A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.
A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.
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DOI：
10.1016/j.eclinm.2022.101348
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发表时间：
2022-04
影响因子：
15.1
通讯作者：
Gao X
中科院分区：
文献类型：
--
作者：
Cui Y;Zhang J;Li Z;Wei K;Lei Y;Ren J;Wu L;Shi Z;Meng X;Yang X;Gao X
准确预测局部晚期胃癌（LAGC）患者对新辅助化疗（NACT）的治疗反应对于个体化治疗至关重要。我们旨在开发并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放射组学图（DLRN），以预测LAGC患者对NACT的反应。回顾性招募2014年12月1日至2020年11月30日期间来自中国四家医院的719例LAGC患者。培训队列和内部验证队列（IVC）分别由243例和103例患者随机从中心I中选择；外部验证队列1 （EVC1）包括来自II中心的207例患者；EVC2包括来自另外两家医院的166名患者。根据预处理门静脉期CT图像构建了反映深度学习表型和手工制作放射组学特征的两个成像特征。采用四步程序，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，用于特征选择和签名构建。然后开发了集成DLRN，以增加成像特征对独立临床病理因素的价值，以预测NACT的反应。对预测性能进行区分、校准和临床有用性评估。采用基于DLRN的Kaplan-Meier生存曲线估计随访队列（n = 300）的无病生存（DFS）。DLRN对NACT具有良好的鉴别性，在内部验证队列和两个外部验证队列中，受试者工作曲线下面积（auc）分别为0.829 （95% CI, 0.739-0.920）、0.804 （95% CI, 0.732-0.877）和0.827 (95% CI, 0.755-0.900)，所有队列均具有良好的校准（p < 0.05）。此外，DLRN的表现明显优于临床模型（p < 0.001）。决策曲线分析证实DLRN在临床上是有用的。此外，DLRN与LAGC患者的DFS有显著相关性（p < 0.05）。基于深度学习的放射组学图在预测LAGC患者的治疗反应和临床结果方面表现良好，可以为个性化治疗提供有价值的信息。
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. 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). 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). 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.
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DOI：
10.1007/s10120-016-0601-9
发表时间：
2017-03
期刊：
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
影响因子：
--
作者：
Sano T;Coit DG;Kim HH;Roviello F;Kassab P;Wittekind C;Yamamoto Y;Ohashi Y
通讯作者：
Ohashi Y
影响因子：
50.5
作者：
Aoyama, T.;Nishikawa, K.;Yoshikawa, T.
通讯作者：
Yoshikawa, T.
影响因子：
8.8
作者：
Jia, Y.;Ye, L.;Ji, K.;Zhang, L.;Hargest, R.;Ji, J.;Jiang, W. G.
通讯作者：
Jiang, W. G.
影响因子：
50.5
作者：
Jiang, Y.;Wang, H.;Li, R.
通讯作者：
Li, R.
影响因子：
3.3
作者：
Giganti, Francesco;Marra, Paolo;De Cobelli, Francesco
通讯作者：
De Cobelli, Francesco