Improving radiologists' recommendations with computer-aided diagnosis for management of small nodules detected by CT.


Journal Article

RATIONALE AND OBJECTIVES: To evaluate how computer-aided diagnosis (CAD) can improve radiologists' recommendations for management of possible early lung cancers on CT. MATERIALS AND METHODS: Twenty-eight lung cancers and 28 benign lesions were employed. Each group of 28 lesions was classified into subgroups of two sizes (9 between 6 and 10 mm and 19 between 11 and 20 mm) and three patterns (8 with pure ground glass opacity [GGO], 12 with mixed GGO and 8 solid lesions). Sixteen radiologists participated in the observer study, first without and then with CAD. Radiologists' recommendations, including (1) follow-up in 12 months, (2) in 6 months, (3) in 3 months, or (4) biopsy, were compared at three levels of their malignancy probability ratings (low: 1%-33%; medium: 34%-66%; high: 67%-99%) for 896 observations (56 lesions by the 16 radiologists) in the two size subgroups and three patterns. RESULTS: The number of recommendations changed by radiologists by use of CAD was 163 (18%) among all 896 observations. Among these changed recommendations, the fraction showing a beneficial effect from CAD was 68% (111/163), and the fraction showing a beneficial effect regarding biopsy recommendations was 69% (48/70). With CAD, the radiologists' performance regarding biopsy recommendations was significantly improved for 43 lung cancers (31 changed to biopsy versus 12 changed away from biopsy; P = .003) and was also improved for 27 benign lesions (10 changed to biopsy versus 17 changed away from biopsy; P = .18). Most of the cancers with improved recommendations were solid lesions or mixed GGO and relatively large. CONCLUSION: CAD has the potential to improve the appropriateness of radiologists' recommendations for small malignant and benign lesions on CT scans.

Full Text

Duke Authors

Cited Authors

  • Li, F; Li, Q; Engelmann, R; Aoyama, M; Sone, S; MacMahon, H; Doi, K

Published Date

  • August 2006

Published In

Volume / Issue

  • 13 / 8

Start / End Page

  • 943 - 950

PubMed ID

  • 16843846

Pubmed Central ID

  • 16843846

International Standard Serial Number (ISSN)

  • 1076-6332

Digital Object Identifier (DOI)

  • 10.1016/j.acra.2006.04.010


  • eng

Conference Location

  • United States