Dynamic contrast-enhanced magnetic resonance imaging of prostate cancer: A review of current methods and applications

[Multiparametric magnetic resonance imaging for hepatocellular carcinoma, part 1 : Morphology and dynamic perfusion imaging in primary diagnostics and treatment monitoring]

Radiology plays a key role in the diagnosis and monitoring of hepatocellular carcinoma (HCC). Ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) are used to identify HCC lesions. Multiparametric MRI provides detailed insights into the tumor biology through the analysis of morphology, perfusion and diffusion. In this way preoperative decisions can be optimized. The guidelines recommend using contrast-enhanced MRI or ultrasound for the diagnosis of HCC. The preferred method is MRI due to its superiority in the detection of small lesions The treatment response is evaluated using modified response evaluation criteria for solid tumors (RECIST) and the European Association for the Study of the Liver (EASL) criteria. The use of multiparametric MRI in conjunction with the liver imaging reporting and data system (LI-RADS) plays overall a central role in the precise diagnosis and monitoring of the treatment of HCC.

Inter-Rater Variability of Prostate Lesion Segmentation on Multiparametric Prostate MRI

INTRODUCTION

External radiotherapy is a major treatment for localized prostate cancer (PCa). Dose escalation to the whole prostate gland increases biochemical relapse-free survival but also acute and late toxicities. Dose escalation to the dominant index lesion (DIL) only is of growing interest. It requires a robust delineation of the DIL. In this context, we aimed to evaluate the inter-observer variability of DIL delineation.

MATERIAL AND METHODS

Two junior radiologists and a senior radiation oncologist delineated DILs on 64 mpMRIs of patients with histologically confirmed PCa. For each mpMRI and each reader, eight individual DIL segmentations were delineated. These delineations were blindly performed from one another and resulted from the individual analysis of the T2, apparent diffusion coefficient (ADC), b2000, and dynamic contrast enhanced (DCE) sequences, as well as the analysis of combined sequences (T2ADC, T2ADCb2000, T2ADCDCE, and T2ADCb2000DCE). Delineation variability was assessed using the DICE coefficient, Jaccard index, Hausdorff distance measure, and mean distance to agreement.

RESULTS

T2, ADC, T2ADC, b2000, T2 + ADC + b2000, T2 + ADC + DCE, and T2 + ADC + b2000 + DCE sequences obtained DICE coefficients of 0.51, 0.50, 0.54, 0.52, 0.54, 0.55, 0.53, respectively, which are significantly higher than the perfusion sequence alone (0.35, p < 0.001). The analysis of other similarity metrics lead to similar results. The tumor volume and PI-RADS classification were positively correlated with the DICE scores.

CONCLUSION

Our study showed that the contours of prostatic lesions were more reproducible on certain sequences but confirmed the great variability of prostatic contours with a maximum DICE coefficient calculated at 0.55 (joint analysis of T2, ADC, and perfusion sequences).

Dynamic contrast-enhanced magnetic resonance imaging parameter changes as an early biomarker of tumor responses following radiation therapy in patients with spinal metastases: a systematic review

PURPOSE

This systematic review aims to assess and summarize the clinical values of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameter changes as early biomarkers of tumor responses following radiation therapy (RT) in patients with spinal metastases.

MATERIALS AND METHODS

A systematic search was conducted on five electronic databases: PubMed, Scopus, Science Direct, Cochrane, and Embase. Studies were included if they mentioned DCE-MRI parameter changes before and after RT in patients with spinal metastases with a correlation to tumor responses based on clinical and imaging criteria. The Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess study quality.

RESULTS

This systematic review included seven studies involving 107 patients. All seven studies evaluated the transfer constant (Ktrans), six studies evaluated the plasma volume fraction (Vp), three studies evaluated the extravascular extracellular space volume fraction, and two studies evaluated the rate constant. There were variations in the type of primary cancer, RT techniques used, post-treatment scan time, and median follow-up time. Despite the variations, however, the collected evidence generally suggested that significant differences could be detected in DCE-MRI parameters between before and after RT, which might reflect treatment success or failures in long-term follow-up. Responders showed higher reduction and lower values of Ktrans and Vp after RT. DCE-MRI parameters showed changes and detectable recurrences significantly earlier (up to 6 months) than conventional MRI with favorable diagnostic values.

CONCLUSION

The results of this systematic review suggested that DCE-MRI parameter changes in patients with spinal metastases could be a promising tool for treatment-response assessment following RT. Lower values and higher reduction of Ktrans and Vp after treatment demonstrated good prediction of local control. Compared to conventional MRI, DCE-MRI showed more rapid changes and earlier prediction of treatment failure.

Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review

The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.

Quantitative dynamic contrast-enhanced parameters and intravoxel incoherent motion facilitate the prediction of TP53 status and risk stratification of early-stage endometrial carcinoma

BACKGROUND

The aim of the study was to investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and intravoxel incoherent motion (IVIM) in differentiating TP53-mutant from wild type, low-risk from non-low-risk early-stage endometrial carcinoma (EC).

PATIENTS AND METHODS

A total of 74 EC patients underwent pelvic MRI. Parameters volume transfer constant (K^trans), rate transfer constant (K_ep), the volume of extravascular extracellular space per unit volume of tissue (V_e), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and microvascular volume fraction (f) were compared. The combination of parameters was investigated by logistic regression and evaluated by bootstrap (1000 samples), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

In the TP53-mutant group, K^trans and K_ep were higher and D was lower than in the TP53-wild group; K^trans, V_e, f, and D were lower in the non-low-risk group than in the low-risk group (all P < 0.05). In the identification of TP53-mutant and TP53-wild early-stage EC, K^trans and D were independent predictors, and the combination of them had an optimal diagnostic efficacy (AUC, 0.867; sensitivity, 92.00%; specificity, 80.95%), which was significantly better than D (Z = 2.169, P = 0.030) and K^trans (Z = 2.572, P = 0.010). In the identification of low-risk and non-low-risk early-stage EC, K^trans, V_e, and f were independent predictors, and the combination of them had an optimal diagnostic efficacy (AUC, 0.947; sensitivity, 83.33%; specificity, 93.18%), which was significantly better than D (Z = 3.113, P = 0.002), f (Z = 4.317, P < 0.001), K^trans (Z = 2.713, P = 0.007), and V_e (Z = 3.175, P = 0.002). The calibration curves showed that the above two combinations of independent predictors, both have good consistency, and DCA showed that these combinations were reliable clinical prediction tools.

CONCLUSIONS

Both DCE-MRI and IVIM facilitate the prediction of TP53 status and risk stratification in early-stage EC. Compare with each single parameter, the combination of independent predictors provided better predictive power and may serve as a superior imaging marker.

Distinguishing mesorectal tumor deposits from metastatic lymph nodes by using diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging in rectal cancer

OBJECTIVES

This study aimed to identify whether apparent diffusion coefficient (ADC) values and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters are helpful in distinguishing mesorectal tumor deposits (TD) from metastatic lymph nodes (MLN) in rectal cancer (RC).

METHODS

Thirty patients (59 lesions, including 30 TD and 29 MLN) with RC who underwent pretreatment-MRI between February 2016 and August 2018 were enrolled. The morphological features, ADC values, and semi-quantitative parameters of DCE-MRI, including relative enhancement (RE), maximum enhancement (ME), maximum relative enhancement (MRE), time to peak (TTP), wash-in rates (WIR), wash-out rates (WOR), brevity of enhancement (BRE), and area under the curve (AUC) were measured on lesions (TD or MLN) and RC. The parameters were compared between TD and MLN, tumor with and without TD group by using Fisher's exact test, independent-samples t-test, and Mann-Whitney U test. The ratio (lesion-to-tumor) of the parameters was compared between TD and MLN. Receiver operating characteristic curve analysis and binary logistic regression analysis were used to assess the diagnostic ability of single and combined metrics for distinguishing TD from MLN.

RESULTS

The morphological features, including size, shape, and border, were significantly different between TD and MLN. TD exhibited significantly lower RE, MRE, RE-ratio, MRE-ratio, ADC_min-ratio, and ADC_mean-ratio than MLN. RE-ratio showed the highest AUC (0.749) and accuracy (77.97%) among single parameters. The combination of DCE-MRI and DWI parameters together showed higher diagnostic efficiency (AUC = 0.825).

CONCLUSIONS

Morphological features, ADC values, and DCE-MRI parameters can preoperatively help distinguish TD from MLN in RC.

KEY POINTS

• DWI and DCE-MRI can facilitate early detection and distinguishing mesorectal TD (tumor deposits) from MLN (metastatic lymph nodes) in rectal cancer preoperatively. • TD has some specific morphological features, including relatively larger size, lower short- to long-axis ratio, irregular shape, and ill-defined border on T2-weighted MR images in rectal cancer. • The combination of ADC values and semi-quantitative parameters of DCE-MRI (RE, MRE) can help to improve the diagnostic efficiency of TD in rectal cancer.

Endothelin-axis antagonism enhances tumor perfusion in pancreatic cancer

Delivery of therapeutic agents in pancreatic cancer (PC) is impaired due to its hypovascular and desmoplastic tumor microenvironment. The Endothelin (ET)-axis is the major regulator of vasomotor tone under physiological conditions and is highly upregulated in multiple cancers. We investigated the effect of dual endothelin receptor antagonist bosentan on perfusion and macromolecular transport in a PC cell-fibroblast co-implantation tumor model using Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI). Following bosentan treatment, the contrast enhancement ratio and wash-in rates in tumors were two- and nine times higher, respectively, compared to the controls, whereas the time to peak was significantly shorter (7.29 ± 1.29 min v/s 22.08 ± 5.88 min; p = 0.04). Importantly, these effects were tumor selective as the magnitudes of change for these parameters were much lower in muscles. Bosentan treatment also reduced desmoplasia and improved intratumoral distribution of high molecular weight FITC-dextran. Overall, these findings support that targeting the ET-axis can serve as a potential strategy to selectively enhance tumor perfusion and improve the delivery of therapeutic agents in pancreatic tumors.

The role of MRI in prostate cancer: current and future directions

There has been an increasing role of magnetic resonance imaging (MRI) in the management of prostate cancer. MRI already plays an essential role in the detection and staging, with the introduction of functional MRI sequences. Recent advancements in radiomics and artificial intelligence are being tested to potentially improve detection, assessment of aggressiveness, and provide usefulness as a prognostic marker. MRI can improve pretreatment risk stratification and therefore selection of and follow-up of patients for active surveillance. MRI can also assist in guiding targeted biopsy, treatment planning and follow-up after treatment to assess local recurrence. MRI has gained importance in the evaluation of metastatic disease with emerging technology including whole-body MRI and integrated positron emission tomography/MRI, allowing for not only better detection but also quantification. The main goal of this article is to review the most recent advances on MRI in prostate cancer and provide insights into its potential clinical roles from the radiologist's perspective. In each of the sections, specific roles of MRI tailored to each clinical setting are discussed along with its strengths and weakness including already established material related to MRI and the introduction of recent advancements on MRI.

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.

An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors

This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast–enhanced MRI (DCE-MRI)–derived perfusion parameters. The clinical data and preoperative DCE–MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate the performance of the prediction models. A predictive model was built based on multi-class classification, which detected lymphoma, thymoma, and thymic carcinoma with sensitivity of 52.9%, 74.2%, and 92.8%, respectively. In addition, two predictive models were built based on binary classification for distinguishing Hodgkin from non-Hodgkin lymphoma and for distinguishing invasive from noninvasive thymoma, with sensitivity of 75% and 71.4%, respectively. In addition to two perfusion parameters (efflux rate constant from tissue extravascular extracellular space into the blood plasma, and extravascular extracellular space volume per unit volume of tissue), age and tumor volume were also essential parameters for predicting PMT subtypes. In conclusion, our machine learning–based predictive model, constructed with clinical data and perfusion parameters, may represent a useful tool for differential diagnosis of PMT subtypes.

Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging

Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.

Can quantitative analysis of multi-parametric MRI independently predict failure of focal salvage HIFU therapy in men with radio-recurrent prostate cancer?

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Quantitative mpMRI parameters predict failure of salvage HIFU in radiorecurrent prostate cancer

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Tumour microenvironment might produce heat-sinks which counter the effect of HIFU

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Ve value measured in the DCE sequence of the mpMRI is an independent predictor of treatment failure

Objectives

Focal salvage HIFU is a feasible therapeutic option in some men who have recurrence after primary radiotherapy for prostate cancer. We aimed to determine if multi-parametric quantitative parameters, in addition to clinical factors, might have a role in independently predicting focal salvage HIFU outcomes.

Methods

A retrospective registry analysis included 150 consecutive men who underwent focal salvage HIFU (Sonablate500) (2006-2015); 89 had mpMRI available. Metastatic disease was excluded by nodal assessment on pelvic MRI, a radioisotope bone-scan and/or choline or FDG PET/CT scan. All men had mpMRI and either transperineal template prostate mapping biopsy or targeted and systematic TRUS-biopsy. mpMRI included T2‐weighted, diffusion‐weighted and dynamic contrast‐enhancement. Pre-HIFU quantitative mpMRI data was obtained using Horos DICOM Viewer v3.3.5 for general MRI parameters and IB DCE v2.0 plug-in. Progression-free survival (PFS) was defined by biochemical failure and/or positive localized or distant imaging results and/or positive biopsy and/or systemic therapy and/or metastases/prostate cancer‐specific death. Potential predictors of PFS were analyzed by univariable and multivariable Cox-regression.

Results

Median age at focal salvage HIFU was 71 years (interquartile range [IQR] 65–74.5) and median PSA pre-focal salvage treatment was 5.8ng/ml (3.8-8). Median follow-up was 35 months (23-47) and median time to failure was 15 months (7.8–24.3). D-Amico low, intermediate and high-risk disease was present in 1% (1/89), 40% (36/89) and 43% (38/89) prior to focal salvage HIFU (16% missing data). 56% (50/89) failed by the composite outcome. A total of 22 factors were evaluated on univariable and 8 factors on multivariable analysis. The following quantitative parameters were included: Ktrans, Kep, Ve, Vp, IS, rTTP and TTP. On univariable analysis, PSA, prostate volume at time of radiotherapy failure and Ve (median) value were predictors for failure. Ve represents extracellular fraction of the whole tissue volume. On multivariable analysis, only Ve (median) value remained as an independent predictor.

Conclusions

One pharmacokinetic quantitative parameter based on DCE sequences seems to independently predict failure following focal salvage HIFU for radio-recurrent prostate cancer. This likely relates to the tumor microenvironment producing heat-sinks which counter the heating effect of HIFU. Further validation in larger datasets and evaluating mechanisms to reduce heat-sinks are required.

Dynamic contrast-enhanced magnetic resonance imaging for differentiating head and neck paraganglioma and schwannoma

BACKGROUND AND PURPOSE

Morphological assessment with conventional magnetic resonance imaging (MRI) sequences has limited specificity to distinguish between paragangliomas and schwannomas. Assessing the differences in microvascular properties through pharmacokinetic parameters of dynamic contrast-enhanced (DCE)-MRI can provide additional information to aid in this differentiation.

MATERIALS AND METHODS

A prospective study on MR characterization of neck masses was performed between January 2017 and March 2019 in our department, out of which 40 patients with head and neck paragangliomas (HNPGLs) (33 lesions) and schwannomas (15 lesions) were included in this analysis. MR perfusion using dynamic axial T1WI fat suppressed fast spoiled gradient recalled sequence with parallel imaging was performed in all the patients, in addition to single-shot turbo spin-echo axial diffusion weighted imaging (DWI) and routine MRI. ROI-based method was used to obtain signal-time curves, permeability measurements, and mean apparent diffusion coefficient (ADC) to differentiate paragangliomas from schwannomas. Statistical analysis was done to assess the significance and establish a cutoff to distinguish between the two entities. The available images of DOTANOC PET/CT (34 lesions) were analyzed retrospectively. Correlations between the perfusion, diffusion, and molecular PET/CT parameters were done.

RESULTS

Paragangliomas had a higher wash-in rate, wash-out rate, K_trans, K_ep , and V_p (p < 0.001); while schwannomas had a higher relative enhancement (p < 0.012), time to peak, time of onset, brevity of enhancement, and V_e (p < 0.001). Among the perfusion parameters, K_ep (area under curve (AUC) 0.994) and V_p (AUC 0.992) were found to have the highest diagnostic value. In diffusion-weighted imaging, paragangliomas had a lower mean ADC compared to schwannomas (p < 0.001). The SUV_max and SUV_mean were significantly associated with K_trans , K_ep , and V_p in paragangliomas.

CONCLUSION

DCE-MRI in addition to DWI-MRI can accurately distinguish HNPGL from schwannoma and may replace the need for any additional imaging and preoperative biopsy in most cases.

Magnetic Resonance-Guided High-Intensity Focused Ultrasound Ablation of Uterine Fibroids-Efficiency Assessment with the Use of Dynamic Contrast-Enhanced Magnetic Resonance Imaging and the Potential Role of the Administration of Uterotonic Drugs

OBJECTIVE

The assessment of the usefulness of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) when qualifying patients with uterine fibroids (UFs) for magnetic resonance-guided high-intensity ultrasound (MR-HIFU).

MATERIAL AND METHODS

This retrospective, single center study included 283 women who underwent DCE-MRI and were treated with MR-HIFU. The patients were divided according to non-perfused volume (NPV) as well as by the type of curve for patients with a washout curve in the DCE-MRI study and patients without a washout curve. The studied women were assessed in three groups according to the type of uterotonics administered. Group A (57 patients) received one dose of misoprostol/diclofenac transvaginally and group B (71 patients) received oxytocin intravenously prior to the MR-HIFU procedure. The remaining 155 women (group C) were treated with the traditional non-drug enhanced MR-HIFU procedure.

RESULTS

The average NPV value was higher in no washout group, and depended on the uterotonics used.

CONCLUSIONS

We demonstrated a correlation between dynamic contrast enhancement curve types and the therapeutic efficacy of MR-HIFU. Our results suggest that DCE-MRI has the potential to assess treatment outcomes among patients with UFs, and patients with UFs that present with a washout curve may benefit from the use of uterotonic drugs. More studies are required to draw final conclusions.

Portable Perfusion Phantom Offers Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Accurate Prostate Cancer Grade Stratification: A Pilot Study

RATIONALE AND OBJECTIVES

The study goal was to test whether the improved accuracy in quantitative dynamic contrast-enhanced magnetic resonance imaging measurement using a point-of-care portable perfusion phantom (P4) leads to better stratification of prostate cancer grade.

MATERIALS AND METHODS

A prospective clinical study was conducted recruiting 44 patients scheduled for multi-parameter MRI prostate exams. All participants were imaged with the P4 placed under their pelvic regions. Tissue sampling was carried out for 25 patients at 22 ± 18 (mean ± SD) days after multi-parameter MRI. On histologic examination, a total of 31 lesions were confirmed as prostate cancer. Tumors were classified into low grade (n = 14), intermediate grade (n = 10), and high grade (n = 7). Tumor perfusion was assessed by volume transfer constant, K^trans, before and after P4-based error correction, and the K^trans of low, intermediate and high-grade tumors were statistically compared.

RESULTS

After P4-based error correction, the K^trans of low, intermediate, and high-grade tumors were 0.109 ± 0.026 min^-1 (95% CI: 0.0094 to 0.124 min^-1), 0.163 ± 0.049 min^-1 (95% CI: 0.129 to 0.198 min^-1) and 0.356 ± 0.156 min^-1 (95% CI: 0.215 to 0.495 min^-1), respectively, with statistically significant difference among the groups (low vs intermediate: p = 0.002; intermediate vs high: p = 0.002; low vs high: p < 0.001). The sensitivity and specificity of K^trans value, 0.14 min^-1, to detect the clinically significant prostate cancer were 88% and 93%, respectively, after P4 based error correction, but those before error correction were 88% and 86%, respectively.

CONCLUSION

The P4 allows to reduce errors in quantitative dynamic contrast-enhanced magnetic resonance imaging measurement, enhancing accuracy in stratification of prostate cancer grade.

Ancillary imaging and clinical features for the characterization of prostate lesions: A proposed approach to reduce false positives

The relatively low specificity and positive predictive value of the Prostate Imaging-Reporting and Data System (PI-RADS) can lead to considerable false-positive results and unnecessary biopsies. The aim of this study was to propose ancillary features (AFs) indicating clinically significant prostate cancer (csPCa) or benign tissues in PI-RADS category ≥3 lesions and determine the usefulness of these AFs in reducing false-positive assessments of suspicious lesions in men at csPCa risk. This was a retrospective study, which included 199 men. A 3T, including turbo spin echo T₂ -weighted, echo-planar diffusion-weighted, and spoiled gradient echo dynamic contrast-enhanced (DCE) images, was used. Five AFs (prostate-specific antigen density ≥0.15 ng/mL² ; size ≥10 mm; heterogeneous T2 signal intensity; circumscribed nodule in the junction of peripheral and transition zone; and DCE time curves) indicating csPCa or non-csPCa were evaluated by three independent readers. The sensitivity and specificity of each AF were calculated. Inter-reader agreement was evaluated using κ statistics. Univariate and multivariate logistic regression analyses were conducted to determine significant AFs. The reduction in positive call rates and csPCa detection rates with combined AF use were calculated and compared with the findings obtained with PI-RADS use alone. The sensitivities and specificities of the AFs indicating csPCa were 72.1%-96.5% and 27.4%-75.2% for reader 1, 66.3%-96.5% and 23.9%-62.0% for reader 2, and 67.4%-96.5% and 34.5%-78.8% for reader 3, with moderate to substantial inter-reader agreement (Fleiss κ, 0.551-0.643). The combined use of two or more AFs for assessing PI-RADS ≥3 lesions resulted in a 19.6%-30.7% reduction in positive calls (p < .05) compared to PI-RADS use alone while preserving the csPCa detection rates (p ≥ .06) for three readers. The use of AFs in combination with PI-RADS can reduce positive calls and false positives without csPCa under-detection.

Fusion US/MRI prostate biopsy using a computer aided diagnostic (CAD) system

BACKGROUND

The aim of this study was to investigate the impact of computer aided diagnostic (CAD) system on the detection rate of prostate cancer (PCa) in a series of fusion prostate biopsy (FPB).

METHODS

Two prospective transperineal FPB series (with or without CAD assistance) were analyzed and PCa detection rates compared with per-patient and per-target analyses. The χ² and Mann-Whitney test were used to compare categorical and continuous variables, respectively. Univariable and multivariable regression analyses were applied to identify predictors of any and clinically significant (cs) PCa detection. Subgroup analyses were performed after stratifying for PI-RADS Score and lesion location.

RESULTS

Out of 183 FPB, 89 were performed with CAD assistance. At per-patient analysis the detection rate of any PCa and of cs PCa were 56.3% and 30.6%, respectively; the aid of CAD was negligible for either any PCa or csPCa detection rates (P=0.45 and P=0.99, respectively). Conversely in a per-target analysis, CAD-assisted biopsy had significantly higher positive predictive value (PPV) for any PCa versus MRI-only group (58% vs. 37.8%, P=0.001). PI-RADS Score was the only independent predictor of any and csPCa, either in per-patient or per-target multivariable regression analysis (all P<0.029). In a subgroup per-patient analysis of anterior/transitional zone lesions, csPCa detection rate was significantly higher in the CAD cohort (54.5%vs.11.1%, respectively; P=0.028), and CAD assistance was the only predictor of csPCa detection (P=0.013).

CONCLUSIONS

CAD assistance for FPB seems to improve detection of csPCa located in anterior/transitional zone. Enhanced identification and improved contouring of lesions may justify higher diagnostic performance.

Exercise-induced calf muscle hyperemia: Rapid mapping of magnetic resonance imaging using deep learning approach

Exercise-induced hyperemia in calf muscles was recently shown to be quantifiable with high-resolution magnetic resonance imaging (MRI). However, processing of the MRI data to obtain muscle-perfusion maps is time-consuming. This study proposes to substantially accelerate the mapping of muscle perfusion using a deep-learning method called artificial neural network (NN). Forty-eight MRI scans were acquired from 21 healthy subjects and patients with peripheral artery disease (PAD). For optimal training of NN, different training-data sets were compared, investigating the effect of data diversity and reference perfusion accuracy. Reference perfusion was estimated by tracer kinetic model fitting initialized with multiple values (multigrid model fitting). Result: The NN method was much faster than tracer kinetic model fitting. To generate a perfusion map of matrix 128 × 128 on a same computer, multigrid model fitting took about 80 min, single-grid or regular model fitting about 3 min, while the NN method took about 1 s. Compared to the reference values, NN trained with a diverse group gave estimates with mean absolute error (MAE) of 15.9 ml/min/100g and correlation coefficient (R) of 0.949, significantly more accurate than regular model fitting (MAE 22.3 ml/min/100g, R 0.889, p < .001). Conclusion: the NN method enables rapid perfusion mapping, and if properly trained, estimates perfusion with accuracy comparable to multigrid model fitting.

The FGF/FGFR system in the physiopathology of the prostate gland

Fibroblast growth factors (FGFs) are a family of proteins possessing paracrine, autocrine, or endocrine functions in a variety of biological processes, including embryonic development, angiogenesis, tissue homeostasis, wound repair, and cancer. Canonical FGFs bind and activate tyrosine kinase FGF receptors (FGFRs), triggering intracellular signaling cascades that mediate their biological activity. Experimental evidence indicates that FGFs play a complex role in the physiopathology of the prostate gland that ranges from essential functions during embryonic development to modulation of neoplastic transformation. The use of ligand- and receptor-deleted mouse models has highlighted the requirement for FGF signaling in the normal development of the prostate gland. In adult prostate, the maintenance of a functional FGF/FGFR signaling axis is critical for organ homeostasis and function, as its disruption leads to prostate hyperplasia and may contribute to cancer progression and metastatic dissemination. Dissection of the molecular landscape modulated by the FGF family will facilitate ongoing translational efforts directed toward prostate cancer therapy.

Preliminary study on identification of estrogen receptor-positive breast cancer subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis

Background

Currently, breast cancer is divided into Luminal A, Luminal B, HER-2 overexpression (HER-2) and basal cell at genetic level. However, the differential diagnosis of estrogen receptor (ER)-positive breast cancer subtypes is rare. Therefore, we aimed to investigate the feasibility of identifying the ER-positive breast cancer subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis.

Methods

A retrospective analysis was performed for clinical data of 51 patients with ER-positive breast invasive ductal carcinoma confirmed by surgery and pathology from January 20 to October 2018. FireVoxel texture analysis software was used to delineate the tumor boundary layer by layer. The differences in the above characteristics between Luminal A and Luminal B breast cancer were compared, and the diagnostic efficacy of statistically significant texture parameters for ER-positive breast cancer subtypes was analyzed.

Results

There were no significant differences in mean, standard deviation (SD), skewness and tumor size between Luminal A and Luminal B groups (P>0.05). The kurtosis, inhomogeneity and entropy could effectively distinguish between the two groups with statistically significant difference (P=0.001, P=0.000, and P=0.000). The area under the receiver operating characteristic (ROC) curve (AUC) of kurtosis, inhomogeneity and entropy diagnosed with malignant mass were 0.832, 0.859 and 0.891, respectively (P<0.01). In addition, the entropy was the best among the three indicators. When the entropy was ≤4.22, the sensitivity of the diagnosis Luminal B was 90.62% and the specificity was 78.95%.

Conclusions

The texture analysis features based on DCE-MRI can help to identify ER-positive breast cancer subtypes. Entropy can be the best single texture indicator.

Double Diffusion Encoding for Probing Radiation-Induced Microstructural Changes in a Tumor Model: A Proof-of-Concept Study With Comparison to the Apparent Diffusion Coefficient and Histology

BACKGROUND

Microstructure analyses are gaining interest in cancer MRI as an alternative to the conventional apparent diffusion coefficient (ADC), of which the determinants remain unclear.

PURPOSE

To assess the sensitivity of parameters calculated from a double diffusion encoding (DDE) sequence to changes in a tumor's microstructure early after radiotherapy and to compare them with ADC and histology.

STUDY TYPE

Cohort study on experimental tumors.

ANIMAL MODEL

Sixteen WAG/Rij rats grafted with one rhabdomyosarcoma fragment in each thigh. Thirty-one were imaged at days 1 and 4, of which 17 tumors received a 20 Gy radiation dose after the first imagery.

FIELD STRENGTH/SEQUENCE

3T. Diffusion-weighted imaging, DDE with flow compensated, and noncompensated measurements.

ASSESSMENTS

1) To compare, after irradiation, DDE-derived parameters (intracellular fraction, cell size, and cell density) to their histological counterparts (fraction of stained area, minimal Feret diameter, and nuclei count, respectively). 2) To compare percentage changes in DDE-derived parameters and ADC. 3) To evaluate the evolution of DDE-derived parameters describing perfusion.

STATISTICAL TESTS

Wilcoxon rank sum test.

RESULTS

1) Intracellular fraction, cell size, and cell density were respectively lower (-24%, P < 0.001), higher (+7.5%, P < 0.001) and lower (-38%, P < 0.001) in treated tumors as compared to controls. Fraction of stained area, minimal Feret diameter, and nuclei count were respectively lower (-20%, P < 0.001), higher (+28%, P < 0.001), and lower (-34%, P < 0.001) in treated tumors. 2) The magnitude of ADC's percentage change due to irradiation (16.4%) was superior to the one of cell size (8.4%, P < 0.01) but inferior to those of intracellular fraction (35.5%, P < 0.001) and cell density (42%, P < 0.001). 3) After treatment, the magnitude of the vascular fraction's decrease was higher than the increase of flow velocity (33.3%, vs. 13.3%, P < 0.001).

DATA CONCLUSION

The DDE sequence allows quantitatively monitoring the effects of radiotherapy on a tumor's microstructure, whereas ADC only reveals global changes.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 4. J. Magn. Reson. Imaging 2020;52:941-951.

[When are contrast agents really needed? : Cross-sectional imaging with computed tomography and magnetic resonance imaging]

CLINICAL ISSUE

The intravenous administration of contrast agents increases the contrast between diverse tissues and vessels against their surroundings in both computed tomography (CT) and magnetic resonance imaging (MRI) scans and has been generously used for years. There are only a few scientific publications that have systematically evaluated the impact of this contrast-enhancing technique over noncontrast enhancing techniques.

RADIOLOGICAL STANDARD

According to these publications and our clinical experiences, there are far more indications to use non-contrast-enhancing techniques as they are used in clinical practice. The most important requirement to renounce the use of a contrast agent is sufficient clinical information and differentiated justified indication. The present review shows useful non-contrast-enhanced examination techniques for neuroradiology, musculoskeletal system, lymphatic system, and thorax, including the hearth, abdomen and breasts.

CLINICAL RECOMMENDATIONS

Good indications for non-contrast imaging are generally follow-ups. In cerebral related questions, like in traumatic or atraumatic emergencies, transient ischemic attacks, minor stroke diagnostic, dementia and in follow-ups of multiple sclerosis, there is usually no need for contrast agent. Examinations of the musculoskeletal systems and follow-up examinations of the lymphatic system can generally be done without a contrast agent. There is no major loss of value in CT and MRI scans of the thorax by examining without contrast. The value of using a contrast agent in the abdomen is far less than expected. Up to now use of a contrast agent is essential in evaluating questions related to vessels or angiomatous tissue and in breast MRI.

Intra- and inter-observer variability in dependence of T1-time correction for common dynamic contrast enhanced MRI parameters in prostate cancer patients

BACKGROUND

Dynamic contrast enhanced (DCE) MRI parameters are potential biomarkers to characterise tumour vasculature and distinguish it from the non-cancerous blood vessel system within the prostate. However, the inevitable presence of intra- and inter-observer variabilities is challenging in this context. Additionally, pre-contrast T1-time correction is a prerequisite to gain quantitative DCE parameters in the first place. The current study investigated the effect of individualized T1-time correction on intra- and inter-reader variability for quantitative DCE-parameters in prostatic lesions.

METHODS

In this IRB-approved retrospective study, two experienced radiologists assessed DCE parameters using individually measured (A) and fixed (B) T1-times twice with a time difference of three weeks. The dataset consisted of 35 MRI-guided biopsy-proven prostate cancer lesions. Limits of agreement (LoA) and coefficients of variability (CoV) were calculated to assess intra- and inter-reader variabilities of the parameters.

RESULTS

With exception of k_ep, for all DCE parameters both intra- and inter-reader CoV were smaller in B compared to A. Absolute k_ep values were largely insensitive to T1-time correction induced bias. The mean intra-reader CoVs [5%, 95% percentile] (over all four DCE parameters and both readers) were 6.7% [0.5%, 15.1%] in A and 3.9% [0.2%, 11.0%] in B. The inter-reader CoVs were 9.0% [0.6%, 25.8%] (A) and 7.0% [0.3%, 25.4%] (B).

CONCLUSIONS

T1-time correction has a significant influence on the intra- and inter-reader variability. By applying individually measured T1-time correction, both intra- and inter-observer variability were found to increase. Out of all investigated DCE parameters, k_ep is the most robust to this investigated bias.

Basic concepts and applications of functional magnetic resonance imaging for radiotherapy of prostate cancer

Recently, the interest to integrate magnetic resonance imaging (MRI) in radiotherapy for prostate cancer has increased considerably. MRI can contribute in all steps of the radiotherapy workflow from diagnosis, staging, and target definition to treatment follow-up. Of particular interest is the ability of MRI to provide a wide range of functional measures. The complexity of MRI as an imaging modality combined with the growing interest of the application to prostate cancer radiotherapy, emphasize the need for dedicated education within the radiation oncology community. In this context, an overview of the most common as well as a few upcoming functional MR imaging techniques is presented: the basic methodology and measurement is described, the link between the functional measures and the underlying biology is established, and finally relevant applications of functional MRI useful for prostate cancer radiotherapy are given.

Evolution of prostate MRI: from multiparametric standard to less-is-better and different-is better strategies

Multiparametric magnetic resonance imaging (mpMRI) has become the standard of care to achieve accurate and reproducible diagnosis of prostate cancer. However, mpMRI is quite demanding in terms of technical rigour, patient's tolerability and safety, expertise in interpretation, and costs. This paper reviews the main technical strategies proposed as less-is-better solutions for clinical practice (non-contrast biparametric MRI, reduction of acquisition time, abbreviated protocols, computer-aided diagnosis systems), discussing them in the light of the available evidence and of the concurrent evolution of Prostate Imaging Reporting and Data System (PI-RADS). We also summarised research results on those advanced techniques representing an alternative different-is-better line of the still ongoing evolution of prostate MRI (quantitative diffusion-weighted imaging, quantitative dynamic contrast enhancement, intravoxel incoherent motion, diffusion tensor imaging, diffusional kurtosis imaging, restriction spectrum imaging, radiomics analysis, hybrid positron emission tomography/MRI).

Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer

OBJECTIVE

To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predict early intensity-modulated radiation therapy (IMRT) response, Gleason scores (GS) and prostate cancer (Pca) stages.

METHODS

Thirty-three Pca patients were included. All patients underwent pre- and post-IMRT T2-weighted (T2 W) and apparent diffusing coefficient (ADC) MRI. IMRT response was calculated in terms of changes in the ADC value, and patients were divided as responders and non-responders. A wide range of radiomic features from different feature sets were extracted from all T2 W and ADC images. Univariate radiomic analysis was performed to find highly correlated radiomic features with IMRT response, and a paired t test was used to find significant features between responders and non-responders. To find high predictive radiomic models, tenfold cross-validation as the criterion for feature selection and classification was applied on the pre-, post- and delta IMRT radiomic features, and area under the curve (AUC) of receiver operating characteristics was calculated as model performance value.

RESULTS

Of 33 patients, 15 patients (45%) were found as responders. Univariate analysis showed 20 highly correlated radiomic features with IMRT response (20 ADC and 20 T2). Two and fifteen T2 and ADC radiomic features were found as significant (P-value ≤ 0.05) features between responders and non-responders, respectively. Several cross-combined predictive radiomic models were obtained, and post-T2 radiomic models were found as high predictive models (AUC 0.632) followed by pre-ADC (AUC 0.626) and pre-T2 (AUC 0.61). For GS prediction, T2 W radiomic models were found as more predictive (mean AUC 0.739) rather than ADC models (mean AUC 0.70), while for stage prediction, ADC models had higher prediction performance (mean AUC 0.675).

CONCLUSIONS

Radiomic models developed by MR image features and machine learning approaches are noninvasive and easy methods for personalized prostate cancer diagnosis and therapy.

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.

Renal cell carcinoma with venous extension: prediction of inferior vena cava wall invasion by MRI

BACKGROUND

Renal cell carcinoma (RCC) are accompanied by inferior vena cava (IVC) thrombus in up to 10% of the cases, with surgical resection remaining the only curative option. In case of IVC wall invasion, the operative procedure is more challenging and may even require IVC resection. This study aims to determine the diagnostic performance of contrast-enhanced magnetic resonance imaging (MRI) for the assessment of wall invasion by IVC thrombus in patients with RCC, validated with intraoperative findings.

METHODS

Data were collected on 81 patients with RCC and IVC thrombus, who received a radical nephrectomy and vena cava thrombectomy between February 2008 and November 2017. Forty eight patients met the inclusion criteria. Sensitivity and specificity as well as the positive and negative predictive values were calculated for preoperative MRI, based on the assessments of the two readers for visual wall invasion. Furthermore, a logistic regression model was used to determine if there was an association between intraoperative wall adherence and IVC diameter.

RESULTS

Complete occlusion of the IVC lumen or vessel breach could reliably assess IVC wall invasion with a sensitivity of 92.3% (95%-CI: 0.75-0.99) and a specificity of 86.4% (95%-CI: 0.65-0.97) (Fisher-test: p-value< 0.001). The positive predictive value (PPV) was 88.9% (95%-CI: 0.71-0.98) and the negative predictive value reached 90.5% (95%-CI: 0.70-0.99). There was an excellent interobserver agreement for determining IVC wall invasion with a kappa coefficient of 0.90 (95%CI: 0.79-1.00).

CONCLUSIONS

The present study indicates that standard preoperative MR imaging can be used to reliably assess IVC wall invasion, evaluating morphologic features such as the complete occlusion of the IVC lumen or vessel breach. Increases in IVC diameter are associated with a higher probability of IVC wall invasion.