{"id":1330,"date":"2019-03-13T15:58:51","date_gmt":"2019-03-13T15:58:51","guid":{"rendered":"https:\/\/csbweb.csb.pitt.edu\/Faculty\/benos\/?p=1330"},"modified":"2019-04-09T16:40:39","modified_gmt":"2019-04-09T16:40:39","slug":"vineets-thorax-paper-gets-some-attention-in-the-media","status":"publish","type":"post","link":"https:\/\/csbweb.csb.pitt.edu\/Faculty\/benos\/?p=1330","title":{"rendered":"Vineet’s Thorax paper gets some attention from the media"},"content":{"rendered":"

\"\"<\/a>Vineet’s\u00a0Thorax<\/strong><\/em> paper was published today and has started gaining some traction. \u00a0Co-senior author, David Wilson, was interviewed by our local NPR news station<\/a>; and this was the first of the references of the publication in the press, which included US News<\/a>, Science Daily<\/a>, \u00a0Health IT Analytics<\/a>\u00a0and others. \u00a0In this study we used probabilistic graphical models to develop a predictor of cancer status in indeterminate lung nodules detected through low-dose CT scans. \u00a0Low-dose CT scans are routinely used for early detection of lung cancer and have helped reduce mortality. \u00a0However, 96% of the detected nodules are benign and currently people have to subject themselves to additional screening that increase the anxiety and costs. \u00a0Our model can potentially reduce subsequent screenings to 28% of people with benign nodules without risking cancer misclassification. \u00a0Congratulations to Vineet and the team that completed this study.<\/p>\n

References of our work in the press<\/strong><\/p>\n