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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

7.6K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
7.6K

You might also read

Related Articles

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

Sort by
Same author

Modulation of smoker brain activity and functional connectivity by tDCS: A go/no-go task-state fMRI study.

Heliyon·2023
Same author

The main strategies for soil pollution apportionment: A review of the numerical methods.

Journal of environmental sciences (China)·2023
Same author

<i>Bacillus velezensis</i> ZN-S10 Reforms the Rhizosphere Microbial Community and Enhances Tomato Resistance to TPN.

Plants (Basel, Switzerland)·2023
Same author

Effect of optimized thrombus aspiration on myocardial perfusion and prognosis in acute ST-segment elevation myocardial infarction patients with primary percutaneous coronary intervention.

Frontiers in cardiovascular medicine·2023
Same author

Erratum: [Corrigendum] Effects of histone deacetylase inhibitors on ATP‑binding cassette transporters in lung cancer A549 and colorectal cancer HCT116 cells.

Oncology letters·2023
Same author

Sulforaphene Inhibits Periodontitis through Regulating Macrophage Polarization <i>via</i> Upregulating Dendritic Cell Immunoreceptor.

Journal of agricultural and food chemistry·2023

Related Experiment Video

Updated: May 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

Interpretable Machine Learning Model for Differentiating Uterine Sarcoma From Atypical Leiomyoma Based on

Zhong Yang1, Wangyang Sun1, Yanran Jiang1

  • 1Department of Graduate, Bengbu Medical University, Bengbu, Anhui 233030, China (Z.Y., W.S., Y.J., K.G., C.W.).

Academic Radiology
|November 13, 2025
PubMed
Summary

Machine learning models integrating MRI features and radiomics can accurately differentiate uterine sarcoma (US) from atypical leiomyoma (ALM) before surgery. This interpretable approach enhances diagnostic confidence and guides precise patient management.

Keywords:
Machine learningMagnetic resonance imagingRadiomicsUterine leiomyomaUterine sarcoma

More Related Videos

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

7.3K
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

478

Related Experiment Videos

Last Updated: May 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
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

7.3K
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

478

Area of Science:

  • Radiology and Medical Imaging
  • Machine Learning in Healthcare
  • Oncology Diagnostics

Background:

  • Accurate preoperative differentiation between uterine sarcoma (US) and atypical leiomyoma (ALM) is crucial for appropriate treatment.
  • Conventional magnetic resonance imaging (MRI) features can be subjective and may not always provide definitive diagnoses.
  • Machine learning (ML) offers potential for objective and enhanced diagnostic accuracy.

Purpose of the Study:

  • To develop interpretable ML models integrating conventional MRI features and radiomics for preoperative differentiation of US and ALM.
  • To evaluate the performance and clinical utility of these models.
  • To enhance clinician trust and guide surgical planning.

Main Methods:

  • Retrospective analysis of 160 patients (47 US, 113 ALM) divided into training and test cohorts.
  • Assessment of 10 MRI features and extraction of radiomics features from T2WI and DWI sequences.
  • Development and comparison of five ML models (LR, RF, XGBoost, SVM, GNB) using MRI predictors and radiomic scores.
  • Evaluation using AUC, calibration curves, and decision curve analysis (DCA); interpretability via SHAP framework.

Main Results:

  • Multivariable analysis identified heterogeneous hyperintensity on T2WI, ill-defined tumor border, interrupted uterine cavity, and low ADC values as significant MRI discriminators.
  • The XGBoost model demonstrated superior performance with AUCs of 0.991 (training) and 0.909 (test).
  • SHAP analysis revealed ADC value as the most influential predictor, followed by tumor border, T2WI signal intensity, radscore, and uterine endometrial cavity.

Conclusions:

  • Interpretable ML models integrating MRI biomarkers and radiomics provide a transparent and clinically actionable tool for preoperative differentiation of US and ALM.
  • The SHAP framework quantifies feature contributions, bridging the 'black-box' gap in ML and fostering clinician trust.
  • This approach empowers clinicians to formulate precise interventions, such as tailored surgical planning to avoid morcellation of suspected US.