The effect of sunitinib treatment assessed by intravital microscopy and DCE-MRI in PDAC xenografts

Abstract Objective To evaluate the performance of dynamic contrast-enhanced CT (DCE-CT) in detecting and quantitatively assessing perfusion parameters in patients with arthritis of the hand compared with dynamic contrast-enhanced MRI (DCE-MRI) as a standard of reference. Materials and methods In this IRB-approved randomized prospective single-centre study, 36 consecutive patients with suspected rheumatoid arthritis underwent DCE-CT (320-row, tube voltage 80 kVp, tube current 8.25 mAs) and DCE-MRI (1.5 T) of the hand. Perfusion maps were calculated separately for mean transit time (MTT), time to peak (TTP), relative blood volume (rBV), and relative blood flow (rBF) using four different decomposition techniques. Region of interest (ROI) analysis was performed in metacarpophalangeal joints II–V and in the wrist. Pairs of perfusion parameters in DCE-CT and DCE-MRI were compared using a two-tailed t test for paired samples and interpreted for effect size (Cohen’s d). According to the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS) scoring results, differentiation of synovitis-positive and synovitis-negative joints with both modalities was assessed with the independent t test. Results The two modalities yielded similar perfusion parameters. Identified differences had small effects (d 0.01–0.4). DCE-CT additionally differentiates inflamed and noninflamed joints based on rBF and rBV but tends to underestimate these parameters in severe inflammation. The total dose-length product (DLP) was 48 mGy*cm with an estimated effective dose of 0.038 mSv. Conclusion DCE-CT is a promising imaging technique in arthritis. In patients with a contraindication to MRI or when MRI is not available, DCE-CT is a suitable alternative to detect and assess arthritis.

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Dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) for differential diagnosis in head and neck cancers

European Journal of Radiology ◽

10.1016/j.ejrad.2011.01.089 ◽

2012 ◽

Vol 81 (4) ◽

pp. 784-788 ◽

Cited By ~ 45

Author(s):

Francis Kar-ho Lee ◽

Ann Dorothy King ◽

Brigitte Buig-Yue Ma ◽

David Ka-wai Yeung

Keyword(s):

Magnetic Resonance Imaging ◽

Differential Diagnosis ◽

Magnetic Resonance ◽

Head And Neck ◽

Contrast Enhancement ◽

Head And Neck Cancers ◽

Resonance Imaging ◽

Dynamic Contrast Enhancement ◽

Dce Mri

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Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions

Applied Sciences ◽

10.3390/app11041880 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1880

Author(s):

Roberta Fusco ◽

Adele Piccirillo ◽

Mario Sansone ◽

Vincenza Granata ◽

Paolo Vallone ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Artificial Intelligence ◽

Feature Selection ◽

Resonance Imaging ◽

Dynamic Features ◽

Surgical Specimen ◽

Dce Mri ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced ◽

Malignant Lesions

Purpose: The aim of the study was to estimate the diagnostic accuracy of textural, morphological and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statistical analyses including artificial intelligence approaches. Methods: In total, 85 patients with known breast lesion were enrolled in this retrospective study according to regulations issued by the local Institutional Review Board. All patients underwent DCE-MRI examination. The reference standard was pathology from a surgical specimen for malignant lesions and pathology from a surgical specimen or fine needle aspiration cytology, core or Tru-Cut needle biopsy for benign lesions. In total, 91 samples of 85 patients were analyzed. Furthermore, 48 textural metrics, 15 morphological and 81 dynamic parameters were extracted by manually segmenting regions of interest. Statistical analyses including univariate and multivariate approaches were performed: non-parametric Wilcoxon–Mann–Whitney test; receiver operating characteristic (ROC), linear classifier (LDA), decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM) were utilized. A balancing approach and feature selection methods were used. Results: The univariate analysis showed low accuracy and area under the curve (AUC) for all considered features. Instead, in the multivariate textural analysis, the best performance (accuracy (ACC) = 0.78; AUC = 0.78) was reached with all 48 metrics and an LDA trained with balanced data. The best performance (ACC = 0.75; AUC = 0.80) using morphological features was reached with an SVM trained with 10-fold cross-variation (CV) and balanced data (with adaptive synthetic (ADASYN) function) and a subset of five robust morphological features (circularity, rectangularity, sphericity, gleaning and surface). The best performance (ACC = 0.82; AUC = 0.83) using dynamic features was reached with a trained SVM and balanced data (with ADASYN function). Conclusion: Multivariate analyses using pattern recognition approaches, including all morphological, textural and dynamic features, optimized by adaptive synthetic sampling and feature selection operations obtained the best results and showed the best performance in the discrimination of benign and malignant lesions.

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Blood Oxygenation Level Dependent Magnetic Resonance Imaging (MRI), Dynamic Contrast Enhanced MRI and Diffusion Weighted MRI for Benign and Malignant Breast Cancer Discrimination: A Preliminary Experience

Cancers ◽

10.3390/cancers13102421 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2421

Author(s):

Roberta Fusco ◽

Vincenza Granata ◽

Mauro Mattace Raso ◽

Paolo Vallone ◽

Alessandro Pasquale De Rosa ◽

...

Keyword(s):

Standard Deviation ◽

Univariate Analysis ◽

Mean Value ◽

Blood Oxygenation ◽

Breast Lesions ◽

Dce Mri ◽

Malignant Lesions ◽

The Mean ◽

And Diffusion ◽

Skewness And Kurtosis

Purpose. To combine blood oxygenation level dependent magnetic resonance imaging (BOLD-MRI), dynamic contrast enhanced MRI (DCE-MRI), and diffusion weighted MRI (DW-MRI) in differentiation of benign and malignant breast lesions. Methods. Thirty-seven breast lesions (11 benign and 21 malignant lesions) pathologically proven were included in this retrospective preliminary study. Pharmaco-kinetic parameters including Ktrans, kep, ve, and vp were extracted by DCE-MRI; BOLD parameters were estimated by basal signal S0 and the relaxation rate R2*; and diffusion and perfusion parameters were derived by DW-MRI (pseudo-diffusion coefficient (Dp), perfusion fraction (fp), and tissue diffusivity (Dt)). The correlation coefficient, Wilcoxon-Mann-Whitney U-test, and receiver operating characteristic (ROC) analysis were calculated and area under the ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis and decision tree) with balancing technique and leave one out cross validation approach were considered. Results. R2* and D had a significant negative correlation (−0.57). The mean value, standard deviation, Skewness and Kurtosis values of R2* did not show a statistical significance between benign and malignant lesions (p > 0.05) confirmed by the ‘poor’ diagnostic value of ROC analysis. For DW-MRI derived parameters, the univariate analysis, standard deviation of D, Skewness and Kurtosis values of D* had a significant result to discriminate benign and malignant lesions and the best result at the univariate analysis in the discrimination of benign and malignant lesions was obtained by the Skewness of D* with an AUC of 82.9% (p-value = 0.02). Significant results for the mean value of Ktrans, mean value, standard deviation value and Skewness of kep, mean value, Skewness and Kurtosis of ve were obtained and the best AUC among DCE-MRI extracted parameters was reached by the mean value of kep and was equal to 80.0%. The best diagnostic performance in the discrimination of benign and malignant lesions was obtained at the multivariate analysis considering the DCE-MRI parameters alone with an AUC = 0.91 when the balancing technique was considered. Conclusions. Our results suggest that the combined use of DCE-MRI, DW-MRI and/or BOLD-MRI does not provide a dramatic improvement compared to the use of DCE-MRI features alone, in the classification of breast lesions. However, an interesting result was the negative correlation between R2* and D.

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77286 Intravital microscopy in the study of the tumor vasculature of patients with peritoneal carcinomatosis

Journal of Clinical and Translational Science ◽

10.1017/cts.2021.506 ◽

2021 ◽

pp. 1-2

Author(s):

Emmanuel Gabriel ◽

Minhyung Kim ◽

Daniel Fisher ◽

Catherine Mangum ◽

Kristopher Attwood ◽

...

Keyword(s):

Peritoneal Carcinomatosis ◽

Intravital Microscopy ◽

Tumor Vasculature

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Sunitinib treatment promotes metastasis of drug-resistant renal cell carcinoma via TFE3 signaling pathway

Cell Death and Disease ◽

10.1038/s41419-021-03511-3 ◽

2021 ◽

Vol 12 (2) ◽

Author(s):

Luchao Li ◽

Shuo Zhao ◽

Zhengfang Liu ◽

Nianzhao Zhang ◽

Shuo Pang ◽

...

Keyword(s):

Renal Cell ◽

Cell Cancer ◽

Acquired Resistance ◽

Resistant Cell ◽

Term Treatment ◽

Acquired Drug Resistance ◽

Sunitinib Treatment ◽

Long Term Treatment

AbstractReceptor tyrosine kinase (RTK) inhibitors, such as sunitinib and sorafenib, remain the first-line drugs for the treatment of mRCC. Acquired drug resistance and metastasis are the main causes of treatment failure. However, in the case of metastasis Renal Cell Cancer (mRCC), which showed a good response to sunitinib, we found that long-term treatment with sunitinib could promote lysosome biosynthesis and exocytosis, thereby triggering the metastasis of RCC. By constructing sunitinib-resistant cell lines in vivo, we confirmed that TFE3 plays a key role in the acquired resistance to sunitinib in RCC. Under the stimulation of sunitinib, TFE3 continued to enter the nucleus, promoting the expression of endoplasmic reticulum (ER) protein E-Syt1. E-Syt1 and the lysosomal membrane protein Syt7 form a heterodimer, which induces ER fragmentation, Ca2+ release, and lysosomal exocytosis. Lysosomal exocytosis has two functions: pumping sunitinib out from the cytoplasm, which promotes resistance to sunitinib in RCC, releasing cathepsin B (CTSB) into the extracellular matrix (ECM), which can degrade the ECM to enhance the invasion and metastasis ability of RCC. Our study found that although sunitinib is an effective drug for the treatment of mRCC, once RCC has acquired resistance to sunitinib, sunitinib treatment will promote metastasis.

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