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 Experiment Videos

Utilizing Clinicopathological and Radiomic Features for Risk Stratification of Lung Cancer Recurrence.

Wai Lone J Ho1, Nikolai Fetisov2, Lawrence O Hall2

  • 1University of South Florida, Morsani College of Medicine, Tampa, Florida (W.L.J.H.).

Academic Radiology
|May 16, 2025
PubMed
Summary
This summary is machine-generated.

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

Leveraging Commercially Available Protein Assays as Biomarkers for Lung Cancer.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Large-scale association study identifies lung cancer susceptibility copy number variants and their potential functional role in genetic instability.

medRxiv : the preprint server for health sciences·2026
Same author

Trans-omics gene-smoking interactions: a new framework for stratifying smoking-related lung cancer risk.

American journal of respiratory and critical care medicine·2026
Same author

Novel risk models based on screening history results and timing of lung cancer diagnosis: <i>Post hoc</i> analysis of the National Lung Cancer Screening Trial.

medRxiv : the preprint server for health sciences·2026
Same author

Preoperative CT-Based Habitat Radiomics Classifiers Predict Recurrence in Non-Small Cell Lung Cancer.

medRxiv : the preprint server for health sciences·2026
Same author

On the road to early detection: A survey study of barriers and facilitators to community participation in a mobile lung cancer screening program.

medRxiv : the preprint server for health sciences·2026
Same journal

Deep Learning for Opportunistic Vertebral Fracture Detection on Routine Thoraco-abdominal Computed Tomography: A Systematic Review and Hierarchical Summary Receiver Operating Characteristic Meta-analysis of Patient-level Diagnostic Test Accuracy.

Academic radiology·2026
Same journal

"Where are They Now?": A Single Institution's 10-Years Experience with an Integrated Nuclear Radiology Fellowship.

Academic radiology·2026
Same journal

Dual-layer Spectral Detector CT Quantitative Parameters for Predicting Tumor Budding Grade and Prognosis in Stage Ⅱ Colorectal Cancer.

Academic radiology·2026
Same journal

Promotion from Associate Professor to Full Professor Should Not Be Monolithic: A National Bibliometric Study by Radiology Subspecialty.

Academic radiology·2026
Same journal

Technological Lag of Digitization for Patient Image Transfer.

Academic radiology·2026
Same journal

Prognostic Value of Coronary Sinus Flow and Aortic Pressure Gradient Quantified by 4D Flow CMR in AMI.

Academic radiology·2026
See all related articles

Radiomics combined with clinical data accurately predicts recurrence in non-small cell lung cancer (NSCLC) patients after surgery. This approach improves risk stratification, identifying patients with significantly higher recurrence risk.

Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging Analysis

Background:

  • Accurate prediction of recurrence risk is crucial for managing patients with surgically resected non-small cell lung cancer (NSCLC).
  • Traditional staging systems may not fully capture individual patient risk profiles.
  • Novel approaches are needed to enhance prognostic accuracy.

Purpose of the Study:

  • To develop and validate a model predicting recurrence risk in NSCLC patients using radiomic features and clinicopathological factors.
  • To compare the performance of radiomic, clinical, and combined radiomic-clinical models in predicting recurrence.

Main Methods:

  • Analysis of 293 patients with surgically resected stage IA-IIIA NSCLC, stratified into development and test cohorts.
  • Extraction of 107 radiomic features from pre-treatment CT scans using pyRadiomics.
Keywords:
Computed tomographyNSCLCPyRadiomicsRadiomicsRecurrence

Related Experiment Videos

  • Development of radiomic, clinical, and radiomic-clinical models using logistic regression and evaluation via Area Under the Curve (AUC).

    • Main Results:

    • The radiomic-clinical model achieved the highest predictive performance on the test set (AUC 0.77), outperforming radiomic (0.76), clinical (0.71), and TNM stage (0.70) models.
    • Patients stratified into a high-risk group by the radiomic-clinical model had a five-fold higher recurrence risk (p<0.01).
    • Key predictors included lymph node metastasis, "GLDM Large Dependence Emphasis" texture, and "Elongation" shape features.

    Conclusions:

    • Radiomics analysis, when integrated with clinicopathological features, offers an effective strategy for recurrence risk stratification in surgically treated NSCLC.
    • This combined approach enhances prognostic accuracy beyond traditional methods.
    • The findings support the use of radiomics in personalized treatment planning for NSCLC.