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

Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
Conservation of Mass in Moving, Nondeforming Control Volume01:14

Conservation of Mass in Moving, Nondeforming Control Volume

Stormwater detention basins are essential in managing runoff during heavy rainfall, particularly in urban areas where impervious surfaces increase the risk of flooding. Understanding the conservation of mass in these systems allows engineers to optimize basin performance, balancing inflow, outflow, and water storage.
In the context of a detention basin, the conservation of mass states that the total mass of water entering the basin must equal the mass leaving the basin plus any accumulation of...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
Gradually Varying Flow01:29

Gradually Varying Flow

Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
Rapidly Varying Flow01:24

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

You might also read

Related Articles

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

Sort by
Same author

Prediction Models for Sentinel Lymph Node Metastasis in Clinically Node-Negative Breast Cancer: Validation of Existing Nomograms, Model Development, and Ensemble Evaluation.

World journal of surgery·2026
Same author

A wireless and handheld optical palpation imaging probe for use in breast-conserving surgery.

APL bioengineering·2026
Same author

Nudging Emergency Department Clinicians to Reduce Unnecessary Diagnostic Test Ordering: A Multi-Arm Vignette-Based Experiment.

Emergency medicine Australasia : EMA·2025
Same author

Association Between Novel Lipid and Anthropometric Indices and Sleep Duration and Disturbance: A Cross-Sectional NHANES Study 2005-2020.

International journal of endocrinology·2025
Same author

Balls on the line: rethinking testing of genital protectors.

BJU international·2025
Same author

The effectiveness of a multisite, multidisciplinary analgesic stewardship program: a 12-month retrospective cohort study.

Pain reports·2025

Related Experiment Video

Updated: May 12, 2026

The Application of 1% Methylene Blue Dye As a Single Technique in Breast Cancer Sentinel Node Biopsy
07:51

The Application of 1% Methylene Blue Dye As a Single Technique in Breast Cancer Sentinel Node Biopsy

Published on: June 1, 2019

21.9K

Mathematical Prediction Models for Sentinel Node Status in Early-Stage Breast Cancer: Protocol for a Systematic

Justin James1,2, Kirti Mehta1, Mohammadali Ahmadipour3

  • 1Eastern Health, Melbourne, Victoria, Australia.

JMIR Research Protocols
|March 23, 2026
PubMed
Summary
This summary is machine-generated.

This systematic review evaluates mathematical models for predicting sentinel node status in early-stage breast cancer. It assesses their performance and quality to guide future AI-based tool development.

Keywords:
AIartificial intelligencebreast cancernomogramssentinel nodesystematic review

More Related Videos

Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy
05:52

Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy

Published on: August 19, 2021

13.8K
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

846

Related Experiment Videos

Last Updated: May 12, 2026

The Application of 1% Methylene Blue Dye As a Single Technique in Breast Cancer Sentinel Node Biopsy
07:51

The Application of 1% Methylene Blue Dye As a Single Technique in Breast Cancer Sentinel Node Biopsy

Published on: June 1, 2019

21.9K
Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy
05:52

Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy

Published on: August 19, 2021

13.8K
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

846

Area of Science:

  • Oncology
  • Medical Statistics
  • Clinical Decision Support

Background:

  • Axillary lymph node status is crucial for prognosis and treatment in early-stage breast cancer (EBC).
  • Sentinel node (SN) biopsy is the standard for nodal staging in clinically node-negative EBC.
  • Mathematical models (MMs) are being explored as alternatives to SN biopsy.

Purpose of the Study:

  • To systematically review and evaluate the predictive performance, methodological quality, and risk of bias of MMs for predicting SN status in EBC.
  • To compare the performance of methodologically robust MMs.
  • To identify key predictors for SN status in MMs.

Main Methods:

  • Systematic search of PubMed, Cochrane CENTRAL, and Embase for studies developing SN status prediction models using mathematical modeling.
  • Inclusion of studies reporting SN status prediction via mathematical techniques.
  • Data extraction and assessment using the Prediction Model Risk of Bias Assessment Tool by two independent reviewers.

Main Results:

  • A total of 3458 records were screened, with 122 selected for data extraction (3.5%).
  • Data extraction and bias assessment were completed by December 2025.
  • Synthesis of findings is planned for March 2026, with results prepared for publication mid-2026.

Conclusions:

  • This review will consolidate evaluations of non-AI MMs for SN status prediction in EBC.
  • Findings will establish a benchmark for emerging AI-based prediction tools.
  • The study will identify consistent predictors, informing future model development.