a l O r i g ${{\Delta}}Initia{{l}_{Orig}}$ and Δ I n i t i a l R P ${{\Delta}}Initia{{l}_{RP}}$ our multivariate analysis showed low R2 values 0.34-0.52 versus 0.14-0.38. The most significant predictor in each multivariate model was the corresponding ∆Initial metric (e.g., Δ I n i t i a l ${{\Delta}}Initial$ Bowel (V40 Gy), p < 1e-05). In the logistic model, the metrics with the strongest correlation to the high-benefit patients were B o w e l V 40 G y $Bowel\ {{V}_{40Gy}}$ (cc), B l a d d e r D 50 % $Bladder\ {{D}_{50{\mathrm{\% }}}}$ (Gy), D o s e T y p e $DoseType$ , and S I B D o s e $SIBDose$ prescription. The models for original and knowledge-based plans had an AUC of 0.85 versus 0.78. The sensitivity and specificity were 0.92/0.72 and 0.69/0.80, respectively.
CONCLUSION: This methodology will allow clinics to prioritize patients for resource-intensive daily online ART.