Dr. William Kostis Profile

Dr. William Kostis

Professor at Massachusetts General Hospital

SPIE Involvement:

Author

Publications (4)

Proceedings Article | 4 May 2004 Paper

Comparison of nodule characteristics on CT and radiographic images

Zhimin Huo, Melisa Gao, David Yankelevitz, Claudia Henschke, William Kostis, John Wandtke

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.537363

KEYWORDS: Chromium, Computed tomography, X-ray computed tomography, Lung, Chest, Feature extraction, Solids, Visualization, Lung cancer, Radiography

Read Abstract +

The purpose of this study is to identify the difference in nodule characteristics manifested on computed topography (CT) and X-ray images and to evaluate the ability of radiographic features to differentiate between benign and malignant nodules, when compared to the features extracted from CT. We collected 79 consecutive computed radiographic (CR) chest images with one or more CT-documented lung nodules. Upon viewing the CT slices, corresponding nodules were localized on CR images by an experienced chest radiologist. Of the 79 CT nodules (19 benign, 60 malignant), 61 (14 benign, 47 malignant) were considered to be definitely visible on the CR, and the rest were considered to be invisible or did not qualify for distinct feature assessment. Eleven nodule features each were visually extracted from CT and CR images. These features were used to characterize the nodule in terms of size, shape, lobulation, spiculation, density, etc. Correlation between the CT and CR features was calculated for the 61 definitely CR-visible nodules. Receiver operating characteristics (ROC) analysis was performed to evaluate the ability of these features in the task of differentiating between benign and malignant nodules. Results showed that CR and CT images agreed well in characterizing nodules in terms of shape, lobulation, spiculation and density features. We found that 40-50% of the cases had same CR and CT ratings and 41-51% of cases were rated by a difference of one between their ratings on CT and CR for shape (3-point scale), lobulation (4-point scale) and speculation (4-point scale) features. Ninety-two percent of the cases had same CT and CR ratings on the density feature. Size yielded a correlation coefficient of 0.84. In the task of differentiating between benign and malignant lung nodules, ROC analysis of individual features yielded an A_z value ranging from 0.52 to 0.77 for the 14 CT features and from 0.52 to 0.75 for the CR features. In addition, we examined the characteristics of the 18 nodules that were excluded from feature analysis. On average, these 18 nodules were smaller in size (15.2 mm measured from CT) than the 61 CR-visible nodules (23.5 mm). We found that CR features agreed reasonably well with CT features and their ability to differentiate between benign and malignant nodules were similar to that of the CT features.

Proceedings Article | 15 May 2003 Paper

Method for analysis and display of distribution of emphysema in CT scans

William Kostis, Simina Fluture, David Yankelevitz, Claudia Henschke

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481415

KEYWORDS: Emphysema, Lung, Computed tomography, Image segmentation, Signal attenuation, Algorithm development, Signal to noise ratio, Lung cancer, Tissues, Visualization

Read Abstract +

Proceedings Article | 15 May 2003 Paper

Volumetric assessment of emphysema on low-dose screening CT scans

William Kostis, Simina Fluture, Ali Farooqi, David Yankelevitz, Claudia Henschke

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481421

KEYWORDS: Emphysema, Lung, Image segmentation, Computed tomography, Signal attenuation, Visualization, Image processing algorithms and systems, Lung cancer, Pulmonary function tests, Digital filtering

Read Abstract +

Proceedings Article | 21 May 1999 Paper

Consistent segmentation of repeat CT scans for growth assessment in pulmonary nodules

Binsheng Zhao, William Kostis, Anthony Reeves, David Yankelevitz, Claudia Henschke

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348494

KEYWORDS: Tissues, Computed tomography, Image segmentation, Natural surfaces, 3D image processing, Algorithm development, Lung, Chest imaging, Scanners, Surgery

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years