LungCAD: a clinically approved, machine learning system for lung cancer detection

We present LungCAD, a computer aided diagnosis (CAD) system that employs a classification algorithm for detecting solid pulmonary nodules from CT thorax studies. We briefly describe some of the machine learning techniques developed to overcome the real world challenges in this medical domain. The most significant hurdle in transitioning from a machine learning research prototype that performs well on an in-house dataset into a clinically deployable system, is the requirement that the CAD system be tested in a clinical trial. We describe the clinical trial in which LungCAD was tested: a large scale multi-reader, multi-case (MRMC) retrospective observational study to evaluate the effect of CAD in clinical practice for detecting solid pulmonary nodules from CT thorax studies. The clinical trial demonstrates that every radiologist that participated in the trial had a significantly greater accuracy with LungCAD, both for detecting nodules and identifying potentially actionable nodules; thi...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: R. Bharat Rao, Jinbo Bi, Glenn Fung, Marcos Salganicoff, Nancy Obuchowski, David P. Naidich
• Keywords: Clinical Trial, CT Thorax Studies, Data Mining, KDD 2007, Solid Pulmonary Nodules