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

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.

[1]  Ansgar Malich,et al.  Are unnecessary follow-up procedures induced by computer-aided diagnosis (CAD) in mammography? Comparison of mammographic diagnosis with and without use of CAD. , 2004, European journal of radiology.

[2]  K Nakamura,et al.  Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. , 2000, Radiology.

[3]  K. Awai,et al.  Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. , 2004, Radiology.

[4]  S. Swensen,et al.  Lung cancer screening with CT: Mayo Clinic experience. , 2003, Radiology.

[5]  Anthony J. Sherbondy,et al.  Pulmonary nodules on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. , 2005, Radiology.

[6]  M. Giger,et al.  Breast cancer: effectiveness of computer-aided diagnosis observer study with independent database of mammograms. , 2002, Radiology.

[7]  O. Miettinen,et al.  Early Lung Cancer Action Project: overall design and findings from baseline screening , 1999, The Lancet.

[8]  William J Kostis,et al.  CT screening for lung cancer. , 2000, Seminars in ultrasound, CT, and MR.

[9]  K. Doi,et al.  Malignant versus benign nodules at CT screening for lung cancer: comparison of thin-section CT findings. , 2004, Radiology.

[10]  S Sone,et al.  Growth rate of small lung cancers detected on mass CT screening. , 2000, The British journal of radiology.

[11]  K. Doi,et al.  Computer-aided diagnosis of pulmonary nodules: results of a large-scale observer test. , 1999, Radiology.

[12]  K. Doi,et al.  Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images. , 2003, Medical physics.

[13]  O. Miettinen,et al.  CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. , 2002, AJR. American journal of roentgenology.

[14]  Luisa P. Wallace,et al.  Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. , 2004, Journal of the National Cancer Institute.

[15]  H. Ohmatsu,et al.  Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. , 1996, Radiology.

[16]  S Sone,et al.  Rapidly growing small peripheral lung cancers detected by screening CT: correlation between radiological appearance and pathological features. , 2000, The British journal of radiology.

[17]  Feng Li,et al.  Mass screening for lung cancer with mobile spiral computed tomography scanner , 1998, The Lancet.

[18]  J. Austin,et al.  Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. , 2005, Radiology.

[19]  Robert M. Nishikawa,et al.  Computer aided diagnosis of breast cancer on mammograms , 1997, Breast cancer.

[20]  Theresa C McLoud,et al.  Small pulmonary nodules: detection at chest CT and outcome. , 2003, Radiology.

[21]  K. Doi,et al.  Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. , 2004, AJR. American journal of roentgenology.

[22]  David Gur,et al.  Trends in recall, biopsy, and positive biopsy rates for screening mammography in an academic practice. , 2005, Radiology.

[23]  N. Petrick,et al.  Improvement in radiologists' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study. , 2004, Radiology.

[24]  OS Miettinen,et al.  Early Lung Cancer Action Project , 1999, The Lancet.

[25]  O. Miettinen,et al.  CT screening for lung cancer: suspiciousness of nodules according to size on baseline scans. , 2004, Radiology.

[26]  Kunio Doi,et al.  Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience. , 2003, Radiology.

[27]  K. Doi,et al.  Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization. , 2005, Radiology.

[28]  K. Doi,et al.  Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis. , 2002, AJR. American journal of roentgenology.

[29]  K. Doi,et al.  Low-dose computed tomography screening for lung cancer in a general population: characteristics of cancer in non-smokers versus smokers. , 2003, Academic radiology.

[30]  W. Heindel,et al.  Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. , 2002, Radiology.