Science Speed Dating mit Olivier Guenat | UniBE Foundation

Europe-China cooperation in personalised medicine: paving the way for a scientific common path

Within the Sino-Eu PerMed Project three delegation visits were organised to foster and increase cooperation be

Machine learning model to preoperatively predict T2/T3 staging of laryngeal and hypopharyngeal cancer based on the CT radiomic signature - European Radiology

Objectives To develop and assess a radiomics-based prediction model for distinguishing T2/T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) Methods A total of 118 patients with pathologically proven LHSCC were enrolled in this retrospective study. We performed feature processing based on 851 radiomic features derived from contrast-enhanced CT images and established multiple radiomic models by combining three feature selection methods and seven machine learning classifiers. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the models. The radiomic signature obtained from the optimal model and statistically significant morphological image characteristics were incorporated into the predictive nomogram. The performance of the nomogram was assessed by calibration curve and decision curve analysis. Results Using analysis of variance (ANOVA) feature selection and logistic regression (LR) classifier produced the best model. The AUCs of the training, validation, and test sets were 0.919, 0.857, and 0.817, respectively. A nomogram based on the model integrating the radiomic signature and a morphological imaging characteristic (suspicious thyroid cartilage invasion) exhibited C-indexes of 0.899 (95% confidence interval (CI) 0.843–0.955), fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram. Conclusions The nomogram based on the radiomics model derived from contrast-enhanced CT images had good diagnostic performance for distinguishing T2/T3 staging of LHSCC. Clinical relevance statement Accurate T2/T3 staging assessment of LHSCC aids in determining whether laryngectomy or laryngeal preservation therapy should be performed. The nomogram based on the radiomics model derived from contrast-enhanced CT images has the potential to predict the T2/T3 staging of LHSCC, which can provide a non-invasive and robust approach for guiding the optimization of clinical decision-making. Key Points • Combining analysis of variance with logistic regression yielded the optimal radiomic model. • A nomogram based on the CT-radiomic signature has good performance for differentiating T2 from T3 staging of laryngeal and hypopharyngeal squamous cell carcinoma. • It provides a non-invasive and robust approach for guiding the optimization of clinical decision-making.

Launch of the European Partnership for Personalised Medicine

Research and innovation news alert: The new European Partnership for Personalised Medicine will promote all areas and disciplines of precision medicine, facilitate innovation and transfer, as well as encourage its consolidation in health systems, allowing continuous improvement.

Diagnostic Accuracy of Hospital Antibiograms in Predicting the Risk of Antimicrobial Resistance in Enterobacteriaceae Isolates: A Nationwide Multicenter Evaluation at the Veterans Health Administration

We conducted a comprehensive evaluation of diagnostic accuracies for conventional antibiograms to predict antimicrobial resistance among E. coli and Klebsiella

AI-powered personalised medicine could revolutionise healthcare (and no, we’re not putting ChatGPT in charge)

Artificial intelligence can’t replace human professionals – but it could transform the way they work, says Mihaela van der Schaar of the University of Cambridge