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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
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...

You might also read

Related Articles

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

Sort by
Same author

Opportunistic Osteoporosis Screening from Routine Knee Radiographs Using a Multi-Stage CNN Framework with External Validation.

Journal of clinical medicine·2026
Same author

Smartphone-Derived Movement Analysis for Musculoskeletal Assessment: Smartphone-Estimated Relative Vertical Power During the Sit-to-Stand Test as an Accessible Predictor of Knee Extensor Strength in Older Adults.

Medicina (Kaunas, Lithuania)·2026
Same author

Full-endoscopic rhizotomy for degenerative lumbar facet joint syndrome: a systematic review and meta-analysis.

Journal of spine surgery (Hong Kong)·2026
Same author

MRI-Based Bladder Cancer Staging via YOLOv11 Segmentation and Deep Learning Classification.

Diseases (Basel, Switzerland)·2026
Same author

Stability-Driven Osteoporosis Screening: Multi-View Consensus Feature Selection with External Validation and Sensitivity Analysis.

Journal of clinical medicine·2026
Same author

Multi-Task Deep Learning Model for Automated Detection and Severity Grading of Lumbar Spinal Stenosis on MRI: Multi-Center External Validation.

Diseases (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jul 2, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Explaining Risk Stratification in Differentiated Thyroid Cancer Using SHAP and Machine Learning Approaches.

Mallika Khwanmuang1, Watcharaporn Cholamjiak2, Pasa Sukson1

  • 1School of Medicine, University of Phayao, Phayao 56000, Thailand.

Biomedicines
|December 30, 2025
PubMed
Summary

This study developed an interpretable machine learning model for differentiated thyroid cancer (DTC) recurrence risk stratification. The model accurately predicts risk using key clinical features, reducing reliance on subjective pathology.

Keywords:
SHAPdifferentiated thyroid cancermachine learningpersonalized medicine

More Related Videos

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
03:55

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

909
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

470

Related Experiment Videos

Last Updated: Jul 2, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
03:55

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

909
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

470

Area of Science:

  • Oncology
  • Machine Learning
  • Medical Informatics

Background:

  • Differentiated thyroid cancer (DTC) has a high recurrence rate (30% within 10 years) despite a generally favorable prognosis.
  • Current risk stratification relies on pathological interpretation, prone to observer variability and incomplete data.
  • Developing objective, data-driven risk assessment tools is crucial for effective DTC management.

Purpose of the Study:

  • To create an interpretable machine learning framework for DTC recurrence risk stratification.
  • To identify key clinical predictors of DTC recurrence using SHapley Additive exPlanations (SHAP).
  • To enhance clinical transparency and support personalized management of DTC.

Main Methods:

  • A retrospective dataset of 345 DTC patients was analyzed.
  • Clinicopathological features were assessed, with feature selection using ReliefF and mRMR.
  • An optimizable neural network classifier was trained and evaluated using SHAP for feature attribution.

Main Results:

  • Reducing features to 6 (T, N, Response, Age, M, Hx Radiotherapy) improved model performance (AUC=0.94, accuracy=92%).
  • SHAP analysis identified N and T as primary drivers of high-risk classification.
  • The model demonstrated strong predictive performance even without postoperative response data, enabling preoperative risk estimation.

Conclusions:

  • An interpretable neural network model effectively stratifies DTC recurrence risk, minimizing dependence on subjective pathology.
  • SHAP analysis provides clinical transparency, aiding in personalized thyroid cancer follow-up.
  • Explainable machine learning offers a promising approach for objective risk assessment in DTC.