AI Medical Compendium Topic

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Carcinoma, Squamous Cell

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[Cox model analysis of curative effect and prognostic factors of oral robot-assisted RPLN dissection for head and neck malignancies].

Shanghai kou qiang yi xue = Shanghai journal of stomatology
PURPOSE: To investigate the efficacy and prognostic factors of oral robot-assisted retropharyngeal lymph node (RPLN) dissection in the treatment of head and neck malignancies.

Squamous Cell Carcinoma of Skin Cancer Margin Classification From Digital Histopathology Images Using Deep Learning.

Cancer control : journal of the Moffitt Cancer Center
OBJECTIVES: Now a days, squamous cell carcinoma (SCC) margin assessment is done by examining histopathology images and inspection of whole slide images (WSI) using a conventional microscope. This is time-consuming, tedious, and depends on experts' ex...

Efficacy and safety of carboplatin with nab-paclitaxel versus docetaxel in older patients with squamous non-small-cell lung cancer (CAPITAL): a randomised, multicentre, open-label, phase 3 trial.

The lancet. Healthy longevity
BACKGROUND: In Japan, docetaxel, a cytotoxic monotherapy, is the standard drug administered to older patients with advanced non-small-cell lung cancer (NSCLC). Carboplatin plus nab-paclitaxel has shown a high objective response rate in patients with ...

Deep Learning for Clinical Image Analyses in Oral Squamous Cell Carcinoma: A Review.

JAMA otolaryngology-- head & neck surgery
IMPORTANCE: Oral squamous cell carcinoma (SCC) is a lethal malignant neoplasm with a high rate of tumor metastasis and recurrence. Accurate diagnosis, prognosis prediction, and metastasis detection can improve patient outcomes. Deep learning for clin...

Detection of Lung Cancer on Computed Tomography Using Artificial Intelligence Applications Developed by Deep Learning Methods and the Contribution of Deep Learning to the Classification of Lung Carcinoma.

Current medical imaging
BACKGROUND: Every year, lung cancer contributes to a high percentage deaths in the world. Early detection of lung cancer is important for its effective treatment, and non-invasive rapid methods are usually used for diagnosis.

Artificial intelligence in upper GI endoscopy - current status, challenges and future promise.

Journal of gastroenterology and hepatology
White-light endoscopy with biopsy is the current gold standard modality for detecting and diagnosing upper gastrointestinal (GI) pathology. However, missed lesions remain a challenge. To overcome interobserver variability and learning curve issues, a...

Common gene signatures and key pathways in hypopharyngeal and esophageal squamous cell carcinoma: Evidence from bioinformatic analysis.

Medicine
BACKGROUND: Hypopharyngeal and esophageal squamous cell carcinoma (ESCC) are the most common double primary tumors with poor prognosis. Intensive work has been made to illuminate the etiology, but the common carcinogenic mechanism remains unclear. Th...

Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been sys...

[Deep Learning in Upper Gastrointestinal Disorders: Status and Future Perspectives].

The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi
Artificial intelligence using deep learning has been applied to gastrointestinal disorders for the detection, classification, and delineation of various lesion images. With the accumulation of enormous medical records, the evolution of computation po...

Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network.

JAMA dermatology
IMPORTANCE: Detection of cutaneous cancer on the face using deep-learning algorithms has been challenging because various anatomic structures create curves and shades that confuse the algorithm and can potentially lead to false-positive results.