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Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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The Impact of Noninvasive Ventilator Assisted Ventilation Nursing Combined with Mechanical Vibration on the Level of Heart Failure Indexes in ICU Patients with Acute Heart Failure.

Journal of healthcare engineeringยท2022
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Related Experiment Video

Updated: May 23, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.

Jinglei Tian1, Qifeng Lou2, Xue Wang2

  • 1Zhejiang Chinese Medical University, Zhejiang Chinese Medical University, Hangzhou, China, Hangzhou, Zhejiang, China.

Journal of Medical Internet Research
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) show promise in colorectal cancer (CRC) care, aiding tasks from data extraction to diagnosis. However, evidence quality is low, necessitating cautious interpretation and further validation for safe clinical use.

Keywords:
PRISMAartificial intelligencecolorectal cancergastroenterologylarge language modelssystematic review

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Last Updated: May 23, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Published on: December 6, 2024

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Health Informatics

Background:

  • Colorectal cancer (CRC) presents a significant global health challenge.
  • The need for advanced decision-support tools is growing due to complex care pathways and unstructured data.
  • Large language models (LLMs) offer potential for processing clinical information and patient communication in CRC.

Purpose of the Study:

  • To systematically review the applications of LLMs across the entire continuum of colorectal cancer care.
  • To evaluate the performance determinants and clinical implications of LLMs in CRC.
  • To synthesize fragmented evidence on LLMs specifically for CRC.

Main Methods:

  • Systematic review following PRISMA guidelines, searching 6 major databases.
  • Inclusion of peer-reviewed original investigations of LLMs on CRC tasks.
  • Quality assessment using QUADAS-2, PROBAST, and ROBINS-I, with narrative synthesis.

Main Results:

  • 37 studies (2023-2026) evaluated LLMs for CRC, primarily GPT models.
  • LLMs demonstrated utility in data extraction, patient education, diagnosis, and decision support.
  • Risk of bias was moderate to high in over half of the studies, with concerns in outcome measurement and patient selection.

Conclusions:

  • LLMs show potential in CRC care, but overall evidence quality is low.
  • Methodological limitations, data privacy, and generalizability remain significant challenges.
  • Future research requires prospective validation, robust privacy measures, and human oversight for safe clinical translation.