Background: Off-clamp robotic partial nephrectomy (Off C-RPN) is a challenging technique, hard to teach since bleeding control is not easily reproducible in training settings. We compared perioperative outcomes of two propensity score matched (PSM) cohorts of patients undergone Off C-RPN by either a training or an expert surgeon in the same Institution.
Methods: The prospectively maintained "renal cancer" database was queried for "off-clamp," "robotic," "partial nephrectomy" performed between January 2017 and June 2018. Achievement of main outcomes along the learning curve of training surgeon was assessed with logistic regression and Lowess analysis. A 1:1 PSM analysis generated two populations homogeneous for demographics, ASA score, tumor size, nephrometry score, baseline hemoglobin and estimated glomerular filtration rate (eGFR). Multiple tumors, and imperative indications were excluded. Categorical and continuous variables were compared by χ and t-test.
Results: Overall, 111 were treated by the expert, 51 by the training surgeon, respectively. Training surgeon experienced a significant decrease of console time (P=0.01). Patients treated by the expert surgeon had significantly larger tumors, higher PADUA and ASA scores (all P≤0.04). After applying the PSM, two cohorts of 29 patients, homogeneous for all baseline demographic and clinical variables (all P≥0.34) were selected. Hilar clamping was never necessary. Hospital stay, hemoglobin and eGFR at discharge, complication and positive surgical margins rates were comparable between the two cohorts (all P≥0.15).
Conclusions: Our results proved that the impact of learning curve on outcomes of Off C-RPN is negligible after completion of a proper training in minimally invasive surgery.
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http://dx.doi.org/10.23736/S2724-6051.20.03673-5 | DOI Listing |
Surg Innov
January 2025
Division of General, Minimally Invasive, and Robotic Surgery, Department of Surgery, University of Illinois at Chicago, Chicago, IL, USA.
Background: Transabdominal pre-peritoneal inguinal hernia repair using the da Vinci Single-Port robot (SP-TAPP) is currently performed in few centers. We aimed to define the learning curve for SP-TAPP by analyzing operative times.
Methods: The operative times of 122 SP-TAPP performed between 2019 and 2024 were retrospectively analyzed.
Alzheimers Res Ther
January 2025
Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, Hunan, 410008, P.R. China.
Purpose: To develop and validate a prostate-specific membrane antigen (PSMA) PET/CT based multimodal deep learning model for predicting pathological lymph node invasion (LNI) in prostate cancer (PCa) patients identified as candidates for extended pelvic lymph node dissection (ePLND) by preoperative nomograms.
Methods: [Ga]Ga-PSMA-617 PET/CT scan of 116 eligible PCa patients (82 in the training cohort and 34 in the test cohort) who underwent radical prostatectomy with ePLND were analyzed in our study. The Med3D deep learning network was utilized to extract discriminative features from the entire prostate volume of interest on the PET/CT images.
Sci Rep
January 2025
Imaging Department, Yantaishan Hospital, Yantai, China.
Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods. fMRI indices such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and sMRI indices such as gray matter volume (GMV), white matter volume (WMV), and cortical thickness were extracted from each brain region.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.
Background: Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging.
Objective: This study aimed to comprehensively collect variables from multiple aspects, develop and validate machine learning models to achieve precise prediction of PPD, and interpret the model to reveal clinical implications.
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