LUNGx Challenge for computerized lung nodule classification

作者：Samuel G. Armato, Karen Drukker, Feng Li, Lubomir M. Hadjiiski, Georgia D. Tourassi, Roger Engelmann, Maryellen L. Giger, George Redmond, Keyvan Farahani, Justin Kirby, Laurence P. Clarke · 发表于：Journal of medical imaging · 年份：2016 · DOI：10.1117/1.jmi.3.4.044506 · 被引用次数：130 · 研究领域：Lung Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Lung Cancer Treatments and Mutations

The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants' computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. Ten groups applied their own methods to 73 lung nodules (37 benign and 36 malignant) that were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists' AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. The continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.