AIMC Topic: Esophageal Squamous Cell Carcinoma
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HRU-Net: A high-resolution convolutional neural network for esophageal cancer radiotherapy target segmentation.
Computer methods and programs in biomedicine
Apr 14, 2024
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 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
Mar 2, 2024
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...
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
Feb 22, 2024
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 prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.
BMC medical informatics and decision making
Jan 2, 2024
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...
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
May 8, 2023
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 ...
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
Feb 17, 2023
BACKGROUND: The feasibility and safety of en bloc robot-assisted minimally invasive oesophagectomy (RAMIE) need to be verified.
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
Dec 5, 2022
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...
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
Nov 29, 2022
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 ...
Deep-learning-based classification of desmoplastic reaction on H&E predicts poor prognosis in oesophageal squamous cell carcinoma.
Histopathology
Jul 11, 2022
AIMS: Desmoplastic reaction (DR) categorisation has been shown to be a promising prognostic factor in oesophageal squamous cell carcinoma (ESCC). The usual DR evaluation is performed using semiquantitative scores, which can be subjective. This study ...
A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal squamous cell carcinoma based on endoscopic images using deep learning.
The British journal of radiology
May 23, 2022
OBJECTIVES: To propose deep-learning (DL)-based predictive model for pathological complete response rate for resectable locally advanced esophageal squamous cell carcinoma (SCC) after neoadjuvant chemoradiotherapy (NCRT) with endoscopic images.
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