AI can detect lung cancers unnoticed in chest radiography: Study

South Korea: A deep learning–based nodule detection algorithm can detect lung cancers that were initially not reported on chest radiographs, finds a recent study in the journal Radiology: Cardiothoracic Imaging. Additionally, the AI algorithm when used as a second reader can significantly reduce reading errors. 

Ju Gang Nam, Seoul National University Hospital, South Korea, and colleagues evaluated the performance of a deep learning–based algorithm in detecting lung cancers not reported on posteroanterior chest radiographs during routine practice.

The retrospective test dataset included 168 posteroanterior chest radiographs acquired between March 2017 and December 2018 (168 patients) with 187 lung cancers undetected during initial clinical evaluation, and 50 normal chest radiographs. CT served as the reference standard for ground truth.

Four thoracic radiologists independently reevaluated the chest radiographs for lung nodules both without and with the aid of the algorithm. The performances of the algorithm and the radiologists were evaluated and compared on a per–chest radiograph basis and a per-lesion basis, according to the area under the receiver operating characteristic curve (AUROC) and area under the jackknife free-response ROC curve (AUFROC). 

"A deep learning–based nodule detection algorithm showed excellent detection performance of lung cancers that were not reported on chest radiographs during routine practice and significantly reduced reading errors when used as a second reader," concluded the authors.

The study, "Undetected Lung Cancer at Posteroanterior Chest Radiography: Potential Role of a Deep Learning–based Detection Algorithm," is published in the journal Radiology: Cardiothoracic Imaging.

DOI: https://pubs.rsna.org/doi/full/10.1148/ryct.2020190222