Results 111 to 120 of about 144,401 (267)

Optimal Indication of D3 Lymph Node Dissection for Non‐Metastatic Colon Cancers by Tumor Stages: Evaluation of Therapeutic Value Index for Each Lymph Node Station

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
ABSTRACT Aims To explore the therapeutic impact of D3 lymph node dissection for non‐metastatic colon cancers, evaluating the therapeutic value index for each lymph node station according to surgical stages. Methods Consecutive patients with surgical Stage I–III colon and rectosigmoid cancer who underwent curative resection between January 2003 and ...
Akira Ouchi   +9 more
wiley   +1 more source

Systematic Review and Meta‐Analysis on the Efficacy and Safety of Salvage Esophagectomy for T4 Esophageal Squamous Cell Carcinoma

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
This meta‐analysis of 208 cases shows that salvage esophagectomy for cT4 esophageal squamous cell carcinoma achieves a 72% R0 resection rate, offering a curative pathway for selected patients. However, it remains a high‐risk procedure with an 18% anastomotic leak rate and 30% major complications (Clavien–Dindo ≥ III).
Makoto Sakai   +4 more
wiley   +1 more source

Clinical utility of pharyngeal high‐resolution manometry with impedance for upper esophageal sphincter dysfunction in gastroenterology

open access: yesAdvances in Digestive Medicine, EarlyView.
Abstract Pharyngeal high‐resolution manometry with impedance (P‐HRM‐I) is an established assessment method used to evaluate pharyngeal swallowing. It provides precise quantification of swallowing biomechanics that enable the detection of alterations in swallowing physiology.
Mistyka Schar   +5 more
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

open access: yesAdvanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang   +3 more
wiley   +1 more source

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