Biomedical subjects

Joseph L Ogarek

Publications and source records attributed to Joseph L Ogarek.

2 recordsLinked to original sources

PubMed2006

Variability in mesothelioma tumor response classification.

OBJECTIVE: The objective of our study was to evaluate observer variability in the measurement of temporal change in mesothelioma tumor thickness and in the resulting tumor response classification from CT scans. In addition, the performance of a semiautomated measurement method was evaluated. MATERIALS AND METHODS: Four observers individually used an interface that displayed two serial CT scans from the same patient to measure mesothelioma tumor thickness on the follow-up CT scans of 22 patients based on baseline scan measurements. During one session, observers acquired measurements on the follow-up scans based on written reports of baseline scan measurements; in another session, baseline scan measurements were superimposed on the baseline scan for direct visual comparison. Follow-up scan measurements also were obtained from a semiautomated method. Measurement variability and tumor response classification concordance were evaluated for manual measurements acquired in both modes and for semiautomated measurements. RESULTS: Although only a small increase in tumor response classification concordance rate was obtained with the visual approach (84.8%) relative to the more standard written-report approach (82.6%), the actual measurements acquired by observers were statistically significantly different between the two approaches (p = 0.03). Both the semiautomated measurements and the resulting tumor response classifications were consistent with manual measurements. CONCLUSION: The presentation of baseline scan tumor measurements affects measurements acquired on follow-up scans and could influence tumor response classification. The potential utility of semiautomated tumor thickness measurements was shown in the context of measuring tumor response.

Adult↗

PubMed2004

Automated matching of temporally sequential CT sections.

In the evaluation of patient response to therapy through measurements on thoracic computed tomography (CT) scans, the selection of anatomically equivalent sections in temporally sequential scans is required. We developed an automated method based on normalized mutual information (NMI) to expedite the selection of anatomically equivalent sections. The method requires as input two temporally sequential CT scans from the same patient. A specified section from the baseline scan is then compared with the sections of a follow-up scan. Each section in the follow-up scan is successively translated and rotated relative to the baseline section, and NMI is calculated. The section in the follow-up scan that yields the highest NMI with respect to the baseline section is selected as the matching section. The method was applied to a database of 22 pairs of temporally sequential CT scans from mesothelioma patients. Five observers manually selected their choice of the best anatomically matched section for each of three predetermined sections in the 22 baseline scans, and the range of selected sections was recorded. The automated method was applied to the same baseline sections to determine the computer-based anatomically matched sections in the corresponding follow-up scan. The automated process was performed using both original CT sections and sections automatically segmented so that only intrathoracic pixels contributed to NMI calculations. The accuracy of the automated method was quantified on a section-by-section basis by comparison with the range of sections selected by the observers. The automated method without segmentation selected equivalent sections within the observers' range for 54 of the 66 matching tasks (81.8%). An 11% improvement was achieved when thoracic segmentation was performed as a pre-processing step.

Adult↗