A tri-exponential model for intravoxel incoherent motion analysis of the human kidney: In silico and during pharmacological renal perfusion modulation

Cisplatin is widely used to treat various types of cancers, but it is often limited by nephrotoxicity. Here, we employed an integrated in silico and in vivo approach to identify potential treatments for cisplatin-induced nephrotoxicity (CIN). Using publicly available mouse kidney and human kidney organoid transcriptome datasets, we first identified a 208-gene expression signature for CIN and then used the bioinformatics database Cmap and Lincs Unified Environment (CLUE) to identify drugs expected to counter the expression signature for CIN. We also searched the adverse event database, Food and Drug Administration. Adverse Event Reporting System (FAERS), to identify drugs that reduce the reporting odds ratio of developing cisplatin-induced acute kidney injury. Palonosetron, a serotonin type 3 receptor (5-hydroxytryptamine receptor 3 (5-HT3R)) antagonist, was identified by both CLUE and FAERS analyses. Notably, clinical data from 103 patients treated with cisplatin for head and neck cancer revealed that palonosetron was superior to ramosetron in suppressing cisplatin-induced increases in serum creatinine and blood urea nitrogen levels. Moreover, palonosetron significantly increased the survival rate of zebrafish exposed to cisplatin but not to other 5-HT3R antagonists. These results not only suggest that palonosetron can suppress CIN but also support the use of in silico and in vivo approaches in drug repositioning studies.

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Error Reduction in Parameter Estimation from the Segmented Intravoxel Incoherent Motion Bi-exponential Model

Journal of Clinical Research in Radiology ◽

10.33309/2639-913x.010101 ◽

2018 ◽

Vol 1 (1) ◽

Author(s):

Luis Agulles-Pedros ◽

John L. Humm ◽

Evis Sala ◽

Hebert A. Vargas ◽

Yousef Mazaheri

Keyword(s):

Parameter Estimation ◽

Exponential Model ◽

Error Reduction ◽

Intravoxel Incoherent Motion

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The diagnostic function of intravoxel incoherent motion for distinguishing between pilocytic astrocytoma and ependymoma

PLoS ONE ◽

10.1371/journal.pone.0247899 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0247899

Author(s):

Nguyen Minh Duc

Keyword(s):

Roc Curve ◽

Pilocytic Astrocytoma ◽

Diagnostic Performance ◽

Exponential Model ◽

Curve Analysis ◽

Intravoxel Incoherent Motion ◽

Contrast Imaging ◽

Roc Curve Analysis ◽

D Values ◽

D Value

Introduction Intravoxel incoherent motion (IVIM) imaging concurrently measures diffusion and perfusion parameters and has potential applications for brain tumor classification. However, the effectiveness of IVIM for the differentiation between pilocytic astrocytoma and ependymoma has not been verified. The aim of this study was to determine the potential diagnostic role of IVIM for the distinction between ependymoma and pilocytic astrocytoma. Methods Between February 2019 and October 2020, 22 children (15 males and 7 females; median age 4 years) with either ependymoma or pilocytic astrocytoma were recruited for this prospective study. IVIM parameters were fitted using 7 b-values (0–1,500 s/mm2), to develop a bi-exponential model. The diffusivity (D), perfusion fraction (f), and pseudo diffusivity (D*) were measured in both tumors and the adjacent normal-appearing parenchyma. These IVIM parameters were compared using the Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis was employed to assess diagnostic performance. Results The median D values for ependymoma and pilocytic astrocytoma were 0.87 and 1.25 × 10−3 mm2/s (p < 0.05), respectively, whereas the f values were 0.11% and 0.15% (p < 0.05). The ratios of the median D values for ependymoma and pilocytic astrocytoma relative to the median D values for the adjacent, normal-appearing parenchyma were 1.45 and 2.10 (p < 0.05), respectively. ROC curve analysis found that the D value had the best diagnostic performance for the differentiation between pilocytic astrocytoma and ependymoma, with an area under the ROC curve of 1. Conclusion IVIM is a beneficial, effective, non-invasive, and endogenous-contrast imaging technique. The D value derived from IVIM was the most essential factor for differentiating ependymoma from pilocytic astrocytoma.

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Intravoxel Incoherent Motion Model in Differentiating the Pathological Grades of Esophageal Carcinoma: Comparison of Mono-Exponential and Bi-Exponential Fit Model

Frontiers in Oncology ◽

10.3389/fonc.2021.625891 ◽

2021 ◽

Vol 11 ◽

Author(s):

Nian Liu ◽

Xiongxiong Yang ◽

Lixing Lei ◽

Ke Pan ◽

Qianqian Liu ◽

...

Keyword(s):

Sensitivity And Specificity ◽

Characteristic Curve ◽

Area Under The Curve ◽

Exponential Model ◽

Intravoxel Incoherent Motion ◽

Imaging Method ◽

Diagnostic Efficiency ◽

Pathological Grade ◽

Significant Difference ◽

Exponential Fit

PurposeTo compare the diagnostic efficiency of the mono-exponential model and bi-exponential model deriving from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating the pathological grade of esophageal squamous cell carcinoma (ESCC).MethodsFifty-four patients with ESCC were divided into three groups of poorly-differentiated (PD), moderately-differentiated (MD), and well-differentiated (WD), and underwent the IVIM-DWI scan. Mono-exponential (Dmono, D*mono, and fmono) and bi-exponential fit parameters (Dbi, D*bi, and fbi) were calculated using the IVIM data for the tumors. Mean parameter values of three groups were compared using a one-way ANOVA followed by post hoc tests. The receiver operating characteristic curve was drawn for differentiating pathological grade of ESCC. Correlations between pathological grades and IVIM parameters were analyzed.ResultsThere were significant differences in fmono and fbi among the PD, MD and WD ESCC groups (all p<0.05). The fmono were 0.32 ± 0.07, 0.23 ± 0.08, and 0.16 ± 0.05, respectively, and the fbi were 0.35 ± 0.08, 0.26 ± 0.10, and 0.18 ± 0.07, respectively. There was a significant difference in the Dmono between the WD and the PD group (1.48 ± 0.51* 10-3 mm2/s versus 1.05 ± 0.44*10-3 mm2/s, p<0.05), but there was no significant difference between the WD and MD groups, MD and PD groups (all p>0.05). The D*mono, Dbi, and D*bi showed no significant difference among the three groups (all p>0.05). The area under the curve (AUC) of Dmono, fmono and fbi in differentiating WD from PD ESCC were 0.764, 0.961 and 0.932, and the sensitivity and specificity were 92.9% and 60%, 92.9% and 90%, 85.7% and 100%, respectively. The AUC of fmono and fbi in differentiating MD from PD ESCC were 0.839 and 0.757, and the sensitivity and specificity were 78.6% and 80%, 85.7% and 70%, respectively. The AUC of fmono and fbi in differentiating MD from WD ESCC were 0.746 and 0.740, and the sensitivity and specificity were 65% and 85%, 80% and 60%, respectively. The pathologically differentiated grade was correlated with all IVIM parameters (all p<0.05).ConclusionsThe mono-exponential IVIM model is superior to the bi-exponential IVIM model in differentiating pathological grades of ESCC, which may be a promising imaging method to predict pathological grades of ESCC.

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Intravoxel Incoherent Motion for Differentiating Spinal Metastasis: A Pilot Study

10.21203/rs.3.rs-495875/v1 ◽

2021 ◽

Author(s):

Yuan Li ◽

Xiaoying Xing ◽

Enlong Zhang ◽

Siyuan Qin ◽

Huishu Yuan ◽

...

Keyword(s):

Logistic Regression ◽

Diffusion Coefficient ◽

Regression Model ◽

Diagnostic Performance ◽

Logistic Regression Model ◽

Spinal Metastasis ◽

Exponential Model ◽

Intravoxel Incoherent Motion ◽

Model Parameters ◽

Tuberculous Spondylitis

Abstract Background: To investigate the value of an intravoxel incoherent motion (IVIM) MRI for discriminating spinal metastasis from tuberculous spondylitis.Methods: This study included 50 patients with spinal metastasis (32 lung cancer, 7 breast cancer, 11 renal cancer), and 20 tuberculous spondylitis. All patients underwent IVIM MRI at 3.0T before treatment. The IVIM parameters including single-index model ( Apparent diffusion coefficient (ADC)-stand), double exponential model (ADCslow，ADCfast and f) and stretched-exponential model parameters (distributed diffusion coefficient (DDC) and α) were acquired. Two radiologists separately measured these parameters for each lesion through drawing region of interest. Receiver operating characteristic (ROC) and the area under the ROC curve analysis was used to evaluate the diagnostic performance. Each parameter was substituted into the Logistic regression model to determine the meaningful parameters, and the combined diagnostic performance was evaluated.Results: The ADCfast and f showed significant differences between spinal metastasis and tuberculous spondylitis. (for all, p < 0.05). The Logistic regression model results showed that ADCfast and f were independent factors affecting the conclusion (P<0.05). The AUC values of ADCfast and f were 0.823 (95%CI:0.719 to 0.927) and 0.876 (95%CI: 0.782 to 0.969), respectively. ADCfast combined with f showed the highest AUC value of 0.925 (95%CI: 0.858 to 0.992). Additional significant differences were found in ADCstand, ADCslow, DDC and α among different metastasis type.Conclusions: IVIM MR imaging may be helpful for differentiating spinal metastasis from tuberculous spondylitis and may be used to detect the origin tumor for those patients who could not identify primary tumors, and provide help for clinical treatment.

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Non-parametric intravoxel incoherent motion analysis in patients with intracranial lesions: Test-retest reliability and correlation with arterial spin labeling

NeuroImage Clinical ◽

10.1016/j.nicl.2016.05.022 ◽

2016 ◽

Vol 11 ◽

pp. 780-788 ◽

Cited By ~ 9

Author(s):

Sonja Stieb ◽

Andreas Boss ◽

Moritz C. Wurnig ◽

Pinar S. Özbay ◽

Tobias Weiss ◽

...

Keyword(s):

Motion Analysis ◽

Arterial Spin Labeling ◽

Spin Labeling ◽

Intravoxel Incoherent Motion ◽

Retest Reliability ◽

Intracranial Lesions ◽

Test Retest Reliability ◽

Non Parametric

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In-silico performance, validation, and modeling of the Nanostring Banff Human Organ transplant gene panel using archival data from human kidney transplants

BMC Medical Genomics ◽

10.1186/s12920-021-00891-5 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

R. N. Smith

Keyword(s):

Gene Expression ◽

Microarray Data ◽

In Silico ◽

Organ Transplant ◽

Human Kidney ◽

Gene Panel ◽

Gene Expressions ◽

Tissue Samples ◽

Human Organ ◽

Archival Tissue

Abstract Background RNA gene expression of renal transplantation biopsies is commonly used to identify the immunological patterns of graft rejection. Mostly done with microarrays, seminal findings defined the patterns of gene sets associated with rejection and non-rejection kidney allograft diagnoses. To make gene expression more accessible, the Molecular Diagnostics Working Group of the Banff Foundation for Allograft Pathology and NanoString Technologies partnered to create the Banff Human Organ Transplant Panel (BHOT), a gene panel set of 770 genes as a surrogate for microarrays (~ 50,000 genes). The advantage of this platform is that gene expressions are quantifiable on formalin fixed and paraffin embedded archival tissue samples, making gene expression analyses more accessible. The purpose of this report is to test in silico the utility of the BHOT panel as a surrogate for microarrays on archival microarray data and test the performance of the modelled BHOT data. Methods BHOT genes as a subset of genes from downloaded archival public microarray data on human renal allograft gene expression were analyzed and modelled by a variety of statistical methods. Results Three methods of parsing genes verify that the BHOT panel readily identifies renal rejection and non-rejection diagnoses using in silico statistical analyses of seminal archival databases. Multiple modelling algorithms show a highly variable pattern of misclassifications per sample, either between differently constructed principal components or between modelling algorithms. The misclassifications are related to the gene expression heterogeneity within a given diagnosis because clustering the data into 9 groups modelled with fewer misclassifications. Conclusion This report supports using the Banff Human Organ Transplant Panel for gene expression of human renal allografts as a surrogate for microarrays on archival tissue. The data modelled satisfactorily with aggregate diagnoses although with limited per sample accuracy and, thereby, reflects and confirms the modelling complexity and the challenges of modelling gene expression as previously reported.

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