Cui Shaoguo, Tang Yibo, Wan Haoming, Wang Rui, Liu Lili
School of Computer and Information Sciences, Chongqing Normal University, Chongqing 401331, P. R. China.
Department of Radiology, Chongqing People's Hospital, Chongqing 401331, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Aug 25;41(4):807-817. doi: 10.7507/1001-5515.202308009.
High-grade serous ovarian cancer has a high degree of malignancy, and at detection, it is prone to infiltration of surrounding soft tissues, as well as metastasis to the peritoneum and lymph nodes, peritoneal seeding, and distant metastasis. Whether recurrence occurs becomes an important reference for surgical planning and treatment methods for this disease. Current recurrence prediction models do not consider the potential pathological relationships between internal tissues of the entire ovary. They use convolutional neural networks to extract local region features for judgment, but the accuracy is low, and the cost is high. To address this issue, this paper proposes a new lightweight deep learning algorithm model for predicting recurrence of high-grade serous ovarian cancer. The model first uses ghost convolution (Ghost Conv) and coordinate attention (CA) to establish ghost counter residual (SCblock) modules to extract local feature information from images. Then, it captures global information and integrates multi-level information through proposed layered fusion Transformer (STblock) modules to enhance interaction between different layers. The Transformer module unfolds the feature map to compute corresponding region blocks, then folds it back to reduce computational cost. Finally, each STblock module fuses deep and shallow layer depth information and incorporates patient's clinical metadata for recurrence prediction. Experimental results show that compared to the mainstream lightweight mobile visual Transformer (MobileViT) network, the proposed slicer visual Transformer (SlicerViT) network improves accuracy, precision, sensitivity, and F1 score, with only 1/6 of the computational cost and half the parameter count. This research confirms that the proposed algorithm model is more accurate and efficient in predicting recurrence of high-grade serous ovarian cancer. In the future, it can serve as an auxiliary diagnostic technique to improve patient survival rates and facilitate the application of the model in embedded devices.
高级别浆液性卵巢癌具有高度恶性，在被发现时，它易于浸润周围软组织，以及转移至腹膜和淋巴结、腹膜种植和远处转移。是否复发成为该疾病手术规划和治疗方法的重要参考。当前的复发预测模型没有考虑整个卵巢内部组织之间潜在的病理关系。它们使用卷积神经网络提取局部区域特征进行判断，但准确率低且成本高。为了解决这个问题，本文提出了一种用于预测高级别浆液性卵巢癌复发的新型轻量级深度学习算法模型。该模型首先使用幽灵卷积（Ghost Conv）和坐标注意力（CA）建立幽灵计数器残差（SCblock）模块，从图像中提取局部特征信息。然后，它通过提出的分层融合Transformer（STblock）模块捕获全局信息并整合多级信息，以增强不同层之间的交互。Transformer模块展开特征图以计算相应的区域块，然后再折叠回去以降低计算成本。最后，每个STblock模块融合深层和浅层深度信息，并纳入患者的临床元数据进行复发预测。实验结果表明，与主流的轻量级移动视觉Transformer（MobileViT）网络相比，所提出的切片视觉Transformer（SlicerViT）网络提高了准确率、精确率、灵敏度和F1分数，计算成本仅为其1/6，参数数量为其一半。这项研究证实，所提出的算法模型在预测高级别浆液性卵巢癌复发方面更准确、高效。未来，它可以作为一种辅助诊断技术来提高患者生存率，并促进该模型在嵌入式设备中的应用。
