MedElevator
Войти в исследования Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2025
Deep learning dosiomics for the pretreatment prediction of radiation dermatitis in nasopharyngeal carcinoma patients treated with radiotherapy.
СтатьяПоддержка исследования: вне правительства СШАНазвание 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.
Xiang Ziqing, Yu Xianwen, Lin Sunzhong, Wang Dong, Huang Weiqian, Fu Wen, Zhu Xuanxuan, Shao Li, Wu Jianping, Zheng Qiao, Ai Yao, Yang Xujing, Guo Mingrou, Jin Xiance
Аннотация
PURPOSE: To develop a combined dosiomics and deep learning (DL) model for predicting radiation dermatitis (RD) of grade ≥ 2 in patients with nasopharyngeal carcinoma (NPC) after radiation therapy (RT) based on radiation dose distribution.
MATERIALS AND METHODS: A retrospective study was performed with 290 NPC patients treated with RT from two medical centers. The patients were categorized into three groups: a training set (n = 167), an internal validation set (n = 72), and an external validation set (n = 51), respectively. Dosiomic features, in conjunction with DL features derived from convolutional neural networks, were extracted and analyzed from the radiation dose distribution to construct an end-to-end model and facilitate the prediction of RD. The efficacy of the developed models was assessed and compared using the area under curve (AUC) of the receiver operating characteristic (ROC) curves.
RESULTS: The XGBoost model with finally screened 25 dosiomic features achieved the best AUC of 0.751 and 0.746 in the internal and external validation sets, respectively. DL model with ResNet-34 achieved the best AUC of 0.820 and 0.812 in the internal and external validation sets, respectively. Combining DL and dosiomic features improved the AUC to 0.863 and 0.832 in the internal and external validation sets, respectively. Nomogram integrating DL, dosiomic features, and clinical factors achieved an AUC of 0.945, 0.916, and 0.832 in the training, internal, and external validation sets, respectively.
CONCLUSION: The integration of DL, dosiomics and clinical features is feasible and effective for predicting RD, thereby enhancing the management of NPC patients treated with RT.
MATERIALS AND METHODS: A retrospective study was performed with 290 NPC patients treated with RT from two medical centers. The patients were categorized into three groups: a training set (n = 167), an internal validation set (n = 72), and an external validation set (n = 51), respectively. Dosiomic features, in conjunction with DL features derived from convolutional neural networks, were extracted and analyzed from the radiation dose distribution to construct an end-to-end model and facilitate the prediction of RD. The efficacy of the developed models was assessed and compared using the area under curve (AUC) of the receiver operating characteristic (ROC) curves.
RESULTS: The XGBoost model with finally screened 25 dosiomic features achieved the best AUC of 0.751 and 0.746 in the internal and external validation sets, respectively. DL model with ResNet-34 achieved the best AUC of 0.820 and 0.812 in the internal and external validation sets, respectively. Combining DL and dosiomic features improved the AUC to 0.863 and 0.832 in the internal and external validation sets, respectively. Nomogram integrating DL, dosiomic features, and clinical factors achieved an AUC of 0.945, 0.916, and 0.832 in the training, internal, and external validation sets, respectively.
CONCLUSION: The integration of DL, dosiomics and clinical features is feasible and effective for predicting RD, thereby enhancing the management of NPC patients treated with RT.