Background: The aim of this study was to establish a deep learning prediction model for neoadjuvant FLOT chemotherapy response. The neural network utilized clinical data and visual information from whole-slide images (WSIs) of therapy-naïve gastroesophageal cancer biopsies. Methods: This study included 78 patients from the University Hospital of Cologne and 59 patients from the University Hospital of Heidelberg used as external validation. Results: After surgical resection, 33 patients from Cologne (42.3%) were ypN0 and 45 patients (57.7%) were ypN+, while 23 patients from Heidelberg (39.0%) were ypN0 and 36 patients (61.0%) were ypN+ (p= 0.695). The neural network had an accuracy of 92.1% to predict lymph node metastasis and the area under the curve (AUC) was 0.726. A total of 43 patients from Cologne (55.1%) had less than 50% residual vital tumor (RVT) compared to 34 patients from Heidelberg (57.6%,p= 0.955). The model was able to predict tumor regression with an error of ±14.1% and an AUC of 0.648. Conclusions: This study demonstrates that visual features extracted by deep learning from therapy-naïve biopsies of gastroesophageal adenocarcinomas correlate with positive lymph nodes and tumor regression. The results will be confirmed in prospective studies to achieve early allocation of patients to the most promising treatment.
背景：本研究旨在建立一种用于预测新辅助FLOT化疗反应的深度学习模型。该神经网络利用了初治胃食管癌活检组织全切片图像（WSIs）的临床数据和视觉信息。方法：本研究纳入来自科隆大学医院的78例患者，并采用海德堡大学医院的59例患者作为外部验证队列。结果：手术切除后，科隆队列中33例患者（42.3%）达到ypN0状态，45例患者（57.7%）为ypN+；海德堡队列中23例患者（39.0%）为ypN0，36例患者（61.0%）为ypN+（p=0.695）。该神经网络预测淋巴结转移的准确率达92.1%，曲线下面积（AUC）为0.726。科隆队列中43例患者（55.1%）残留活性肿瘤（RVT）比例低于50%，海德堡队列中34例患者（57.6%）达到此标准（p=0.955）。该模型预测肿瘤消退的误差为±14.1%，AUC为0.648。结论：本研究表明，通过深度学习从初治胃食管腺癌活检组织中提取的视觉特征与淋巴结阳性状态及肿瘤消退程度具有相关性。该结果将在前瞻性研究中进一步验证，以期实现早期将患者分配至最具前景的治疗方案。
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