Background And Purpose: Laparoscopic radical prostatectomy (LRP) is an established treatment for patients with prostate cancer in selected centers with appropriate expertise. We studied our single-center experience of developing a LRP service and subsequent training of two additional surgeons by the initial surgeon. We assessed the learning curve of the three surgeons with regard to perioperative outcomes and oncologic results.
Patients And Methods: Three hundred consecutive patients underwent a LRP between January 2005 and April 2011. Patients were divided into three equal groups (1-100 group 1], 101-200 [group 2], and 201-300 [group 3]). Age, American Society of Anesthesiologists score, preoperative comorbidities, and indications for LRP were comparable for all three patient groups. Perioperative and oncologic outcomes were compared across all three groups to assess the impact of the learning curve for LRP. All surgical complications were classified using the Clavien-Dindo system (CDS).
Results: The mean age was 61.9 years (range 46-74 y). There was a significant reduction in the mean operative time (P<0.05), mean blood loss (P<0.05), mean duration of hospital stay (P<0.05), and duration of catherization (P<0.05) between the three groups as the series progressed. The two most important factors predictive of positive surgical margins at LRP were the initial prostate-specific antigen level and tumor stage at diagnosis. The overall positive margin rate was 27.7%. For pT(2) tumors, the positive margin rate was 21%, while patients with pT(3) tumors had a positive margin of 44%. For pT(2) tumors, positive margin rates decreased with increasing experience (group 1, 27% vs group 2, 17% vs group 3, 19%). The incidence of major complications--ie, grade CDS score ≤ III--was 4.6% (14/300).
Conclusion: LRP is a safe procedure with low morbidity. As surgeons progress through the learning curve, perioperative parameters and oncologic outcomes improve. Using a carefully mentored approach, LRP can be safely introduced as a new procedure without compromising patient outcomes.
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http://dx.doi.org/10.1089/end.2011.0635 | DOI Listing |
BMC Pulm Med
January 2025
Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Objective: Lung cancer (LC), the primary cause for cancer-related death globally is a diverse illness with various characteristics. Saliva is a readily available biofluid and a rich source of miRNA. It can be collected non-invasively as well as transported and stored easily.
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January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
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January 2025
Department of Psychology, Faculty of Behavioural and Social Sciences, University of Groningen, Grote Kruisstraat 2/1, 9712TS, Groningen, The Netherlands.
Recruits are exposed to high levels of psychological and physical stress during the special forces selection period, resulting in dropout rates of up to 80%. To identify who likely drops out, we assessed a group of 249 recruits, every week of the selection program, on their self-efficacy, motivation, experienced psychological and physical stress, and recovery. Using linear regression as well as state-of-the-art machine learning techniques, we aimed to build a model that could meaningfully predict dropout while remaining interpretable.
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January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
View Article and Find Full Text PDFJ Prev Alzheimers Dis
February 2025
Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA. Electronic address:
Background: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is challenging by their high dimensionality and inherent correlations.
Methods: Our study analyzed 1128 plasma proteins, measured by the SOMAscan platform, from 858 participants 55 years and older (mean age 63 years, 52.
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