International journal of radiation oncology, biology, physics · 2025
Forecasting Chemoradiation Response Midtreatment for High-Grade Gliomas Through Patient-Specific Biology-Based Modeling.
СтатьяПоддержка исследования: вне правительства СШАПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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.
Hormuth David A, Farhat Maguy, Panthi Bikash, Langshaw Holly, Shanker Mihir D, Talpur Wasif, Thrower Sara, Goldman Jodi, Ty Sophia, Custer Calliope, Kowalski Jeanne, Yankeelov Thomas E, Chung Caroline
Аннотация
PURPOSE: The entire course of radiation therapy (RT) for high-grade glioma (HGG) is currently derived from pre-RT magnetic resonance imaging (MRI). Although it is possible to adapt RT during the course of treatment, it is often guided only by anatomical changes to the tumor. This study seeks to determine if a biology-based mathematical model, parameterized by patient-specific, multiparametric MRI (mpMRI) data, can accurately forecast HGG response during RT.
METHODS AND MATERIALS: Twenty one patients with HGG planned for 6 weeks of concurrent RT and chemotherapy were imaged weekly with mpMRI during RT and at 1, 2, and 3 months post-RT. Each patient's MRI data from baseline to midtreatment were used to personalize a family of biology-based mathematical models, from which the most parsimonious was selected and used to predict response at the volume and voxel levels at the remaining mpMRI visits. The model family consists of varied descriptions of how tumor cells proliferate, diffuse, and respond to RT and chemotherapy.
RESULTS: At the volume level, Pearson correlation coefficients >0.86 (P < .0001) were observed between the predicted and observed total tumor cellularity and volume up to the 2 months post-RT. A high level of spatial overlap was measured between the predicted and observed tumor extent with Dice values >0.87 and >0.74 during and following RT, respectively. At the voxel level, Pearson correlation coefficients were >0.90 and >0.71 (P < .0001) during and following RT, respectively.
CONCLUSIONS: By leveraging patient-specific mpMRI data before and during adaptive RT, this biology-based computational framework yields accurate spatiotemporal forecasts of tumor response at the volume and voxel levels during and following RT.
METHODS AND MATERIALS: Twenty one patients with HGG planned for 6 weeks of concurrent RT and chemotherapy were imaged weekly with mpMRI during RT and at 1, 2, and 3 months post-RT. Each patient's MRI data from baseline to midtreatment were used to personalize a family of biology-based mathematical models, from which the most parsimonious was selected and used to predict response at the volume and voxel levels at the remaining mpMRI visits. The model family consists of varied descriptions of how tumor cells proliferate, diffuse, and respond to RT and chemotherapy.
RESULTS: At the volume level, Pearson correlation coefficients >0.86 (P < .0001) were observed between the predicted and observed total tumor cellularity and volume up to the 2 months post-RT. A high level of spatial overlap was measured between the predicted and observed tumor extent with Dice values >0.87 and >0.74 during and following RT, respectively. At the voxel level, Pearson correlation coefficients were >0.90 and >0.71 (P < .0001) during and following RT, respectively.
CONCLUSIONS: By leveraging patient-specific mpMRI data before and during adaptive RT, this biology-based computational framework yields accurate spatiotemporal forecasts of tumor response at the volume and voxel levels during and following RT.