arXiv 2308.005072023-09-13eess.IVcs.CVcs.LG

通过集成神经距离与纹理感知Transformer改进基于多期CT的胰腺癌预后预测

Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer

DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Peking University（北京大学）；Hupan Lab（湖畔实验室）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；South China Hospital, Shenzhen University（深圳大学华南医院）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）；Changsha Institute for Computing and Digital Economy（长沙计算与数字经济研究院）；Guangdong Provincial People’s Hospital（广东省人民医院）；Shengjing Hospital（盛京医院）

Hexin Dong, Jiawen Yao, Yuxing Tang, Mingze Yuan, Yingda Xia, Jian Zhou, Hong Lu, Jingren Zhou, Bin Dong, Le Lu, Li Zhang, Zaiyi Liu, Yu Shi, Ling Zhang

备注：: MICCAI 2023

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AI总结：

提出可学习神经距离描述肿瘤与血管关系，并结合CNN与Transformer提取多期CT动态纹理特征，在多中心数据集上显著提升PDAC预后预测性能。

摘要：

胰腺导管腺癌（PDAC）是一种高度致命的癌症，其中肿瘤-血管累及极大地影响可切除性，从而影响患者的总生存期。然而，当前的预后预测方法未能明确且准确地研究肿瘤与附近重要血管之间的关系。本文提出了一种新颖的可学习神经距离，用于描述不同患者CT图像中肿瘤与血管之间的精确关系，并将其作为预后预测的主要特征。此外，与使用CNN或LSTM在动态对比增强CT成像上利用肿瘤增强模式的现有模型不同，我们通过使用CNN和transformer模块融合局部与全局特征，改进了多期对比增强CT中动态肿瘤相关纹理特征的提取，进一步增强了跨多期CT图像提取的特征。我们在包含1,070名PDAC患者的多中心（n=4）数据集中对所提方法进行了广泛评估并与现有方法比较，统计分析证实了其在由三个中心组成的外部测试集中的临床有效性。所开发的风险标志物是术前因素中总生存期的最强预测因子，并且有潜力与已确立的临床因素相结合，以筛选可能从新辅助治疗中获益的高风险患者。

英文摘要：

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients. However, current prognostic prediction methods fail to explicitly and accurately investigate relationships between the tumor and nearby important vessels. This paper proposes a novel learnable neural distance that describes the precise relationship between the tumor and vessels in CT images of different patients, adopting it as a major feature for prognosis prediction. Besides, different from existing models that used CNNs or LSTMs to exploit tumor enhancement patterns on dynamic contrast-enhanced CT imaging, we improved the extraction of dynamic tumor-related texture features in multi-phase contrast-enhanced CT by fusing local and global features using CNN and transformer modules, further enhancing the features extracted across multi-phase CT images. We extensively evaluated and compared the proposed method with existing methods in the multi-center (n=4) dataset with 1,070 patients with PDAC, and statistical analysis confirmed its clinical effectiveness in the external test set consisting of three centers. The developed risk marker was the strongest predictor of overall survival among preoperative factors and it has the potential to be combined with established clinical factors to select patients at higher risk who might benefit from neoadjuvant therapy.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。