DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

Abstract

Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. A DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PCdefined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.

Accelerated Brain DCE-MRI Using Iterative Reconstruction With Total Generalized Variation Penalty for Quantitative Pharmacokinetic Analysis: A Feasibility Study

“…78 To maintain AIF's enhancement feature, some studies used only a certain number or percentage of voxels based on the peak enhancement ratio in AIF generation. 60,78 However, such a method is sensitive to the absolute CA concentration value, and the data reproducibility may be reduced when intensity-based image reconstructed methods were used. In this work, we selected a group of voxels based on the timeto-peak distribution and average the resultant time course to create AIF.…”

“…Then a histogram of each arterial structure voxel's time-topeak (T max ) was generated. 60 Next, the T max of the C p (t) was determined by the peak position of the generated histogram, and the C p (t) was calculated by averaging the CA concentration of the voxels that constituted the histogram's peak.…”

Temporal Feature Extraction from DCE-MRI to Identify Poorly Perfused Subvolumes of Tumors Related to Outcomes of Radiation Therapy in Head and Neck Cancer

“…The curves need to be preprocessed to remove variations in intensity magnitude and onset time of contrast enhancement because of individual hemodynamics and various acquisition protocols. A signal intensity change at time t , Δ S ( t ), after contrast injection compared with a baseline intensity, ( S 0 ), is computed, and then normalized to the peak of the arterial input function ( AIF max ) of the patient as shown in the study by Farjam et al (11) and using the following equation: $$\begin{array}{l}\mathrm{normal\Delta }\mathrm{normalS}false(\mathrm{normalt}false)=\frac{\mathrm{normalS}false(\mathrm{normalt}false)-{\mathrm{S}}_0}{{\mathrm{normalS}}_0}\\ \mathrm{normal\Delta }{\mathrm{S}}_{\mathrm{N}}false(\mathrm{normalt}false)=\mathrm{normal\Delta }\mathrm{normalS}false(\mathrm{normalt}false)\frac{1}{{\text{AIF}}_{max}}\end{array}$$ where S ( t ) is the signal intensity of a DCE curve at time t , Δ S N ( t ) is normalized S ( t ), and AIF max is the peak enhancement in Δ S ( t ) of the AIF. The AIF was determined by thresholding the intensity changes in a region of interest in a large artery, for example, carotid artery in this study.…”

Quantitative Imaging in Radiation Oncology: An Emerging Science and Clinical Service

“…As an alternative to more formal pharmacokinetic modelling, analysis of the shape of concentration-time signal curves may present a simplification for DCE-MR applications (Woolf, Padhani et al 2014(Farjam, Tsien et al 2014)(Stoyanova, Huang et al 2012) For breast cancer patients treated with neoadjuvant chemotherapy prior to surgery and RT, changes in signal curve shape after chemotherapy correlated with pharmacokinetic model output, and with clinical and pathological response (Woolf, Padhani et al 2014). This has been further formalized using a principal component analysis approach, which demonstrated that much of the response-related information in DCE-MR curves of brain metastases is contained within the first projection coefficient, corresponding to the area-under-the-curve (Farjam, Tsien et al 2014).…”

“…)(Farjam, Tsien et al 2014)(Stoyanova, Huang et al 2012) For breast cancer patients treated with neoadjuvant chemotherapy prior to surgery and RT, changes in signal curve shape after chemotherapy correlated with pharmacokinetic model output, and with clinical and pathological response (Woolf, Padhani et al 2014). This has been further formalized using a principal component analysis approach, which demonstrated that much of the response-related information in DCE-MR curves of brain metastases is contained within the first projection coefficient, corresponding to the area-under-the-curve (Farjam, Tsien et al 2014). More recently, principal component analysis applied to DCE-MR data in a preclinical tumor model found that the second projection coefficient corresponded to 18 FMISO-PET time activity curves and ex vivo pimonidazole stained tissue sections (Stoyanova, Huang et al 2012).…”