文章：

集成深度学习模型预测胃癌淋巴血管侵犯

Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer

原文发布日期：19 January 2024

DOI: 10.3390/cancers16020430

类型: Article

开放获取: 是

英文摘要：

Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images. The ConViT model showed the best performance in terms of both AUROC and AURPC among the classification models (AUROC: 0.9796; AUPRC: 0.9648). The AUROC and AUPRC of YOLOX computed based on the augmented patch-level confidence score were slightly lower (AUROC: −0.0094; AUPRC: −0.0225) than those of the ConViT classification model. With weighted averaging of the patch-level confidence scores, the ensemble model exhibited the best AUROC, AUPRC, and F1 scores of 0.9880, 0.9769, and 0.9280, respectively. The proposed model is expected to contribute to precision medicine by potentially saving examination-related time and labor and reducing disagreements among pathologists.

摘要翻译： 

淋巴血管侵犯（LVI）是胃癌最重要的预后因素之一，其存在提示患者淋巴结转移风险更高、总体预后更差。尽管LVI的评估至关重要，但在胃癌组织病理学标本中检测LVI阳性对病理医师仍具挑战性，因其侵袭表现可能较为隐匿且难以辨识。本研究提出一种基于深度学习的LVI阳性检测方法，利用H&E染色全切片图像进行分析。在分类模型中，ConViT模型在受试者工作特征曲线下面积和精确率-召回率曲线下面积两项指标上均表现最优（AUROC：0.9796；AUPRC：0.9648）。基于增强切片级置信度计算的YOLOX模型AUROC与AUPRC略低于ConViT分类模型（AUROC降低0.0094；AUPRC降低0.0225）。通过加权平均切片级置信度，集成模型取得了最佳性能指标：AUROC为0.9880，AUPRC为0.9769，F1分数达0.9280。该模型有望通过节约检测时间与人力成本、减少病理医师间诊断差异，为精准医疗发展提供支持。

原文链接：

Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer