DOI: 10.1118/1.1543575. Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low‐dose CT images

Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low‐dose CT images

10.1118/1.1543575

Crossref journal-article

Wiley

Medical Physics (311)

Abstract

An automated computerized scheme has been developed for determination of the likelihood measure of malignancy of pulmonary nodules on low‐dose helical CT (LDCT) images. Our database consisted of 76 primary lung cancers (147 slices) and 413 benign nodules (576 slices). With this automated computerized scheme, the location of a nodule was first indicated by a radiologist. The outline of the nodule was segmented automatically by use of a dynamic programming technique. Various objective features on the nodules were determined by use of outline analysis and image analysis, and the likelihood measure of malignancy was determined by use of linear discriminant analysis (LDA). The effect of many different combinations of features and the performance of LDA in distinguishing benign nodules from malignant ones were evaluated by means of receiver operating characteristic (ROC) analysis. The Az value (area under the ROC curve) obtained by the computerized scheme in distinguishing benign nodules from malignant ones was 0.828 when a single slice was employed for each of the nodules. However, the Az value was improved to 0.846 when multiple slices were used for determination of the likelihood measure of malignancy. The Az values obtained by the computerized scheme on LDCT images were significantly greater than the Az value of 0.70, which was obtained from our previous observer studies by radiologists in distinguishing benign nodules from malignant ones on LDCT images. The automated computerized scheme for determination of the likelihood measure of malignancy would be useful in assisting radiologists to distinguish between benign and malignant pulmonary nodules on LDCT images.

Bibliography

Aoyama, M., Li, Q., Katsuragawa, S., Li, F., Sone, S., & Doi, K. (2003). Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on lowâdose CT images. Medical Physics, 30(3), 387â394. Portico.

Authors 6

Masahito Aoyama (first)
Qiang Li (additional)
Shigehiko Katsuragawa (additional)
Feng Li (additional)
Shusuke Sone (additional)
Kunio Doi (additional)

References 29 Referenced 88

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{'key': 'e_1_2_1_4_1', 'first-page': '487', 'article-title': 'CT screening for lung cancer', 'volume': '38', 'author': 'Henschke C. I.', 'year': '2000', 'journal-title': 'Lung Cancer'} / Lung Cancer / CT screening for lung cancer by Henschke C. I. (2000)
10.2214/ajr.179.4.1790833
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Y. Kawata et al. “Classification of pulmonary nodules in thin section CT images based on shape characterization ” Proceeding of IEEE Int. Conference on Image Processing3 528–531 (1997). (10.1109/ICIP.1997.632174)
Y. Kawata et al. “Computer aided differential diagnosis of pulmonary nodules using carvature based analysis ” Proceeding of IEEE Int. Conference on Image and Acoustics Processing 470–475 (1999). (10.1109/ICIAP.1999.797640)
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10.1109/34.6784

Dates

Type	When
Created	22 years, 6 months ago (March 6, 2003, 6:03 p.m.)
Deposited	8 months, 3 weeks ago (Dec. 11, 2024, 5:34 p.m.)
Indexed	5 months, 1 week ago (March 29, 2025, 12:14 p.m.)
Issued	22 years, 6 months ago (Feb. 10, 2003)
Published	22 years, 6 months ago (Feb. 10, 2003)
Published Online	22 years, 6 months ago (Feb. 10, 2003)
Published Print	22 years, 6 months ago (March 1, 2003)

Funders 0

None

BibTeX

@article{Aoyama_2003, title={Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low‐dose CT images}, volume={30}, ISSN={2473-4209}, url={http://dx.doi.org/10.1118/1.1543575}, DOI={10.1118/1.1543575}, number={3}, journal={Medical Physics}, publisher={Wiley}, author={Aoyama, Masahito and Li, Qiang and Katsuragawa, Shigehiko and Li, Feng and Sone, Shusuke and Doi, Kunio}, year={2003}, month=feb, pages={387–394} }

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