Blar i NTNU Open på forfatter "Lagani, Vincenzo"
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A Validated Clinical Risk Prediction Model for Lung Cancer in Smokers of All Ages and Exposure Types: A HUNT Study
Markaki, Maria Dorothea Haaberg; Tsamardinos, Ioannis; Langhammer, Arnulf; Lagani, Vincenzo; Hveem, Kristian; Røe, Oluf Dimitri (Journal article; Peer reviewed, 2018)Lung cancer causes >1·6 million deaths annually, with early diagnosis being paramount to effective treatment. Here we present a validated risk assessment model for lung cancer screening. The prospective HUNT2 population ... -
Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets
Papoutsoglou, Georgios; Karaglani, Makrina; Lagani, Vincenzo; Thomson, Naomi; Røe, Oluf Dimitri; Tsamardinos, Ioannis; Chatzaki, Ekaterini (Peer reviewed; Journal article, 2021)COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three ... -
Hidden treasures in "ancient" microarrays: Gene expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal tissue
Kerkentzes, Konstantinos; Lagani, Vincenzo; Tsamardinos, Ioannis; Vyberg, Mogens; Røe, Oluf Dimitri (Journal article; Peer reviewed, 2014)Objective: Novel statistical methods and increasingly more accurate gene annotations can transform “old” biological data into a renewed source of knowledge with potential clinical relevance. Here, we provide an in silico ... -
‘Reduced’ HUNT model outperforms NLST and NELSON study criteria in predicting lung cancer in the Danish screening trial
Røe, Oluf Dimitri; Markaki, Maria; Tsamardinos, Ioannis; Lagani, Vincenzo; Nguyen, Olav Toai Duc; Pedersen, Jesper Holst; Saghir, Zaigham; Ashraf, Haseem (Journal article; Peer reviewed, 2019)Hypothesis We hypothesise that the validated HUNT Lung Cancer Risk Model would perform better than the NLST (USA) and the NELSON (Dutch‐Belgian) criteria in the Danish Lung Cancer Screening Trial (DLCST). Methods The ...