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Tumor perfusion evaluation using dynamic contrast-enhanced ultrasound after electrochemotherapy and IL-12 plasmid electrotransfer in murine melanoma.

ABSTRACT: Electrochemotherapy with bleomycin (ECT BLM) is an effective antitumor treatment already used in clinical oncology. However, ECT alone is still considered a local antitumor therapy because it cannot induce systemic immunity. When combined with adjuvant gene electrotransfer of plasmid DNA encoding IL-12 (GET pIL-12), the combined therapy leads to a systemic effect on untreated tumors and distant metastases. Although the antitumor efficacy of both therapies alone or in combination has been demonstrated at both preclinical and clinical levels, data on the predictors of efficacy of the treatments are still lacking. Herein, we evaluated the results of dynamic contrast-enhanced ultrasound (DCE-US) as a predictive factor for ECT BLM and GET pIL-12 in murine melanoma. Melanoma B16F10 tumors grown in female C57Bl/6NCrl mice were treated with GET pIL-12 and ECT BLM. Immediately after therapy, 6 h and 1, 3, 7 and 10 days later, tumors were examined by DCE-US. Statistical analysis was performed to inspect the correlation between tumor doubling time (DT) and DCE-US measurements using semilinear regression models and Bland-Altman plots. Therapeutic groups in which DCE-US showed reduced tumor perfusion had longer tumor DTs. It was confirmed that the DCE-US parameter peak enhancement (PE), reflecting relative blood volume, had predictive value for the outcome of therapy: larger PE correlated with shorter DT. In addition, perfusion heterogeneity was also associated with outcome: tumors that had more heterogeneous perfusion had faster growth, i.e., shorter DTs. This study demonstrates that DCE-US can be used as a method to predict the efficacy of electroporation-based treatment.

SUBMITTER: Brloznik M

PROVIDER: S-EPMC8242003 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Key Points Contrast-enhanced ultrasound is a noninvasive imaging modality that may noninvasively assess kidney cortical microvascular perfusion. Contrast-enhanced ultrasound–derived kidney cortical microvascular perfusion is associated with eGFR. Visual Abstract Background Individuals with chronic kidney disease (CKD) have decreased kidney cortical microvascular perfusion, which may lead to worsening kidney function over time, but methods to quantify kidney cortical microvascular perfusion are not feasible to incorporate into clinical practice. Contrast-enhanced ultrasound (CEUS) may quantify kidney cortical microvascular perfusion, which requires further investigation in individuals across the spectrum of kidney function. Methods We performed CEUS on a native kidney of 83 individuals across the spectrum of kidney function and calculated quantitative CEUS-derived kidney cortical microvascular perfusion biomarkers. Participants had a continuous infusion of the microbubble contrast agent (Definity) with a flash-replenishment sequence during their CEUS scan. Lower values of the microbubble velocity (β) and perfusion index (β×A) may represent lower kidney cortical microvascular perfusion. Multivariable linear regression models tested the associations of the microbubble velocity (β) and perfusion index (β×A) with estimated glomerular filtration rate (eGFR). Results Thirty-eight individuals with CKD (mean age±SD 65.2±12.6 years, median [IQR] eGFR 31.5 [18.9–41.5] ml/min per 1.73 m2), 37 individuals with end stage kidney disease (ESKD; age 54.8±12.3 years), and eight healthy volunteers (age 44.1±15.0 years, eGFR 117 [106–120] ml/min per 1.73 m2) underwent CEUS without side effects. Individuals with ESKD had the lowest microbubble velocity (β) and perfusion index (β×A) compared with individuals with CKD and healthy volunteers. The microbubble velocity (β) and perfusion index (β×A) had moderate positive correlations with eGFR (β: rs=0.44, P<0.001; β×A: rs=0.50, P<0.001). After multivariable adjustment, microbubble velocity (β) and perfusion index (β×A) remained significantly associated with eGFR (change in natural log transformed eGFR per 1 unit increase in natural log transformed biomarker: β, 0.38 [95%, CI 0.17 to 0.59]; β×A, 0.79 [95% CI, 0.45 to 1.13]). Conclusions CEUS-derived kidney cortical microvascular perfusion biomarkers are associated with eGFR. Future studies are needed to determine if CEUS-derived kidney cortical microvascular perfusion biomarkers have prognostic value.

Project description:PurposeContrast-enhanced ultrasound imaging has expanded the diagnostic potential of ultrasound by enabling real-time imaging and quantification of tissue perfusion. Several perfusion models and curve fitting methods have been developed to quantify the temporal behavior of tracer signal and standardize perfusion quantification. While the least-squares approach has traditionally been applied for curve fitting, it can be inadequate for noisy and complex data. Moreover, previous research suggests that certain perfusion models may be more relevant depending on the organ or tissue imaged. We propose a multi-model framework to select the most appropriate perfusion model and curve fitting method for each diagnostic application.MethodsOur multi-model approach uses a system identification method, which estimates perfusion parameters from the model with the best fit to a given time-intensity curve. We compared current perfusion quantification methods that use a single perfusion model and curve fitting method and our proposed multi-model framework on bolus 3D dynamic contrast-enhanced ultrasound (DCE-US) in vivo images obtained in mice implanted with a colon cancer, as well as on simulation data. The quality of fit in estimating perfusion parameters was evaluated using the Spearman correlation coefficient, the coefficient of determination (R2 ), and the normalized root-mean-square error (NRMSE) to ensure that the multi-model framework finds the best perfusion model and curve fitting algorithm.ResultsOur multi-model framework outperforms conventional single perfusion model approaches with least-squares optimization, providing more robust perfusion parameter estimation. R2 and NRMSE are 0.98 and 0.18, respectively, for our proposed method. By comparison, the performance of the traditional approach is much more dependent upon the selection of the appropriate model. The R2 and NRMSE are 0.91 and 0.31, respectively.ConclusionsThe proposed multi-model framework for perfusion modeling outperforms the current approach of single perfusion modeling using least-squares optimization and more robustly estimates perfusion parameters when using empiric data labeled by an expert as the gold standard. Our technique is minimally sensitive to issues affecting the accuracy of perfusion parameter estimation, including rise time, noise, region of interest size, and frame rate. This framework could be of key utility in modeling different perfusion systems in different tissues and organs.

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Tumor perfusion evaluation using dynamic contrast-enhanced ultrasound after electrochemotherapy and IL-12 plasmid electrotransfer in murine melanoma.

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