Liao Hongfan, Yuan Jiang, Liu Chunhua, Zhang Jiao, Yang Yaying, Liang Hongwei, Jiang Song, Chen Shanxiong, Li Yongmei, Liu Yanbing
College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China.
Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Insights Imaging. 2023 Dec 21;14(1):223. doi: 10.1186/s13244-023-01553-z.
This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC).
A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR. Deep learning and radiomics models were developed including ShuffulNetV2, Xception, MobileNetV3, ResNet18, support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and logistic regression (LR). Additionally, the clinical models were constructed through univariate and multivariate logistic regression. Kaplan-Meier survival analysis and log-rank tests were conducted to compare the overall survival time between different TSR groups.
To differentiate low TSR from high TSR, the deep learning models based on ShuffulNetV2, Xception, MobileNetV3, and ResNet18 achieved AUCs of 0.846, 0.924, 0.930, and 0.941, respectively, outperforming the radiomics models based on SVM, KNN, RF, and LR with AUCs of 0.739, 0.717, 0.763, and 0.756, respectively. Resnet 18 achieved the best predictive performance. The clinical model based on T stage alone performed worse than deep learning models and radiomics models. The survival analysis based on 142 of the 207 patients demonstrated that patients with low TSR had longer overall survival.
Deep learning models demonstrate feasibility and superiority over radiomics in differentiating TSR in PDAC. The tumor stroma ratio in the PDAC microenvironment plays a significant role in determining prognosis.
The objective was to compare the feasibility and effectiveness of automatic deep learning networks and radiomics models in identifying the tumor-stroma ratio in pancreatic ductal adenocarcinoma. Our findings demonstrate deep learning models exhibited superior performance compared to traditional radiomics models.
• Deep learning demonstrates better performance than radiomics in differentiating tumor-stroma ratio in pancreatic ductal adenocarcinoma. • The tumor-stroma ratio in the pancreatic ductal adenocarcinoma microenvironment plays a protective role in prognosis. • Preoperative prediction of tumor-stroma ratio contributes to clinical decision-making and guiding precise medicine.
本研究旨在比较自动深度学习网络和放射组学模型在区分胰腺导管腺癌（PDAC）低肿瘤基质比（TSR）与高TSR方面的可行性和有效性。
对来自三个中心的207例PDAC患者进行回顾性分析（训练队列：n = 160；测试队列：n = 47）。由经验丰富的病理学家在苏木精和伊红染色的标本上评估TSR，并分为低TSR和高TSR。开发了深度学习和放射组学模型，包括ShuffulNetV2、Xception、MobileNetV3、ResNet18、支持向量机（SVM）、k近邻（KNN）、随机森林（RF）和逻辑回归（LR）。此外，通过单变量和多变量逻辑回归构建临床模型。进行Kaplan-Meier生存分析和对数秩检验，以比较不同TSR组之间的总生存时间。
为了区分低TSR和高TSR，基于ShuffulNetV2、Xception、MobileNetV3和ResNet18的深度学习模型的曲线下面积（AUC）分别为0.846、0.924、0.930和0.941，优于基于SVM、KNN、RF和LR的放射组学模型，其AUC分别为0.739、0.717、0.763和0.756。Resnet 18具有最佳的预测性能。仅基于T分期的临床模型表现不如深度学习模型和放射组学模型。对207例患者中的142例进行的生存分析表明，低TSR患者的总生存期更长。
在区分PDAC中的TSR方面，深度学习模型显示出比放射组学更好的可行性和优越性。PDAC微环境中的肿瘤基质比在决定预后方面起着重要作用。
目的是比较自动深度学习网络和放射组学模型在识别胰腺导管腺癌肿瘤基质比方面的可行性和有效性。我们的研究结果表明，深度学习模型表现出比传统放射组学模型更优越的性能。
• 在区分胰腺导管腺癌的肿瘤基质比方面，深度学习比放射组学表现更好。• 胰腺导管腺癌微环境中的肿瘤基质比在预后方面起保护作用。• 术前预测肿瘤基质比有助于临床决策和指导精准医学。
