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Axial CT scan of a 68-year-old male with lung squamous cell carcinomas at the left upper lobe before (a) and after (b) three months of chemotherapy. At the maximum transverse dimension, about 12.0% increase of the tumor size was observed, and the patient was categorized as a nonresponder. The DCE-MRI map (c) and TIC map (d) are shown. Images of color MR PEI mapping before (e) and after (f) one week of the first course of chemotherapy showed not significant increase in tumor perfusion (the value was 914 vs. 1078).
Source publication
Objective:
To evaluate the early chemotherapy response in patients with lung cancer using semiquantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI).
Methods:
Twenty-two patients with lung cancer treated with chemotherapy were subjected to DCE-MRI at two time points: before starting treatment and after one week...
Citations
... The semiquantitative parameters C 1 and C 2 , defined in Figure 4 can be used to determine semi-quantitative evaluations that help to characterize the pathology or changes during a therapy follow-up. [33][34][35][36] An example of this approach is shown in the right-hand side of Figure 5, where a distribution is obtained for the C 2 /C 1 ratio. ...
Tumor vascularity detection and quantification are of high relevance in the assessment of cancer lesions not only for disease diagnostics but for therapy considerations and monitoring. The present work addressed the quantification of pharmacokinetic parameters derived from the two-compartment Brix model by analyzing and processing Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI) of prostate cancer lesions. The 3D image sets were acquired at regular time intervals, covering all the phases implied in contrast injection (wash-in and wash-out phases), and the standardized image intensity is determined for each voxel, conforming to a 4D data set. Previous voxel classification was carried out by the three-time-point method proposed by Degani et al. (1997) and Furman-Haran et al. (1998) to identify regions of interest. Relevant pharmacokinetic parameters, such as kel, the vascular elimination rate, and kep, the extravascular transfer rate, are extracted by a novel interpolation method applicable to compartment models. Parameter distribution maps were obtained for either pathological or unaffected glandular regions indicating that a three-compartment model, including fast and slow exchange compartments, provides a more suitable description of the contrast kinetics. Results can be applied to prostate cancer diagnostic evaluation and therapy follow-up.
... DCE-MRI has been established as a useful, noninvasive, and non-ionizing method for quantitative evaluation and characterization of tumor microvasculature and permeability parameters [60][61][62][63][64][65][66][67][68][69][70] . DCE-MRI experiment is performed by applying a fast T1-weighted MR pulse sequence to repeatedly image the volume of interest during the intravenous injection of a contrast agent (CA). ...
Purpose: Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI.
Methods: Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC) and feature engineering analyses were performed on the feature spaces of the K-SOMs to quantify the distinction power of different radiomics features compared to raw-DCE-MRI for classification of different nested models.
Results: Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875%±12.922%, p<0.001.
Conclusions: This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs’ feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
