Matsuda Satoru, Irino Tomoyuki, Kitagawa Yuko, Okamura Akihiko, Mayanagi Shuhei, Booka Eisuke, Takeuchi Masashi, Kitadani Junya, Kanda Mitsuro, Abe Tetsuya, Bamba Takeo, Iwatsuki Masaaki, Kagaya Takehiro, Kurogochi Takanori, Tsubosa Yasuhiro, Kawakubo Hirofumi, Kakeji Yoshihiro, Kono Koji, Watanabe Masayuki, Takeuchi Hiroya
Department of Surgery, Keio University School of Medicine, Tokyo, Japan.
Department of Gastroenterological Surgery, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan.
Esophagus. 2025 Apr 28. doi: 10.1007/s10388-025-01130-x.
Detecting pathological complete response (pCR) preoperatively facilitated a non-surgical approach after neoadjuvant chemotherapy (NAC). We previously developed a deep neural network-based endoscopic evaluation to determine pCR preoperatively. Its quality warrants improvement with a larger data series for clinical application.
This study retrospectively reviewed patients with esophageal squamous cell carcinoma (ESCC) receiving NAC at 46 Japanese esophageal centers certified by the Japan Esophageal Society. Endoscopic images after NAC were collected with clinicopathological factors and long-term outcomes. We randomly selected the same number of patients with Grades 0-1a and Grades 1b-2 based on those with pCR (Grade 3). A deep neural network was used for endoscopic image analyses. A test data set, consisting of 100 photos, was utilized for validation. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the deep neural network-based model and experienced physicians were calculated.
The study enrolled 1041 patients, including 354 (33%) patients with pCR, the same number of histological non-responders (Grade 0-1a/1b-2, 352 [33%]/368 [34%]). The median values of sensitivity, specificity, PPV, NPV, and accuracy for pCR detection were 80%, 90%, 89%, 82%, and 85%, respectively. The patients with pCR preoperatively demonstrated significantly better overall survival and recurrence-free survival.
This large-scale study revealed that the deep neural network-based endoscopic evaluation after NAC identified pCR with feasible accuracy. The current artificial intelligence technology may guide an individualized treatment strategy, including a non-surgical approach, in patients with ESCC through prospective studies with careful external validation.
术前检测病理完全缓解（pCR）有助于在新辅助化疗（NAC）后采取非手术治疗方法。我们之前开发了一种基于深度神经网络的内镜评估方法，用于术前确定pCR。其质量有待通过更大的数据系列进行改进，以用于临床应用。
本研究回顾性分析了在日本食管学会认证的46家日本食管中心接受NAC的食管鳞状细胞癌（ESCC）患者。收集NAC后的内镜图像以及临床病理因素和长期预后。我们根据pCR患者（3级）随机选择相同数量的0 - 1a级和1b - 2级患者。使用深度神经网络进行内镜图像分析。由100张照片组成的测试数据集用于验证。计算基于深度神经网络的模型和经验丰富的医生的敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。
该研究共纳入1041例患者，其中354例（33%）为pCR患者，组织学无反应者数量相同（0 - 1a/1b - 2级，分别为352例[33%]/368例[34%]）。pCR检测的敏感性、特异性、PPV、NPV和准确性的中位数分别为80%、90%、89%、82%和85%。术前达到pCR的患者总体生存率和无复发生存率显著更好。
这项大规模研究表明，NAC后基于深度神经网络的内镜评估能够以可行的准确性识别pCR。当前的人工智能技术可能通过经过仔细外部验证的前瞻性研究，为ESCC患者指导个性化治疗策略，包括非手术治疗方法。
