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

Cancer02:18

Cancer

54.6K
Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
54.6K
Electric Potential and Potential Difference01:16

Electric Potential and Potential Difference

5.8K
Suppose a positive test charge moves away from a positive static charge, then the Coulomb force does positive work, and its electric potential energy decreases. The potential energy per unit charge is defined as the electric potential. The electric potential is independent of the test charge.
When a test charge moves from the initial to the final position, the electric potential difference between those positions is defined as the ratio of the change in the potential energy to the charge on the...
5.8K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.5K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.5K
Identifying Statistically Significant Differences: The F-Test01:14

Identifying Statistically Significant Differences: The F-Test

3.9K
The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
3.9K
Sum and Difference OpAmps01:22

Sum and Difference OpAmps

1.4K
Operational amplifiers (op-amps) are versatile devices that extend beyond amplification. In this context, two specific op-amp configurations are explored: the summing and difference amplifiers.
A summing amplifier, or an adder, utilizes an op-amp to merge multiple input signals into a single output signal. When audio signals are introduced into its input channels, the input resistors initiate currents that traverse feedback resistors, resulting in an output voltage. Applying Kirchhoff's current...
1.4K
Difference Equation Solution using z-Transform01:24

Difference Equation Solution using z-Transform

656
The z-transform is a powerful tool for analyzing practical discrete-time systems, often represented by linear difference equations. Solving a higher-order difference equation requires knowledge of the input signal and the initial conditions up to one term less than the order of the equation.
The z-transform facilitates handling delayed signals by shifting the signal in the z-domain, which corresponds to delaying the signal in the time domain, and advancing signals by similarly shifting in the...
656

You might also read

Related Articles

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

Sort by
Same author

Bias in Low Dose Risk Estimation due to the Constant RBE Assumption in RERF Studies.

Radiation research·2026
Same author

Cataracts in Atomic-bomb Survivors More than 70 Years after Radiation Exposure.

Radiation research·2026
Same author

Patient-Reported Outcomes After Same-Day Mastectomy Among Older Breast Cancer Patients: Results From a Prospective Clinical Trial.

The breast journal·2025
Same author

Recent evolution of risk analyses in atomic bomb survivor studies: new methods and applications.

Carcinogenesis·2025
Same author

Real-World Application of Alliance ACOSOG Z11102: How Many Patients Can be Spared Mastectomy?

Annals of surgical oncology·2025
Same author

Calculations of Mean Quality Factors and Their Implications for Organ-specific Relative Biological Effectiveness (RBE) in Analysis of Radiation-related Risk in the Atomic Bomb Survivors.

Radiation research·2025

Related Experiment Video

Updated: Feb 16, 2026

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System
10:51

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Published on: April 23, 2021

4.7K

Breast cancer: do specialists make a difference?

Kristin A Skinner1, James T Helsper, Dennis Deapen

  • 1Department of Surgery, Norris Comprehensive Cancer Center, University of Southern California/Keck School of Medicine, Los Angeles, California, USA. kristin.skinner@med.nyu.edu

Annals of Surgical Oncology
|July 4, 2003
PubMed
Summary

Treatment by a surgical oncologist significantly improves breast cancer survival rates. Surgical specialization, not just hospital type, is a key factor in reducing mortality risk.

More Related Videos

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
07:45

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer

Published on: May 18, 2020

7.1K
Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
09:29

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Published on: March 20, 2020

19.0K

Related Experiment Videos

Last Updated: Feb 16, 2026

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System
10:51

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Published on: April 23, 2021

4.7K
Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
07:45

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer

Published on: May 18, 2020

7.1K
Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
09:29

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Published on: March 20, 2020

19.0K

Area of Science:

  • Oncology
  • Surgical Oncology
  • Public Health

Background:

  • Growing belief that breast cancer treatment requires specialized care.
  • Need to investigate the impact of surgeon and hospital specialization on patient survival.
  • Study conducted on a large, well-defined patient population.

Purpose of the Study:

  • To determine the effect of surgeon and hospital specialization on survival after breast cancer treatment.
  • To analyze the association between various patient and treatment factors and 5-year survival.
  • To identify independent predictors of survival in breast cancer patients.

Main Methods:

  • Utilized the Cancer Surveillance Program database for Los Angeles County (1990-1998).
  • Analyzed 29,666 breast cancer cases with complete surgeon, hospital, and staging data.
  • Stratified patients by surgeon/hospital specialization, demographics, disease stage, procedure, and case volume; performed survival analysis.

Main Results:

  • Factors associated with 5-year survival included age, race, socioeconomic status, tumor size, nodal status, disease extent, surgeon specialization, and case volumes.
  • Treatment at a specialty center did not independently affect survival.
  • Multivariate analysis confirmed surgeon type and hospital/surgeon case volume as independent predictors of survival.

Conclusions:

  • Treatment by a surgical oncologist was associated with a 33% reduction in 5-year mortality risk.
  • The positive impact of surgical specialization on survival is not solely due to increased case volume.
  • Highlights the importance of surgeon specialization in optimizing breast cancer patient outcomes.