Pulmonary Nodules and Lung Cancer

Dual-energy computed tomography as a lower radiation dose alternative to perfusion computed tomography in tumor viability assessment

To present the utility of dual-energy computed tomography (DECT) in the assessment of angiogenesis of focal lesions as an example of a solitary pulmonary nodule (SPN). This prospective study comprised 28 patients with SPN who underwent DECT and perfusion computed tomography (CTP), according to a proprietary protocol. Two radiologists independently analyzed four perfusion parameters, namely blood flow (BF), blood volume (BV), the time to maximum of the tissue residue function (Tmax), permeability surface area product (PS) from CTP, in addition to the iodine concentration (IC) and normalized iodine concentration (NIC) of the SPN from DECT. We used the Pearson R correlation and interclass correlation coefficients (ICC_s). Statistical significance was assumed at p < 0.05. The mean tumor size was 23.5 ± 6.5 mm. We observed good correlations between IC and BF (r = 0.78, p < 0.000) and NIC and BF (r = 0.71, p < 0.000) as well as between IC and BV (r = 0.73, p < 0.000) and NIC and BV (r = 0.73, p < 0.000) and poor correlation between IC and PS (r = 0.38, p = 0.044).There was no correlation between NIC and PS (r = 0.35, p = 0.064), IC content and Tmax (r = - 0.28, p = 0.147) and NIC and Tmax (r = - 0.21, p = 0.266). Inter-reader agreement on quantitative parameters at CTP (ICC_PS = 0.97, ICC_Tmax = 0.96, ICC_BV = 0.98, and ICC_BF = 0.99) and DECT (ICC_IC = 0.98) were excellent. The radiation dose was significantly lower in DECT than that in CTP (4.84 mSv vs. 9.07 mSv, respectively). DECT is useful for the functional assessment of oncological lesions with less exposure to radiation compared to perfusion computed tomography.

Advances and perspectives in lung cancer imaging using multidetector row computed tomography

The introduction of multidetector row computed tomography (CT) into clinical practice has revolutionized many aspects of the clinical work-up. Lung cancer imaging has benefited from various breakthroughs in computing technology, with advances in the field of lung cancer detection, tissue characterization, lung cancer staging and response to therapy. Our paper discusses the problems of radiation, image visualization and CT examination comparison. It also reviews the most significant advances in lung cancer imaging and highlights the emerging clinical applications that use state of the art CT technology in the field of lung cancer diagnosis and follow-up.