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Robot-assisted partial nephrectomy (RAPN) has become the standard treatment for small renal tumors, offering better perioperative outcomes than open surgery. However, objective evaluations of the RAPN learning curve are limited. While the Trifecta criteria-comprising negative surgical margins, no perioperative complications, and warm ischemia time (WIT) ≤ 25 min-are commonly used to assess surgical outcomes, they are inadequate for continuous proficiency assessment. This study aimed to evaluate the RAPN learning curve using the cumulative sum (CUSUM) method based on Trifecta achievement and its components. We retrospectively analyzed 119 RAPN cases performed by three surgeons at a single institution between 2017 and 2022. All surgeons (≥ 30 cases; ≥ 15 year experience) were included. CUSUM charts were created using Trifecta achievement rates with thresholds (p₀ = 0.4, p₁ = 0.8), and further analysis was performed on individual components. Distinct learning curve transitions were observed only in Surgeon B, with proficiency achieved at the 9th case for complication rates and the 4th case for overall Trifecta achievement. No clear transitions were seen in WIT or surgical margins, or in any component for Surgeons A and C. These findings suggest that Surgeons A and C may have already attained proficiency before the study period. The CUSUM method offers a practical tool for visualizing and quantifying individual learning curves in RAPN based on clinically relevant criteria. Despite some limitations, CUSUM enables continuous, surgeon-specific assessment. Future studies should integrate additional metrics to develop more comprehensive training programs and improve surgical safety and outcomes.
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http://dx.doi.org/10.1007/s11701-025-02599-5 | DOI Listing |
Eur J Radiol
September 2025
Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China. Electronic address:
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma.
Materials And Methods: This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent.
JMIR Med Inform
September 2025
College of Medical Informatics, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China, 86 13500303273.
Background: Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health care outcomes. However, the quality and integrity of real-world electronic health records (EHRs) limit their utility in developing risk assessment tools.
View Article and Find Full Text PDFJMIR AI
September 2025
Faculty of Medicine, Universidade Federal de Alagoas, Av. Lourival Melo Mota, S/n - Tabuleiro do Martins, Maceió, 57072-900, Brazil, 558232141461.
Background: Artificial intelligence (AI) has the potential to transform global health care, with extensive application in Brazil, particularly for diagnosis and screening.
Objective: This study aimed to conduct a systematic review to understand AI applications in Brazilian health care, especially focusing on the resource-constrained environments.
Methods: A systematic review was performed.
Arq Gastroenterol
September 2025
Faculdade de Medicina da Universidade de São Paulo, Departamento de Gastroenterologia, São Paulo, SP, Brasil.
Background: Accurate evaluation of the invasion depth of superficial esophageal squamous cell carcinoma (SESCC) is crucial for optimal treatment. While magnifying endoscopy (ME) using the Japanese Esophageal Society (JES) classification is reported as the most accurate method to predict invasion depth, its efficacy has not been tested in the Western world. This study aims to evaluate the interobserver agreement of the JES classification for SESCC and its accuracy in estimating invasion depth in a Brazilian tertiary hospital.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Computer Science, Osun State University, Osogbo, Nigeria.
Probabilistic Random Forest is an extension of the traditional Random Forest machine learning algorithm that is one of the frequently used machine learning algorithms employed for species distribution modeling. However, with the use of complex dataset for predicting the presence or absence of the species, It is essential that feature extraction is important to generate optimal prediction that can affect the model accuracy and AUC score of the model simulation. In this paper, we integrated the Genetic Algorithm Optimization technique, which is popular for its excellent feature extraction technique, to enhance the predictive performance of the PRF Model.
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