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Open-source Large Language Models can Generate Labels from Radiology Reports for Training Convolutional Neural Networks.
Acad Radiol
January 2025
Department of Radiology and Nuclear Medicine, German Heart Center Munich, Lazarettstraße 36, 80636 Munich, Germany (K.K.B.).
Rationale And Objectives: Training Convolutional Neural Networks (CNN) requires large datasets with labeled data, which can be very labor-intensive to prepare. Radiology reports contain a lot of potentially useful information for such tasks. However, they are often unstructured and cannot be directly used for training.
View Article and Find Full Text PDFPreTKcat: A pre-trained representation learning and machine learning framework for predicting enzyme turnover number.
Comput Biol Chem
January 2025
College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin, 300457, China. Electronic address:
The enzyme turnover number (k) is crucial for understanding enzyme kinetics and optimizing biotechnological processes. However, experimentally measured k values are limited due to the high cost and labor intensity of wet-lab measurements, necessitating robust computational methods. To address this issue, we propose PreTKcat, a framework that integrates pre-trained representation learning and machine learning to predict k values.
View Article and Find Full Text PDFComprehensive histopathological analysis of gastric cancer in European and Latin America populations reveals differences in PDL1, HER2, p53 and MUC6 expression.
Gastric Cancer
January 2025
Department of Medical Oncology, Hospital Clinico Universitario, INCLIVA, Biomedical Research Institute, University of Valencia, Avenida Menendez Pelayo nro 4 accesorio, Valencia, Spain.
Introduction: Gastric cancer (GC) burden is currently evolving with regional differences associated with complex behavioural, environmental, and genetic risk factors. The LEGACy study is a Horizon 2020-funded multi-institutional research project conducted prospectively to provide comprehensive data on the tumour biological characteristics of gastroesophageal cancer from European and LATAM countries.
Material And Methods: Treatment-naïve advanced gastroesophageal adenocarcinoma patients were prospectively recruited in seven European and LATAM countries.
Multiparametric MRI and artificial intelligence in predicting and monitoring treatment response in bladder cancer.
Insights Imaging
January 2025
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Bladder cancer is the 10th most common and 13th most deadly cancer worldwide, with urothelial carcinomas being the most common type. Distinguishing between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is essential due to significant differences in management and prognosis. MRI may play an important diagnostic role in this setting.
View Article and Find Full Text PDFBody Composition Measurements as Predictive Variables for Outcomes of Canine Appendicular Osteosarcoma Treated With Stereotactic Body Radiation Therapy.
Vet Comp Oncol
January 2025
Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Colorado, Fort Collins, US.
Body composition measurements (BCM), obtained via computed tomography (CT), have been used as predictors of survival, tumour recurrence, and post-surgical infections in human oncology. There are no reports on using BCM to predict outcomes of dogs with cancer. Elevated BCM is hypothesised to place extra stress on bones weakened by cancer.
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