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Radiomics and machine learning have been extensively utilized in the realm of urinary stones, particularly in forecasting stone treatment outcomes. The objective of this study was to integrate clinical variables and radiomic features to develop a machine learning model for predicting the stone-free rate (SFR) following percutaneous nephrolithotomy (PCNL).A total of 212 eligible patients who underwent PCNL surgery at the Second Affiliated Hospital of Nanchang University were included in a retrospective analysis. Preoperative clinical variables and non-contrast-enhanced CT images of all patients were collected, and radiomic features were extracted after delineating the stone ROI. Univariate analysis was conducted to identify clinical variables strongly correlated with the stone-free rate after PCNL, and the least absolute shrinkage and selection operator algorithm (lasso regression) was utilized to screen radiomic features. Four supervised machine learning algorithms, including Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were employed. The clinical variables with strong correlation and screened radiomic features were integrated into the four machine learning algorithms to construct a prediction model, and the receiver operating curve was plotted. The area under the receiver operating curve (AUC), the accuracy rate, the specificity, etc., were used to evaluate the predictive performance of the four models.After analyzing postoperative statistics, the stone-free rate following the procedure was found to be 70.3% (n = 149). Among the various clinical variables examined, factors, such as stone number, stone diameter, stone CT value, stone location, and history of stone surgery, were identified as statistically significant in relation to the stone-free rate after PCNL. A total of 121 radiomic features were extracted, and through lasso regression, 7 features most closely associated with the stone-free rate post-PCNL were identified. The predictive accuracy of different models (Logistic Regression, RF, XGBoost, and GBDT) for determining the stone-free rate after PCNL was evaluated, yielding accuracies of 78.1%, 76.6%, 75.0%, and 73.4%, respectively. The corresponding area under the curve AUC (95%CI) were 0.85 (0.83-0.89), 0.81 (0.76-0.85), 0.82 (0.78-0.85), and 0.77 (0.73-0.81), positioning these models among the top performers in logistic regression prediction. In terms of predictive importance scores, the key factors identified by the logistic regression model were number of stone, zone percentage, stone diameter, and surface area. Similarly, the RF model highlighted number of stone, stone CT value, stone diameter, and surface area as the top predictors.Among the four machine learning models, the logistic regression model demonstrated the highest accuracy and discrimination ability in predicting the stone-free rate following PCNL. In comparison to XGBoost and GBDT, RF also exhibited superior accuracy and a certain level of discrimination ability. However, based on the performance of all four models, logistic regression is more likely to aid in clinical decision-making by assisting clinicians in diagnosing PCNL in patients. This enables us to effectively predict the presence of residual stones post-surgery and ultimately select patients who are suitable candidates for PCNL.
8Percutaneous Nephrolithotomy in Patients With BMI >50: Single Surgeon Outcomes and Feasibility
Percutaneous Nephrolithotomy in Patients With BMI >50: Single Surgeon Outcomes and Feasibility
Development and validation of machine learning-based prediction for in-hospital mortality in ICU patients with severe community-acquired pneumonia and respiratory failure.
Accurate prediction of in-hospital mortality for patients with severe community-acquired pneumonia (SCAP) complicated by respiratory failure admitted to the intensive care unit (ICU) remains a critical challenge. This study aimed to develop and validate a machine learning (ML) model to predict this risk and compare its performance with conventional scoring systems. In this retrospective study, data from 164 patients with SCAP and respiratory failure admitted to the ICU between January 2017 and January 2024 were analyzed. Patients were randomly divided into a training set (n = 114) and a validation (test) set (n = 50). Forty-five clinical features collected at admission were used as candidate predictors. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for feature selection. Six ML models, including Gradient Boosting Decision Tree (GBDT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Decision Tree(DT),Support Vector Machine (SVM), and Logistic Regression(LR), were constructed and evaluated.Use SHAP analysis to assess the contribution of each feature in a machine learning model. Construct a nomogram using the top six most influential features. The GBDT model demonstrated the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.83 (95% CI: 0.757-0.927) in the internal validation set, significantly outperforming the Acute Physiology and Chronic Health Evaluation II (APACHE-II, AUC = 0.70). Calibration curves demonstrated good agreement between predicted and observed mortality risks, particularly across the mid-probability range. Decision curve analysis indicated that the model provided a higher net benefit than "treat-all" and "treat-none" strategies across a broad range of threshold probabilities. SHapley Additive exPlanations (SHAP) analysis identified lactate, D-dimer, temperature, albumin, Prothrombin Time and Fraction of Inspired Oxygen as the six most influential predictors of in-hospital mortality. Based on these key predictors, we further developed a simplified nomogram to facilitate bedside risk estimation. The GBDT ML model, developed from routinely available clinical data, provides a highly accurate and clinically interpretable tool for predicting in-hospital mortality in SCAP patients with respiratory failure. It outperforms traditional severity scores and holds promise for assisting clinicians in risk stratification and early intervention.
Read more4Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms
To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms. The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-IV v2.0 (MIMIC-IV v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA). A total of 3 295 patients with septic shock were included, with 2 164 surviving and 1 131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2 307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95%CI) was 0.795-0.849], 0.823 (95%CI was 0.796-0.849), and 0.810 (95%CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95%CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95%CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score III (SAPS III), the highest WBC, acute physiology score III (APS III), the lowest Na+, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95%CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APS III score, SAPS III score, and sequential organ failure assessment (SOFA) score] with AUC (95%CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca2+, Hb, WBC, SAPS III score, APS III score, Na+, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.
Read more4Glass forming ability prediction of bulk metallic glasses based on fused strategy
Glass forming ability prediction of bulk metallic glasses based on fused strategy
41Prediction of glass forming ability in amorphous alloys based on different machine learning algorithms
Prediction of glass forming ability in amorphous alloys based on different machine learning algorithms
Interpretable machine learning using CT radiomics predicts pathological upgrading after secondary resection in non-muscle-invasive bladder cancer.
Repeat transurethral resection (ReTUR) is essential for reducing residual and recurrent non-muscle-invasive bladder cancer (NMIBC). Pathological upstaging after ReTUR significantly influences prognosis. This study aimed to develop an interpretable machine learning model using CT radiomics to predict the risk of pathological upstaging following ReTUR in NMIBC patients. We retrospectively analyzed 104 NMIBC patients who underwent ReTUR at the Second Affiliated Hospital of Nanchang University from March 2019 to July 2022. Data were split 7:3 into training and internal validation sets. An external validation set included 40 patients from two other hospitals. Radiomic features were extracted from preoperative CT scans. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression were used to identify predictors of pathological upstaging. Four machine learning models, including Extreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Linear Discriminant Analysis (LDA), were constructed and evaluated using AUC, accuracy, precision, F1 score, calibration curves, and decision curve analysis (DCA). The best model was interpreted via SHapley Additive exPlanations (SHAP) to identify key predictive features. umor grade (OR = 7.02, 95% CI: 1.17-42.21), tumor size (OR = 5.83, 95% CI: 1.21-28.15), and tumor number (OR = 6.83, 95% CI: 1.18-39.52) were independent risk factors. From 4,738 radiomic features, nine were selected. The XGBoost model outperformed others, with an AUC of 0.804 (95% CI: 0.756-0.862), accuracy of 77.4%, precision of 82.7%, and F1 score of 0.701 in internal validation. External validation confirmed its robustness. SHAP analysis highlighted Wavelet_LLH_firstorder_Maximum.1, Gradient_ngtdm_Complexity, and tumor grade as top predictors. The model showed good calibration and clinical utility on DCA. An interpretable CT radiomics-based machine learning model integrating clinical and imaging features was developed to accurately predict pathological upstaging risk after ReTUR in NMIBC patients. This tool may support clinical decision-making for individualized treatment after multicenter validation.
Read more6MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer
ObjectiveLymphovascular invasion (LVI) is critical for the effective treatment and prognosis of breast cancer (BC). This study aimed to investigate the value of eight machine learning models based on MRI radiomic features for the preoperative prediction of LVI status in BC.MethodsA total of 454 patients with BC with known LVI status who underwent breast MRI were enrolled and randomly assigned to the training and validation sets at a ratio of 7:3. Radiomic features were extracted from T2WI and dynamic contrast-enhanced (DCE) of MRI sequences, the optimal feature filter and LASSO algorithm were used to obtain the optimal features, and eight machine learning algorithms, including LASSO, logistic regression, random forest, k-nearest neighbor (KNN), support vector machine, gradient boosting decision tree, extreme gradient boosting, and light gradient boosting machine, were used to construct models for predicating LVI status in BC. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate the performance of the models.ResultsEighteen radiomic features were retained to construct the radiomic signature. Among the eight machine learning algorithms, the KNN model demonstrated superior performance to the other models in assessing the LVI status of patients with BC, with an accuracy of 0.696 and 0.642 in training and validation sets, respectively.ConclusionThe eight machine learning models based on MRI radiomics serve as reliable indicators for identifying LVI status, and the KNN model demonstrated superior performance.This model offers substantial clinical utility, facilitating timely intervention in invasive BC and ultimately aiming to enhance patient survival rates.
Read more10Safety and outcome of percutaneous nephrolithotomy in patients with solitary kidney: A tertiary care center experience.
Introduction:Percutaneous nephrolithotomy (PCNL) for stones in solitary kidney poses a significant challenge and potential threat for acute kidney injury or progression of chronic kidney disease (CKD). We present our experience of PCNL in solitary functioning kidney (SFK) to evaluate the safety, efficacy, and postoperative complications and highlight the differences between these outcomes with respect to the stage of CKD.Methods:We carried out a retrospective study of patients with SFK, who underwent PCNL at our center from April 2010 to March 2018. Patients who had a minimum of 6 months of follow-up were included. Patients were classified into CKD groups based on the National Kidney Foundation's Kidney Disease Outcomes Quality Initiative. Group 1 included Stages 1, 2, and 3A and Group 2 included Stages 3B, 4, and 5. Postoperative complications and stone-free rate were recorded and graded according to the Clavien–Dindo classification and compared between the two groups.Results:We had a total of 128 patients (Group 1 – 84 and Group 2 – 44). Stone-free rate after the first PCNL was higher in Group 1 as compared to Group 2 (88.1% [n = 74] vs. 50% [n = 22], P = 0.02). Overall, 48 patients (37.5%) had postoperative complications, but most were minor. Clavien Grade 1 and 2 complications were seen in 34 patients (Group 1, n = 18 and Group 2, n = 16, P = 0.069), whereas Grade 3 and 4 complications were seen in 14 patients (Group 1, n = 2 and Group 2, n = 12, P < 0.001), respectively. Need for postoperative (number of sessions) dialysis was seen with increased frequency in patients with higher chronic kidney stages (Group 1 vs. Group 2; 6 vs. 22 sessions, P < 0.001).Conclusion:PCNL in SFK is safe, with satisfactory outcome, but patients with advanced CKD stage have higher risk of complications including need for dialysis and may require multiple sessions for complete stone clearance. Hence, they should be managed at high output tertiary centers.
Read more154Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples
Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples
7Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames.
To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4. This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign). Two 2D-US images and five CEUS key frames ("2nd second after the arrival time" frame, "time to peak" frame, "2nd second after peak" frame, "first-flash" frame, and "second-flash" frame) were selected to manually label the region of interest using the "Labelme" tool. A total of 7 images of each nodule and their annotates were imported into the Darwin Research Platform for radiomics analysis. The datasets were randomly split into training and test cohorts in a 9:1 ratio. Six classifiers, namely, support vector machine, logistic regression, decision tree, random forest (RF), gradient boosting decision tree and extreme gradient boosting, were used to construct and test the models. Performance was evaluated using a receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and F1-score were calculated. One junior radiologist and one senior radiologist reviewed the 2D-US image and CEUS videos of each nodule and made a diagnosis. We then compared their AUC and ACC with those of our best model. The AUC of the diagnosis of US, CEUS and US combined CEUS by junior radiologist and senior radiologist were 0.755, 0.750, 0.784, 0.800, 0.873, 0.890, respectively. The RF classifier performed better than the other five, with an AUC of 1 for the training cohort and 0.94 (95% confidence interval 0.88-1) for the test cohort. The sensitivity, specificity, accuracy, PPV, NPV, and F1-score of the RF model in the test cohort were 0.82, 0.93, 0.90, 0.85, 0.92, and 0.84, respectively. The RF model with 2D-US combined with CEUS key frames achieved equivalent performance as the senior radiologist (AUC: 0.94 vs. 0.92, P = 0.798; ACC: 0.90 vs. 0.92) and outperformed the junior radiologist (AUC: 0.94 vs. 0.80, P = 0.039, ACC: 0.90 vs. 0.81) in the test cohort. Our model, based on 2D-US and CEUS key frames radiomics features, had good diagnostic efficacy for thyroid nodules, which are classified as C-TIRADS 4. It shows promising potential in assisting less experienced junior radiologists.
Read morePredicting fall risk among older adults in Chinese communities with advanced machine learning techniques: a retrospective study
BackgroundThis study aims to develop a advanced machine learning model to predict the fall risk among community-dwelling elders. This study could present actionable advices for early prevention of fall risk.MethodsBetween October and December 2022, 977 older adults from the Hannan District of Wuhan were recruited. Data was collected using structured questionnaires. The sample was randomly split into training (732 participants) and testing (245 participants) sets at a 3:1 ratio. The primary outcome was the occurrence of fall. Five machine learning models—Random Forest (RF), Gradient Boosted Decision Tree (GBDT), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Categorical Features Gradient Boosting (CatBoost)—were evaluated against a Logistic Regression (LR) model. Model performance was assessed using AUC, accuracy, precision, sensitivity, specificity, and F1 score.ResultsAmong the 977 older adults, 195 experienced falls (20.0%). ROC curve analysis showed AUC values of LR, RF, LGBM, GBDT, XGBoost, and CatBoost were, respectively, 0.8390, 0.8632, 0.8614, 0.8544, 0.8705, and 0.8719. CatBoost had the highest AUC, indicating the best predictive performance. SHapley Additive exPlanations (SHAP) analysis identified key features influencing the CatBoost model: history of falls, comorbidities, polypharmacy, sleep disorders, ADL, TUG results, frailty status, and use of assistive devices.ConclusionThe fall risk prediction model for community-dwelling older adults, developed with CatBoost, showed excellent performance and can aid in early clinical assessment and fall prevention.
Read more3Preoperative ternary classification using DCE-MRI radiomics and machine learning for HCC, ICC, and HIPT
ObjectivesThis study develops a machine learning model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and clinical data to preoperatively differentiate hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and hepatic inflammatory pseudotumor (HIPT), addressing limitations of conventional diagnostics.Materials and methodsThis retrospective study included 280 patients (HCC = 160, ICC = 80, HIPT = 40) who underwent DCE-MRI from 2008 to 2024 at three hospitals. Radiomics features and clinical data were extracted and analyzed using LASSO regression and machine learning algorithms (Logistic Regression, Random Forest, and Extreme Gradient Boosting), with class weighting (HCC:ICC:HIPT = 1:2:4) to address class imbalance. Models were compared using macro-average Area Under the Curve (AUC), accuracy, recall, and precision.ResultsThe fusion model, integrating radiomics and clinical features, achieved an AUC of 0.933 (95% CI: 0.91–0.95) and 84.5% accuracy, outperforming radiomics-only (AUC = 0.856, 72.6%) and clinical-only (AUC = 0.795, 66.7%) models (p < 0.05). Rim enhancement is a key model feature for distinguishing HCC from ICC and HIPT, while hepatic lobe atrophy distinguishes ICC and HIPT from HCC.ConclusionThis study developed a novel preoperative imaging-based model to differentiate HCC, ICC, and HIPT. The fusion model performed exceptionally well, demonstrating superior accuracy in ICC identification, significantly outperforming traditional diagnostic methods (e.g., radiology and biomarkers) and single-modality machine learning models (p < 0.05). This noninvasive approach enhances diagnostic precision and supports personalized treatment planning in liver disease management.Critical relevance statementThis study develops a novel preoperative imaging-based machine learning model to differentiate hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and hepatic inflammatory pseudotumor (HIPT), improving diagnostic accuracy and advancing personalized treatment strategies in clinical radiology.Key PointsA machine learning model integrates DCE-MRI radiomics and clinical data for liver lesion differentiation.The fusion model outperforms single-modality models with 0.933 AUC and 84.5% accuracy.This model provides a noninvasive, reliable tool for personalized liver disease diagnosis and treatment planning.Graphical
Read moreEfficacy Between Percutaneous Nephrolithotomy and Retrograde Intrarenal Surgery For Renal Stones
Background: Renal stones, particularly those between 2 cm and 3.5 cm in size, present a significant challenge in urology. Despite the availability of modern techniques, the optimal treatment for stones within this size range remains a contentious issue. Percutaneous Nephrolithotomy (PCNL) and Retrograde Intrarenal Surgery (RIRS) are two popular minimally invasive procedures used to treat renal stones. However, there is limited consensus on the superiority of one over the other in terms of clinical outcomes, complications, and patient satisfaction. Objective: To compare the clinical outcomes of PCNL and RIRS in the treatment of renal stones sized between 2 cm and 3.5 cm, specifically focusing on stone-free rate (SFR), complications, hospital stay, blood loss, the need for repeat procedures, and patient satisfaction. Method: This was a prospective, randomized study conducted from June 2024 TO December 2024 at CMH nephrology and urology department district peshawer, with 150 patients presenting with renal stones between 2 cm and 3.5 cm. The patients were randomly assigned to either the PCNL group (n = 75) or the RIRS group (n = 75). Preoperative imaging (CT or ultrasound) confirmed stone size. Patients with bilateral kidney stones, a history of previous kidney surgeries, or significant comorbidities were excluded. Clinical outcomes, including SFR, hospital stay, complications (postoperative fever, infection, stent-related symptoms), blood loss, and the need for repeat procedures, were analyzed. Patient satisfaction was also evaluated using a 1-10 scale. Result: The study found that PCNL had a significantly higher stone-free rate (95%) compared to RIRS (82%). The mean hospital stay for RIRS was shorter (1.56 ± 0.4 days) compared to PCNL (6.5 ± 1.2 days). While the incidence of postoperative postoperative infection was higher in the RIRS group (44% vs. 2.7%), blood loss was greater in the PCNL group (12% vs. 4%). The need for additional procedures was higher in the RIRS group, with 44% requiring further sessions, compared to just 2.7% in the PCNL group. Patient satisfaction was higher in the PCNL group (94.7%) compared to RIRS (77.3%). The treatment of renal stones larger than 2 cm and smaller than 3.5 cm remains controversial, and Percutaneous Nephrolithotomy (PCNL) and Retrograde Intrarenal Surgery (RIRS) are two popular treatment options. This study compares these two techniques in terms of clinical outcomes, including stone-free rate (SFR), complications, hospital stay, and patient satisfaction. This prospective study was conducted June 2024 TO December 2024 at CMH neprology and urology department district peshawer involving 150 patients with renal stones who were randomized into two treatment arms, PCNL and RIRS, with 75 patients in each arm. They critically evaluated the following parameters: SFR, duration of hospital stay, blood loss during surgery, complications, requirement for re-procedures, and patient satisfaction. The puncture access of the PCNL group was decided according to the Guy's Score classification. The stone-free rate was significantly higher in the PCNL group (95%) compared to the group treated with other methods (82%). The mean hospitalization time was shorter for RIRS (6.5 ± 1.2 vs. 1.56 ± 0.4 days). PostoperativePostoperative infective complications were seen more in the RIRS group (44%) along with the stent -related symptoms (34.6%) but had the least postoperative morbidity in the PCNL group (2.7% infection). Blood loss was greater for the PCNL group (12%) compared to the RIRS group (4%), but both groups had low requirements for blood transfusions (2.7% for PCNL and 1.3% for RIRS). Furthermore, RIRS required a greater proportion of repeat sessions (44%) for total stone clearance, especially in cases involving larger stones or increased stone density, whereas PCNL required fewer repeat procedures (2.7%). The patient satisfaction was excellent in the PCNL group (94.7%) and was slightly higher than in the RIRS group (77.3%). Conclusion: Both PCNL and RIRS are effective treatments for renal stones between 2 and 3.5 cm, with PCNL offering superior stone-free rates and greater patient satisfaction, particularly for stones with favorable anatomy. RIRS is a less invasive approach but may require multiple sessions for complete stone clearance and is associated with a higher incidence of postoperative postoperative complications. The choice of procedure should be individualized based on patient characteristics, stone features, and treatment preferences.
Read more34Safety and Efficacy of Supracostal Percutaneous Nephrolithotomy in Pediatric Patients
Safety and Efficacy of Supracostal Percutaneous Nephrolithotomy in Pediatric Patients
70Percutaneous Nephrolithotomy in Patients Who Previously Underwent Open Nephrolithotomy
Open stone surgery nowdays is rare. However, some patients who are treated today have in the past undergone open nephrolithotomy. The aim of this study was to determine the possible impact of open nephrolithotomy on the efficacy and morbidity of subsequent percutaneous nephrolithotomy (PCNL). We reviewed the files of all 167 patients undergoing PCNL at our institution between December 2000 and December 2003. The same surgeon performed all of the procedures. We compared 21 patients undergoing PCNL after open nephrolithotomy to the same kidney with all other patients undergoing PCNL. The groups did not differ in terms of age or stone burden (mean size, number of stones, percentage with staghorn calculi). The outcomes measured were operating time, necessity for secondary procedures, stonefree rate, and intraoperative and postoperative complications. The operating time (203+/-92 v 177+/-52 minutes) and percentage of secondary procedures (29% v 12%) were significantly higher in patients who had previously undergone open stone surgery. However, the stone-free rate (95% v 93%), intraoperative complication rate (10% v 9%), and postoperative complication rate (10% v 11%) did not differ significantly. A PCNL in a patient with a history of open nephrolithotomy may take longer and lead to a higher percentage of auxiliary procedures, probably because of scar tissue and anatomic changes in the kidney. However, the morbidity and efficacy of PCNL appear to remain the same in these patients.
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