Abstract
Pulmonary perfusion with dynamic contrast‐enhanced (DCE‐) MRI is typically assessed using a single‐input tracer kinetic model. Preliminary studies based on perfusion CT are indicating that dual‐input perfusion modeling of lung tumors may be clinically valuable as lung tumors have a dual blood supply from the pulmonary and aortic system. This study aimed to investigate the feasibility of fitting dual‐input tracer kinetic models to DCE‐MRI datasets of thoracic malignancies, including malignant pleural mesothelioma (MPM) and nonsmall cell lung cancer (NSCLC), by comparing them to single‐input (pulmonary or systemic arterial input) tracer kinetic models for the voxel‐level analysis within the tumor with respect to goodness‐of‐fit statistics. Fifteen patients (five MPM, ten NSCLC) underwent DCE‐MRI prior to radiotherapy. DCE‐MRI data were analyzed using five different single‐ or dual‐input tracer kinetic models: Tofts‐Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH) and distributed parameter (DP) models. The pulmonary blood flow (BF), blood volume (BV), mean transit time (MTT), permeability‐surface area product (PS), fractional interstitial volume (v I), and volume transfer constant (K Trans) were calculated for both single‐ and dual‐input models. The pulmonary arterial flow fraction (γ), pulmonary arterial blood flow (BFPA) and systemic arterial blood flow (BFA) were additionally calculated for only dual‐input models. The competing models were ranked and their Akaike weights were calculated for each voxel according to corrected Akaike information criterion (cAIC). The optimal model was chosen based on the lowest cAIC value. In both types of tumors, all five dual‐input models yielded lower cAIC values than their corresponding single‐input models. The 2CX model was the best‐fitted model and most optimal in describing tracer kinetic behavior to assess microvascular properties in both MPM and NSCLC. The dual‐input 2CX‐model‐derived BFA was the most significant parameter in differentiating adenocarcinoma from squamous cell carcinoma histology for NSCLC patients.
Highlights
Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is a well‐established imaging technique for the estimation of tissue microvascular function in clinical settings.[1,2,3,4,5] To obtain dynamic contrast‐enhanced (DCE‐)MRI data, a contrast agent (CA) is injected into the patient and multiple MR images are acquired at the same spatial location over a time period of approximately five minutes
Malignant pleural mesothelioma occurs in any part of the visceral pleura that covers the lungs and the parietal pleura that lines the inner surfaces of the chest wall of the pleural cavity; about 80% occurs in the visceral pleura and 20% occurs in the parietal pleura.[10]
Two examples each for malignant pleural mesothelioma (MPM) and nonsmall cell lung cancer (NSCLC) cases are shown in Figs. 2– 4, with voxel‐level fittings and parameter maps generated from the five models with the pulmonary [Figs. 2(a) and 2(b)], systemic [Figs. 3(a) and 3(b)] and dual arterial input function (AIF) [Figure 4(a) and 4(b)], respectively
Summary
Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is a well‐established imaging technique for the estimation of tissue microvascular function in clinical settings.[1,2,3,4,5] To obtain DCE‐MRI data, a contrast agent (CA) is injected into the patient and multiple MR images are acquired at the same spatial location over a time period of approximately five minutes. The visceral pleura derives its arterial blood supply from bronchial arterial circulation and from the pulmonary arteries which arise beneath the pleura from the pulmonary circulation.[11,13] Lung tumors may have a dual blood supply, due to the pulmonary and aortic systems, with a circulatory pattern that is specific to their histologic types.[14] Previous studies have performed a single‐input pulmonary or aortic perfusion computed tomography (PCT),[15,16,17,18,19] DCE‐MRI assessment[20,21] or, in contrast to the above studies, which measured the arterial input function (AIF), a few DCE‐MRI studies used a reduced model, e.g., Brix model in MPM, where the model assumed a predefined AIF.[22,23] Recent studies using PCT have reported that a dual‐input maximum (or steepest) slope analysis is valuable in NSCLC.[24,25,26,27] The dual‐input implementation identified the proportion of the pulmonary (or systemic) arterial perfusion to the total perfusion in the lung tissue, and indicated that perfusion index derived from dual‐input maximum slope PCT analysis has potential to be an important biomarker for thoracic malignancies
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