Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

6.9K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
6.9K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.9K
Cancer Survival Analysis01:21

Cancer Survival Analysis

645
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...
645
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

6.4K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
6.4K
Cancer Therapies02:49

Cancer Therapies

9.8K
Cancer therapies are various modes of treatment, such as surgery, radiation therapy, and chemotherapy that are administered to cancer patients.
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...
9.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Synchronous Surgical Therapy for Bilateral Multiple Pulmonary Nodules: A Single-Center Analysis of 108 Cases.

Thoracic cancer·2026
Same author

Global, regional and national burden of tracheal, bronchus and lung cancer attributable to occupational carcinogens from 1990 to 2021 and projections to 2050: A finding from the global burden of disease study 2021 and Mendelian randomization.

Science progress·2026
Same author

[Safety and Clinical Benefit Analysis of No-chest-tube Strategy After Uniportal Video-assisted Thoracoscopic Pulmonary Wedge Resection Based on Propensity Score Matching].

Zhongguo fei ai za zhi = Chinese journal of lung cancer·2026
Same author

Optimizing Surgery Strategies in Stage IB Lung Squamous Cell Carcinoma: Insights from Interpretable Machine Learning.

Thoracic cancer·2026
Same author

SFTPB: A signature gene for lung adenocarcinoma development.

Computational biology and chemistry·2026
Same author

The combined inhibitory effect of butaselen and decitabine against lung cancer cells.

Scientific reports·2026
Same journal

Whole body CT attenuation and volume charts from routine clinical scans via LLM report filtering.

NPJ digital medicine·2026
Same journal

Fast information and slow evidence in the large language models era.

NPJ digital medicine·2026
Same journal

Predicting response to neoadjuvant therapy using artificial intelligence on digitized histopathology slides: a systematic review.

NPJ digital medicine·2026
Same journal

Automated diagnosis of keratitis from low-quality slit-lamp images using an improved generative adversarial network.

NPJ digital medicine·2026
Same journal

The ethics of listening walls: patient autonomy and consent in the age of ambient clinical AI.

NPJ digital medicine·2026
Same journal

Targeting m6A-SCG2-TAMs axis overcomes 5-FU resistance in colorectal cancer via a multi-omics model.

NPJ digital medicine·2026
See all related articles

Related Experiment Video

Updated: Jan 13, 2026

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
10:24

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

6.7K

EvoMDT: a self-evolving multi-agent system for structured clinical decision-making in multi-cancer.

Qicai Liu1,2, Zhichao Hu3, Tao Huang4

  • 1Department of Reproductive Medicine Centre, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.

NPJ Digital Medicine
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

EvoMDT enhances cancer care by using AI to improve multidisciplinary tumor board (MDT) decisions. This adaptive system provides evidence-linked recommendations, boosting accuracy and efficiency in oncology practice.

More Related Videos

Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression
07:53

Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression

Published on: March 17, 2020

7.6K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.8K

Related Experiment Videos

Last Updated: Jan 13, 2026

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
10:24

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

6.7K
Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression
07:53

Multidimensional Coculture System to Model Lung Squamous Carcinoma Progression

Published on: March 17, 2020

7.6K
Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
05:18

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

1.8K

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Clinical Decision Support

Background:

  • Multidisciplinary tumor boards (MDTs) are crucial for cancer care but face challenges with expert availability and consistent decision quality.
  • Evolving clinical evidence and increasing workloads necessitate adaptive and auditable AI-driven decision support systems.

Purpose of the Study:

  • To introduce EvoMDT, a novel AI framework designed to enhance the robustness, traceability, and efficiency of MDT decision-making.
  • To evaluate EvoMDT's performance against leading Large Language Models (LLMs) and human MDTs in oncology settings.

Main Methods:

  • EvoMDT utilizes a self-evolution loop to dynamically update prompts, consensus weights, and retrieval scope based on expert feedback and outcome signals.
  • The system employs domain-specific agents for inference on lesion-level data, coupled with structured knowledge retrieval and a consensus protocol for evidence-linked recommendations.
  • Performance was assessed using public oncology QA benchmarks, real-world datasets (breast, liver, lung, lymphoma), and single-blind physician evaluations.

Main Results:

  • EvoMDT demonstrated superior performance compared to frontier LLMs, achieving higher guideline concordance and semantic alignment (BERTScore 0.62-0.68) with expert plans.
  • The system significantly reduced safety violations and achieved decision quality comparable to human MDTs, with a 30-40% reduction in response time.
  • Quantitative metrics and physician assessments confirmed EvoMDT's factuality, guideline adherence, clinical appropriateness, and usability.

Conclusions:

  • EvoMDT offers an interpretable and evidence-traceable AI framework for operationalizing reasoning in multidisciplinary oncology practice.
  • The system provides a scalable foundation for trustworthy, lesion-level precision cancer care, addressing the limitations of current MDT processes.
  • EvoMDT represents a significant advancement in AI-assisted clinical decision support for complex cancer management.