Objective

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.

Methods

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.

Results

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.

Conclusion

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.

中文翻译：

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开发基于深度学习的混合型胃癌准确诊断模型

客观的

鉴于混合型胃癌复杂的特征以及与其他亚型胃癌的相似性，从病理图像中准确诊断混合型胃癌对病理学家来说是一个巨大的挑战。人工智能有潜力克服这一障碍。这项研究旨在利用深度机器学习技术为这种癌症类型建立一种精确有效的诊断方法，该方法还可以使用 U-Net 和 QuPath 两种软件预测转移风险，这两种软件尚未在胃癌中进行试验。

方法

训练 U-Net 神经网络来识别和分割 186 幅混合型胃癌病理图像中的差异成分。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。U-Net和QuPath的结果用于计算与淋巴结转移相关的分​​化/未分化成分的比率。

结果

U-Net建立的模型识别了~91%的感兴趣区域，准确率、召回率和F1值分别为90.2%、90.9%和94.6%，表明了较高的准确性和可靠性。此外，受试者工作特征曲线分析显示治愈面积为 91%，表明性能良好。发现分化/未分化比率与淋巴转移之间存在钟形曲线相关性（最高风险在 0.683 至 1.03 之间），这是范式转变的。

结论

U-Net 和 QuPath 在识别混合型胃癌的分化和未分化成分以及转移预测方面表现出令人鼓舞的准确性。这些发现使我们离其潜在的临床应用又近了一步。