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Background: Stone nomogram by Micali et al., able topredict treatment failure of shock-wave lithotripsy (SWL), retrograde intrarenal surgery (RIRS) and percutaneous nephrolithotomy (PNL) in the management of single 1-2 cm renal stones, was developed on 2605 patients and showed a high predictive accuracy, with an area under ROC curve of 0.793 at internal validation. The aim of the present study is to externally validate the model to assess whether it displayed a satisfactory predictive performance if applied to different populations.
Methods: External validation was retrospectively performed on 3025 patients who underwent an active stone treatment from December 2010 to June 2021 in 26 centers from four countries (Italy, USA, Spain, Argentina). Collected variables included: age, gender, previous renal surgery, preoperative urine culture, hydronephrosis, stone side, site, density, skin-to-stone distance. Treatment failure was the defined outcome (residual fragments >4 mm at three months CT-scan).
Results: Model discrimination in external validation datasets showed an area under ROC curve of 0.66 (95% 0.59-0.68) with adequate calibration. The retrospective fashion of the study and the lack of generalizability of the tool towards populations from Asia, Africa or Oceania represent limitations of the current analysis.
Conclusions: According to the current findings, Micali's nomogram can be used for treatment prediction after SWL, RIRS and PNL; however, a lower discrimination performance than the one at internal validation should be acknowledged, reflecting geographical, temporal and domain limitation of external validation studies. Further prospective evaluation is required to refine and improve the nomogram findings and to validate its clinical value.
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http://dx.doi.org/10.23736/S2724-6051.24.05672-6 | DOI Listing |
PLoS One
September 2025
Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan, Kunming, China.
Purpose: Bronchiolar adenoma (BA) is a rare benign pulmonary neoplasm originating from the bronchial mucosal epithelium and mimics lung adenocarcinoma (LAC) both radiographically and microscopically. This study aimed to develop a nomogram for distinguishing BA from LAC by integrating clinical characteristics and artificial intelligence (AI)-derived histogram parameters across two medical centers.
Methods: This retrospective study included 215 patients with diagnoses confirmed by postoperative pathology from two medical centers.
JMIR Res Protoc
September 2025
Institute of Higher Education and Research in Healthcare, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
Background: In pediatric intensive care units, pain, sedation, delirium, and iatrogenic withdrawal syndrome (IWS) must be managed as interrelated conditions. Although clinical practice guidelines (CPGs) exist, new evidence needs to be incorporated, gaps in recommendations addressed, and recommendations adapted to the European context.
Objective: This protocol describes the development of the first patient- and family-informed European guideline for managing pain, sedation, delirium, and IWS by the European Society of Paediatric and Neonatal Intensive Care.
Target Oncol
September 2025
Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
Background: Population pharmacokinetic models can potentially provide suggestions for an initial dose and the magnitude of dose adjustment during therapeutic drug monitoring procedures of imatinib. Several population pharmacokinetic models for imatinib have been developed over the last two decades. However, their predictive performance is still unknown when extrapolated to different populations, especially children.
View Article and Find Full Text PDFJ Ultrasound Med
September 2025
Department of Ultrasound, Donghai Hospital Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China.
Objective: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics (DLRN) features derived from ultrasound and multi-sequence magnetic resonance imaging (MRI) for predicting survival, recurrence, and metastasis in patients diagnosed with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC).
Methods: This retrospective, multicenter study included 103 patients with histopathologically confirmed TNBC across four institutions. The training group comprised 72 cases from the First People's Hospital of Lianyungang, while the validation group included 31 cases from three external centers.
J Thorac Imaging
September 2025
Department of Radiology, the Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University.
Purpose: To establish an explainable machine learning (ML) approach using patient-related and noncontrast chest CT-derived features to predict the contrast material arrival time (TARR) in CT pulmonary angiography (CTPA).
Materials And Methods: This retrospective study included consecutive patients referred for CTPA between September 2023 to October 2024. Sixteen clinical and 17 chest CT-derived parameters were used as inputs for the ML approach, which employed recursive feature elimination for feature selection and XGBoost with SHapley Additive exPlanations (SHAP) for explainable modeling.