Dynamic Contrast-Enhanced Imaging as a Prognostic Tool in Early Diagnosis of Prostate Cancer: Correlation with PSA and Clinical Stage

Parametric maps of spatial two-tissue compartment model for prostate dynamic contrast enhanced MRI - comparison with the standard tofts model in the diagnosis of prostate cancer

The spatial two-tissue compartment model (2TCM) was used to analyze prostate dynamic contrast enhanced (DCE) MRI data and compared with the standard Tofts model. A total of 29 patients with biopsy-confirmed prostate cancer were included in this IRB-approved study. MRI data were acquired on a Philips Achieva 3T-TX scanner. After T2-weighted and diffusion-weighted imaging, DCE data using 3D T1-FFE mDIXON sequence were acquired pre- and post-contrast media injection (0.1 mmol/kg Multihance) for 60 dynamic scans with temporal resolution of 8.3 s/image. The 2TCM has one fast ([Formula: see text] and [Formula: see text]) and one slow ([Formula: see text] and [Formula: see text]) exchanging compartment, compared with the standard Tofts model parameters (Ktrans and kep). On average, prostate cancer had significantly higher values (p < 0.01) than normal prostate tissue for all calculated parameters. There was a strong correlation (r = 0.94, p < 0.001) between Ktrans and [Formula: see text] for cancer, but weak correlation (r = 0.28, p < 0.05) between kep and [Formula: see text]. Average root-mean-square error (RMSE) in fits from the 2TCM was significantly smaller (p < 0.001) than the RMSE in fits from the Tofts model. Receiver operating characteristic (ROC) analysis showed that fast [Formula: see text] had the highest area under the curve (AUC) than any other individual parameter. The combined four parameters from the 2TCM had a considerably higher AUC value than the combined two parameters from the Tofts model. The 2TCM is useful for quantitative analysis of prostate DCE-MRI data and provides new information in the diagnosis of prostate cancer.

Parametric maps of spatial two-tissue compartment model for prostate dynamic contrast enhanced MRI - comparison with the standard Tofts model in the diagnosis of prostate cancer

The spatial two-tissue compartment model (2TCM) was used to analyze prostate dynamic contrast enhanced (DCE) MRI data and compared with the standard Tofts model. A total of 29 patients with biopsy-confirmed prostate cancer were included in this IRB-approved study. MRI data were acquired on a Philips Achieva 3T-TX scanner. After T2-weighted and diffusion-weighted imaging, DCE data using 3D T1-FFE mDIXON sequence were acquired pre- and post-contrast media injection (0.1 mmol/kg Multihance) for 60 dynamic scans with temporal resolution of 8.3 s/image. The 2TCM has one fast (K 1 trans and k 1 ep ) and one slow (K 2 trans and k 2 ep ) exchanging compartment, compared with the standard Tofts model parameters (K trans and k ep ). On average, prostate cancer had significantly higher values (p < 0.007) than normal prostate tissue for all calculated parameters. There was a strong correlation (r = 0.94, p < 0.0001) between K trans and K 1 trans for cancer, but weak correlation (r = 0.28, p < 0.05) between k ep and k 1 ep . Average root-mean-square error (RMSE) in fits from the 2TCM was significantly smaller (p < 0.001) than the RMSE in fits from the Tofts model. Receiver operating characteristic (ROC) analysis showed that fast K 1 trans had the highest area under the curve (AUC) than any other individual parameter. The combined four parameters from the 2TCM had a considerably higher AUC value than the combined two parameters from the Tofts model. The 2TCM may be useful for quantitative analysis of prostate DCE-MRI data and may provide new information in the diagnosis of prostate cancer.

DCE-MRI and DWI can differentiate benign from malignant prostate tumors when serum PSA is ≥10 ng/ml

Background

This study investigated the diagnostic utility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters for distinguishing between benign and malignant prostate tumors when serum prostate-specific antigen (PSA) level is ≥10 ng/ml.

Methods

Patients with prostate cancer (PCa) and benign prostatic hyperplasia (BPH) with serum PSA ≥10 ng/ml before treatment were recruited. Transrectal ultrasound-guided biopsy or surgery was performed for tumor classification and patients were stratified accordingly into PCa and BPH groups. Patients underwent DCE-MRI and DWI scanning and the transfer constant (K_trans), rate constant (K_ep), fractional volume of the extravascular extracellular space, plasma volume (V_p), and Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2) score were determined. The apparent diffusion coefficient (ADC) was calculated from DWI. The diagnostic performance of these parameters was assessed by receiver operating characteristic (ROC) curve analysis, and those showing a significant difference between the PCa and BPH groups were combined into a multivariate logistic regression model for PCa diagnosis. Spearman's correlation was used to analyze the relationship between Gleason score and imaging parameters.

Results

The study enrolled 65 patients including 32 with PCa and 33 with BPH. Ktrans (P=0.006), Kep (P=0.001), and Vp (P=0.009) from DCE-MRI and ADC (P<0.001) from DWI could distinguish between the 2 groups when PSA was ≥10 ng/ml. PI-RADS score (area under the ROC curve [AUC]=0.705), Ktrans (AUC=0.700), Kep (AUC=0.737), Vp (AUC=0.688), and ADC (AUC=0.999) showed high diagnostic performance for discriminating PCa from BPH. A combined model based on PI-RADS score, Ktrans, Kep, Vp, and ADC had a higher AUC (1.000), with a sensitivity of 0.998 and specificity of 0.999. Imaging markers showed no significant correlation with Gleason score in PCa.

Conclusion

DCE-MRI and DWI parameters can distinguish between benign and malignant prostate tumors in patients with serum PSA ≥10 ng/ml.

Dynamic Contrast Enhanced Study in Multiparametric Examination of the Prostate—Can We Make Better Use of It?

We sought to investigate whether quantitative parameters from a dynamic contrast-enhanced study can be used to differentiate cancer from normal tissue and to determine a cut-off value of specific parameters that can predict malignancy more accurately, compared to the obturator internus muscle as a reference tissue. This retrospective study included 56 patients with biopsy proven prostate cancer (PCa) after multiparametric magnetic resonance imaging (mpMRI), with a total of 70 lesions; 39 were located in the peripheral zone, and 31 in the transition zone. The quantitative parameters for all patients were calculated in the detected lesion, morphologically normal prostate tissue and the obturator internus muscle. Increase in the K_trans value was determined in lesion-to-muscle ratio by 3.974368, which is a cut-off value to differentiate between prostate cancer and normal prostate tissue, with specificity of 72.86% and sensitivity of 91.43%. We introduced a model to detect prostate cancer that combines K_trans lesion-to-muscle ratio value and iAUC lesion-to-muscle ratio value, which is of higher accuracy compared to individual variables. Based on this model, we identified the optimal cut-off value with 100% sensitivity and 64.28% specificity. The use of quantitative DCE pharmacokinetic parameters compared to the obturator internus muscle as reference tissue leads to higher diagnostic accuracy for prostate cancer detection.

Effectiveness of Dynamic Contrast Enhanced MRI with a Split Dose of Gadoterate Meglumine for Detection of Prostate Cancer

RATIONALE AND OBJECTIVES

To evaluate whether dynamic contrast enhanced (DCE) MRI with a split injection of 30% followed by 70% of a standard dose (30PSD and 70PSD) of gadoterate meglumine (DOTAREM) can improve diagnosis of prostate cancer (PCa).

MATERIALS AND METHODS

MRI for twenty patients was performed on a Philips Ingenia 3T scanner without an endorectal coil followed by subsequent radical prostatectomy. DCE 3D T1-FFE data were acquired with injection of 0.03 mmol/kg followed after 2 minutes by 0.07 mmol/kg of DOTAREM. Regions-of-interest on histologically verified PCa and normal tissue in different prostate zones and the iliac artery were drawn. Average signal intensity as function of time was calculated for each ROI and fitted by using the signal intensity form of the Tofts (SI-Tofts) model to extract physiological parameters (K^trans and v_e). In addition, the scaled arterial input function (AIF) obtained from 30PSD data was used to analyze 70PSD data.

RESULTS

The AIF obtained from 30PSD data showed both first and second passes clearly and had much higher peak magnitude than AIFs from 70PSD data. K^trans was significantly (p < 0.05) larger in PCa than in normal tissue in peripheral zone (PZ) and central zone (CZ) for both 70PSD and 70PSD data analyzed with a scaled AIF. K^trans in cancer overlapped with that of normal tissue in the transition zone (TZ). There was no statistical difference in v_e between cancer and normal tissue. Receiver operating characteristic analysis showed that use of the AIF from 30PSD data to analyze 70PSD data increased the diagnostic efficacy of K^trans in the PZ and CZ.

CONCLUSION

The split dose protocol for injection of Dotarem increased diagnostic accuracy of quantitative analysis with the SI-Tofts model.

Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature

Purpose

This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI).

Methods

This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1.

Results

In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases (P < 0.05) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade.

Conclusions

The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.

Signal intensity form of the Tofts model for quantitative analysis of prostate dynamic contrast enhanced MRI data

The aim of this study is to develop a signal intensity (S(t)) form of the standard Tofts pharmacokinetic model that avoids the need to calculate tissue contrast agent concentration (C(t)) as function of time (t). We refer to this as 'SI-Tofts' model. Physiological parameters (K ^trans and v _e) calculated using the SI-Tofts and standard Tofts models were compared by using simulations and human prostate dynamic contrast enhanced (DCE) MRI data. This approach was also applied to the Patlak model to compare K ^trans values calculated from C(t) and S(t). Simulations were performed on DCE-MRI data from the quantitative imaging biomarkers alliance to validate SI-Tofts model. In addition, ultrafast DCE-MRI data were acquired from 18 prostate cancer patients on a Philips Achieva 3T-TX scanner. Regions-of-interest (ROIs) for prostate cancer, normal tissue, gluteal muscle, and iliac artery were manually traced. The C(t) was calculated for each ROI using the standard model with measured pre-contrast tissue T ₁ values. Both the simulation and clinical results showed strong correlation (r = 0.87-0.99, p < 0.001) for K ^trans and v _e calculated from the SI-Tofts and standard Tofts models. The SI-Tofts model with a correction factor using the T ₁ ratio of blood to tissue significantly improved the K ^trans estimates. The correlation of K ^trans obtained from the Patlak model with C(t) vs S(t) was also strong (r = 0.95-0.99, p < 0.001). These preliminary results suggest that physiological parameters from DCE-MRI can be reliably estimated from the SI-Tofts model without contrast agent concentration calculation.

APRICOT: Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools

Background  Scientific publications are meant to exchange knowledge among researchers but the inability to properly reproduce computational experiments limits the quality of scientific research. Furthermore, bibliography shows that irreproducible preclinical research exceeds 50%, which produces a huge waste of resources on nonprofitable research at Life Sciences field. As a consequence, scientific reproducibility is being fostered to promote Open Science through open databases and software tools that are typically deployed on existing computational resources. However, some computational experiments require complex virtual infrastructures, such as elastic clusters of PCs, that can be dynamically provided from multiple clouds. Obtaining these infrastructures requires not only an infrastructure provider, but also advanced knowledge in the cloud computing field.

Objectives  The main aim of this paper is to improve reproducibility in life sciences to produce better and more cost-effective research. For that purpose, our intention is to simplify the infrastructure usage and deployment for researchers.

Methods  This paper introduces Advanced Platform for Reproducible Infrastructures in the Cloud via Open Tools (APRICOT), an open source extension for Jupyter to deploy deterministic virtual infrastructures across multiclouds for reproducible scientific computational experiments. To exemplify its utilization and how APRICOT can improve the reproduction of experiments with complex computation requirements, two examples in the field of life sciences are provided. All requirements to reproduce both experiments are disclosed within APRICOT and, therefore, can be reproduced by the users.

Results  To show the capabilities of APRICOT, we have processed a real magnetic resonance image to accurately characterize a prostate cancer using a Message Passing Interface cluster deployed automatically with APRICOT. In addition, the second example shows how APRICOT scales the deployed infrastructure, according to the workload, using a batch cluster. This example consists of a multiparametric study of a positron emission tomography image reconstruction.

Conclusion  APRICOT's benefits are the integration of specific infrastructure deployment, the management and usage for Open Science, making experiments that involve specific computational infrastructures reproducible. All the experiment steps and details can be documented at the same Jupyter notebook which includes infrastructure specifications, data storage, experimentation execution, results gathering, and infrastructure termination. Thus, distributing the experimentation notebook and needed data should be enough to reproduce the experiment.

Heterogeneous Enhancement Pattern in DCE-MRI Reveals the Morphology of Normal Lymph Nodes: An Experimental Study

Purpose

To investigate the heterogeneous enhancement pattern in normal lymph nodes of healthy mice by different albumin-binding contrast agents.

Methods

The enhancement of normal lymph nodes was assessed in mice by dynamic contrast-enhanced MRI (DCE-MRI) after the administration of two contrast agents characterized by different albumin-binding properties: gadopentetate dimeglumine (Gd-DTPA) and gadobenate dimeglumine (Gd-BOPTA). To take into account potential heterogeneities of the contrast uptake in the lymph nodes, k-means cluster analysis was performed on DCE-MRI data. Cluster spatial distribution was visually assessed. Statistical comparison among clusters and contrast agents was performed on semiquantitative parameters (AUC, wash-in rate, and wash-out rate) and on the relative size of the segmented clusters.

Results

Cluster analysis of DCE-MRI data revealed at least two main clusters, localized in the outer portion and in the inner portion of each lymph node. With both contrast agents, AUC (p < 0.01) and wash-in (p < 0.05) rates were greater in the inner cluster, which also showed a steeper wash-out rate than the outer cluster (Gd-BOPTA, p < 0.01; Gd-DTPA, p=0.056). The size of the outer cluster was greater than that of the inner cluster by Gd-DTPA (p < 0.05) and Gd-BOPTA (p < 0.01). The enhancement pattern of Gd-DTPA was not significantly different from the enhancement pattern of Gd-BOPTA.

Conclusion

DCE-MRI in normal lymph nodes shows a characteristic heterogeneous pattern, discriminating the periphery and the central portion of the lymph nodes. Such a pattern deserves to be investigated as a diagnostic marker for lymph node staging.