AIMC Topic: Colorectal Neoplasms

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Prediction of early-onset colorectal cancer mortality rates in the United States using machine learning.

Cancer medicine
INTRODUCTION: The current study, focusing on a significant US (United States) colorectal cancer (CRC) burden, employs machine learning for predicting future rates among young population.

A Cohort Study Investigating Zearalenone Concentrations and Selected Steroid Levels in Patients with Sigmoid Colorectal Cancer or Colorectal Cancer.

Toxins
THE AIM: In this study was to determine if sigmoid colorectal cancer (SCC) and colorectal cancer (CRC) in women (W) and men (M) is accompanied by zearalenone (ZEN) mycotoxicosis and changes in selected steroid levels.

Development of fecal microbial diagnostic marker sets of colorectal cancer using natural language processing method.

The International journal of biological markers
BACKGROUND: Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early screening biomarkers. In recent years, natural language processing (NLP)-based text-mining methods ...

Diagnostic accuracy of endocytoscopy via artificial intelligence in colorectal lesions: A systematic review and meta‑analysis.

PloS one
BACKGROUND: Endocytoscopy (EC) is a nuclei and micro-vessels visualization in real-time and can facilitate "optical biopsy" and "virtual histology" of colorectal lesions. This study aimed to investigate the significance of employing artificial intell...

Enhancing artificial intelligence-doctor collaboration for computer-aided diagnosis in colonoscopy through improved digital literacy.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Establishing appropriate trust and maintaining a balanced reliance on digital resources are vital for accurate optical diagnoses and effective integration of computer-aided diagnosis (CADx) in colonoscopy. Active learning using diverse polyp image da...

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases.

European radiology experimental
BACKGROUND: We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM).

Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging.

Journal of biomedical optics
SIGNIFICANCE: Current treatment for stage III colorectal cancer (CRC) patients involves surgery that may not be sufficient in many cases, requiring additional adjuvant systemic therapy. Identification of this latter cohort that is likely to recur fol...

Shedding Light on Colorectal Cancer: An In Vivo Raman Spectroscopy Approach Combined with Deep Learning Analysis.

International journal of molecular sciences
Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic ...