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

What is Variation?01:14

What is Variation?

17.6K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
17.6K
Variation01:19

Variation

7.7K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
7.7K
Position-effect Variegation02:32

Position-effect Variegation

7.0K
In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
7.0K
Radical Reactivity: Nucleophilic Radicals01:16

Radical Reactivity: Nucleophilic Radicals

2.6K
Radicals adjacent to electron-donating groups are called nucleophilic radicals. These radicals readily react with electrophilic alkenes. The SOMO–LUMO interactions are the driving force for the reaction, where the high-energy SOMO of the electron-rich, nucleophilic radicals interacts with the low-energy LUMO of the electron-deficient, electrophilic alkenes. Such SOMO–LUMO interactions are the basis of reactive radical traps, affecting the selectivity in radical reactions. For...
2.6K
Margin of Error01:27

Margin of Error

7.0K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.0K
Radical Reactivity: Electrophilic Radicals01:02

Radical Reactivity: Electrophilic Radicals

2.4K
Radicals adjacent to electron‐withdrawing groups are called electrophilic radicals. These radicals readily react with nucleophilic alkenes. For example, the malonate radical, in which the radical center is flanked by two electron‐withdrawing groups, reacts readily with butyl vinyl ether, which consists of an electron‐donating oxygen substituent. The reaction between electrophilic malonate radical and nucleophilic vinyl ether is favored because the radical has a...
2.4K

You might also read

Related Articles

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

Sort by
Same author

Genomic analysis of BCG unresponsive non-muscle-invasive bladder cancer identifies drivers of sensitivity to intravesical Gemcitabine/Docetaxel.

bioRxiv : the preprint server for biology·2026
Same author

Perioperative Apalutamide in High-Risk Localized Prostate Cancer.

The New England journal of medicine·2026
Same author

Polygenic risk score with KLK3 SNP-SNP interaction pairs for predicting prostate cancer aggressiveness.

Communications medicine·2026
Same author

Contemporary Perioperative Outcomes of Robotic Retroperitoneal Lymph Node Dissection for Testicular Cancer.

Journal of endourology·2026
Same author

Germline polygenic score for prostate cancer aggressiveness.

medRxiv : the preprint server for health sciences·2026
Same author

Testosterone Treatment in Prostate Cancer Survivors With Hypogonadism: A Randomized Clinical Trial.

JAMA internal medicine·2026

Related Experiment Video

Updated: Jan 22, 2026

Retzius-Sparing Robot-Assisted Radical Prostatectomy
12:10

Retzius-Sparing Robot-Assisted Radical Prostatectomy

Published on: May 19, 2022

9.1K

Variation in Positive Surgical Margin Status After Radical Prostatectomy for pT2 Prostate Cancer.

Wei Shen Tan1, Marieke J Krimphove2, Alexander P Cole3

  • 1Center for Surgery and Public Health, Division of Urological Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA; Division of Surgery and Interventional Science, University College London, London, United Kingdom; Department of Urology, University College London Hospitals, London, United Kingdom.

Clinical Genitourinary Cancer
|July 16, 2019
PubMed
Summary

Radical prostatectomy outcomes vary due to patient, hospital, and cancer factors. While cancer characteristics explain 15.2% of positive surgical margin (PSM) variability, non-cancer factors are crucial for improving patient results.

Keywords:
LocalizedPositive surgical marginPractice patternsProstate cancerRadical prostatectomy

More Related Videos

Laparoscopic Radical Left Pancreatectomy for Pancreatic Cancer: Surgical Strategy and Technique Video
10:04

Laparoscopic Radical Left Pancreatectomy for Pancreatic Cancer: Surgical Strategy and Technique Video

Published on: June 6, 2020

10.2K
Isolation, Culture, and Characterization of Prostate Cancer-Associated Fibroblasts
09:43

Isolation, Culture, and Characterization of Prostate Cancer-Associated Fibroblasts

Published on: August 1, 2025

753

Related Experiment Videos

Last Updated: Jan 22, 2026

Retzius-Sparing Robot-Assisted Radical Prostatectomy
12:10

Retzius-Sparing Robot-Assisted Radical Prostatectomy

Published on: May 19, 2022

9.1K
Laparoscopic Radical Left Pancreatectomy for Pancreatic Cancer: Surgical Strategy and Technique Video
10:04

Laparoscopic Radical Left Pancreatectomy for Pancreatic Cancer: Surgical Strategy and Technique Video

Published on: June 6, 2020

10.2K
Isolation, Culture, and Characterization of Prostate Cancer-Associated Fibroblasts
09:43

Isolation, Culture, and Characterization of Prostate Cancer-Associated Fibroblasts

Published on: August 1, 2025

753

Area of Science:

  • Urology
  • Surgical Oncology
  • Health Services Research

Background:

  • Positive surgical margin (PSM) after radical prostatectomy impacts patient outcomes.
  • Variability in PSM rates suggests influence from patient, hospital, and cancer-specific factors.

Purpose of the Study:

  • To evaluate patient, hospital, and cancer-specific factors associated with PSM variability in pT2 prostate cancer.
  • To assess the relative contributions of different factors to PSM status.

Main Methods:

  • Analysis of 45,426 men with pT2 prostate cancer from the National Cancer Database (2010-2015).
  • Utilized a mixed-effects logistic regression model to identify factors associated with PSM.
  • Calculated partial R-squared values to determine the contribution of patient, cancer, and hospital variables.

Main Results:

  • Robotic/laparoscopic approaches, academic institutions, and high hospital volume were linked to lower PSM rates.
  • Black race and adverse cancer features (elevated PSA, advanced stage, high Gleason score) were associated with higher PSM.
  • Cancer-specific factors contributed 15.2%, hospital factors 23.7%, patient factors 2.3%, and other factors 3.9% to PSM variation.

Conclusions:

  • Cancer-specific factors explain a portion of PSM variability, but non-cancer factors are significant.
  • Patient and hospital factors represent addressable targets for improving outcomes and reducing PSM rates.