Background: Radical antegrade modular pancreatosplenectomy (RAMPS), a new surgical approach for pancreatic ductal adenocarcinoma of the body and tail, has become increasingly accepted and performed in recent years. Robotic surgery has advantages over open and laparoscopic surgeries in terms of surgical vision and instrument flexibility. However, the lack of comprehension of the learning curve has limited its generalization. This study aimed to evaluate the learning curve of robotic posterior RAMPS.
Methods: Patients who underwent robotic posterior RAMPS between February 2017 and April 2021 at our institution were included in this study. Data on patient characteristics, perioperative outcomes, and pathological outcomes were summarized and analyzed. The cumulative sum (CUSUM) method was used to assess the learning curve and inflection points based on operation time and estimated blood loss.
Results: One hundred consecutive patients who underwent robotic posterior RAMPS were enrolled. The median operation time was 235.0 (interquartile range [IQR], 210.0-270.0) min, and the estimated blood loss was 210.0 (IQR, 165.0-245.0) mL. The grade 3/4 Clavien-Dindo complication rate was 8% (8/100). According to the CUSUM plot, the inflection points of the learning curve were 25 and 65 cases, dividing the case series into the learning (1-25 cases), plateau (26-65 cases), and maturation (66-100 cases) phases. The operation time was relatively high in the learning phase, reached a plateau between 25 and 65 cases (270.0 min vs. 220.0 min, p < 0.01), and decreased significantly in the maturation phase (p < 0.01). Estimated blood loss improved in the maturation phase compared to the learning phase (150.0 vs. 245.0 mL, p < 0.01). No significant differences in conversion rate, complications, or mortality were observed among the three phases.
Conclusion: The inflection points of the learning and plateau phases were the 25th and 65th cases, respectively. Robotic RAMPS is safe and feasible even in the learning phase.
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http://dx.doi.org/10.1016/j.ijsu.2022.106612 | DOI Listing |
J Imaging Inform Med
January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.
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January 2025
Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China.
With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, surgical decision-making and optimal strategies still lack standardization and personalized customization. Our study aims to devise proper deep learning (DL) models that incorporate key factors influencing surgical outcomes on the coronal plane in AIS patients to facilitate surgical decision-making and predict surgical results for AIS patients.
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January 2025
Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Radiology, Xinhua Hospital, Shanghai Jiaotong University Medical School, Shanghai 200092, China (Z.H.W., Y.Q.M., X.Y.W., N.X.Y., X.Y.W., G.R.). Electronic address:
Rationale And Objectives: The expression of human epidermal growth factor receptor 2 (HER2) in gastric cancer is closely associated with its treatment outcomes and prognosis. This study aims to develop and validate a HER2 prediction model based on computed tomography (CT). Additionally, the study evaluates the robustness of the proposed model.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Ultrasound, Chengdu Second People's Hospital, Chengdu 610000, China (X.L., X.Q.). Electronic address:
Rationale And Objectives: This study aims to develop a radiopathomics model based on preoperative ultrasound and fine-needle aspiration cytology (FNAC) images to enable accurate, non-invasive preoperative risk stratification for patients with papillary thyroid carcinoma (PTC). The model seeks to enhance clinical decision-making by optimizing preoperative treatment strategies.
Methods: A retrospective analysis was conducted on data from PTC patients who underwent thyroidectomy between October 2022 and May 2024 across six centers.
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