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

Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

You might also read

Related Articles

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

Sort by
Same author

Barriers to Healthcare Access and Utilization Among Immigrants in Host Countries: A Systematic Review of Qualitative and Quantitative Evidence.

Cureus·2026
Same author

Perioperative Communication Between Nurse Anesthetists and Patients: A 30-Year Experience From Sweden.

Cureus·2025
Same author

Perceptions and Experiences of Surgical Nurses in Using the WHO Checklist in a Perioperative Setting: a Mixed-method Study.

Materia socio-medica·2025
Same author

Favorite colors of patients with drug-resistant epilepsy: pilot study.

Acta neurologica Belgica·2024
Same author

Evaluation of Inflammatory Parameters Following Extracorporeal Shock Wave Lithotripsy (ESWL) and Ureteroscopy for the Treatment of Proximal Ureteral Stones.

Cureus·2024
Same author

The Role of an Assistant Nurse in Implementing the WHO Surgical Safety Checklist: Perception and Perspectives.

Cureus·2023

Related Experiment Video

Updated: Jun 29, 2026

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy
11:27

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy

Published on: April 29, 2014

16.5K

Histopathologic features and parameters predicting recurrence potential of small renal masses.

Senad Bajramović1, Berina Hasanović2, Jasmin Alić1

  • 1Clinic of Urology, Clinical Center University of Sarajevo, Sarajevo, Bosnia and Herzegovina.

Current Urology
|May 16, 2025
PubMed
Summary

This study analyzed small renal masses (SRMs), finding that malignancy is common, especially in larger tumors. Histopathological features and treatment impact recurrence risk, highlighting the need for careful management of SRMs.

Keywords:
MetastasisNephrectomyRecurrenceRenal cell carcinomaSmall renal mass

More Related Videos

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
09:31

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses

Published on: March 30, 2015

8.8K
Author Spotlight: Developing a Bedside Protocol for Kidney and Genitourinary Ultrasonography
03:19

Author Spotlight: Developing a Bedside Protocol for Kidney and Genitourinary Ultrasonography

Published on: June 21, 2024

905

Related Experiment Videos

Last Updated: Jun 29, 2026

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy
11:27

Modeling Spontaneous Metastatic Renal Cell Carcinoma mRCC in Mice Following Nephrectomy

Published on: April 29, 2014

16.5K
In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
09:31

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses

Published on: March 30, 2015

8.8K
Author Spotlight: Developing a Bedside Protocol for Kidney and Genitourinary Ultrasonography
03:19

Author Spotlight: Developing a Bedside Protocol for Kidney and Genitourinary Ultrasonography

Published on: June 21, 2024

905

Area of Science:

  • Urology
  • Oncology
  • Pathology

Background:

  • Small renal masses (SRMs) are defined as contrast-enhanced masses ≤4 cm, often T1a renal cell carcinoma (RCC).
  • Understanding SRM histopathology is crucial for predicting clinical behavior.
  • This study investigated pathological features, metastatic potential, and recurrence risk in SRMs.

Purpose of the Study:

  • To explore histopathological features of contemporary SRMs.
  • To identify predictors of pathological nature, metastatic potential, and recurrence.
  • To analyze factors influencing tumor recurrence in SRMs.

Main Methods:

  • Retrospective analysis of 166 patients undergoing surgery for suspected SRMs.
  • Comparison of radical vs. partial nephrectomy (1:44 ratio).
  • Statistical analysis (chi-squared, logistic regression) of variables associated with metastatic recurrence.

Main Results:

  • 86% of SRMs were malignant (RCC), 14% benign. 17% of RCCs were high-grade (G3-4).
  • Malignant SRMs were larger (31 ± 8 mm vs. 24 ± 9 mm) and occurred in older patients.
  • Tumor size, grade, stage, metastasis, localization, and treatment modality predicted recurrence.

Conclusions:

  • Malignant neoplasms were the majority of treated SRMs, but less common in masses <2 cm.
  • Histopathological features and treatment modalities are key to predicting malignant recurrence in SRMs.