Published in:
01-01-2012 | Chest
Small peripheral lung carcinomas with five-year post-surgical follow-up: assessment by semi-automated volumetric measurement of tumour size, CT value and growth rate on TSCT
Authors:
Shusuke Sone, Takaomi Hanaoka, Hiroyuki Ogata, Fumiyoshi Takayama, Tomofumi Watanabe, Masayuki Haniuda, Kazuhiko Kaneko, Ryoichi Kondo, Kazuo Yoshida, Takayuki Honda
Published in:
European Radiology
|
Issue 1/2012
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Abstract
Objectives
To retrospectively assess the utility of semi-automated measurements by stratification of CT values of tumour size, CT value and doubling time (DT) using thin-section computed tomography (CT) images. The post-surgical outcomes of favourable and problematic tumours (more advanced p stage than IA, post-surgical recurrence or mortality from lung cancer) were compared using the measured values. The computed DTs were compared with manually measured values.
Methods
The study subjects comprised 85 patients (aged 33–80 years, 48 women, 37 men), followed-up for more than 5 years postoperatively, with 89 lung lesions, including 17 atypical adenomatous hyperplasias and 72 lung cancers. DTs were determined in 45 lesions.
Results
For problematic lesions, whole tumour diameter and density were >18 mm and >−400 HU, respectively. The respective values for the tumour core (with CT values of −350 to 150 HU) were >15 mm and >−70 HU. Analysis of tumour core DTs showed interval tumour progression even if little progress was seen by standard tumour volume DT (TVDT).
Conclusion
Software-based volumetric measurements by stratification of CT values provide valuable information on tumour core and help estimate tumour aggressiveness and interval tumour progression better than standard manually measured 2D-VDTs
Key Points:
• Quantitative analysis of thin-section CT according to treatment outcome.
• Semi-automated CT analysis provides fast and reliable assessment of tumour aggressiveness.
• Tumour core doubling time provides further information about tumor aggressiveness.
• More appropriate management of patients can be made with 3D-quantitative CT data.