Wang Huitao, Nakajima Takahiro, Shikano Kohei, Nomura Yukihiro, Nakaguchi Toshiya
Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, Japan.
Department of General Thoracic Surgery, Dokkyo Medical University, Mibu 321-0293, Japan.
Tomography. 2025 Feb 27;11(3):24. doi: 10.3390/tomography11030024.
Lung cancer is the leading cause of cancer-related deaths globally and ranks among the most common cancer types. Given its low overall five-year survival rate, early diagnosis and timely treatment are essential to improving patient outcomes. In recent years, advances in computer technology have enabled artificial intelligence to make groundbreaking progress in imaging-based lung cancer diagnosis. The primary aim of this study is to develop a computer-aided diagnosis (CAD) system for lung cancer using endobronchial ultrasonography (EBUS) images and deep learning algorithms to facilitate early detection and improve patient survival rates. We propose M3-Net, which is a multi-branch framework that integrates multiple features through an attention-based mechanism, enhancing diagnostic performance by providing more comprehensive information for lung cancer assessment. The framework was validated on a dataset of 95 patient cases, including 13 benign and 82 malignant cases. The dataset comprises 1140 EBUS images, with 540 images used for training, and 300 images each for the validation and test sets. The evaluation yielded the following results: accuracy of 0.76, F1-score of 0.75, AUC of 0.83, PPV of 0.80, NPV of 0.75, sensitivity of 0.72, and specificity of 0.80. These findings indicate that the proposed attention-based multi-feature fusion framework holds significant potential in assisting with lung cancer diagnosis.
肺癌是全球癌症相关死亡的主要原因，也是最常见的癌症类型之一。鉴于其总体五年生存率较低，早期诊断和及时治疗对于改善患者预后至关重要。近年来，计算机技术的进步使人工智能在基于成像的肺癌诊断方面取得了突破性进展。本研究的主要目的是开发一种使用支气管内超声（EBUS）图像和深度学习算法的肺癌计算机辅助诊断（CAD）系统，以促进早期检测并提高患者生存率。我们提出了M3-Net，这是一个多分支框架，通过基于注意力的机制整合多种特征，通过为肺癌评估提供更全面的信息来提高诊断性能。该框架在一个包含95例患者病例的数据集上进行了验证，其中包括13例良性病例和82例恶性病例。该数据集包含1140张EBUS图像，其中540张用于训练，验证集和测试集各300张。评估结果如下：准确率为0.76，F1分数为0.75，AUC为0.83，PPV为0.80，NPV为0.75，灵敏度为0.72，特异性为0.80。这些发现表明，所提出的基于注意力的多特征融合框架在辅助肺癌诊断方面具有巨大潜力。
