PDF(Pubmed)
Abstract：
OBJECTIVE: Neoadjuvant chemoradiotherapy has been the standard practice for patients with locally advanced rectal cancer. However, the treatment response varies greatly among individuals, how to select the optimal candidates for neoadjuvant chemoradiotherapy is crucial. This study aimed to develop an endoscopic image-based deep learning model for predicting the response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
METHODS: In this multicenter observational study, pre-treatment endoscopic images of patients from two Chinese medical centers were retrospectively obtained and a deep learning-based tumor regression model was constructed. Treatment response was evaluated based on the tumor regression grade and was defined as good response and non-good response. The prediction performance of the deep learning model was evaluated in the internal and external test sets. The main outcome was the accuracy of the treatment prediction model, measured by the AUC and accuracy.
RESULTS: This deep learning model achieved favorable prediction performance. In the internal test set, the AUC and accuracy were 0.867 (95% CI: 0.847-0.941) and 0.836 (95% CI: 0.818-0.896), respectively. The prediction performance was fully validated in the external test set, and the model had an AUC of 0.758 (95% CI: 0.724-0.834) and an accuracy of 0.807 (95% CI: 0.774-0.843).
CONCLUSIONS: The deep learning model based on endoscopic images demonstrated exceptional predictive power for neoadjuvant treatment response, highlighting its potential for guiding personalized therapy.
METHODS: In this multicenter observational study, pre-treatment endoscopic images of patients from two Chinese medical centers were retrospectively obtained and a deep learning-based tumor regression model was constructed. Treatment response was evaluated based on the tumor regression grade and was defined as good response and non-good response. The prediction performance of the deep learning model was evaluated in the internal and external test sets. The main outcome was the accuracy of the treatment prediction model, measured by the AUC and accuracy.
RESULTS: This deep learning model achieved favorable prediction performance. In the internal test set, the AUC and accuracy were 0.867 (95% CI: 0.847-0.941) and 0.836 (95% CI: 0.818-0.896), respectively. The prediction performance was fully validated in the external test set, and the model had an AUC of 0.758 (95% CI: 0.724-0.834) and an accuracy of 0.807 (95% CI: 0.774-0.843).
CONCLUSIONS: The deep learning model based on endoscopic images demonstrated exceptional predictive power for neoadjuvant treatment response, highlighting its potential for guiding personalized therapy.
摘要：
目的：新辅助放化疗已成为局部晚期直肠癌患者的标准治疗方法。然而,个体之间的治疗反应差异很大，如何选择新辅助放化疗的最佳候选人至关重要.本研究旨在开发一种基于内窥镜图像的深度学习模型，用于预测局部晚期直肠癌对新辅助放化疗的反应。
方法：在这项多中心观察研究中，回顾性获得了来自两个中国医学中心的患者的治疗前内镜图像,并构建了基于深度学习的肿瘤回归模型.基于肿瘤消退等级评估治疗反应,并将其定义为良好反应和非良好反应。在内部和外部测试集中评估了深度学习模型的预测性能。主要结果是治疗预测模型的准确性，通过AUC和准确性测量。
结果：该深度学习模型实现了良好的预测性能。在内部测试集中，AUC和准确性分别为0.867（95％CI：0.847-0.941）和0.836（95％CI：0.818-0.896），分别。预测性能在外部测试集中得到了充分验证,模型的AUC为0.758(95%CI:0.724-0.834),准确度为0.807(95%CI:0.774-0.843).
结论：基于内窥镜图像的深度学习模型对新辅助治疗反应具有出色的预测能力，突出了其指导个性化治疗的潜力。
方法：在这项多中心观察研究中，回顾性获得了来自两个中国医学中心的患者的治疗前内镜图像,并构建了基于深度学习的肿瘤回归模型.基于肿瘤消退等级评估治疗反应,并将其定义为良好反应和非良好反应。在内部和外部测试集中评估了深度学习模型的预测性能。主要结果是治疗预测模型的准确性，通过AUC和准确性测量。
结果：该深度学习模型实现了良好的预测性能。在内部测试集中，AUC和准确性分别为0.867（95％CI：0.847-0.941）和0.836（95％CI：0.818-0.896），分别。预测性能在外部测试集中得到了充分验证,模型的AUC为0.758(95%CI:0.724-0.834),准确度为0.807(95%CI:0.774-0.843).
结论：基于内窥镜图像的深度学习模型对新辅助治疗反应具有出色的预测能力，突出了其指导个性化治疗的潜力。
