Abstract：
OBJECTIVE: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of its significance in T-classification and lymph node metastasis prediction.
METHODS: This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation.
RESULTS: Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction.
CONCLUSIONS: AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.
METHODS: This retrospective analysis was conducted on preoperative HRCT images of 472 patients with stage I non-small cell lung cancer (NSCLC), focusing on lesions adjacent to the pleura to predict VPI. YOLOv4.0 was utilized for tumor localization, and EfficientNetv2 was applied for VPI prediction with HRCT images meticulously annotated for AI model training and validation.
RESULTS: Of the 472 lung cancer cases (500 CT images) studied, the AI algorithm successfully identified tumors, with YOLOv4.0 accurately localizing tumors in 98% of the test images. In the EfficientNet v2-M analysis, the receiver operating characteristic curve exhibited an area under the curve of 0.78. It demonstrated powerful diagnostic performance with a sensitivity, specificity, and precision of 76.4% in VPI prediction.
CONCLUSIONS: AI is a promising tool for improving the diagnostic accuracy of VPI for NSCLC. Furthermore, incorporating AI into the diagnostic workflow is advocated because of its potential to improve the accuracy of preoperative diagnosis and patient outcomes in NSCLC.
摘要：
目的：本研究旨在评估人工智能（AI）在使用高分辨率计算机断层扫描（HRCT）图像检测肺癌内脏胸膜侵犯（VPI）中的效率，这对专家来说是具有挑战性的，因为它在T分类和淋巴结转移预测中具有重要意义。
方法：对472例I期非小细胞肺癌（NSCLC）患者的术前HRCT图像进行回顾性分析。关注邻近胸膜的病变以预测VPI。YOLOv4.0用于肿瘤定位，和EfficientNetv2应用于VPI预测，HRCT图像精心注释，用于AI模型训练和验证。
结果：在所研究的472例肺癌病例（500张CT图像）中，人工智能算法成功识别出肿瘤，YOLOv4.0在98%的测试图像中准确定位肿瘤。在EfficientNetv2-M分析中，受试者工作特征曲线显示曲线下面积为0.78。它展示了强大的诊断性能和灵敏度，特异性，VPI预测精度为76.4%。
结论：AI是提高NSCLCVPI诊断准确性的一种有前途的工具。此外，由于AI有可能提高NSCLC的术前诊断准确性和患者预后,因此提倡将AI纳入诊断工作流程.
方法：对472例I期非小细胞肺癌（NSCLC）患者的术前HRCT图像进行回顾性分析。关注邻近胸膜的病变以预测VPI。YOLOv4.0用于肿瘤定位，和EfficientNetv2应用于VPI预测，HRCT图像精心注释，用于AI模型训练和验证。
结果：在所研究的472例肺癌病例（500张CT图像）中，人工智能算法成功识别出肿瘤，YOLOv4.0在98%的测试图像中准确定位肿瘤。在EfficientNetv2-M分析中，受试者工作特征曲线显示曲线下面积为0.78。它展示了强大的诊断性能和灵敏度，特异性，VPI预测精度为76.4%。
结论：AI是提高NSCLCVPI诊断准确性的一种有前途的工具。此外，由于AI有可能提高NSCLC的术前诊断准确性和患者预后,因此提倡将AI纳入诊断工作流程.
