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Abstract：
UNASSIGNED: Accurate prediction of treatment response to neoadjuvant chemotherapy (NACT) in individual patients with locally advanced gastric cancer (LAGC) is essential for personalized medicine. We aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on pretreatment contrast-enhanced computed tomography (CT) images and clinical features to predict the response to NACT in patients with LAGC.
UNASSIGNED: 719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort (n = 300).
UNASSIGNED: The DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739-0.920), 0.804 (95% CI, 0.732-0.877), and 0.827 (95% CI, 0.755-0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts (p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model (p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC (p < 0.05).
UNASSIGNED: A deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment.
UNASSIGNED: 719 patients with LAGC were retrospectively recruited from four Chinese hospitals between Dec 1st, 2014 and Nov 30th, 2020. The training cohort and internal validation cohort (IVC), comprising 243 and 103 patients, respectively, were randomly selected from center I; the external validation cohort1 (EVC1) comprised 207 patients from center II; and EVC2 comprised 166 patients from another two hospitals. Two imaging signatures, reflecting the phenotypes of the deep learning and handcrafted radiomics features, were constructed from the pretreatment portal venous-phase CT images. A four-step procedure, including reproducibility evaluation, the univariable analysis, the LASSO method, and the multivariable logistic regression analysis, was applied for feature selection and signature building. The integrated DLRN was then developed for the added value of the imaging signatures to independent clinicopathological factors for predicting the response to NACT. The prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the DLRN were used to estimate the disease-free survival (DFS) in the follow-up cohort (n = 300).
UNASSIGNED: The DLRN showed satisfactory discrimination of good response to NACT and yielded the areas under the receiver operating curve (AUCs) of 0.829 (95% CI, 0.739-0.920), 0.804 (95% CI, 0.732-0.877), and 0.827 (95% CI, 0.755-0.900) in the internal and two external validation cohorts, respectively, with good calibration in all cohorts (p > 0.05). Furthermore, the DLRN performed significantly better than the clinical model (p < 0.001). Decision curve analysis confirmed that the DLRN was clinically useful. Besides, DLRN was significantly associated with the DFS of patients with LAGC (p < 0.05).
UNASSIGNED: A deep learning-based radiomics nomogram exhibited a promising performance for predicting therapeutic response and clinical outcomes in patients with LAGC, which could provide valuable information for individualized treatment.
摘要：
UNASSIGNED：准确预测局部晚期胃癌（LAGC）患者对新辅助化疗（NACT）的治疗反应对于个性化医疗至关重要。我们旨在开发和验证基于预处理对比增强计算机断层扫描（CT）图像和临床特征的深度学习影像组学列线图（DLRN），以预测LAGC患者对NACT的反应。
UNASSIGNED：12月1日之间从四家中国医院回顾性招募了719名LAGC患者，2014年和11月30日，2020年。训练队列和内部验证队列(IVC)，包括243名和103名患者，分别,从中心I随机选择;外部验证队列1(EVC1)包括来自中心II的207名患者;EVC2包括来自另外两家医院的166名患者。两个影像特征,反映了深度学习和手工制作的影像组学特征的表型，从预处理门静脉期CT图像构建。一个四步程序，包括再现性评估，单变量分析，LASSO方法,和多变量逻辑回归分析，被应用于特征选择和签名构建。然后开发综合DLRN，以增加成像特征对独立临床病理因素的价值，以预测对NACT的反应。在歧视方面评估了预测性能，校准,和临床有用性。使用基于DLRN的Kaplan-Meier存活曲线来估计随访队列(n=300)中的无病存活期(DFS)。
UNASSIGNED：DLRN对NACT的良好反应表现出令人满意的辨别，并产生了0.829（95％CI，0.739-0.920）的受试者工作曲线下面积（AUC），0.804（95%CI，0.732-0.877），内部和两个外部验证队列中的0.827（95%CI，0.755-0.900），分别,在所有队列中具有良好的校准(p>0.05)。此外，DLRN的表现明显优于临床模型(p<0.001)。判定曲线剖析证实DLRN是临床有用的。此外,DLRN与LAGC患者的DFS显著相关(p<0.05)。
UNASSIGNED：基于深度学习的影像组学列线图在预测LAGC患者的治疗反应和临床结果方面表现出了有希望的表现，这可以为个体化治疗提供有价值的信息。
UNASSIGNED：12月1日之间从四家中国医院回顾性招募了719名LAGC患者，2014年和11月30日，2020年。训练队列和内部验证队列(IVC)，包括243名和103名患者，分别,从中心I随机选择;外部验证队列1(EVC1)包括来自中心II的207名患者;EVC2包括来自另外两家医院的166名患者。两个影像特征,反映了深度学习和手工制作的影像组学特征的表型，从预处理门静脉期CT图像构建。一个四步程序，包括再现性评估，单变量分析，LASSO方法,和多变量逻辑回归分析，被应用于特征选择和签名构建。然后开发综合DLRN，以增加成像特征对独立临床病理因素的价值，以预测对NACT的反应。在歧视方面评估了预测性能，校准,和临床有用性。使用基于DLRN的Kaplan-Meier存活曲线来估计随访队列(n=300)中的无病存活期(DFS)。
UNASSIGNED：DLRN对NACT的良好反应表现出令人满意的辨别，并产生了0.829（95％CI，0.739-0.920）的受试者工作曲线下面积（AUC），0.804（95%CI，0.732-0.877），内部和两个外部验证队列中的0.827（95%CI，0.755-0.900），分别,在所有队列中具有良好的校准(p>0.05)。此外，DLRN的表现明显优于临床模型(p<0.001)。判定曲线剖析证实DLRN是临床有用的。此外,DLRN与LAGC患者的DFS显著相关(p<0.05)。
UNASSIGNED：基于深度学习的影像组学列线图在预测LAGC患者的治疗反应和临床结果方面表现出了有希望的表现，这可以为个体化治疗提供有价值的信息。
