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Esophageal Squamous Cell Carcinoma

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Robot-assisted versus thoracolaparoscopic oesophagectomy for locally advanced oesophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: Robot-assisted oesophagectomy (RAE) and thoracolaparoscopic oesophagectomy (TLE) are surgical techniques for the treatment of oesophageal cancer. This study aimed to compare the perioperative and mid-term outcomes of RAE versus TLE for ...

A one-dimensional convolutional neural network based deep learning for high accuracy classification of transformation stages in esophageal squamous cell carcinoma tissue using micro-FTIR.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Among the most frequently diagnosed cancers in developing countries, esophageal squamous cell carcinoma (ESCC) ranks among the top six causes of death. It would be beneficial if a rapid, accurate, and automatic ESCC diagnostic method could be develop...

Initial experience with modified en bloc robot-assisted minimally invasive oesophagectomy for thoracic oesophageal squamous cell carcinoma.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The feasibility and safety of en bloc robot-assisted minimally invasive oesophagectomy (RAMIE) need to be verified.

A novel staging system based on deep learning for overall survival in patients with esophageal squamous cell carcinoma.

Journal of cancer research and clinical oncology
PURPOSE: We developed DeepSurv, a deep learning approach for predicting overall survival (OS) in patients with esophageal squamous cell carcinoma (ESCC). We validated and visualized the novel staging system based on DeepSurv using data from multiple ...

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.

BMC medical informatics and decision making
BACKGROUND: Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despi...

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.

Journal of translational medicine
BACKGROUND: Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was limited to only a small portion of patients. This study aimed to perform a deep learning signature base...

Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study.

International journal of radiation oncology, biology, physics
PURPOSE: To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation ther...

HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The effective segmentation of esophageal squamous carcinoma lesions in CT scans is significant for auxiliary diagnosis and treatment. However, accurate lesion segmentation is still a challenging task due to the irregular for...

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Science translational medicine
Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) fo...

Machine learning to predict lymph node metastasis in T1 esophageal squamous cell carcinoma: a multicenter study.

International journal of surgery (London, England)
BACKGROUND: Existing models do poorly when it comes to quantifying the risk of lymph node metastases (LNM). This study aimed to develop a machine-learning model for LNM in patients with T1 esophageal squamous cell carcinoma (ESCC).