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

You might also read

Related Articles

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

Sort by
Same author

Pulsed Electric Field Ablation Reprograms Tumor Immunity and Stimulates Germinal Center Formation in Tertiary Lymphoid Structures in Patients with Non-Small Cell Lung Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Clinical and translational roles of circulating tumor cells in non-small cell and small cell lung cancer: a narrative review.

Journal of thoracic disease·2026
Same author

Post-Transbronchial Microwave Ablation Bronchopleural Fistula-A Case Series and Unique Insight.

European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery·2026
Same author

Treatment patterns and clinical outcomes of neoadjuvant chemoimmunotherapy for patients with resected oesophageal squamous cell carcinoma: a Chinese multicentre retrospective study.

Therapeutic advances in medical oncology·2026
Same author

The global cancer crisis: a review of growing burden, deepening inequality and initiatives for prevention and early detection.

Ecancermedicalscience·2026
Same author

Clinicopathological characteristics associated with intrapulmonary metastasis rather than single primary lung cancer at first diagnosis: a study based on the Surveillance, Epidemiology, and End Results database using Bayesian networks and structural equation modeling.

Translational lung cancer research·2026
Same journal

RETRACTED: Sabir et al. DNA Based and Stimuli-Responsive Smart Nanocarrier for Diagnosis and Treatment of Cancer: Applications and Challenges. <i>Cancers</i> 2021, <i>13</i>, 3396.

Cancers·2026
Same journal

Correction: Adeluola et al. Chemoprevention of 4-NQO-Induced Oral Cancer by the Combination of Resveratrol and EGCG: In Vivo, In Silico and In Vitro Studies. <i>Cancers</i> 2026, <i>18</i>, 1098.

Cancers·2026
Same journal

Correction: Peñalver et al. Guidelines for Diagnosis, Treatment, and Follow-Up of Patients with Follicular Lymphoma-Spanish Lymphoma Group (GELTAMO) 2026. <i>Cancers</i> 2026, <i>18</i>, 395.

Cancers·2026
Same journal

Correction: Accorsi Buttini et al. Development of a Simplified Geriatric Score-4 (SGS-4) to Predict Outcomes After Allogeneic Hematopoietic Stem Cell Transplantation in Patients Aged over 50. <i>Cancers</i> 2025, <i>17</i>, 3278.

Cancers·2026
Same journal

Age-Stratified Long-Term Outcomes of Immune Checkpoint Inhibitors for Stage IV Melanoma and NSCLC in The Netherlands: A Population-Based Study.

Cancers·2026
Same journal

Targeting Ferroptosis in Glioblastoma: Molecular Mechanisms, Tumor Microenvironment, and Therapeutic Opportunities.

Cancers·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

5.8K

Integrating Machine Learning and Dynamic Bayesian Networks to Identify the Factors Associated with Subsequent

Wei Liu1, Aliss T C Chang1, Joyce W Y Chan1

  • 1Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong, China.

Cancers
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence identified key factors predicting intrapulmonary metastasis (IPM) after initial single primary lung cancer (SPLC). Surgery type and pleural invasion significantly influence IPM risk, guiding future research.

Keywords:
SEERdynamic Bayesian networkintrapulmonary metastasislung cancerrandom forestregistry-based classificationsingle primary lung cancer

More Related Videos

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

6.4K
Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells
08:54

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published on: May 20, 2020

8.8K

Related Experiment Videos

Last Updated: May 5, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

5.8K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

6.4K
Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells
08:54

Pathological Analysis of Lung Metastasis Following Lateral Tail-Vein Injection of Tumor Cells

Published on: May 20, 2020

8.8K

Area of Science:

  • Oncology
  • Artificial Intelligence in Medicine
  • Cancer Epidemiology

Background:

  • Intrapulmonary metastasis (IPM) following single primary lung cancer (SPLC) is a critical adverse outcome.
  • Determinants of subsequent IPM in population-based studies remain incompletely understood.
  • There is a need for advanced analytical methods to identify risk factors for IPM.

Purpose of the Study:

  • To identify factors associated with subsequent IPM after initial SPLC.
  • To utilize artificial intelligence (AI)-driven analytical approaches for this identification.
  • To explore the predictive power of machine learning and dynamic Bayesian networks.

Main Methods:

  • Analysis of Surveillance, Epidemiology, and End Results (SEER) lung cancer records (2000-2019).
  • Development of a random forest machine learning model for prediction.
  • Utilized a dynamic Bayesian network (DBN) for simulated intervention (SI) analyses to estimate risk ratios.

Main Results:

  • The random forest model demonstrated high predictive accuracy (AUC 0.929 in temporal validation).
  • Key predictors identified include type of surgery, pleural invasion level, and timing of records.
  • Lobectomy with mediastinal lymph node dissection significantly reduced IPM probability compared to wedge resection (RR 0.378).

Conclusions:

  • AI-based time-sequence modeling effectively identified surgery, pleural invasion, and record timing as crucial factors for IPM.
  • The study highlights the potential of integrating predictive and probabilistic modeling for understanding disease patterns.
  • This framework can aid in generating hypotheses for future prospective investigations into lung cancer metastasis.