Dynamic contrast-enhanced MRI of prostate cancer at 3 T: a study of pharmacokinetic parameters

Auto-segmentation of thoraco-abdominal organs in pediatric dynamic MRI

Purpose

Analysis of the abnormal motion of thoraco-abdominal organs in respiratory disorders such as the Thoracic Insufficiency Syndrome (TIS) and scoliosis such as adolescent idiopathic scoliosis (AIS) or early onset scoliosis (EOS) can lead to better surgical plans. We can use healthy subjects to find out the normal architecture and motion of a rib cage and associated organs and attempt to modify the patient's deformed anatomy to match to it. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for capturing dynamic images of healthy pediatric subjects. In this paper, we propose an auto-segmentation set-up for the lungs, kidneys, liver, spleen, and thoraco-abdominal skin in these dMRI images which have their own challenges such as poor contrast, image non-standardness, and similarity in texture amongst gas, bone, and connective tissue at several inter-object interfaces.

Methods

The segmentation set-up has been implemented in two steps: recognition and delineation using two deep neural network (DL) architectures (say DL-R and DL-D) for the recognition step and delineation step, respectively. The encoder-decoder framework in DL-D utilizes features at four different resolution levels to counter the challenges involved in the segmentation. We have evaluated on dMRI sagittal acquisitions of 189 (near-)normal subjects. The spatial resolution in all dMRI acquisitions is 1.46 mm in a sagittal slice and 6.00 mm between sagittal slices. We utilized images of 89 (10) subjects at end inspiration for training (validation). For testing we experimented with three scenarios: utilizing (1) the images of 90 (=189-89-10) different (remaining) subjects at end inspiration for testing, (2) the images of the aforementioned 90 subjects at end expiration for testing, and (3) the images of the aforesaid 99 (=89+10) subjects but at end expiration for testing. In some situations, we can take advantage of already available ground truth (GT) of a subject at a particular respiratory phase to automatically segment the object in the image of the same subject at a different respiratory phase and then refining the segmentation to create the final GT. We anticipate that this process of creating GT would require minimal post hoc correction. In this spirit, we conducted separate experiments where we assume to have the ground truth of the test subjects at end expiration for scenario (1), end inspiration for (2), and end inspiration for (3).

Results

Amongst these three scenarios of testing, for the DL-R, we achieve a best average location error (LE) of about 1 voxel for the lungs, kidneys, and spleen and 1.5 voxels for the liver and the thoraco- abdominal skin. The standard deviation (SD) of LE is about 1 or 2 voxels. For the delineation approach, we achieve an average Dice coefficient (DC) of about 0.92 to 0.94 for the lungs, 0.82 for the kidneys, 0.90 for the liver, 0.81 for the spleen, and 0.93 for the thoraco-abdominal skin. The SD of DC is lower for the lungs, liver, and the thoraco-abdominal skin, and slightly higher for the spleen and kidneys.

Conclusions

Motivated by applications in surgical planning for disorders such as TIS, AIS, and EOS, we have shown an auto-segmentation system for thoraco-abdominal organs in dMRI acquisitions. This proposed setup copes with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness quite well. We are in the process of testing its effectiveness on TIS patient dMRI data.

Magnetic Resonance Imaging Biomarkers of Bone and Soft Tissue Tumors

Magnetic resonance imaging (MRI) is essential in the management of musculoskeletal (MSK) tumors. This review delves into the diverse MRI modalities, focusing on anatomical, functional, and metabolic sequences that provide essential biomarkers for tumor detection, characterization, disease extent determination, and assessment of treatment response. MRI's multimodal capabilities offer a range of biomarkers that enhance MSK tumor evaluation, aiding in better patient management.

Quantitative Analysis of Prostate MRI: Correlation between Contrast-Enhanced Magnetic Resonance Fingerprinting and Dynamic Contrast-Enhanced MRI Parameters

This research aimed to assess the relationship between contrast-enhanced (CE) magnetic resonance fingerprinting (MRF) values and dynamic contrast-enhanced (DCE) MRI parameters including (Ktrans, Kep, Ve, and iAUC). To evaluate the correlation between the MRF-derived values (T1 and T2 values, CE T1 and T2 values, T1 and T2 change) and DCE-MRI parameters and the differences in the parameters between prostate cancer and noncancer lesions in 68 patients, two radiologists independently drew regions-of-interest (ROIs) at the focal prostate lesions. Prostate cancer was identified in 75% (51/68) of patients. The CE T2 value was significantly lower in prostate cancer than in noncancer lesions in the peripheral zone and transition zone. Ktrans, Kep, and iAUC were significantly higher in prostate cancer than noncancer lesions in the peripheral zone (p < 0.05), but not in the transition zone. The CE T1 value was significantly correlated with Ktrans, Ve, and iAUC in prostate cancer, and the CE T2 value was correlated to Ve in noncancer. Some CE MRF values are different between prostate cancer and noncancer tissues and correlate with DCE-MRI parameters. Prostate cancer and noncancer tissues may have different characteristics regarding contrast enhancement.

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.

Emerging Role of Nuclear Medicine in Prostate Cancer: Current State and Future Perspectives

Prostate cancer is the most frequent epithelial neoplasia after skin cancer in men starting from 50 years and prostate-specific antigen (PSA) dosage can be used as an early screening tool. Prostate cancer imaging includes several radiological modalities, ranging from ultrasonography, computed tomography (CT), and magnetic resonance to nuclear medicine hybrid techniques such as single-photon emission computed tomography (SPECT)/CT and positron emission tomography (PET)/CT. Innovation in radiopharmaceutical compounds has introduced specific tracers with diagnostic and therapeutic indications, opening the horizons to targeted and very effective clinical care for patients with prostate cancer. The aim of the present review is to illustrate the current knowledge and future perspectives of nuclear medicine, including stand-alone diagnostic techniques and theragnostic approaches, in the clinical management of patients with prostate cancer from initial staging to advanced disease.

Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer

BACKGROUND

To assess the performance of prospectively accelerated and deep learning (DL) reconstructed T2-weighted (T2w) imaging in volunteers and patients with histologically proven prostate cancer (PCa).

METHODS

Prospectively undersampled T2w datasets were acquired with acceleration factors of 1.7 (reference), 3.4 and 4.8 in 10 healthy volunteers and 23 patients with histologically proven PCa. Image reconstructions using compressed SENSE (C-SENSE) and a combination of C-SENSE and DL-based artificial intelligence (C-SENSE AI) were analyzed. Qualitative image comparison was performed using a 6-point Likert scale (overall image quality, noise, motion artifacts, lesion detection, diagnostic certainty); the T2 and PI-RADS scores were compared between the two reconstructions. Additionally, quantitative image parameters were assessed (apparent SNR, apparent CNR, lesion size, line profiles).

RESULTS

All C-SENSE AI-reconstructed images received a significantly higher qualitative rating compared to the C-SENSE standard images. Analysis of the quantitative parameters supported this finding, with significantly higher aSNR and aCNR. The line profiles demonstrated a significantly steeper signal change at the border of the prostatic lesion and the adjacent normal tissue in the C-SENSE AI-reconstructed images, whereas the T2 and PI-RADS scores as well as the lesion size did not differ.

CONCLUSION

In this prospective study, we demonstrated the clinical feasibility of a novel C-SENSE AI reconstruction enabling a 58% acceleration in T2w imaging of the prostate while obtaining significantly better image quality.

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.

Identification of prostate cancer using multiparametric MR imaging characteristics of prostate tissues referenced to whole mount histopathology

In this study, the objective was to characterize the MR signatures of the various benign prostate tissues and to differentiate them from cancer. Data was from seventy prostate cancer patients who underwent multiparametric MRI (mpMRI) and subsequent prostatectomy. The scans included T2-weighted imaging (T2W), diffusion weighted imaging, dynamic contrast-enhanced MRI (DCE MRI), and MR spectroscopic imaging. Histopathology tissue information was translated to MRI images. The mpMRI parameters were characterized separately per zone and by tissue type. The tissues were ordered according to trends in tissue parameter means. The peripheral zone tissue order was cystic atrophy, high grade prostatic intraepithelial neoplasia (HGPIN), normal, atrophy, inflammation, and cancer. Decreasing values for tissue order were exhibited by ADC (1.8 10^-3 mm²/s to 1.2 10^-3 mm²/s) and T2W intensity (3447 to 2576). Increasing values occurred for DCE MRI peak (143% to 157%), DCE MRI slope (101%/min to 169%/min), fractional anisotropy (FA) (0.16 to 0.19), choline (7.2 to 12.2), and choline / citrate (0.3 to 0.9). The transition zone tissue order was cystic atrophy, mixed benign prostatic hyperplasia (BPH), normal, atrophy, inflammation, stroma, anterior fibromuscular stroma, and cancer. Decreasing values occurred for ADC (1.6 10^-3 mm²/s to 1.1 10^-3 mm²/s) and T2W intensity (2863 to 2001). Increasing values occurred for DCE MRI peak (143% to 150%), DCE MRI slope (101%/min to 137%/min), FA (0.18 to 0.25), choline (7.9 to 11.7), and choline / citrate (0.3 to 0.7). Logistic regression was used to create parameter model fits to differentiate cancer from benign prostate tissues. The fits achieved AUCs ≥0.91. This study quantified the mpMRI characteristics of benign prostate tissues and demonstrated the capability of mpMRI to discriminate among benign as well as cancer tissues, potentially aiding future discrimination of cancer from benign confounders.

Revisiting DCE-MRI: Classification of Prostate Tissue Using Descriptive Signal Enhancement Features Derived From DCE-MRI Acquisition With High Spatiotemporal Resolution

METHODS

A retrospective study (from January 2016 to July 2019) including 75 subjects (mean, 65 years; 46-80 years) with 2.5-second temporal resolution DCE-MRI and PIRADS 4 or 5 lesions was performed. Fifty-four subjects had biopsy-proven prostate cancer (Gleason 6, 15; Gleason 7, 20; Gleason 8, 13; Gleason 9, 6), whereas 21 subjects had negative MRI/ultrasound fusion-guided biopsies. Voxel-wise analysis of contrast signal enhancement was performed for all time points using custom-developed software, including automatic arterial input function detection. Seven descriptive parameter maps were calculated: normalized maximum signal intensity, time to start, time to maximum, time-to-maximum slope, and maximum slope with normalization on maximum signal and the arterial input function (SMN1, SMN2). The parameters were compared with ADC using multiparametric machine-learning models to determine classification accuracy. A Wilcoxon test was used for the hypothesis test and the Spearman coefficient for correlation.

RESULTS

There were significant differences (P < 0.05) for all 7 DCE-derived parameters between the normal peripheral zone versus PIRADS 4 or 5 lesions and the biopsy-positive versus biopsy-negative lesions. Multiparametric analysis showed better performance when combining ADC + DCE as input (accuracy/sensitivity/specificity, 97%/93%/100%) relative to ADC alone (accuracy/sensitivity/specificity, 94%/95%/95%) and to DCE alone (accuracy/sensitivity/specificity, 78%/79%/77%) in differentiating the normal peripheral zone from PIRADS lesions, biopsy-positive versus biopsy-negative lesions (accuracy/sensitivity/specificity, 68%/33%/81%), and Gleason 6 versus ≥7 prostate cancer (accuracy/sensitivity/specificity, 69%/60%/72%).

CONCLUSIONS

Descriptive perfusion characteristics derived from high-resolution DCE-MRI using model-free computations show significant differences between normal and cancerous tissue but do not reach the accuracy achieved with solely ADC-based classification. Combining ADC with DCE-based input features improved classification accuracy for PIRADS lesions, discrimination of biopsy-positive versus biopsy-negative lesions, and differentiation between Gleason 6 versus Gleason ≥7 lesions.

Impact of qualitative, semi-quantitative, and quantitative analyses of dynamic contrast-enhanced magnet resonance imaging on prostate cancer detection

Dynamic contrast enhanced imaging (DCE) as an integral part of multiparametric prostate magnet resonance imaging (mpMRI) can be evaluated using qualitative, semi-quantitative, or quantitative assessment methods. Aim of this study is to analyze the clinical benefits of these evaluations of DCE regarding clinically significant prostate cancer (csPCa) detection and grading. 209 DCE data sets of 103 consecutive patients with mpMRI (T2, DWI, and DCE) and subsequent MRI-(in-bore)-biopsy were retrospectively analyzed. Qualitative DCE evaluation according to PI-RADS v2.1, semi-quantitative (curve type; DCE score according to PI-RADS v1), and quantitative Tofts analyses (Ktrans, kep, and ve) as well as PI-RADS v1 and v2.1 overall classification of 209 lesions (92 PCa, 117 benign lesions) were performed. Of each DCE assessment method, cancer detection, discrimination of csPCa, and localization were assessed and compared to histopathology findings. All DCE analyses (p<0.01-0.05), except ve (p = 0.02), showed significantly different results for PCa and benign lesions in the peripheral zone (PZ) with area under the curve (AUC) values of up to 0.92 for PI-RADS v2.1 overall classification. In the transition zone (TZ) only the qualitative DCE evalulation within PI-RADS (v1 and v2.1) could distinguish between PCa and benign lesions (p<0.01; AUC = 0.95). None of the DCE parameters could differentiate csPCa from non-significant (ns) PCa (p ≥ 0.1). Qualitative analysis of DCE within mpMRI according to PI-RADS version 2.1 showed excellent results regarding (cs)PCa detection. Semi-quantitative and quantitative parameters provided no additional improvements. DCE alone wasn't able to discriminate csPCa from nsPCa.

Multiparametric MRI as a Biomarker of Response to Neoadjuvant Therapy for Localized Prostate Cancer-A Pilot Study

RATIONALE AND OBJECTIVES

To explore a role for multiparametric MRI (mpMRI) as a biomarker of response to neoadjuvant androgen deprivation therapy (ADT) for prostate cancer (PCa).

MATERIALS AND METHODS

This prospective study was approved by the institutional review board and was HIPAA compliant. Eight patients with localized PCa had a baseline mpMRI, repeated after 6-months of ADT, followed by prostatectomy. mpMRI indices were extracted from tumor and normal regions of interest (TROI/NROI). Residual cancer burden (RCB) was measured on mpMRI and on the prostatectomy specimen. Paired t-tests compared TROI/NROI mpMRI indices and pre/post-treatment TROI mpMRI indices. Spearman's rank tested for correlations between MRI/pathology-based RCB, and between pathological RCB and mpMRI indices.

RESULTS

At baseline, TROI apparent diffusion coefficient (ADC) was lower and dynamic contrast enhanced (DCE) metrics were higher, compared to NROI (ADC: 806 ± 137 × 10^-6 vs. 1277 ± 213 × 10^-6 mm²/sec, p = 0.0005; K^trans: 0.346 ± 0.16 vs. 0.144 ± 0.06 min^-1, p = 0.002; AUC₉₀: 0.213 ± 0.08 vs. 0.11 ± 0.03, p = 0.002). Post-treatment, there was no change in TROI ADC, but a decrease in TROI K^trans (0.346 ± 0.16 to 0.188 ± 0.08 min^-1; p = 0.02) and AUC₉₀ (0.213 ± 0.08 to 0.13 ± 0.06; p = 0.02). Tumor volume decreased with ADT. There was no difference between mpMRI-based and pathology-based RCB, which positively correlated (⍴ = 0.74-0.81, p < 0.05). Pathology-based RCB positively correlated with post-treatment DCE metrics (⍴ = 0.76-0.70, p < 0.05) and negatively with ADC (⍴ = -0.79, p = 0.03).

CONCLUSION

Given the heterogeneity of PCa, an individualized approach to ADT may maximize potential benefit. This pilot study suggests that mpMRI may serve as a biomarker of ADT response and as a surrogate for RCB at prostatectomy.

Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy

PURPOSE

Hierarchical clustering (HC), an unsupervised machine learning (ML) technique, was applied to multi-parametric MR (mp-MR) for prostate cancer (PCa). The aim of this study is to demonstrate HC can diagnose PCa in a straightforward interpretable way, in contrast to deep learning (DL) techniques.

METHODS

HC was constructed using mp-MR including intravoxel incoherent motion, diffusion kurtosis imaging, and dynamic contrast-enhanced MRI from 40 tumor and normal tissues in peripheral zone (PZ) and 23 tumor and normal tissues in transition zone (TZ). HC model was optimized by assessing the combinations of several dissimilarity and linkage methods. Goodness of HC model was validated by internal methods.

RESULTS

Accuracy for differentiating tumor and normal tissue by optimal HC model was 96.3% in PZ and 97.8% in TZ, comparable to current clinical standards. Relationship between input (DWI and permeability parameters) and output (tumor and normal tissue cluster) was shown by heat maps, consistent with literature.

CONCLUSION

HC can accurately differentiate PCa and normal tissue, comparable to state-of-the-art diffusion based parameters. Contrary to DL techniques, HC is an operator-independent ML technique producing results that can be interpreted such that the results can be knowledgeably judged.

Prostatitis, the Great Mimicker of Prostate Cancer: Can We Differentiate Them Quantitatively With Multiparametric MRI?

OBJECTIVE. The purpose of this study was to investigate the diagnostic performance of semiquantitative and quantitative pharmacokinetic parameters and quantitative apparent diffusion coefficient (ADC) values obtained from prostate multiparametric MRI (mpMRI) to differentiate prostate cancer (PCa) and prostatitis objectively. MATERIALS AND METHODS. We conducted a retrospective review of patients with biopsy-proven PCa or prostatitis who underwent mpMRI study between January 2015 and February 2018. Mean ADC, forward volume transfer constant (K^trans), reverse volume transfer constant (k_ep), plasma volume fraction (V_p), extravascular extracellular space volume fraction (V_e), and time to peak (TTP) values were calculated for both lesions and contralateral normal prostate tissue. Signal intensity-time curves were analyzed. Lesion-to-normal prostate tissue ratios of pharmacokinetic parameters were also calculated. The diagnostic accuracy and cutoff points of all parameters were analyzed to differentiate PCa from prostatitis. RESULTS. A total of 138 patients (94 with PCa and 44 with prostatitis) were included in the study. Statistically, ADC, quantitative pharmacokinetic parameters (K^trans, k_ep, V_e, and V_p), their lesion-to-normal prostate tissue ratios, and TTP values successfully differentiated PCa and prostatitis. Surprisingly, we found that V_e values were significantly higher in prostatitis lesions. The combination of these parameters had 92.7% overall diagnostic accuracy. ADC, k_ep, and TTP made up the most successful combination for differential diagnosis. Analysis of the signal intensity-time curves showed mostly type 2 and type 3 enhancement curve patterns for patients with PCa. Type 3 curves were not seen in any prostatitis cases. CONCLUSION. Quantitative analysis of mpMRI differentiates PCa from prostatitis with high sensitivity and specificity, appears to have significant potential, and may improve diagnostic accuracy. In addition, evaluating these parameters does not cause any extra burden to the patients.

Ultrasound elastography in characterization of prostatic lesions: correlation with histopathological findings

OBJECTIVE

Ultrasound elastography is increasingly used in the diagnosis of prostate cancer, however results are heterogeneous. We correlate in a large sample-size prospective study the accuracy of elastography, aiming to settle an accurate cut-off point for diagnosis and possibility of use as a screening tool.

METHODS

Prospective study that included 120 patients with mean age 59.5 ± 9.8 years, showing enlarged prostate by clinical examination with prostate-specific antigen >4 ng ml-1.The study was done using high frequency high resolution endorectal probe with real time tissue elastography.Grayscale ultrasound examination was done first with Doppler followed by elastography color-coded map and strain ratio measurement. Then, transrectal ultrasound-guided core biopsy was done from suspicious areas detected by elastography (totally or partly stiff by color-coded map or with relative increased strain ratio), besides standard six-quadrant core biopsy samples.

RESULTS

There was statistically significant difference (p < 0.001) regarding strain ratio in benign and malignant lesions. Strain ratio showed significant proportionate correlation with prostate-specific antigen level and Gleason pathological score, while no significant correlation noted with the age or the prostatic volume. A strain ratio with a cut-off value of 1.9 showed a sensitivity of 100%, specificity 93.8%, positive predictive value of 79.3%, negative predictive value 100 and 95% accuracy in differentiating between malignant and benign lesions.

CONCLUSION

Strain ratio improves the detection of prostatic cancer with high sensitivity (100%) and high negative predictive value (100%).

ADVANCES IN KNOWLEDGE

Different prostatic lesions are mostly similar in grayscale ultrasound.Imaging plays an important role in differentiation of prostatic nodules.Ultrasound elastography may play an important role in distinguishing benign from malignant nodules.

Magnetic Resonance Imaging Assessment After Therapy in Prostate Cancer

Prostate cancer is the fifth leading cause of death worldwide. A variety of treatment options is available for localized prostate cancer and may range from active surveillance to focal therapy or whole gland treatment, that is, surgery or radiotherapy. Serum prostate-specific antigen levels are an important tool to monitor treatment success after whole gland treatment, unfortunately prostate-specific antigen is unreliable after focal therapy. Multiparametric magnetic resonance imaging of the prostate is rapidly gaining field in the management of prostate cancer and may play a crucial role in the evaluation of recurrent prostate cancer. This article will focus on postprocedural magnetic resonance imaging after different forms of local therapy in patients with prostate cancer.

Evaluating the scope of intramedullary invasion of malignant bone tumor by DCE-MRI quantitative parameters in animal study

The purpose was to analyze the value of quantitative parameters of DCE-MRI in evaluating micro-infiltration of malignant bone tumors.

METHODS

Thirty-nine New Zealand white rabbits were used to establish malignant bone tumor models by implanting VX2 tumor fragments into the right tibiae. After three weeks, models were examined by conventional MRI and DCE-MRI; then the right tibiae were cut into sagittal sections and partitioned into histology slices for comparison with microscopic findings. Micro-infiltration groups were selected and the range of infiltration was determined under the microscope, and corresponding DCE images analyzed to obtain the quantitative parameters include Ktrans, Kep, v_e and v_p in parenchyma areas, micro-infiltration areas and simple edema areas. One-way ANOVA was used to compare the differences of the parameters between the three areas. Receiver operating characteristic curves (ROCs) were plotted to determine the accuracy of different parameters by area under curves (AUCs).

RESULTS

22 cases (22/39, 56.4%) were included in the micro-infiltration group and the infiltration depth ranged from 1.3 mm to 4.6 mm, with an average depth of 3.2 mm ± 0.8 mm. The statistical results of quantitative parameters in the three areas were as follows: Ktrans values were (0.494 ± 0.052), (0.403  ±  0.049), (0.173 ± 0.047) min^-1 (p = =0.000), Kep values were (1.959 ± 0.65), (1.528 ± 0.372), (1.174 ± 0.486) min^-1 (p = =0.000), v_e values were (0.247 ± 0.068), (0.283 ± 0.057), (0.168 ± 0.062) min^-1 (p = =0.000), v_p values were (0.125 ± 0.036), (0.108 ± 0.033), (0.098 ± 0.025) min^-1 (p = =0.022), respectively. Ktrans and Kep values had significant difference in the three areas after comparing between-groups, respectively. However, there were no significant difference in v_p values between parenchyma and micro-infiltration areas (p = =0.078), micro-infiltration and simple edema areas (p = =0.315), and v_e values between parenchyma and micro-infiltration areas (p = =0.056). The v_e values were higher in parenchyma and micro-infiltration areas then simple edema areas. Ktrans had highest accuracy in differentiating different areas (AUC > 0.9), respectively.

CONCLUSION

Quantitative parameters Ktrans, Kep and v_e can assess the extent of intramedullary invasion of malignant bone tumors. Ktrans have highest accuracy in differentiating different regions.

Volumetric and Voxel-Wise Analysis of Dominant Intraprostatic Lesions on Multiparametric MRI

Introduction: Multiparametric MR imaging (mpMRI) has shown promising results in the diagnosis and localization of prostate cancer. Furthermore, mpMRI may play an important role in identifying the dominant intraprostatic lesion (DIL) for radiotherapy boost. We sought to investigate the level of correlation between dominant tumor foci contoured on various mpMRI sequences. Methods: mpMRI data from 90 patients with MR-guided biopsy-proven prostate cancer were obtained from the SPIE-AAPM-NCI Prostate MR Classification Challenge. Each case consisted of T2-weighted (T2W), apparent diffusion coefficient (ADC), and K^trans images computed from dynamic contrast-enhanced sequences. All image sets were rigidly co-registered, and the dominant tumor foci were identified and contoured for each MRI sequence. Hausdorff distance (HD), mean distance to agreement (MDA), and Dice and Jaccard coefficients were calculated between the contours for each pair of MRI sequences (i.e., T2 vs. ADC, T2 vs. K^trans, and ADC vs. K^trans). The voxel wise spearman correlation was also obtained between these image pairs. Results: The DILs were located in the anterior fibromuscular stroma, central zone, peripheral zone, and transition zone in 35.2, 5.6, 32.4, and 25.4% of patients, respectively. Gleason grade groups 1-5 represented 29.6, 40.8, 15.5, and 14.1% of the study population, respectively (with group grades 4 and 5 analyzed together). The mean contour volumes for the T2W images, and the ADC and K^trans maps were 2.14 ± 2.1, 2.22 ± 2.2, and 1.84 ± 1.5 mL, respectively. K^trans values were indistinguishable between cancerous regions and the rest of prostatic regions for 19 patients. The Dice coefficient and Jaccard index were 0.74 ± 0.13, 0.60 ± 0.15 for T2W-ADC and 0.61 ± 0.16, 0.46 ± 0.16 for T2W-K^trans. The voxel-based Spearman correlations were 0.20 ± 0.20 for T2W-ADC and 0.13 ± 0.25 for T2W-K^trans. Conclusions: The DIL contoured on T2W images had a high level of agreement with those contoured on ADC maps, but there was little to no quantitative correlation of these results with tumor location and Gleason grade group. Technical hurdles are yet to be solved for precision radiotherapy to target the DILs based on physiological imaging. A Boolean sum volume (BSV) incorporating all available MR sequences may be reasonable in delineating the DIL boost volume.

Quantitative contrast-enhanced perfusion kinetics in multiparametric MRI in differentiating prostate cancer from chronic prostatitis: results from a pilot study

OBJECTIVE

The objectives of the study were to analyze the apparent diffusion coefficient (ADC), k^trans, k_ep metrics in dynamic contrast-enhanced multiparametric MRI (DCE-mpMRI) in biopsy proven cases of prostate cancer (PCa) and prostatitis and to establish "cut-off" values for various pharmacokinetic parameters that may distinguish PCa from chronic prostatitis.

METHODS

A retrospective review of all cases of PCa and chronic prostatitis patients, who underwent DCE-mpMRI in our institute was done from July 2017 to January 2019. Mean ADC, k^trans , k_ep for lesion "L" (ADCL, k^trans _L , k_epL) and normal prostate tissue "N" (ADCN, k^trans _N , k_epN ,) were calculated for each region of interest. Different ratios ADC ratio (defined as ADCL/ ADCN), k^trans ratio (k^trans _L/ k^trans _N), k_epratio (k_epL/k_epN) were calculated to differentiate PCa from chronic prostatitis.

RESULTS

Total of biopsy proven 14 cases of PCa and 18 cases of chronic prostatitis were included in the study. For k^trans ratio, the optimal cut-off was at 1.49 units where sensitivity was 85.7%, specificity was 61.1 % and Youden's index was 0.468 %. Similarly, optimal cut-offs determined for k_ep lesion was 0.86 (sensitivity 85.7%, specificity 66.7%, J = 0.524) and for k_ep ratio was 1.34 units (sensitivity 78.6%, specificity 66.7%, J = 0.543).

CONCLUSION

DCE-mpMRI metrics could differentiate between PCa and chronic prostatitis with good specificity and sensitivity, while ProstateImaging Reporting and Data System v. 2 alone, did not differentiate between these patterns.

ADVANCES IN KNOWLEDGE

k^trans ratio, k_ep lesion and k_ep ratio can differentiate PCa from chronic prostatitis.

Comparison of Qualitative (Time Intensity Curve Analysis), Semi-Quantitative, and Quantitative Multi-Phase 3T DCEMRI Parameters as Predictors of Malignancy in Adnexal

Objective:

The present study aimed to compare the qualitative (time intensity curve analysis), the semi-quantitative and the quantitative multiphase 3T dynamic contrast-enhanced (DCE) MRI parameters as predictors of malignancy in adnexal masses. Materials and

Methods:

In this prospective study, women with an adnexal mass who were scheduled for surgical resection or were followed for more than one year period to confirm the benignity of their lesions, underwent multiphase 3T DCE-MRI. The qualitative (time intensity curve), semi-quantitative (SImax, SIrel, WIR) and quantitative (Ktrans, Kep, Vb) analyses were performed on DCE-MRI sequences and their predictive values were compared.

Results:

A total of 17 benign and 14 malignant lesions were included. According to the qualitative analysis, none of the lesions with Type I time intensity curves (TIC) were malignant and none of the masses with Type III TICs were benign. The accuracy of the quantitative parameters in detection of malignancy was found to be higher than that of semi-quantitative variables, particularly when calculated for a small ROI within the high signal area of the mass (sROI) rather than the largest ROI including the whole mass (lROI), and when inter-MRI variations were omitted using ratios. The Kep(tumor)/Kep(myometrium) ratio measured from sROI was the best parameter for differentiating a malignant lesion with a sensitivity of 100% and a specificity of 92.3%.

Conclusion:

We concluded that a Type I TIC confirms a benign lesion, and a type III TIC confirms the malignancy and further evaluation is not recommended for these lesions. So complementary quantitative analysis is only recommended for adnexal masses with type II TICs.

Multiparametric MRI to improve detection of prostate cancer compared with transrectal ultrasound-guided prostate biopsy alone: the PROMIS study

BACKGROUND

Men with suspected prostate cancer usually undergo transrectal ultrasound (TRUS)-guided prostate biopsy. TRUS-guided biopsy can cause side effects and has relatively poor diagnostic accuracy. Multiparametric magnetic resonance imaging (mpMRI) used as a triage test might allow men to avoid unnecessary TRUS-guided biopsy and improve diagnostic accuracy.

OBJECTIVES

To (1) assess the ability of mpMRI to identify men who can safely avoid unnecessary biopsy, (2) assess the ability of the mpMRI-based pathway to improve the rate of detection of clinically significant (CS) cancer compared with TRUS-guided biopsy and (3) estimate the cost-effectiveness of a mpMRI-based diagnostic pathway.

DESIGN

A validating paired-cohort study and an economic evaluation using a decision-analytic model.

SETTING

Eleven NHS hospitals in England.

PARTICIPANTS

Men at risk of prostate cancer undergoing a first prostate biopsy.

INTERVENTIONS

Participants underwent three tests: (1) mpMRI (the index test), (2) TRUS-guided biopsy (the current standard) and (3) template prostate mapping (TPM) biopsy (the reference test).

MAIN OUTCOME MEASURES

Diagnostic accuracy of mpMRI, TRUS-guided biopsy and TPM-biopsy measured by sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using primary and secondary definitions of CS cancer. The percentage of negative magnetic resonance imaging (MRI) scans was used to identify men who might be able to avoid biopsy.

RESULTS

Diagnostic study - a total of 740 men were registered and 576 underwent all three tests. According to TPM-biopsy, the prevalence of any cancer was 71% [95% confidence interval (CI) 67% to 75%]. The prevalence of CS cancer according to the primary definition (a Gleason score of ≥ 4 + 3 and/or cancer core length of ≥ 6 mm) was 40% (95% CI 36% to 44%). For CS cancer, TRUS-guided biopsy showed a sensitivity of 48% (95% CI 42% to 55%), specificity of 96% (95% CI 94% to 98%), PPV of 90% (95% CI 83% to 94%) and NPV of 74% (95% CI 69% to 78%). The sensitivity of mpMRI was 93% (95% CI 88% to 96%), specificity was 41% (95% CI 36% to 46%), PPV was 51% (95% CI 46% to 56%) and NPV was 89% (95% CI 83% to 94%). A negative mpMRI scan was recorded for 158 men (27%). Of these, 17 were found to have CS cancer on TPM-biopsy. Economic evaluation - the most cost-effective strategy involved testing all men with mpMRI, followed by MRI-guided TRUS-guided biopsy in those patients with suspected CS cancer, followed by rebiopsy if CS cancer was not detected. This strategy is cost-effective at the TRUS-guided biopsy definition 2 (any Gleason pattern of ≥ 4 and/or cancer core length of ≥ 4 mm), mpMRI definition 2 (lesion volume of ≥ 0.2 ml and/or Gleason score of ≥ 3 + 4) and cut-off point 2 (likely to be benign) and detects 95% (95% CI 92% to 98%) of CS cancers. The main drivers of cost-effectiveness were the unit costs of tests, the improvement in sensitivity of MRI-guided TRUS-guided biopsy compared with blind TRUS-guided biopsy and the longer-term costs and outcomes of men with cancer.

LIMITATIONS

The PROstate Magnetic resonance Imaging Study (PROMIS) was carried out in a selected group and excluded men with a prostate volume of > 100 ml, who are less likely to have cancer. The limitations in the economic modelling arise from the limited evidence on the long-term outcomes of men with prostate cancer and on the sensitivity of MRI-targeted repeat biopsy.

CONCLUSIONS

Incorporating mpMRI into the diagnostic pathway as an initial test prior to prostate biopsy may (1) reduce the proportion of men having unnecessary biopsies, (2) improve the detection of CS prostate cancer and (3) increase the cost-effectiveness of the prostate cancer diagnostic and therapeutic pathway. The PROMIS data set will be used for future research; this is likely to include modelling prognostic factors for CS cancer, optimising MRI scan sequencing and biomarker or translational research analyses using the blood and urine samples collected. Better-quality evidence on long-term outcomes in prostate cancer under the various management strategies is required to better assess cost-effectiveness. The value-of-information analysis should be developed further to assess new research to commission.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN16082556 and NCT01292291.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 22, No. 39. See the NIHR Journals Library website for further project information. This project was also supported and partially funded by the NIHR Biomedical Research Centre at University College London (UCL) Hospitals NHS Foundation Trust and UCL and by The Royal Marsden NHS Foundation Trust and The Institute of Cancer Research Biomedical Research Centre and was co-ordinated by the Medical Research Council's Clinical Trials Unit at UCL (grant code MC_UU_12023/28). It was sponsored by UCL. Funding for the additional collection of blood and urine samples for translational research was provided by Prostate Cancer UK.

Quantitative assessment of dynamic 18F-flumethycholine PET and dynamic contrast enhanced MRI in high risk prostate cancer

OBJECTIVE:

To describe dynamic ¹⁸F-flumethycholine PET (dPET) and dynamic contrast enhancement MR (DCE MR) parameters in localized high-risk prostate cancer (PCa), and determine whether these differ from normal prostate. Furthermore, to determine whether a correlation exists between dPET and DCE MR parameters.

METHODS:

41 consenting patients who underwent prostate DCE MR and dPET were included in this institutionally approved study. Intraprostatic lesions on MR were assigned a PI-RADS v2 score, and focal lesions on PET were documented. All lesions were correlated with pathology. Quantitative and semi-quantitative DCE MR and two-tissue compartmental model dPET parameters were determined and tumor-to-normal gland ratios (T/N) for these parameters were calculated. Finally, dPET and DCE MR correlation was estimated using Spearman correlation coefficients.

RESULTS:

There were 46 malignant lesions per standard of reference. On dPET, peripheral zone (PZ) tumors had higher K1 (p < 0.001), and a T/N ratio ≥2 was significant (p < 0.001). On DCE MR, the parameters in, kep, Ktrans and quantitative iAUC were higher for PZ and non-PZ tumors than corresponding normal tissue (p < 0.001); for PZ tumors, a T/N ratio ≥ 1.5 for Ktrans and pei was significant (p = 0.0019 and 0.0026, respectively). Moderate Spearman correlation (0.40 < ρ < 0.59) was found between dPET K1 and DCE MR Ktrans and pei.

CONCLUSION:

In patients with high-risk PCa, quantitative dPET and DCE-MR parameters in primary tumors differ from normal tissue. Only moderate correlation exists between K1 (dPET) and Ktrans and pei (DCE MR). The incremental value of any of these parameters to PI-RADS v2 warrants further investigation.

ADVANCES IN KNOWLEDGE:

Unique quantitative and semi-quantitative FCH PET/MR parameters in PCa differ from normal gland, and should be further investigated to determine their potential contribution to PI-RADS v2 in the detection of clinically significant PCa.

Needle-based optical coherence tomography for the detection of prostate cancer: a visual and quantitative analysis in 20 patients

Diagnostic accuracy of needle-based optical coherence tomography (OCT) for prostate cancer detection by visual and quantitative analysis is defined. 106 three-dimensional (3-D)-OCT data sets were acquired in 20 prostates after radical prostatectomy and precisely matched with pathology. OCT images were grouped per histological category. Two reviewers performed blind assessments of the OCT images. Sensitivity and specificity for malignancy detection were calculated. Quantitative analyses by automated optical attenuation coefficient calculation were performed. OCT can reliably differentiate between fat, cystic, and regular atrophy and benign glands. The overall sensitivity and specificity for malignancy detection was 79% and 88% for reviewer 1 and 88% and 81% for reviewer 2. Quantitative analysis for differentiation between stroma and malignancy showed a significant difference (4.6  mm  -  1 versus 5.0  mm  -  1 Mann-Whitney U-test p  <  0.0001). A Kruskal-Wallis test showed a significant difference in median attenuation coefficient between stroma, inflammation, Gleason 3, and Gleason 4 (4.6, 4.1, 5.9, and 5.0  mm  -  1, respectively). However, attenuation coefficient varied per patient and a related-samples Wilcoxon signed-rank test showed no significant difference per patient (p  =  0.17). This study confirmed the one to one correlation of histopathology and OCT. Precise matching showed that most histological tissues categories in the prostate could be distinguished by their unique pattern in OCT images. In addition, the optical attenuation coefficient can play a role in the differentiation between stroma and malignancy; however, a per patient analysis of the optical attenuation coefficient did not show a significant difference.

Contrast-enhanced magnetic resonance imaging with a novel nano-size contrast agent for the clinical diagnosis of patients with lung cancer

Recent studies have indicated that magnetic resonance imaging (MRI) efficiently diagnoses lung cancer. However, the efficacy of MRI in diagnosing lung cancer requires improving for patients in the early stage of the disease. In the present study, a novel nano-sized contrast agent of chistosan/Fe₃O₄-enclosed bispecific antibodies (BsAbCENS) was introduced, which targeted carcino-embryonic antigen (CEA) and neuron-specific enolase (NSE) in lung cancer cells. The diagnostic efficacy of contrast-enhanced MRI with BsAbCENS (CEMRI-BsAbCENS) was investigated in a total of 182 patients with suspected lung cancer who had high serum levels of CEA and NSE. BsAbCENS was administered by pulmonary inhalation prior to the MRI scan. The results revealed that CEA and NSE were overexpressed in human lung cancer cell lines. BsAbCENS bound with CEA and NSE on the surface of human lung cancer cells and produced a higher signal intensity than MRI alone for the diagnosis of patients with lung cancer. The diagnostic data revealed that CEMRI-BsAbCENS diagnosed 124/182 lung cancer cases, whereas CEMRI only diagnosed 98/182, which was significantly less (P<0.01). In addition, the survival rate of patients with lung cancer diagnosed by CEMRI-BsAbCENS was significantly higher than the mean 5-year survival rate (P<0.01). Furthermore, the pharmacodynamics demonstrated that BsAbCENS was metabolized within 24 h. The results of the present study indicate that the efficacy and accuracy of lung cancer diagnosis are improved by CEMRI-BsAbCENS. In conclusion, these results provide a potential novel protocol for the diagnosis of tumors in patients with suspected early stage lung cancer.

Prostate DCE-MRI with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction using an approximated analytical approach

PURPOSE

To develop and evaluate a practical <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction method for prostate dynamic contrast-enhanced (DCE) MRI analysis.

THEORY

We proposed a simple analytical <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction method using a Taylor series approximation to the steady-state spoiled gradient echo signal equation. This approach only requires <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> maps and uncorrected pharmacokinetic (PK) parameters as input to estimate the corrected PK parameters.

METHODS

The proposed method was evaluated using a prostate digital reference object (DRO), and 82 in vivo prostate DCE-MRI cases. The approximated analytical correction was compared with the ground truth PK parameters in simulation, and compared with the reference numerical correction in in vivo experiments, using percentage error as the metric.

RESULTS

The prostate DRO results showed that our approximated analytical approach provided residual error less than 0.4% for both K^trans and v_e , compared to the ground truth. This noise-free residual error was smaller than the noise-induced error using the reference numerical correction, which had a minimum error of 2.1+4.3% with baseline signal-to-noise ratio of 234.5. For the 82 in vivo cases, K^trans and v_e percentage error compared to the reference numerical correction method had a mean of 0.1% (95% central range of [0.0%, 0.2%]) across the prostate volume.

CONCLUSION

The approximated analytical <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction method provides comparable results with less than 0.2% error within 95% central range, compared to reference numerical <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction. The proposed method is a practical solution for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msubsup><mml:mi>B</mml:mi> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo></mml:msubsup> </mml:mrow> </mml:math> correction in prostate DCE-MRI because of its simple implementation.

Quantitative parameters in dynamic contrast-enhanced magnetic resonance imaging for the detection and characterization of prostate cancer

Objectives

to assess the diagnostic accuracy of quantitative parameters of DCE-MRI in multi-parametric MRI (mpMRI) in comparison to the histopathology (including Gleason grade) of prostate cancer.

Patients and methods

150 men with suspected prostate cancer (abnormal digital rectum examination and or elevated prostate-specific antigen) received pre-biopsy 3T mpMRI and were recruited into peer-reviewed, protocol-based prospective study. The DCE-MRI quantitative parameters (K^trans (influx transfer constant) and k_ep (efflux rate constant)) of the cancerous and normal areas were recorded using four different kinetic models employing Olea Sphere (Olea Medical, La Ciotat, France). The correlation between these parameters and the histopathology of the lesions (biopsy and in a sub-cohort 41 radical prostatectomy specimen) was assessed.

Results

The quantitative parameters showed a significant difference between non-cancerous (benign) and cancerous lesions (Gleason score≥3+3) in the prostate gland. The cut-off values for prostate cancer differentiation were: K^trans (0.205 min⁻¹) and k_ep (0.665 min⁻¹) in the extended Tofts model (ET) and K^trans(0.205 min⁻¹ and k_ep (0.63 min⁻¹) in the Lawrence and Lee delay (LD) models respectively. The mean K^trans value also showed a difference between low-grade cancer (Gleason score=3+3) and high-grade cancer (Gleason score ≥ 3+4). With the addition of DCE-MRI quantitative parameters, the sensitivity of the PIRAD scoring system was increased from 56.6% to 92.1% (K^trans_ET), 93.1% (k_ep_ET), 91.0%, (K^trans_LD) and 89.4% (k_ep_LD).

Conclusion

Quantitative DCE-MRI parameters improved the diagnostic performance of conventional MRI in distinguishing normal and prostate cancers, including characterization of grade of cancers. The ET and LD models in post-image processing analysis provided better cut-off values for prostate cancer differentiation than the other quantitative DCE-MRI parameters.

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

In many areas of oncology, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has proven to be a clinically useful, non-invasive functional imaging technique to quantify tumor vasculature and tumor perfusion characteristics. Tumor angiogenesis is an essential process for tumor growth, proliferation, and metastasis. Malignant lesions demonstrate rapid extravasation of contrast from the intravascular space to the capillary bed due to leaky capillaries associated with tumor neovascularity. DCE-MRI has the potential to provide information regarding blood flow, areas of hypoperfusion, and variations in endothelial permeability and microvessel density to aid treatment selection, enable frequent monitoring during treatment and assess response to targeted therapy following treatment. This review will discuss the current status of DCE-MRI in cancer imaging, with a focus on its use in imaging prostate malignancies as well as weaknesses that limit its widespread clinical use. The latest techniques for quantification of DCE-MRI parameters will be reviewed and compared.

Robust and efficient pharmacokinetic parameter non-linear least squares estimation for dynamic contrast enhanced MRI of the prostate

PURPOSE

To describe an efficient numerical optimization technique using non-linear least squares to estimate perfusion parameters for the Tofts and extended Tofts models from dynamic contrast enhanced (DCE) MRI data and apply the technique to prostate cancer.

METHODS

Parameters were estimated by fitting the two Tofts-based perfusion models to the acquired data via non-linear least squares. We apply Variable Projection (VP) to convert the fitting problem from a multi-dimensional to a one-dimensional line search to improve computational efficiency and robustness. Using simulation and DCE-MRI studies in twenty patients with suspected prostate cancer, the VP-based solver was compared against the traditional Levenberg-Marquardt (LM) strategy for accuracy, noise amplification, robustness to converge, and computation time.

RESULTS

The simulation demonstrated that VP and LM were both accurate in that the medians closely matched assumed values across typical signal to noise ratio (SNR) levels for both Tofts models. VP and LM showed similar noise sensitivity. Studies using the patient data showed that the VP method reliably converged and matched results from LM with approximate 3× and 2× reductions in computation time for the standard (two-parameter) and extended (three-parameter) Tofts models. While LM failed to converge in 14% of the patient data, VP converged in the ideal 100%.

CONCLUSION

The VP-based method for non-linear least squares estimation of perfusion parameters for prostate MRI is equivalent in accuracy and robustness to noise, while being more reliably (100%) convergent and computationally about 3× (TM) and 2× (ETM) faster than the LM-based method.

Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness

Purpose

To develop a robust and clinically applicable automated method for analyzing Dynamic Contrast Enhanced (DCE-) MRI of the prostate as a guide for targeted biopsies and treatments.

Materials and methods

An unsupervised pattern recognition (PR) method was used to analyze prostate DCE-MRI from 71 sequential radiotherapy patients. Identified regions of interest (ROIs) with increased perfusion were assigned either to the peripheral (PZ) or transition zone (TZ). Six quantitative features, associated with the washin and washout part of the weighted average DCE curve from the ROI, were calculated. The associations between the assigned DCE-scores and Gleason Score (GS) were investigated. A heatmap of tumor aggressiveness covering the entire prostate was generated and validated with histopathology from MRI-ultrasound fused (MRI-US) targeted biopsies.

Results

The volumes of the PR-identified ROI’s were significantly correlated with the highest GS from the biopsy session for each patient. Following normalization (and only after normalization) with gluteus maximus muscle’s DCE signal, the quantitative features in PZ were significantly correlated with GS. These correlations straightened in subset of patients with available MRI-US biopsies when GS from the individual biopsies were used. Area under the receiver operating characteristics curve for discrimination between indolent vs aggressive cancer for the significant quantitative features reached 0.88–0.95. When DCE-scores were calculated in normal appearing tissues, the features were highly discriminative for cancer vs no cancer both in PZ and TZ. The generated heatmap of tumor aggressiveness coincided with the location and GS of the MRI-US biopsies.

Conclusion

A quantitative approach for DCE-MRI analysis was developed. The resultant map of aggressiveness correlated well with tumor location and GS and is applicable for integration in radiotherapy/radiology imaging software for clinical translation.

Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments

Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (K ^trans and v _e) and the Parker population arterial input function. The average C(t) for each ROI was calculated and then K ^trans and v _e for the ROI was extracted. The simulations were run 100 times for each ROI with new K ^trans and v _e generated. In addition, white Gaussian noise was added to C(t) with 3, 6, and 12 dB signal-to-noise ratios to each C(t). For pre-clinical experiments, Copenhagen rats (n  =  6) with implanted prostate tumors in the hind limb were used in this study. The DCE-MRI data were acquired with a temporal resolution of ~5 s in a 4.7 T animal scanner, before, during, and after a bolus injection (<5 s) of Gd-DTPA for a total imaging duration of ~10 min. K ^trans and v _e were calculated in two ways: (i) by fitting C(t) for each pixel, and then averaging the pixel values over the entire ROI, and (ii) by averaging C(t) over the entire ROI, and then fitting averaged C(t) to extract K ^trans and v _e. The simulation results showed that in heterogeneous ROIs, the pixel-by-pixel averaged K ^trans was ~25% to ~50% larger (p  <  0.01) than the ROI-averaged K ^trans. At higher noise levels, the pixel-averaged K ^trans was greater than the 'true' K ^trans, but the ROI-averaged K ^trans was lower than the 'true' K ^trans. The ROI-averaged K ^trans was closer to the true K ^trans than pixel-averaged K ^trans for high noise levels. In pre-clinical experiments, the pixel-by-pixel averaged K ^trans was ~15% larger than the ROI-averaged K ^trans. Overall, with the Tofts model, the extracted physiological parameters from the pixel-by-pixel averages were larger than the ROI averages. These differences were dependent on the heterogeneity of the ROI.

Diagnostic value of semi-quantitative and quantitative analysis of functional parameters in multiparametric MRI of the prostate

OBJECTIVE

To determine the diagnostic value of semi-quantitative and quantitative parameters of three functional techniques in multiparametric (mp)-MRI of the prostate.

METHODS

Mp-MRI was performed in 110 patients with suspicion of prostate cancer (PCA) before transrectal ultrasound (TRUS)-guided core biopsy. Peak-enhancement, initial and post-initial enhancement, initial area under gadolinium curve, Ktrans (forward rate constant), Kep (efflux rate constant), Ve (extracellular volume), ADC (apparent diffusion coefficient) and MR spectroscopy ratio were obtained for malignant and benign lesions. For iAUGC, Ktrans, Kep and Ve we evaluated median, mean and the difference (Diff) between mean and median. For ADC we evaluated mean, median, Diff between mean and median, and min. In addition, we evaluated these parameters in dependence of Gleason score in PCA. Receiver operating characteristic analysis and area under curve (AUC) were determined.

RESULTS

ADC min and Kep Diff were the best predictors of malignancy in all lesions (AUC: 0.765). ADC min was the best predictor of malignancy for lesions in peripheral zone (PZ) (AUC: 0.7506) and Kep Diff was the best predictor of malignancy for lesions in transitional zone (AUC: 0.7514). Peak enhancement was the best parameter in differentiation of low-grade PCA with Gleason score 6 from high-grade PCA with Gleason score ≥ 7 (AUC: 0.7692).

CONCLUSION

ADC min can differentiate PCA from benign prostate lesions in PZ. Kep Diff could possibly improve prostate cancer detection in. Peak enhancement might be able to differentiate low grade from high-grade PCA. Semi-quantitative and quantitative parameters may be useful for the functional techniques in mp-MRI. Advances in knowledge: ADC min can differentiate PCA from benign prostate lesions in PZ. Peak enhancement might be able to differentiate low grade from high-grade PCA.

Intravoxel Incoherent Motion (IVIM) Diffusion Weighted Imaging (DWI) in the Periferic Prostate Cancer Detection and Stratification

The aim of this study was to compare the Intravoxel Incoherent Motion (IVIM) parameters between healthy Peripheral Zone (PZ), Benign Prostatic Hyperplasia (BPH) and Prostate Cancer (PCa) and compare them to assess whether there was correlation with Gleason Score (GS) grading system. Thirty-one patients with suspect of PCa underwent 1.5T Multi-Parametric Magnetic Resonance Imaging (MP-MRI) with endorectal coil with a protocol including T2WI, DWI using 10 b values (0, 10, 20, 30, 50, 80, 100, 200, 400, 1000 s/mm²) and DCE. Monoexponential and IVIM model fits were used to calculate both apparent diffusion coefficient (ADC) and the following IVIM parameters: molecular diffusion coefficient (D), perfusion-related diffusion coefficient (D*) and perfusion fraction (f). The ADC and D values were significantly lower in the PCa (0.70 ± 0.16 × 10^-3 mm²/s and 0.88 ± 0.31 × 10^-3 mm²/s) compared to those found in the PZ (1.22 ± 0.20 × 10^-3 mm²/s and 1.78 ± 0.34 × 10^-3 mm²/s) and in the BPH (1.53 ± 0.23 × 10^-3 mm²/s and 1.11 ± 0.28 × 10^-3 mm²/s). The D* parameter was significantly increased in the PCa (5.35 ± 5.12 × 10^-3 mm²/s) compare to the healthy PZ (3.02 ± 2.86 × 10^-3 mm²/s), instead there was not significantly difference in the PCa compare to the BPH (5.61 ± 6.77 × 10^-3 mm²/s). The f was statistically lower in the PCa (9.01 ± 5.20%) compared to PZ (10.57 ± 9.30%), but not significantly different between PCa and BPH (9.29 ± 7.29%). The specificity, sensitivity and accuracy of T2WI associated with DWI and IVIM were higher (100, 98 and 99%, respectively) than for T2WI/DWI and IVIM alone (89, 92 and 90%, respectively). Only for ADC was found a statistical difference between low- and intermediate-/high-grade tumors. Adding IVIM to the MP-MRI could increase the diagnostic performance to detect clinically relevant PCa. ADC values have been found to have a rule to discriminate PCa reliably from normal areas and differed significantly in low- and intermediate-/high-grade PCa. In contrast, IVIM parameters were unable to distinguish between the different GS.

Improved performance of prostate DCE-MRI using a 32-coil vs. 12-coil receiver array

PURPOSE

To assess whether acquisition with 32 receiver coils rather than the vendor-recommended 12 coils provides significantly improved performance in 3D dynamic contrast-enhanced MRI (DCE-MRI) of the prostate.

MATERIALS

The study was approved by the institutional review board and was compliant with HIPAA. 50 consecutive male patients in whom prostate MRI was clinically indicated were prospectively imaged in March 2015 with an accelerated DCE-MRI sequence in which image reconstruction was performed using 12 and 32 coil elements. The two reconstructions were compared quantitatively and qualitatively. The first was done using signal-to-noise ratio (SNR) and g-factor analysis to assess sensitivity to acceleration. The second was done using a five-point scale by two experienced radiologists using criteria of perceived SNR, artifact, sharpness, and overall preference. Significance was assessed with the Wilcoxon signed rank test. Extension to T2-weighted spin-echo and diffusion sequences was assessed in phantom studies.

RESULTS

Reconstruction using 32 vs. 12 coil elements provided improved performance in DCE-MRI based on intrinsic SNR (18% higher) and g-factor statistics (14% higher), with a median 32% higher overall SNR within the prostate volume over all subjects. Reconstruction using 32 coils was qualitatively rated significantly improved (p<0.001) vs. 12 coils on the basis of perceived SNR and radiologist preference and equivalent for sharpness and artifact. Phantom studies suggested the improvement in intrinsic SNR could extend to T2-weighted spin-echo and diffusion sequences.

CONCLUSIONS

Reconstruction of 3D accelerated DCE-MRI studies of the prostate using 32 independent receiver coils provides improved overall performance vs. using 12 coils.

DCE-MRI of the prostate using shutter-speed vs. Tofts model for tumor characterization and assessment of aggressiveness

PURPOSE

To quantify Tofts model (TM) and shutter-speed model (SSM) perfusion parameters in prostate cancer (PCa) and noncancerous peripheral zone (PZ) and to compare the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to Prostate Imaging and Reporting and Data System (PI-RADS) classification for the assessment of PCa aggressiveness.

MATERIALS AND METHODS

Fifty PCa patients (mean age 60 years old) who underwent MRI at 3.0T followed by prostatectomy were included in this Institutional Review Board-approved retrospective study. DCE-MRI parameters (K^trans , v_e , k_ep [TM&SSM] and intracellular water molecule lifetime τ_i [SSM]) were determined in PCa and PZ. Differences in DCE-MRI parameters between PCa and PZ, and between models were assessed using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis for differentiation between PCa and PZ was performed for individual and combined DCE-MRI parameters. Diagnostic performance of DCE-MRI parameters for identification of aggressive PCa (Gleason ≥8, grade group [GG] ≥3 or pathology stage pT3) was assessed using ROC analysis and compared with PI-RADSv2 scores.

RESULTS

DCE-MRI parameters were significantly different between TM and SSM and between PZ and PCa (P < 0.037). Diagnostic performances of TM and SSM for differentiation of PCa from PZ were similar (highest AUC TM: K^trans +k_ep 0.76, SSM: τ_i +k_ep 0.80). PI-RADS outperformed TM and SSM DCE-MRI for identification of Gleason ≥8 lesions (AUC PI-RADS: 0.91, highest AUC DCE-MRI: K^trans +τ_i SSM 0.61, P = 0.002). The diagnostic performance of PI-RADS and DCE-MRI for identification of GG ≥3 and pT3 PCa was not significantly different (P > 0.213).

CONCLUSION

SSM DCE-MRI did not increase the diagnostic performance of DCE-MRI for PCa characterization. PI-RADS outperformed both TM and SSM DCE-MRI for identification of aggressive cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:837-849.

PRECISION MANAGEMENT OF LOCALIZED PROSTATE CANCER

INTRODUCTION

The vast majority of men who are diagnosed with prostate cancer die of other causes, highlighting the importance of determining which patient has a risk of death from prostate cancer. Precision management of prostate cancer patients includes distinguishing which men have potentially lethal disease and employing strategies for determining which treatment modality appropriately balances the desire to achieve a durable response while preventing unnecessary overtreatment.

AREAS COVERED

In this review, we highlight precision approaches to risk assessment and a context for the precision-guided application of definitive therapy. We focus on three dilemmas relevant to the diagnosis of localized prostate cancer: screening, the decision to treat, and postoperative management.

EXPERT COMMENTARY

In the last five years, numerous precision tools have emerged with potential benefit to the patient. However, to achieve optimal outcome, the decision to employ one or more of these tests must be considered in the context of prevailing conventional factors. Moreover, performance and interpretation of a molecular or imaging precision test remains practitioner-dependent. The next five years will witness increased marriage of molecular and imaging biomarkers for improved multi-modal diagnosis and discrimination of disease that is aggressive versus truly indolent.

Differential diagnosis of prostate cancer and noncancerous tissue in the peripheral zone and central gland using the quantitative parameters of DCE-MRI: A meta-analysis

BACKGROUND

The objective of this meta-analysis was to evaluate the clinical usefulness of K, Kep, and Ve values in the differential diagnosis of prostate cancer (PCa) and noncancerous tissue in the peripheral zone (PZ) and central gland (CG).

METHODS

A search was conducted of the PubMed, MEDLINE, EMBASE, Cochrane Library, China National Knowledge Infrastructure, and Wanfang databases from January 2000 to October 2015 using the search terms "prostate cancer," " dynamic contrast-enhanced (DCE)," "magnetic resonance imaging," "K," "Kep," and "Ve." Studies were selected and included according to strict eligibility criteria. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) were used to compare K, Kep, and Ve values between PCa and noncancerous tissue.

RESULTS

Fourteen studies representing 484 patients highly suspicious for prostate adenocarcinoma were selected for the meta-analysis. We found that K values measured by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were significantly higher in PCa tissue than in noncancerous tissue in the PZ (SMD 1.57, 95% CI 0.98-2.16; z = 5.21, P <0.00001) and CG (SMD 1.19, 95% CI 0.46-1.91; z = 3.21, P = 0.001). Kep values measured by DCE-MRI were significantly higher in PCa than in noncancerous tissue in the PZ (SMD 1.41, 95% CI 0.92-1.91; z = 5.59, P < 0.00001) and CG (SMD 1.57, 95% CI 0.69-2.46; z = 3.49, P = 0.0005). Ve values generated by DCE-MRI were slightly higher in PCa than in noncancerous tissue in the PZ (SMD 0.72, 95% CI 0.17-1.27; z = 2.58, P = 0.010), but sensitivity analysis found that the Ve value was unstable for differentiation between PCa and noncancerous PZ tissue. However, there was no significant difference in the Ve value between PCa and noncancerous CG tissue (SMD -0.29, 95% CI -1.18, 0.59; z = 0.65, P = 0.51).

CONCLUSION

Our meta-analysis shows that K and Kep were the most reliable parameters for differentiating PCa from noncancerous tissue and were critical for evaluation of the internal structure of cancer. The Ve value was not helpful for distinguishing PCa from noncancerous CG tissue; its ability to distinguish between PCa and noncancerous PZ tissue remains uncertain.

Dynamic contrast-enhanced MRI of gastric cancer: Correlations of the pharmacokinetic parameters with histological type, Lauren classification, and angiogenesis

PURPOSE

To compare the pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in gastric cancers of different histological type and Lauren classification, and to investigate whether DCE-MRI parameters correlate with vascular endothelial growth factor (VEGF) expression levels in gastric cancer.

METHODS

Included were 32 patients with gastric cancer who underwent DCE-MRI of the upper abdomen before tumor resection. DCE-MRI parameters including the volume transfer coefficient (K^trans), reverse reflux rate constant (K_ep), and extracellular extravascular volume fraction (V_e) were calculated from the tumor region. Post-operative specimens were used for determination of histological differentiation (i.e., non-mucinous, mucinous, or signet-ring-cell adenocarcinoma) as well as Lauren classification (intestinal type or diffuse type). VEGF expression was examined for assessing angiogenesis. DCE-MRI parameters with different histological type and Lauren classification were compared using independent samples t-test and analysis of variance, respectively. Correlations between DCE-MRI parameters and VEGF expression grades were tested using Spearman correlation analysis.

RESULTS

Among gastric adenocarcinomas of three different histological types, mucinous adenocarcinomas showed a higher V_e and lower K^trans than the others (P<0.01). Between the two Lauren classifications, the diffuse type showed a higher V_e than the intestinal type (P<0.001). The mean K^trans showed a significantly positive correlation with VEGF (r=0.762, P<0.001).

CONCLUSION

DCE-MRI permits noninvasive prediction of tumor histological type and Lauren classification and estimation of tumor angiogenesis in gastric cancer. DCE-MRI parameters can be used as imaging biomarkers to predict the biologic aggressiveness of a tumor as well as patient prognosis.

Computer aided diagnosis of prostate cancer: A texton based approach

PURPOSE

In this paper the authors propose a texton based prostate computer aided diagnosis approach which bypasses the typical feature extraction process such as filtering and convolution which can be computationally expensive. The study focuses the peripheral zone because 75% of prostate cancers start within this region and the majority of prostate cancers arising within this region are more aggressive than those arising in the transitional zone.

METHODS

For the model development, square patches were extracted at random locations from malignant and benign regions. Subsequently, extracted patches were aggregated and clustered using k-means clustering to generate textons that represent both regions. All textons together form a texton dictionary, which was used to construct a texton map for every peripheral zone in the training images. Based on the texton map, histogram models for each malignant and benign tissue samples were constructed and used as a feature vector to train our classifiers. In the testing phase, four machine learning algorithms were employed to classify each unknown sample tissue based on its corresponding feature vector.

RESULTS

The proposed method was tested on 418 T2-W MR images taken from 45 patients. Evaluation results show that the best three classifiers were Bayesian network (Az = 92.8% ± 5.9%), random forest (89.5% ± 7.1%), and k-NN (86.9% ± 7.5%). These results are comparable to the state-of-the-art in the literature.

CONCLUSIONS

The authors have developed a prostate computer aided diagnosis method based on textons using a single modality of T2-W MRI without the need for the typical feature extraction methods, such as filtering and convolution. The proposed method could form a solid basis for a multimodality magnetic resonance imaging based systems.

Dynamic contrast-enhanced case-control analysis in 3T MRI of prostate cancer can help to characterize tumor aggressiveness

PURPOSE

The aim of this work is to establish normality and tumor tissue ranges for perfusion parameters from dynamic contrast-enhanced (DCE) MR of the peripheral prostate at 3T and to compare the diagnostic performance of quantitative and semi-quantitative parameters.

MATERIALS AND METHODS

Thirty-six patients with prostate carcinomas (18 Gleason-6, 15 Gleason-7, and 3 Gleason-8) and 33 healthy subjects were included. Image analysis workflow comprised four steps: manual segmentation of whole prostate and lesions, series registration, voxelwise T1 mapping and calculation of pharmacokinetic and semi-quantitative parameters.

RESULTS

K^trans, v_e, upslope and AUC60 showed statistically significant differences between healthy peripheral areas and tumors. Curve type showed no association with healthy/tumor peripheral areas (chi-square=0.702). Areas under the ROC curves were 0.64 (95% CI: 0.54-0.75), 0.70 (0.60-0.80), 0.62 (0.51-0.72) and 0.63 (0.52-0.74) for K^trans, v_e, upslope and AUC60, respectively. The optimal cutoff values were: K^trans=0.21min^-1 (sensitivity=0.61, specificity=0.64), v_e=0.36 (0.63, 0.71), upslope=0.59 (0.59, 0.59) and AUC60=2.4 (0.63, 0.64). Significant differences were found between Gleason scores 6 and 7 for normalized K^trans, upslope and AUC60, with good diagnostic accuracy (area under ROC curve 0.80, 95% CI: 0.60-1.00).

CONCLUSION

Quantitative (K^trans and v_e) and semi-quantitative (upslope and AUC60) perfusion parameters showed significant differences between tumors and control areas in the peripheral prostate. Normalized K^trans, upslope and AUC60 values might characterize tumor aggressiveness.

Dynamic contrast-enhanced MRI of the prostate: An intraindividual assessment of the effect of temporal resolution on qualitative detection and quantitative analysis of histopathologically proven prostate cancer

PURPOSE

To assess the effects of temporal resolution (R_T ) in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on qualitative tumor detection and quantitative pharmacokinetic parameters in prostate cancer.

MATERIALS AND METHODS

This retrospective Institutional Review Board (IRB)-approved study included 58 men (64 ± 7 years). They underwent 3T prostate MRI showing dominant peripheral zone (PZ) tumors (24 with Gleason ≥ 4 + 3), prior to prostatectomy. Continuously acquired DCE utilizing GRASP (Golden-angle RAdial Sparse Parallel) was retrospectively reconstructed at R_T of 1.4 sec, 3.7 sec, 6.0 sec, 9.7 sec, and 14.9 sec. A reader placed volumes-of-interest on dominant tumors and benign PZ, generating quantitative pharmacokinetic parameters (k^trans , v_e ) at each R_T . Two blinded readers assessed each R_T for lesion presence, location, conspicuity, and reader confidence on a 5-point scale. Data were assessed by mixed-model analysis of variance (ANOVA), generalized estimating equation (GEE), and receiver operating characteristic (ROC) analysis.

RESULTS

R_T did not affect sensitivity (R1_all : 69.0%-72.4%, all P_adj  = 1.000; R1_{GS≥4 + 3} : 83.3-91.7%, all P_adj  = 1.000; R2_all : 60.3-69.0%, all P_adj  = 1.000; R2_{GS≥4 + 3} : 58.3%-79.2%, all P_adj  = 1.000). R1 reported greater conspicuity of GS ≥ 4 + 3 tumors at R_T of 1.4 sec vs. 14.9 sec (4.29 ± 1.23 vs. 3.46 ± 1.44; P_adj  = 0.029). No other tumor conspicuity pairwise comparison reached significance (R1_all : 2.98-3.43, all P_adj ≥ 0.205; R2_all : 2.57-3.19, all P_adj ≥ 0.059; R1_{GS≥4 + 3} : 3.46-4.29, all other P_adj ≥ 0.156; R2_{GS≥4 + 3} : 2.92-3.71, all P_adj ≥ 0.439). There was no effect of R_T on reader confidence (R1_all : 3.17-3.34, all P_adj  = 1.000; R2_all : 2.83-3.19, all P_adj ≥ 0.801; R1_{GS≥4 + 3} : 3.79-4.21, all P_adj  = 1.000; R2_{GS≥4 + 3} : 3.13-3.79, all P_adj  = 1.000). k^trans and v_e of tumor and benign tissue did not differ across R_T (all adjusted P values [P_adj ] = 1.000). R_T did not significantly affect area under the curve (AUC) of K^trans or v_e for differentiating tumor from benign (all P_adj  = 1.000).

CONCLUSION

Current PI-RADS recommendations for R_T of 10 seconds may be sufficient, with further reduction to the stated PI-RADS preference of R_T ≤ 7 seconds offering no benefit in tumor detection or quantitative analysis.

LEVEL OF EVIDENCE

3 J. MAGN. RESON. IMAGING 2017;45:1464-1475.

Application of an unsupervised multi-characteristic framework for intermediate-high risk prostate cancer localization using diffusion-weighted MRI

PURPOSE

The aim of this proof-of-concept work is to propose an unsupervised framework that combines multiple parameters, in "positive-if-all-positive" manner, from different models to localize tumors.

METHODS

A voxel-by-voxel analysis of the DW-MRI images of whole prostate was performed to obtain parametric maps for D*, D, f, and K using the IVIM and kurtosis models. Ten patients with moderate or high-risk prostate cancer were included in study. The mean age and serum PSA for these 10 patients were 65years (range 54-78) and 21.9ng/mL (range 4.84-44.81), respectively. These patients were scanned using a DW spin-echo sequence with echo-planar readout with 16 equidistantly spaced b-values in the range of 0-2000s/mm² (TE=58ms; TR=3990ms; spatial resolution 2.19×2.19×2.73mm³, slices =26, FOV=140×140mm, slice gap =0.27mm, NSA=2).

RESULTS

The proposed framework detected 24 lesions of which 14 were true positive with 58% tumor detection rate on lesion-based analysis with sensitivity of 100%. The mpMRI evaluation (PIRADSv2) identified 12 of 14 true positive lesions with sensitivity of 86%; positive predictive value of mpMRI was 92%. The index lesions were visible on all framework maps and were coded as the most suspicious in 9 of 10 patients.

CONCLUSION

Preliminary results of the proposed framework indicate high patient-based sensitivity with 100% detection rate for identifying moderate-high risk aggressive index lesions.