ArticleAnnals of surgical oncology2023
Evaluation of Endoscopic Response Using Deep Neural Network in Esophageal Cancer Patients Who Received Neoadjuvant Chemotherapy.
Article in Annals of surgical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Detection of pathologic complete response using deep neural network-based endoscopic evaluation in patients with esophageal cancer receiving neoadjuvant chemotherapy: a nationwide multicenter retrospective study from 46 Japanese esophageal centers.Esophagus : official journal of the Japan Esophageal Society · 2025Article
- Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).Oncology letters · 2025Review
- Multimodal deep-learning model using pre-treatment endoscopic images and clinical information to predict efficacy of neoadjuvant chemotherapy in esophageal squamous cell carcinoma.Esophagus : official journal of the Japan Esophageal Society · 2025Article
- Current Role of Artificial Intelligence in the Management of Esophageal Cancer.Journal of clinical medicine · 2025Review
- Review
- Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer.Cancers · 2024Review
- Article
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Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe previously reported that endoscopic response evaluation can preoperatively predict the prognosis and distribution of residual tumors after neoadjuvant chemotherapy (NAC). In this study, we developed artificial intelligence (AI)-guided endoscopic response evaluation using a deep neural network to discriminate endoscopic responders (ERs) in patients with esophageal squamous cell carcinoma (ESCC) after NAC.
methodSurgically resectable ESCC patients who underwent esophagectomy following NAC were retrospectively analyzed in this study. Endoscopic images of the tumors were analyzed using a deep neural network. The model was validated with a test data set using 10 newly collected ERs and 10 newly collected non-ER images. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the endoscopic response evaluation by AI and endoscopists were calculated and compared.
resultsOf 193 patients, 40 (21%) were diagnosed as ERs. The median sensitivity, specificity, PPV, and NPV values for ER detection in 10 models were 60%, 100%, 100%, and 71%, respectively. Similarly, the median values by the endoscopist were 80%, 80%, 81%, and 81%, respectively.
conclusionThis proof-of-concept study using a deep learning algorithm demonstrated that the constructed AI-guided endoscopic response evaluation after NAC could identify ER with high specificity and PPV. It would appropriately guide an individualized treatment strategy that includes an organ preservation approach in ESCC patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.