Lung cancer is the leading cause of cancer-related deaths worldwide. Two of the crucial factors contributing to these fatalities are delayed diagnosis and suboptimal prognosis. The rapid advancement of deep learning (DL) approaches provides a significant opportunity for medical imaging techniques to play a pivotal role in the early detection of lung tumors and subsequent monitoring during treatment. This study presents a DL-based model for efficient lung cancer detection using whole-slide images. Our methodology combines convolutional neural networks (CNNs) and separable CNNs with residual blocks, thereby improving classification performance. Our model improves accuracy (96% to 98%) and robustness in distinguishing between cancerous and non-cancerous lung cell images in less than 10 s. Moreover, the model’s overall performance surpassed that of active pathologists, with an accuracy of 100% vs. 79%. There was a significant linear correlation between pathologists’ accuracy and years of experience (r Pearson = 0.71, 95% CI 0.14 to 0.93,p= 0.022). We conclude that this model enhances the accuracy of cancer detection and can be used to train junior pathologists.
肺癌是全球癌症相关死亡的主要原因。导致这些死亡的两个关键因素是诊断延迟和预后不佳。深度学习方法的快速发展为医学影像技术在肺部肿瘤早期检测及后续治疗监测中发挥关键作用提供了重要机遇。本研究提出了一种基于深度学习的模型，利用全切片图像实现高效肺癌检测。我们的方法将卷积神经网络和可分离卷积神经网络与残差块相结合，从而提升了分类性能。该模型在10秒内区分癌性与非癌性肺细胞图像的准确率（96%至98%）和鲁棒性均有提高。此外，模型的整体表现超越了执业病理医师，准确率达100%对比79%。病理医师诊断准确率与工作年限呈显著线性相关（皮尔逊相关系数r=0.71，95%置信区间0.14-0.93，p=0.022）。我们得出结论：该模型提高了癌症检测的准确性，并可用于培训初级病理医师。
肿瘤(癌症)患者之家