Computer-Aided Nodule Detection on Digital Chest Radiography: Validation Test on Consecutive T1 Cases of Resectable Lung Cancer

Abstract

Purpose. To evaluate the usefulness of a commercially available computer-assisted diagnosis (CAD) system on operable T1 cases of lung cancer by use of digital chest radiography equipment. Materials and Methods. Fifty consecutive patients underwent surgery for primary lung cancer, and 50 normal cases were selected. All cancer cases were histopathologically confirmed T1 cases. All normal individuals were selected on the basis of chest computed tomography (CT) confirmation and were matched with cancer cases in terms of age and gender distributions. All chest radiographs were obtained with one computed radiography or two flat-panel detector systems. Eight radiologists (four chest radiologists and four residents) participated in observer tests and interpreted soft copy images by using an exclusive display system without and with CAD output. When radiologists diagnosed cases as positives, the locations of lesions were recorded on hard copies. The observers , performance was evaluated by receiver operating characteristic analysis. Results. The overall detectability of lung cancer cases with CAD system was 74% (37/50), and the false-positive rate was 2.28 (114/50) false positives per case for normal cases. The mean A z value increased significantly from 0.896 without CAD output to 0.923 with CAD output (P = 0.018). The main cause of the improvement in performance is attributable to changes from false negatives without CAD to true positives with CAD (19/31, 61%). Moreover, improvement in the location of the tumor was observed in 1.5 cases, on average, for radiology residents. Conclusion. This CAD system for digital chest radiographs is useful in assisting radiologists in the detection of early resectable lung cancer.

Impact of a Computer-Aided Detection (CAD) System Integrated into a Picture Archiving and Communication System (PACS) on Reader Sensitivity and Efficiency for the Detection of Lung Nodules in Thoracic CT Exams

“…In addition, while the integration of CAD into a routine clinical PACS environment is gaining momentum [25][26][27][28], the interactive functionality for reviewing CAD marks within PACS is currently limited. The main purpose of the present report, therefore, has been to assess the impact on reader's identification of solid lung nodules employing a CAD server integrated into a PACS environment as a potential component of routine clinical CT interpretation.…”

A review of factors influencing radiologists’ visual search behaviour

“…Over the years, there were many reasons that were proposed as primary causes for the SOS effect, including faulty visual search, recognition and decision-making errors. [16][17][18] There are various solutions proposed to counteract these perceptual and interpretational errors and improve radiologists' detection of abnormalities, including double reading, 19,20 computer-aided detection 21,22 and the introduction of educational strategies. 23,24 However, to identify the underlying errors in perception and interpretation of radiologists leading to missed abnormalities and to successfully aid in improving their ability to detect abnormalities, it is vital to understand the visual search behaviour of radiologists when viewing medical images.…”

Computer-aided Detection of Lung Cancer on Chest Radiographs: Effect on Observer Performance

“…Even the lowest rating was already counted as a positive reading. This threshold is also used in other studies that evaluated CAD ( 22,26 ) and ensures that all changes made owing to CAD are accounted for in the evaluation, because observers had a low confi dence in most markings that were placed after seeing the CAD results.…”