Cited by Radiomics applications in the modern management of esophageal squamous cell carcinoma

Advancing Esophageal Cancer Staging and Restaging: The Role of MRI in Precision Diagnosis DOI

Laura Haefliger, Pauline Chapellier, Naïk Vietti Violi

et al.

Cancers, Journal Year: 2025, Volume and Issue: 17(8), P. 1351 - 1351

Published: April 17, 2025

This review provides an in-depth analysis and comprehensive overview of recent advancements in MRI techniques for evaluating esophageal cancer (EC). It discusses the specific acquisition protocols parameters that enhance image quality diagnostic accuracy. The highlights MRI’s role performance initial TNM staging its potential to refine treatment strategies by improving tumor delineation characterization. Additionally, paper explores utility restaging after NAT, focusing on accuracy assessing response detecting residual or recurrent disease. Comparisons with other imaging modalities currently used—such as endoscopic ultrasound (EUS), contrast-enhanced computed tomography (CE-CT), 18F-fluorodeoxyglucose (FDG) positron emission tomography/CT (PET/CT)—are included highlight strengths limitations each method. Illustrated numerous Figures, this article proposes a novel MRI-based strategy EC restaging. aims integrate into clinical practice leveraging superior soft-tissue contrast functional capabilities precision improve patient outcomes. Through evaluation, underscores become cornerstone diagnosis management EC.

Language: Английский

Citations

Research status and progress of deep learning in automatic esophageal cancer detection DOI

Jing Chen,

Xin Fan,

Qiao-Liang Chen

et al.

World Journal of Gastrointestinal Oncology, Journal Year: 2025, Volume and Issue: 17(5)

Published: May 15, 2025

Esophageal cancer (EC), a common malignant tumor of the digestive tract, requires early diagnosis and timely treatment to improve patient prognosis. Automated detection EC using medical imaging has potential increase screening efficiency diagnostic accuracy, thereby significantly improving long-term survival rates quality life patients. Recent advances in deep learning (DL), particularly convolutional neural networks, have demonstrated remarkable performance analysis. These techniques shown significant progress automated identification tumors, quantitative analysis lesions, improvement accuracy efficiency. This article comprehensively examines research DL for EC, covering various modalities such as digital pathology, endoscopy, computed tomography, etc. It explores clinical value application prospects diagnosis. Additionally, addresses several critical challenges that must be overcome translation techniques, including constructing high-quality datasets, promoting multimodal feature fusion, optimizing artificial intelligence-clinical workflow integration. By providing detailed overview current state highlighting key future directions, this aims guide facilitate implementation technologies management, ultimately contributing better outcomes.

Language: Английский

Citations

A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights DOI