PDF(Pubmed)
Abstract：
OBJECTIVE: Synchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM.
METHODS: A total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC).
RESULTS: The AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874-0.978), 0.897 (95% CI: 0.801-0.994), 0.885 (95% CI: 0.795-0.975), and 0.889 (95% CI: 0.823-0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model.
CONCLUSIONS: The radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans.
UNASSIGNED: The onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans.
CONCLUSIONS: Prognosis for patients with CRPM is bleak, and early detection poses challenges. The synergy between radiomics and deep learning proves advantageous in evaluating CRPM. The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients.
METHODS: A total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC).
RESULTS: The AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874-0.978), 0.897 (95% CI: 0.801-0.994), 0.885 (95% CI: 0.795-0.975), and 0.889 (95% CI: 0.823-0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model.
CONCLUSIONS: The radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans.
UNASSIGNED: The onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans.
CONCLUSIONS: Prognosis for patients with CRPM is bleak, and early detection poses challenges. The synergy between radiomics and deep learning proves advantageous in evaluating CRPM. The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients.
摘要：
目的：同步结直肠癌腹膜转移（CRPM）预后较差。本研究旨在通过PET/CT图像创建一个影像组学增强的深度学习模型，用于同步CRPM的风险评估。
方法：本研究共纳入220例结直肠癌（CRC）病例。我们通过2D滑动内核在CT和PET图像块上映射了放射学特征的特征图（放射学特征图（RFM））。基于ResNet50，使用PET/CT图像补丁和RFM训练了一个影像组学增强的深度学习模型。我们探讨了瘤周区域是否有助于CRPM的评估.在这项研究中,通过曲线下面积（AUC）评估每个模型的性能。
结果：培训中的影像组学增强的深度学习模型的AUC，内部,外部,所有验证数据集均为0.926（95％置信区间（CI）：0.874-0.978），0.897（95%CI：0.801-0.994），0.885（95%CI：0.795-0.975），和0.889（95%CI：0.823-0.954），分别。该模型在校准曲线中表现出一致性，德隆检验和IDI将其确定为最具预测性的模型。
结论：影像组学增强的深度学习模型在术前预测PET/CT同步CRPM方面显示出优越的估计性能，在开发更个性化的治疗方法和后续计划方面提供潜在的帮助。
■同步结直肠CRPM的发作是阴险的，使用影像组学增强的深度学习模型在治疗前评估CRPM的风险，可以帮助做出个性化的临床治疗决策或选择更敏感的后续计划。
结论：CRPM患者的预后暗淡，早期检测带来了挑战。影像组学和深度学习之间的协同作用在评估CRPM方面被证明是有利的。影像组学增强的深度学习模型在为CRC患者量身定制治疗方法方面被证明是有价值的。
方法：本研究共纳入220例结直肠癌（CRC）病例。我们通过2D滑动内核在CT和PET图像块上映射了放射学特征的特征图（放射学特征图（RFM））。基于ResNet50，使用PET/CT图像补丁和RFM训练了一个影像组学增强的深度学习模型。我们探讨了瘤周区域是否有助于CRPM的评估.在这项研究中,通过曲线下面积（AUC）评估每个模型的性能。
结果：培训中的影像组学增强的深度学习模型的AUC，内部,外部,所有验证数据集均为0.926（95％置信区间（CI）：0.874-0.978），0.897（95%CI：0.801-0.994），0.885（95%CI：0.795-0.975），和0.889（95%CI：0.823-0.954），分别。该模型在校准曲线中表现出一致性，德隆检验和IDI将其确定为最具预测性的模型。
结论：影像组学增强的深度学习模型在术前预测PET/CT同步CRPM方面显示出优越的估计性能，在开发更个性化的治疗方法和后续计划方面提供潜在的帮助。
■同步结直肠CRPM的发作是阴险的，使用影像组学增强的深度学习模型在治疗前评估CRPM的风险，可以帮助做出个性化的临床治疗决策或选择更敏感的后续计划。
结论：CRPM患者的预后暗淡，早期检测带来了挑战。影像组学和深度学习之间的协同作用在评估CRPM方面被证明是有利的。影像组学增强的深度学习模型在为CRC患者量身定制治疗方法方面被证明是有价值的。
