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Related Concept Videos

Urinary Tract Calculi III: Medical Management01:30

Urinary Tract Calculi III: Medical Management

The diagnosis of renal calculi involves several imaging techniques, including non-contrast CT scans and ultrasound. These methods help visualize kidney stones, assess their size and location, and detect possible obstructions. Additionally, Measuring urine pH is useful for diagnosing specific stone types, such as struvite (alkaline pH) and uric acid stones (acidic pH). Cystine stones are primarily linked to cystinuria, a genetic condition. A urinalysis helps detect blood in the urine (hematuria)...
Urinary Tract Calculi IV: Nutrition Therapy and Prevention01:27

Urinary Tract Calculi IV: Nutrition Therapy and Prevention

Management of renal calculi focuses on effective strategies like tailored nutrition and hydration therapy. Adjusting diet and fluid intake reduces stone formation and recurrence, making these interventions simple yet powerful in kidney stone prevention and management.Understanding Kidney StonesKidney stones form when calcium, oxalate, uric acid, and cystine concentrate and crystallize in urine. Factors contributing to their formation include genetic predisposition, certain medical conditions,...
Urinary Tract Calculi V: Nursing Management01:28

Urinary Tract Calculi V: Nursing Management

AssessmentSubjective Data: Obtain a detailed health history, including any recent or chronic urinary tract infections, periods of immobilization, previous episodes of renal calculi, and medical conditions such as gout, benign prostatic hyperplasia, or hyperparathyroidism. Review the medication history for drugs that may influence stone formation, including allopurinol, analgesics, loop diuretics, or thiazide diuretics. Document the use of long-term indwelling catheters and any past surgical...
Urinary Tract Calculi VI: Surgical Management01:25

Urinary Tract Calculi VI: Surgical Management

Procedures for Kidney StonesMedical intervention is necessary when kidney stones or renal calculi are too large to pass spontaneously (typically greater than 5 millimeters) when stones are accompanied by symptomatic infection (such as fever or pyelonephritis), when they impair kidney function, or when they cause persistent symptoms like severe pain, nausea, or urinary retention. Additionally, patients with only one kidney or those who cannot be treated with medical management also require...
Imaging Studies V: Intravenous Urography and Retrograde Pyelography01:22

Imaging Studies V: Intravenous Urography and Retrograde Pyelography

IntroductionIntravenous Urography (IVU) and Retrograde Pyelography (RP) are important diagnostic imaging techniques used to evaluate the urinary system. These methods help identify structural abnormalities, obstructions, and functional issues in the kidneys, ureters, and bladder. Both procedures use iodine-based contrast media to enhance the visibility of urinary tract structures on X-ray images, though they differ in their methods and indications.1. Intravenous Urography (IVU)Intravenous...

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy.

Xin Chang Zou1, Cheng Wei Luo1, Rong Man Yuan2

  • 1Department of Urology, Second Affiliated Hospital of Nanchang University, Nanchang, 330008, China.

Urolithiasis
|April 13, 2024
PubMed
Summary

This study developed a machine learning model to predict the stone-free rate after percutaneous nephrolithotomy (PCNL) for urinary stones. Logistic regression showed the highest accuracy, aiding clinical decisions for PCNL suitability.

Keywords:
Clinical variablesMachine learningPercutaneous nephrolithotomyRadiomicsStone-free rate

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Area of Science:

  • Urology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Radiomics and machine learning are increasingly used for predicting urinary stone treatment outcomes.
  • Percutaneous nephrolithotomy (PCNL) is a common procedure for urinary stone removal.

Purpose of the Study:

  • To develop and evaluate a machine learning model integrating clinical variables and radiomic features for predicting the stone-free rate (SFR) after PCNL.
  • To identify key predictors of successful PCNL outcomes.

Main Methods:

  • Retrospective analysis of 212 patients undergoing PCNL.
  • Extraction of radiomic features from CT images and collection of clinical variables.
  • Development and comparison of four machine learning models: Logistic Regression, Random Forest (RF), XGBoost, and GBDT.
  • Evaluation using Area Under the Curve (AUC) and accuracy.

Main Results:

  • The overall stone-free rate after PCNL was 70.3%.
  • Significant clinical predictors included stone number, diameter, CT value, location, and prior surgery history.
  • Lasso regression identified 7 key radiomic features.
  • Logistic Regression achieved the highest accuracy (78.1%) and AUC (0.85), outperforming RF, XGBoost, and GBDT.

Conclusions:

  • Logistic regression demonstrated superior performance in predicting SFR after PCNL.
  • The developed model, integrating clinical and radiomic data, can assist clinicians in patient selection and predicting residual stones post-PCNL.
  • This approach enhances clinical decision-making for optimizing PCNL outcomes.