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

Determination of Renal Drug Clearance: Graphical and Midpoint Methods01:07

Determination of Renal Drug Clearance: Graphical and Midpoint Methods

357
Renal clearance, a crucial parameter in pharmacokinetics, can be determined using two different methods: the graphical method and the midpoint method. These methods provide insights into the rate of drug excretion by the kidneys and aid in assessing renal function.
The graphical method involves plotting the rate of drug excretion in urine against the plasma drug concentration. By analyzing the graph, the clearance can be calculated and obtained. Drugs rapidly excreted by the kidneys exhibit a...
357

You might also read

Related Articles

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

Sort by
Same author

Influence of food groups on plasma total homocysteine for specific MTHFR C677T genotypes in Chinese population.

Molecular nutrition & food research·2016
Same author

NIR Light Propulsive Janus-like Nanohybrids for Enhanced Photothermal Tumor Therapy.

Small (Weinheim an der Bergstrasse, Germany)·2016
Same author

Smart Hydrogels with Inhomogeneous Structures Assembled Using Nanoclay-Cross-Linked Hydrogel Subunits as Building Blocks.

ACS applied materials & interfaces·2016
Same author

Synergy between von Hippel-Lindau and P53 contributes to chemosensitivity of clear cell renal cell carcinoma.

Molecular medicine reports·2016
Same author

Aerobic Degradation of Sulfadiazine by Arthrobacter spp.: Kinetics, Pathways, and Genomic Characterization.

Environmental science & technology·2016
Same author

Downregulation of ClC-3 in dorsal root ganglia neurons contributes to mechanical hypersensitivity following peripheral nerve injury.

Neuropharmacology·2016

Related Experiment Video

Updated: Jan 8, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K

Clinical decision system for renal cell carcinoma integrating interpretable machine learning algorithms.

Tianhong Zhang1, Tian Tian1, Yifan Zhang2

  • 1Department of Oncology, Xi Chang People's Hospital, Xi Chang, China.

Frontiers in Surgery
|December 15, 2025
PubMed
Summary

Accurate prediction of kidney cancer metastasis is crucial for patient prognosis. Machine learning models, particularly Extreme Gradient Boosting (XGB), effectively predict distal metastasis risk, aiding clinical strategy development.

Keywords:
distal metastasiskidney cancermachine learningnomogrampredictive model

More Related Videos

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

470
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Related Experiment Videos

Last Updated: Jan 8, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K
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

470
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Kidney cancer is a heterogeneous disease with a historically poor prognosis.
  • Accurate prediction of distal metastasis is vital for risk stratification and improving patient outcomes.
  • Identifying high-risk patients enables tailored clinical strategies.

Purpose of the Study:

  • To develop and validate a predictive model for distal metastasis in kidney cancer patients.
  • To identify independent risk factors associated with kidney cancer metastasis.
  • To establish a nomogram and web calculator for predicting metastasis risk.

Main Methods:

  • Utilized data from 40,527 kidney cancer patients (2010-2017) from the SEER database.
  • Employed LASSO, univariate, and multivariate logistic regression to identify risk factors.
  • Compared six machine learning algorithms (LR, NBC, DT, RF, GBM, XGB) for predictive modeling.
  • Validated models using ten-fold cross-validation and ROC analysis.

Main Results:

  • The Extreme Gradient Boosting (XGB) model demonstrated superior performance (AUC=0.91 training, 0.851 testing).
  • Key predictors included marital status, primary site, grade, pathological type, T-stage, N-stage, and treatment modalities.
  • A nomogram incorporating XGB-derived risk was developed to predict 1-, 3-, and 5-year survival probabilities.
  • Nomogram utility was confirmed by calibration plots, DCA, ROC, and KM curves.

Conclusions:

  • A robust machine learning model was established for predicting kidney cancer distal metastasis.
  • The XGB-based nomogram effectively identifies high-risk patients.
  • This tool can inform clinical decision-making and optimize treatment strategies for kidney cancer.