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First-pass perfusion of non-small-cell lung cancer (NSCLC) with 64-detector-row CT: A study of technique repeatability and intra- and interobserver variability

Radiologia Medica, ISSN: 0033-8362, Vol: 119, Issue: 1, Page: 4-12
2014
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Article Description

Purpose: This study was done to prospectively assess the repeatability and intra- and interobserver variability of first-pass perfusion with 64-detector-row computed tomography (CT) in non-small-cell lung cancer (NSCLC) with a maximum diameter of up to 8 cm. Materials and methods: Twelve patients with NSCLC underwent 64-detector-row first-pass CT perfusion (CTP) of the whole tumour. Two different techniques were used according to lesion size (cine mode; sequential mode). After 24 h, each study was repeated to assess repeatability. Lesion blood volume (BV), blood flow (BF), mean transit time (MTT) and peak enhancement intensity (PEI) were automatically calculated by two chest radiologists in two different reading sessions. Intra- and interobserver variability was also assessed. Results: The first-pass CTP technique was repeatable and precise with within-subject coefficient of variation (WCV) of 9.3, 16.4, 11.2 and 14.9 %, respectively, for BV, BF, MTT and PEI. High intra- and interobserver agreement was demonstrated for each perfusion parameter, with Cronbach's a coefficients and intraclass correlation coefficients ranging from 0.99 to 1. Precision of measurements was slightly better for intraobserver analysis with WCV ranging between 1.05 and 3.03 %. Conclusions: Non-small-cell lung cancer first-pass perfusion performed with 64-detector-row CT showed good repeatability and high intra- and interobserver agreement for all perfusion parameters and may be considered a reliable and robust tool for assessing tumour vascularisation. © Italian Society of Medical Radiology 2013.

Bibliographic Details

Larici, Anna Rita; Calandriello, Lucio; Amato, Michele; Silvestri, Roberta; del Ciello, Annemilia; Molinari, Francesco; de Waure, Chiara; Vita, Maria Letizia; Carnassale, Giulia; Bonomo, Lorenzo

Springer Science and Business Media LLC

Medicine

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