Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China.
Department of Pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China.
Int J Biochem Cell Biol. 2023 Sep;162:106452. doi: 10.1016/j.biocel.2023.106452. Epub 2023 Jul 21.
The accurate diagnosis of mixed-type gastric cancer from pathology images presents a formidable challenge for pathologists, given its intricate features and resemblance to other subtypes of gastric cancer. Artificial Intelligence has the potential to overcome this hurdle. This study aimed to leverage deep machine learning techniques to establish a precise and efficient diagnostic approach for this cancer type which can also predict the metastatic risk using two software, U-Net and QuPath, which have not been trialled in gastric cancers.
A U-Net neural network was trained to recognise, and segment differentiated components from 186 pathology images of mixed-type gastric cancer. Undifferentiated components in the same images were annotated using the open-source pathology imaging software QuPath. The outcomes from U-Net and QuPath were used to calculate the ratios of differentiation/undifferentiated components which were correlated to lymph node metastasis.
The models established by U-Net recognised ∼91% of the regions of interest, with precision, recall, and F1 values of 90.2%, 90.9% and 94.6%, respectively, indicating a high level of accuracy and reliability. Furthermore, the receiver operating characteristic curve analysis showed an area under the cure of 91%, indicating good performance. A bell-curve correlation between the differentiated/undifferentiated ratio and lymphatic metastasis was found (highest risk between 0.683 and 1.03), which is paradigm-shifting.
U-Net and QuPath exhibit promising accuracy in the identification of differentiated and undifferentiated components in mixed-type gastric cancer, as well as paradigm-shifting prediction of metastasis. These findings bring us one step closer to their potential clinical application.
由于混合性胃癌具有复杂的特征且与其他胃癌亚型相似，病理学家准确诊断混合性胃癌具有挑战性。人工智能有可能克服这一障碍。本研究旨在利用深度学习技术为这种癌症类型建立一种精确且高效的诊断方法，同时还可以使用两种软件（U-Net 和 QuPath）预测转移风险，这两种软件尚未在胃癌中进行试验。
训练 U-Net 神经网络识别和分割 186 张混合性胃癌病理图像中的分化成分。使用开源病理成像软件 QuPath 对同一图像中的未分化成分进行注释。使用 U-Net 和 QuPath 的结果计算分化/未分化成分的比例，该比例与淋巴结转移相关。
U-Net 建立的模型识别了约 91%的感兴趣区域，其精度、召回率和 F1 值分别为 90.2%、90.9%和 94.6%，表明具有较高的准确性和可靠性。此外，接收器工作特征曲线分析显示曲线下面积为 91%，表明性能良好。发现分化/未分化比例与淋巴转移之间存在钟形曲线相关性（风险最高在 0.683 和 1.03 之间），这是一个范式转变。
U-Net 和 QuPath 在识别混合性胃癌中的分化和未分化成分以及范式转变预测转移方面表现出有希望的准确性。这些发现使我们更接近它们的潜在临床应用。
