Computer-aided nodule detection on digital chest radiography: Validation test on consecutive T1 cases of resectable lung cancer

Shuji Sakai, Hiroyasu Soeda, Naoki Takahashi, Takashi Okafuji, Tadamasa Yoshitake, Hidetake Yabuuchi, Ichiro Yoshino, Keiji Yamamoto, Hiroshi Honda, Kunio Doi

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)376-382
Number of pages7
JournalJournal of Digital Imaging
Issue number4
Publication statusPublished - Dec 2006

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications


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