Liver cancer remains the leading cause of cancer death globally, and the treatment strategies are distinct for each type of malignant hepatic tumors. However, the differential diagnosis before surgery is challenging and subjective. This study aims to build an automatic diagnostic model for differentiating malignant hepatic tumors based on patients’ multimodal medical data including multi-phase contrast-enhanced computed tomography and clinical features. Our study consisted of 723 patients from two centers, who were pathologically diagnosed with HCC, ICC or metastatic liver cancer. The training set and the test set consisted of 499 and 113 patients from center 1, respectively. The external test set consisted of 111 patients from center 2. We proposed a deep learning model with the modular design of SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC), which take the advantage of deep CNN and gated RNN to effectively extract and integrate the diagnosis-related radiological and clinical features of patients. The code is publicly available at https://github.com/ruitian-olivia/STIC-model . The STIC model achieved an accuracy of 86.2% and AUC of 0.893 for classifying HCC and ICC on the test set. When extended to differential diagnosis of malignant hepatic tumors, the STIC model achieved an accuracy of 72.6% on the test set, comparable with the diagnostic level of doctors’ consensus (70.8%). With the assistance of the STIC model, doctors achieved better performance than doctors’ consensus diagnosis, with an increase of 8.3% in accuracy and 26.9% in sensitivity for ICC diagnosis on average. On the external test set from center 2, the STIC model achieved an accuracy of 82.9%, which verify the model’s generalization ability. We incorporated deep CNN and gated RNN in the STIC model design for differentiating malignant hepatic tumors based on multi-phase CECT and clinical features. Our model can assist doctors to achieve better diagnostic performance, which is expected to serve as an AI assistance system and promote the precise treatment of liver cancer.

中文翻译：

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基于多相增强 CT 和临床数据的深度学习鉴别诊断肝脏恶性肿瘤

肝癌仍然是全球癌症死亡的主要原因，每种类型的恶性肝肿瘤的治疗策略各不相同。然而，手术前的鉴别诊断具有挑战性和主观性。本研究旨在基于患者的多模态医学数据（包括多相对比增强计算机断层扫描和临床特征）建立用于区分恶性肝脏肿瘤的自动诊断模型。我们的研究包括来自两个中心的 723 名病理诊断为 HCC、ICC 或转移性肝癌的患者。训练集和测试集分别由来自中心 1 的 499 名和 113 名患者组成。外部测试集由来自中心 2 的 111 名患者组成。我们提出了一种采用 SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC) 模块化设计的深度学习模型，利用深度 CNN 和门控 RNN 的优势，有效地提取和整合患者的诊断相关放射学和临床特征。该代码可在 https://github.com/ruitian-olivia/STIC-model 上公开获得。STIC 模型实现了 86.2% 的准确率和 0.893 的 AUC，用于在测试集上对 HCC 和 ICC 进行分类。当扩展到肝脏恶性肿瘤的鉴别诊断时，STIC模型在测试集上的准确率达到了72.6%，与医生共识的诊断水平（70.8%）相当。在 STIC 模型的辅助下，医生取得了比医生共识诊断更好的性能，准确率提高了 8.3% 和 26。ICC 诊断的敏感性平均为 9%。在来自中心2的外部测试集上，STIC模型达到了82.9%的准确率，验证了模型的泛化能力。我们在 STIC 模型设计中加入了深度 CNN 和门控 RNN，用于根据多期 CECT 和临床特征区分恶性肝肿瘤。我们的模型可以帮助医生获得更好的诊断性能，有望作为人工智能辅助系统，促进肝癌的精准治疗。