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

Nodule size significantly affects the diagnostic value of chinese thyroid imaging reporting and data system: a multi-center retrospective study.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·2026
Same author

Ultrasound-Guided Percutaneous Microwave Coagulation Studies on VX2 Rabbit Models for Breast Cancer Treatment and Ultrasound Imaging Assessment.

Breast cancer (Dove Medical Press)·2025
Same author

A Machine-Learning Model Based on Clinical Features for the Prediction of Severe Dysphagia After Ischemic Stroke.

International journal of general medicine·2024
Same author

Diagnostic Value of Artificial Intelligence in Minimal Breast Lesions Based on Real-Time Dynamic Ultrasound Imaging.

International journal of general medicine·2024
Same author

Alteration Trend and Overlap Analysis of Positive Features in Different-Sized Benign and Malignant Thyroid Nodules: Based on Chinese Thyroid Imaging Reporting and Data System.

International journal of general medicine·2024
Same author

Comparison of Diagnostic Values of ACR TI-RADS versus C-TIRADS Scoring and Classification Systems for the Elderly Thyroid Cancers.

International journal of general medicine·2023

Related Experiment Video

Updated: Jun 4, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

213

Nomogram Model for Predicting Minimal Breast Cancer Based on Clinical and Ultrasonic Characteristics.

Liang-Ling Cheng1, Feng Ye1, Tian Xu2

  • 1Wuxi school of medicine, Jiangnan University, Wuxi, People's Republic of China.

International Journal of Women'S Health
|December 23, 2024
PubMed
Summary

This study developed a nomogram prediction model for minimal breast cancer using clinical and ultrasound data. The model demonstrates good accuracy, aiding in the early detection of small breast lesions.

Keywords:
minimal breast cancernomogrampredictive modelultrasonography

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K
Ultrasonographic Evaluation of Breast Cancer-related Lymphedema
05:44

Ultrasonographic Evaluation of Breast Cancer-related Lymphedema

Published on: January 12, 2017

10.0K

Related Experiment Videos

Last Updated: Jun 4, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

213
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K
Ultrasonographic Evaluation of Breast Cancer-related Lymphedema
05:44

Ultrasonographic Evaluation of Breast Cancer-related Lymphedema

Published on: January 12, 2017

10.0K

Area of Science:

  • Oncology
  • Radiology
  • Medical Informatics

Background:

  • Early detection of breast cancer is crucial for improving patient outcomes.
  • Minimal breast cancer (lesions ≤ 10 mm) presents diagnostic challenges.
  • Accurate prediction models can aid clinicians in managing small breast lesions.

Purpose of the Study:

  • To construct a nomogram prediction model for minimal breast cancer (≤ 10 mm).
  • To utilize clinical and ultrasound parameters for predicting minimal breast cancer.
  • To evaluate the diagnostic efficacy of the developed nomogram.

Main Methods:

  • Retrospective analysis of clinical and ultrasound data from 433 patients.
  • Development of a nomogram using LASSO and multivariable logistic regression.
  • Validation of the model using calibration curves, DCA, and ROC curve analysis (AUC).

Main Results:

  • Age, margin, shape, and breast density identified as independent risk factors for malignancy.
  • Nomogram achieved an AUC of 0.875 in both training and validation sets.
  • High sensitivity and specificity observed, with good calibration (MAE of 0.01 and 0.024).

Conclusions:

  • The nomogram prediction model exhibits strong discrimination and calibration.
  • The model demonstrates significant clinical practical value for early minimal breast cancer diagnosis.
  • Clinical and ultrasonic features integrated into the nomogram facilitate early detection.