Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization

Feng Li, Hidetaka Arimura, Kenji Suzuki, Junji Shiraishi, Qiang Li, Hiroyuki Abe, Roger Engelmann, Shusuke Sone, Heber MacMahon, Kunio Doi

Research output: Contribution to journalArticle

95 Citations (Scopus)

Abstract

PURPOSE: To retrospectively evaluate whether a difference-image computer-aided detection (CAD) scheme can help radiologists detect peripheral lung cancers missed at low-dose computed tomography (CT). MATERIALS AND METHODS: Institutional review board approval and informed patient and observer consent were obtained. Seventeen patients (eight men and nine women; mean age, 60 years) with a missed peripheral lung cancer and 10 control subjects (five men and five women; mean age, 63 years) without cancer at low-dose CT were included in an observer study. Fourteen radiologists were divided into two groups on the basis of different image display formats: Six radiologists (group 1) reviewed CT scans with a multiformat display, and eight radiologists (group 2) reviewed images with a "stacked" cine-mode display. The radiologists, first without and then with the CAD scheme, indicated their confidence level regarding the presence (or absence) of cancer and the most likely position of a lesion on each CT scan. Receiver operating characteristic (ROC) curves were calculated without and with localization to evaluate the observers' performance. RESULTS: With the CAD scheme, the average area under the ROC curve improved from 0.763 to 0.854 for all radiologists (P = .002), from 0.757 to 0.862 for group 1 (P = .04), and from 0.768 to 0.848 for group 2 (P = .01). The average sensitivity in the detection of 17 cancers improved from 52% (124 of 238 observations) to 68% (163 of 238 observations) for all radiologists (P < .001), from 49% (50 of 102 observations) to 71% (72 of 102 observations) for group 1 (P = .02), and from 54% (74 of 136 observations) to 67% (91 of 136 observations) for group 2 (P = .006). The localization ROC curve also improved. CONCLUSION: Lung cancers missed at low-dose CT were very difficult to detect, even in an observer study. The use of CAD, however, can improve radiologists' performance in the detection of these subtle cancers.

Original languageEnglish
Pages (from-to)684-690
Number of pages7
JournalRadiology
Volume237
Issue number2
DOIs
Publication statusPublished - Nov 1 2005

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization'. Together they form a unique fingerprint.

  • Cite this

    Li, F., Arimura, H., Suzuki, K., Shiraishi, J., Li, Q., Abe, H., Engelmann, R., Sone, S., MacMahon, H., & Doi, K. (2005). Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. Radiology, 237(2), 684-690. https://doi.org/10.1148/radiol.2372041555