The goal of the study described here was to define the predictive value of pre-operative clinical information and contrast-enhanced ultrasound (CEUS) imaging characteristics in combined hepatocellular-cholangiocarcinoma (CHC) patients with microvascular invasion (MVI). Seventy-six patients with pathologically confirmed CHC were enrolled in this study, comprising 18 patients with MVI-positive status and 58 with MVI-negative CHC nodules. The pre-operative clinical data and CEUS imaging features were retrospectively analyzed. Univariate and multivariate analyses were performed to identify the potential predictors of MVI in CHC. Recurrence-free survival (RFS) after hepatectomy was compared between patients with different MVI status using the log-rank test and Kaplan-Meier survival curves. Univariate analysis indicated that the following parameters of patients with CHC significantly differed between the MVI-positive and MVI-negative groups (p<0.05): tumor size, α-fetoprotein ≥400 ng/mL, enhancement patterns in arterial phase and marked washout during the portal venous phase on CEUS. On multivariate logistic regression analysis, only the CEUS characteristics of heterogeneous enhancement (odds ratio = 6.807; 95% confidence interval [CI]: 1.099, 42.147; p = 0.039) and marked washout (odds ratio = 4.380; 95% CI: 1.050,18.270; p = 0.043) were identified as independent predictors of MVI in CHC. The combination of the two risk factors in predicting MVI achieved a better diagnostic performance than each parameter alone, with an area under the receiver operating characteristic curve of 0.736 (0.622, 0.830). After hepatectomy, CHC patients with MVI exhibited earlier recurrence compared with those without MVI (hazard ratio = 1.859; 95% CI: 0.8699-3.9722, p = 0.046). The CEUS imaging features of heterogeneous enhancement in the arterial phase and marked washout during the portal venous phase were the potential predictors of MVI in CHC. Aside from that, CHC patients with MVI had an earlier recurrence rate than those without MVI after surgery.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2022.02.014 | DOI Listing |
Curr Res Transl Med
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Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.
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February 2025
Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
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View Article and Find Full Text PDFJ Foot Ankle Surg
January 2025
Clinical Orthopeadics, University of Campania Luigi Vanvitelli, Vico L. de Crecchio, 80128 Naples Italy.
Subluxation or dislocation of the second metatarsophalangeal joint may be commonly associated with crossover toe, metatarsalgia, and painful calluses. This retrospective study aims to evaluate the clinical and functional results in patients with irreducible second metatarsophalangeal joint dislocation treated by double percutaneous osteotomy in one step: Haspell's osteotomy and Distal Metatarsal Mini-Invasive Osteotomy. A total of 39 patients were included in this study.
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University of Pisa, Department of Orthopedic and Trauma Surgery, Pisa, Italy.
Purpose Of The Study: Hand enchondromas are benign cartilage bone tumors. Curettage represents the actual gold standard for hand enchondromas. Little has been written about the effectiveness of curettage on hand functionality.
View Article and Find Full Text PDFAnaesthesia
January 2025
Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Introduction: Understanding 1-year mortality following major surgery offers valuable insights into patient outcomes and the quality of peri-operative care. Few models exist that predict 1-year mortality accurately. This study aimed to develop a predictive model for 1-year mortality in patients undergoing complex non-cardiac surgery using a novel machine-learning technique called multi-objective symbolic regression.
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