Magnetic resonance imaging · 2025
Predicting progression of enhancing non-measurable disease in high-grade glioma by using dynamic contrast-enhanced MR imaging.
СтатьяПоддержка исследования: вне правительства СШАНазвание 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.
Cao Haimei, Huang Zhousan, Qiu Ruowei, Xiao Xiang, Li Zhiyong, Pillai Jay J, Hua Jun, Huang Guanglong, Xu Yikai, Liang Wen, Wu Yuankui
Аннотация
OBJECTIVES: To investigate the potential of histogram models derived from dynamic contrast-enhanced (DCE) MR imaging in predicting the progression of enhancing non-measurable disease (NMD) persisting after chemoradiotherapy in patients with high-grade glioma (HGG).
MATERIALS AND METHODS: A total of 97 glioma patients (mean age ± standard deviation, 46.7 years ±12.1; 73 men) who underwent temozolomide-based chemoradiation following gross total resection were enrolled retrospectively, including 55 (57 %) in the progression group and 42 (43 %) in the non-progression group. The histogram features of Ktrans (volume transfer constant between the plasma and extravascular extracellular space) and Ve (extravascular volume) for enhancing NMDs were extracted and compared between the two groups. Histogram features with significant differences were included in binary logistic regression to construct models to predict progression within 2 to 3 months. The models were constructed based on Ktrans and Ve alone or combined. Receiver operating characteristic curves were used to evaluate the prediction performance of the different models. The models were testified in a prospective cohort consisting of 15 patients with HGG.
RESULTS: The histogram model of Ktrans showed an area under the curve (AUC) of 0.900 in predicting progression. The model of Ve had an AUC of 0.879. When combining Ktrans and Ve, the model achieved an AUC of 0.927. These models showed excellent predictive performance in the prospective study.
CONCLUSION: The histogram models based on DCE MRI can predict the progression of enhancing NMDs in HGG following chemoradiotherapy 2 to 3 months in advance.
MATERIALS AND METHODS: A total of 97 glioma patients (mean age ± standard deviation, 46.7 years ±12.1; 73 men) who underwent temozolomide-based chemoradiation following gross total resection were enrolled retrospectively, including 55 (57 %) in the progression group and 42 (43 %) in the non-progression group. The histogram features of Ktrans (volume transfer constant between the plasma and extravascular extracellular space) and Ve (extravascular volume) for enhancing NMDs were extracted and compared between the two groups. Histogram features with significant differences were included in binary logistic regression to construct models to predict progression within 2 to 3 months. The models were constructed based on Ktrans and Ve alone or combined. Receiver operating characteristic curves were used to evaluate the prediction performance of the different models. The models were testified in a prospective cohort consisting of 15 patients with HGG.
RESULTS: The histogram model of Ktrans showed an area under the curve (AUC) of 0.900 in predicting progression. The model of Ve had an AUC of 0.879. When combining Ktrans and Ve, the model achieved an AUC of 0.927. These models showed excellent predictive performance in the prospective study.
CONCLUSION: The histogram models based on DCE MRI can predict the progression of enhancing NMDs in HGG following chemoradiotherapy 2 to 3 months in advance.