Reproducibility of Dynamic Contrast-Enhanced MRI in Renal Cell Carcinoma: A Prospective Analysis on Intra- and Interobserver and Scan-Rescan Performance of Pharmacokinetic Parameters

Reproducibility of dynamic contrast enhanced MRI derived transfer coefficient Ktrans in lung cancer

Dynamic contrast enhanced MRI (DCE-MRI) is a useful method to monitor therapy assessment in malignancies but must be reliable and comparable for successful clinical use. The aim of this study was to evaluate the inter- and intrarater reproducibility of DCE-MRI in lung cancer. At this IRB approved single centre study 40 patients with lung cancer underwent up to 5 sequential DCE-MRI examinations. DCE-MRI were performed using a 3.0T system. The volume transfer constant Ktrans was assessed by three readers using the two-compartment Tofts model. Inter- and intrarater reliability and agreement was calculated by wCV, ICC and their 95% confident intervals. DCE-MRI allowed a quantitative measurement of Ktrans in 107 tumors where 91 were primary carcinomas or intrapulmonary metastases and 16 were extrapulmonary metastases. Ktrans showed moderate to good interrater reliability in overall measurements (ICC 0.716-0.841; wCV 30.3-38.4%). Ktrans in pulmonary lesions ≥ 3 cm showed a good to excellent reliability (ICC 0.773-0.907; wCV 23.0-29.4%) compared to pulmonary lesions < 3 cm showing a moderate to good reliability (ICC 0.710-0.889; wCV 31.6-48.7%). Ktrans in intrapulmonary lesions showed a good reliability (ICC 0.761-0.873; wCV 28.9-37.5%) compared to extrapulmonary lesions with a poor to moderate reliability (ICC 0.018-0.680; wCV 28.1-51.8%). The overall intrarater agreement was moderate to good (ICC 0.607-0.795; wCV 24.6-30.4%). With Ktrans, DCE MRI offers a reliable quantitative biomarker for early non-invasive therapy assessment in lung cancer patients, but with a coefficient of variation of up to 48.7% in smaller lung lesions.

Prediction of low-risk breast cancer using quantitative DCE-MRI and its pathological basis

Purpose

This study aimed to evaluate the difference of mass in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) characteristics between low-risk and non-low-risk breast cancers and to explore the possible pathological basis.

Materials and Methods

Approval from the institutional review board and informed consent were acquired for this study. The MR images of 104 patients with pathologically proven breast cancer (104 lesions) were prospectively analyzed. All of included patients were Chinese woman. The DCE-MRI morphologic findings, apparent diffusion coefficient (ADC) values, quantitative DCE-MRI parameters, and pathological biomarkers between the two subtypes of breast cancer were compared. The quantitative DCE-MRI parameters and ADC values were added to the morphologic features in multivariate models to evaluate diagnostic performance in predicting low-risk breast cancer. The values were further subjected to the receiver operating characteristic (ROC) curve analysis.

Results

Low-risk tumors showed significantly lower K^trans and K_epvalues (t = 2.065, P = 0.043 and t = 3.548, P = 0.001, respectively) and higher ADC value (t = 4.713, P = 0.000) than non-low-risk breast cancers. Our results revealed no significant differences in clinic data and conventional imaging findings between the two breast cancer subtypes. Adding the quantitative DCE-MRI parameters and ADC values to conventional MRI improved the diagnostic performance of MRI: The area under the ROC improved from 0.63 to 0.91. Low-risk breast cancers showed significantly lower matrix metalloproteinase (MMP)-2 expression (P = 0.000), lower MMP-9 expression (P = 0.001), and lower microvessel density (MVD) values (P = 0.008) compared with non-low-risk breast cancers. K^trans and K_ep values were positively correlated with pathological biomarkers. The ADC value showed a significant inverse correlation with pathological biomarkers.

Conclusions

The prediction parameter using K^trans, K_ep, and ADC obtained on DCE-MRI and diffusion-weighted imaging could facilitate the identification of low-risk breast cancers. Decreased biological factors, including MVD, vascular endothelial growth factor, MMP-2, and MMP-9, may explain the possible pathological basis.

Dynamic Contrast-enhanced MRI in Renal Tumors: Common Subtype Differentiation using Pharmacokinetics

Preoperative renal tumor subtype differentiation is important for radiology and urology in clinical practice. Pharmacokinetic data (K^trans & V_e, etc.) derived from dynamic contrast-enhanced MRI (DCE-MRI) have been used to investigate tumor vessel permeability. In this prospective study on DCE-MRI pharmacokinetic studies, we enrolled patients with five common renal tumor subtypes: clear cell renal cell carcinoma (ccRCC; n = 65), papillary renal cell carcinoma (pRCC; n = 12), chromophobic renal cell carcinoma (cRCC; n = 9), uroepithelial carcinoma (UEC; n = 14), and fat-poor angiomyolipoma (fpAML; n = 10). The results show that K^trans of ccRCC, pRCC, cRCC, UEC and fpAML (0.459 ± 0.190 min⁻¹, 0.206 ± 0.127 min⁻¹, 0.311 ± 0.111 min⁻¹, 0.235 ± 0.116 min⁻¹, 0.511 ± 0.159 min⁻¹, respectively) were different, but V_e was not. K^trans could distinguish ccRCC from non-ccRCC (pRCC & cRCC) with a sensitivity of 76.9% and a specificity of 71.4%, respectively, as well as to differentiate fpAML from non-ccRCC with a sensitivity of 100% and a specificity of 76.2%, respectively. Our findings suggest that DCE-MRI pharmacokinetics are promising for differential diagnosis of renal tumors, especially for RCC subtype characterization and differentiation between fpAML and non-ccRCC, which may facilitate the treatment of renal tumors.

Radiogenomics Monitoring in Breast Cancer Identifies Metabolism and Immune Checkpoints as Early Actionable Mechanisms of Resistance to Anti-angiogenic Treatment

Anti-VEGF antibody bevacizumab has prolonged progression-free survival in several cancer types, however acquired resistance is common. Adaption has been observed pre-clinically, but no human study has shown timing and genes involved, enabling formulation of new clinical paradigms. In a window-of-opportunity study in 35 ductal breast cancer patients for 2weeks prior to neoadjuvant chemotherapy, we monitored bevacizumab response by Dynamic Contrast-Enhanced Magnetic Resonance [DCE-MRI], transcriptomic and pathology. Initial treatment response showed significant overall decrease in DCE-MRI median K(trans), angiogenic factors such ESM1 and FLT1, and proliferation. However, it also revealed great heterogeneity, spanning from downregulation of blood vessel density and central necrosis to continued growth with new vasculature. Crucially, significantly upregulated pathways leading to resistance included glycolysis and pH adaptation, PI3K-Akt and immune checkpoint signaling, for which inhibitors exist, making a strong case to investigate such combinations. These findings support that anti-angiogenesis trials should incorporate initial enrichment of patients with high K(trans), and a range of targeted therapeutic options to meet potential early resistance pathways. Multi-arm adaptive trials are ongoing using molecular markers for targeted agents, but our results suggest this needs to be further modified by much earlier adaptation when using drugs affecting the tumor microenvironment.

Dynamic Contrast-enhanced MR Imaging in Renal Cell Carcinoma: Reproducibility of Histogram Analysis on Pharmacokinetic Parameters

Pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been increasingly used to evaluate the permeability of tumor vessel. Histogram metrics are a recognized promising method of quantitative MR imaging that has been recently introduced in analysis of DCE-MRI pharmacokinetic parameters in oncology due to tumor heterogeneity. In this study, 21 patients with renal cell carcinoma (RCC) underwent paired DCE-MRI studies on a 3.0 T MR system. Extended Tofts model and population-based arterial input function were used to calculate kinetic parameters of RCC tumors. Mean value and histogram metrics (Mode, Skewness and Kurtosis) of each pharmacokinetic parameter were generated automatically using ImageJ software. Intra- and inter-observer reproducibility and scan–rescan reproducibility were evaluated using intra-class correlation coefficients (ICCs) and coefficient of variation (CoV). Our results demonstrated that the histogram method (Mode, Skewness and Kurtosis) was not superior to the conventional Mean value method in reproducibility evaluation on DCE-MRI pharmacokinetic parameters (K^trans & V_e) in renal cell carcinoma, especially for Skewness and Kurtosis which showed lower intra-, inter-observer and scan-rescan reproducibility than Mean value. Our findings suggest that additional studies are necessary before wide incorporation of histogram metrics in quantitative analysis of DCE-MRI pharmacokinetic parameters.