Introduction: Robot-assisted partial nephrectomy (RAPN) is an established, minimally invasive technique to treat patients with renal masses. The aim of this study was to assess the learning curve (LC) of RAPN, evaluate its impact on perioperative outcomes following RAPN and to study the role of surgeon experience in achieving "trifecta" outcomes following RAPN.
Methods: We prospectively analyzed the clinical and pathological outcomes of 108 consecutive patients who underwent RAPN for renal tumors from January 2012 to December 2016 by a laparoscopy trained surgeon with no prior robotic experience. We used warm ischemia time (WIT) <20 min, operative time <120 min, and blood loss <100 ml as endpoints for plotting the LCs. Trifecta was analyzed in relation to our LC.
Results: Surgeon experience was found to correlate with WIT, operative time, and blood loss. Overall 18.5% of patients developed complications. Complication rate reduced with increasing surgeon experience. LC was 44 cases for WIT ≤20 min, 44 cases for operative time <120 min, and 54 cases for blood loss <100 ml. Trifecta outcome was achieved in 67.6% patients overall and was found to correlate with increasing surgeon experience. Improvement in trifecta outcomes continued to occur beyond the LC.
Conclusions: RAPN is a viable option for nephron-sparing surgery in patients with renal carcinoma. For a surgeon trained in laparoscopy, acceptable perioperative outcomes following RAPN can be achieved after an LC of about 44 cases. Increasing surgeon experience was associated with improved "trifecta" achievement following RAPN.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5769252 | PMC |
| http://dx.doi.org/10.4103/iju.IJU_169_17 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A machine learning-based model to predict POD24 in follicular lymphoma: a study by the Chinese workshop on follicular lymphoma.
Biomark Res
January 2025
Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Background: Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.
Methods: A cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio).
Preoperative prediction of the selection of the NOTES approach for patients with symptomatic simple renal cysts via an interpretable machine learning model: a retrospective study of 264 patients.
Langenbecks Arch Surg
January 2025
Department of Urology, Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Background: There are multiple surgical approaches for treating symptomatic simple renal cysts (SSRCs). The natural orifice transluminal endoscopic surgery (NOTES) approach has gradually been applied as an emerging minimally invasive approach for the treatment of SSRCs. However, there are no clear indicators for selecting the NOTES approach for patients with SSRCs.
View Article and Find Full Text PDFComputer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images.
Sci Rep
January 2025
College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China.
Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-cystic, polycystic, internal capsule collapse, solid mass, and calcified subtypes.
View Article and Find Full Text PDFNoninvasive diagnosis of significant liver fibrosis in patients with chronic hepatitis B using nomogram and machine learning models.
Sci Rep
January 2025
Department of Infectious Diseases, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, Hunan, China.
This study aims to construct and validate noninvasive diagnosis models for evaluating significant liver fibrosis in patients with chronic hepatitis B (CHB). A cohort of 259 CHB patients were selected as research subjects. Through random grouping, 182 cases were included in the training set and 77 cases in the validation set.
View Article and Find Full Text PDFRadiomics and Deep Learning Model for Benign and Malignant Soft Tissue Tumors Differentiation of Extremities and Trunk.
Acad Radiol
January 2025
Department of Radiology, Southeast University Zhongda Hospital, No. 87 Dingjiaqiao Road, Gulou District, Nanjing, Jiangsu Province, China (M.Y., J.J.). Electronic address:
Rationale And Objectives: To develop radiomics and deep learning models for differentiating malignant and benign soft tissue tumors (STTs) preoperatively based on fat saturation T2-weighted imaging (FS-T2WI) of patients.
Materials And Methods: Data of 115 patients with STTs of extremities and trunk were collected from our hospital as the training set, and data of other 70 patients were collected from another center as the external validation set. Outlined Regions of interest included the intratumor and the peritumor region extending outward by 5 mm, then the corresponding radiomics features were extracted respectively.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!