Whole-lesion apparent diffusion coefficient metrics as a marker of percentage Gleason 4 component within Gleason 7 prostate cancer at radical prostatectomy

Histomic and transcriptomic features of MRI-visible and invisible clinically significant prostate cancers are associated with prognosis

Magnetic resonance imaging (MRI) is increasingly used to triage patients for prostate biopsy. However, 9% to 24% of clinically significant (cs) prostate cancers (PCas) are not visible in MRI. We aimed to identify histomic and transcriptomic determinants of MRI visibility and their association to metastasis, and PCa-specific death (PCSD). We studied 45 radical prostatectomy-treated patients with csPCa (grade group [GG]2-3), including 30 with MRI-visible and 15 with MRI-invisible lesions, and 18 men without PCa. First, histological composition was quantified. Next, transcriptomic profiling was performed using NanoString technology. MRI visibility-associated differentially expressed genes (DEGs) and Reactome pathways were identified. MRI visibility was classified using publicly available genes in MSK-IMPACT and Decipher, Oncotype DX, and Prolaris. Finally, DEGs and clinical parameters were used to classify metastasis and PCSD in an external cohort, which included 76 patients with metastatic GG2-4 PCa, and 84 baseline-matched controls without progression. Luminal area was lower in MRI-visible than invisible lesions and low luminal area was associated with short metastasis-free and PCa-specific survival. We identified 67 DEGs, eight of which were associated with survival. Cell division, inflammation and transcriptional regulation pathways were upregulated in MRI-visible csPCas. Genes in Decipher, Oncotype DX and MSK-IMPACT performed well in classifying MRI visibility (AUC = 0.86-0.94). DEGs improved classification of metastasis (AUC = 0.69) and PCSD (AUC = 0.68) over clinical parameters. Our data reveals that MRI-visible csPCas harbor more aggressive histomic and transcriptomic features than MRI-invisible csPCas. Thus, targeted biopsy of visible lesions may be sufficient for risk stratification in patients with a positive MRI.

Up-to-Date Imaging and Diagnostic Techniques for Prostate Cancer: A Literature Review

Prostate cancer (PCa) faces great challenges in early diagnosis, which often leads not only to unnecessary, invasive procedures, but to over-diagnosis and treatment as well, thus highlighting the need for modern PCa diagnostic techniques. The review aims to provide an up-to-date summary of chronologically existing diagnostic approaches for PCa, as well as their potential to improve clinically significant PCa (csPCa) diagnosis and to reduce the proliferation and monitoring of PCa. Our review demonstrates the primary outcomes of the most significant studies and makes comparisons across the diagnostic efficacies of different PCa tests. Since prostate biopsy, the current mainstream PCa diagnosis, is an invasive procedure with a high risk of post-biopsy complications, it is vital we dig out specific, sensitive, and accurate diagnostic approaches in PCa and conduct more studies with milestone findings and comparable sample sizes to validate and corroborate the findings.

Predicting prostate cancer in men with PSA levels of 4-10 ng/mL: MRI-based radiomics can help junior radiologists improve the diagnostic performance

To develop MRI-based radiomics model for predicting prostate cancer (PCa) in men with prostate-specific antigen (PSA) levels of 4-10 ng/mL, to compare the performance of radiomics model and PI-RADS v2.1, and to further verify the predictive ability of radiomics model for lesions with different PI-RADS v2.1 score. 171 patients with PSA levels of 4-10 ng/mL were divided into training (n = 119) and testing (n = 52) groups. PI-RADS v2.1 score was assessed by two radiologists. All volumes of interest were segmented on T₂-weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences, from which quantitative radiomics features were extracted. Multivariate logistic regression analysis was performed to establish radiomics model for predicting PCa. The diagnostic performance was assessed using receiver operating characteristic curve analysis. The radiomics model exhibited the best performance in predicting PCa, which was better than the performance of PI-RADS v2.1 scoring by the junior radiologist in the training group [area under the curve (AUC): 0.932 vs 0.803], testing group (AUC: 0.922 vs 0.797), and the entire cohort (AUC: 0.927 vs 0.801) (P < 0.05). The radiomics model performed well for lesions with PI-RADS v2.1 score of 3 (AUC = 0.854, sensitivity = 84.62%, specificity = 84.34%) and PI-RADS v2.1 score of 4-5 (AUC = 0.967, sensitivity = 98.11%, specificity = 86.36%) assigned by junior radiologist. The radiomics model quantitatively outperformed PI-RADS v2.1 for noninvasive prediction of PCa in men with PSA levels of 4-10 ng/mL. The model can help improve the diagnostic performance of junior radiologists and facilitate better decision-making by urologists for management of lesions with different PI-RADS v2.1 score.

Is perfect the enemy of good? Weighing the evidence for biparametric MRI in prostate cancer

The role of multiparametric MRI in diagnosis, staging and treatment planning for prostate cancer is well established. However, there remain several challenges to widespread adoption. One such challenge is the duration and cost of the examination. Abbreviated exams omitting contrast-enhanced sequences may help address this challenge. In this review, we will discuss the rationale for biparametric MRI for detection and characterization of clinically significant prostate cancer prior to biopsy and synthesize the published literature. We will weigh up the advantages and disadvantages to this approach and lay out a conceptual cost/benefit analysis regarding adoption of biparametric MRI.

Relationship between Apparent Diffusion Coefficient Distribution and Cancer Grade in Prostate Cancer and Benign Prostatic Hyperplasia

The aim of this paper was to assess the associations between prostate cancer aggressiveness and histogram-derived apparent diffusion coefficient (ADC) parameters and determine which ADC parameters may help distinguish among stromal hyperplasia (SH), glandular hyperplasia (GH), and low-grade, intermediate-grade, and high-grade prostate cancers. The mean, median, minimum, maximum, and 10th and 25th percentile ADC values were determined from the ADC histogram and compared among two benign prostate hyperplasia (BPH) groups and three Gleason score (GS) groups. Seventy lesions were identified in 58 patients who had undergone proctectomy. Thirty-nine lesions were prostate cancers (GS 6 = 7 lesions, GS 7 = 19 lesions, GS 8 = 11 lesions, GS 9 = 2 lesions), and thirty-one lesions were BPH (SH = 15 lesions, GH = 16 lesions). There were statistically significant differences in 10th percentile and 25th percentile ADC values when comparing GS 6 to GS 7 (p < 0.05). The 10th percentile ADC values yielded the highest area under the curve (AUC). Tenth and 25th percentile ADCs can be used to more accurately differentiate lesions with GS 6 from those with GS 7 than other ADC parameters. Our data indicate that the major challenge with ADC mapping is to differentiate between SH and GS 6, and SH and GS 7.

DWI-related texture analysis for prostate cancer: differences in correlation with histological aggressiveness and data repeatability between peripheral and transition zones

Background

We investigated the correlation between texture features extracted from apparent diffusion coefficient (ADC) maps or diffusion-weighted images (DWIs), and grade group (GG) in the prostate peripheral zone (PZ) and transition zone (TZ), and assessed reliability in repeated examinations.

Methods

Patients underwent 3-T pelvic magnetic resonance imaging (MRI) before radical prostatectomy with repeated DWI using b-values of 0, 100, 1,000, and 1,500 s/mm². Region of interest (ROI) for cancer was assigned to the first and second DWI acquisition separately. Texture features of ROIs were extracted from comma-separated values (CSV) data of ADC maps generated from several sets of two b-value combinations and DWIs, and correlation with GG, discrimination ability between GG of 1–2 versus 3–5, and data repeatability were evaluated in PZ and TZ.

Results

Forty-four patients with 49 prostate cancers met the eligibility criteria. In PZ, ADC 10% and 25% based on ADC map of two b-value combinations of 100 and 1,500 s/mm² and 10% based on ADC map with b-value of 0 and 1,500 s/mm² showed significant correlation with GG, acceptable discrimination ability, and good repeatability. In TZ, higher-order texture feature of busyness extracted from ADC map of 100 and 1,500 s/mm², and high gray-level run emphasis, short-run high gray-level emphasis, and high gray-level zone emphasis from DWI with b-value of 100 s/mm² demonstrated significant correlation, excellent discrimination ability, but moderate repeatability.

Conclusions

Some DWI-related features showed significant correlation with GG, acceptable to excellent discrimination ability, and moderate to good data repeatability in prostate cancer, and differed between PZ and TZ.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41747-021-00252-y.

A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation

In the last years, the widespread use of the prostate-specific antigen (PSA) blood examination to triage patients who will enter the diagnostic/therapeutic path for prostate cancer (PCa) has almost halved PCa-specific mortality. As a counterpart, millions of men with clinically insignificant cancer not destined to cause death are treated, with no beneficial impact on overall survival. Therefore, there is a compelling need to develop tools that can help in stratifying patients according to their risk, to support physicians in the selection of the most appropriate treatment option for each individual patient. The aim of this study was to develop and validate on multivendor data a fully automated computer-aided diagnosis (CAD) system to detect and characterize PCas according to their aggressiveness. We propose a CAD system based on artificial intelligence algorithms that a) registers all images coming from different MRI sequences, b) provides candidates suspicious to be tumor, and c) provides an aggressiveness score of each candidate based on the results of a support vector machine classifier fed with radiomics features. The dataset was composed of 131 patients (149 tumors) from two different institutions that were divided in a training set, a narrow validation set, and an external validation set. The algorithm reached an area under the receiver operating characteristic (ROC) curve in distinguishing between low and high aggressive tumors of 0.96 and 0.81 on the training and validation sets, respectively. Moreover, when the output of the classifier was divided into three classes of risk, i.e., indolent, indeterminate, and aggressive, our method did not classify any aggressive tumor as indolent, meaning that, according to our score, all aggressive tumors would undergo treatment or further investigations. Our CAD performance is superior to that of previous studies and overcomes some of their limitations, such as the need to perform manual segmentation of the tumor or the fact that analysis is limited to single-center datasets. The results of this study are promising and could pave the way to a prediction tool for personalized decision making in patients harboring PCa.

De Novo Radiomics Approach Using Image Augmentation and Features From T1 Mapping to Predict Gleason Scores in Prostate Cancer

OBJECTIVES

The aims of this study were to discriminate among prostate cancers (PCa's) with Gleason scores 6, 7, and ≥8 on biparametric magnetic resonance imaging (bpMRI) of the prostate using radiomics and to evaluate the added value of image augmentation and quantitative T1 mapping.

MATERIALS AND METHODS

Eighty-five patients with subsequently histologically proven PCa underwent bpMRI at 3 T (T2-weighted imaging, diffusion-weighted imaging) with 66 patients undergoing additional T1 mapping at 3 T. The PCa lesions as well as the peripheral and transition zones were segmented pixel by pixel in multiple slices of the 3D MRI data sets (T2-weighted images, apparent diffusion coefficient, and T1 maps). To increase the size of the data set, images were augmented for contrast, brightness, noise, and perspective multiple times, effectively increasing the sample size 10-fold, and 322 different radiomics features were extracted before and after augmentation. Four different machine learning algorithms, including a random forest (RF), stochastic gradient boosting (SGB), support vector machine (SVM), and k-nearest neighbor, were trained with and without features from T1 maps to differentiate among 3 different Gleason groups (6, 7, and ≥8).

RESULTS

Support vector machine showed the highest accuracy of 0.92 (95% confidence interval [CI], 0.62-1.00) for classifying the different Gleason scores, followed by RF (0.83; 95% CI, 0.52-0.98), SGB (0.75; 95% CI, 0.43-0.95), and k-nearest neighbor (0.50; 95% CI, 0.21-0.79). Image augmentation resulted in an average increase in accuracy between 0.08 (SGB) and 0.48 (SVM). Removing T1 mapping features led to a decline in accuracy for RF (-0.16) and SGB (-0.25) and a higher generalization error.

CONCLUSIONS

When data are limited, image augmentations and features from quantitative T1 mapping sequences might help to achieve higher accuracy and lower generalization error for classification among different Gleason groups in bpMRI by using radiomics.

Optimal Method for Reporting Prostate Cancer Grade in MRI-targeted Biopsies

When multiple cores are biopsied from a single magnetic resonance imaging (MRI)-targeted lesion, Gleason grade may be assigned for each core separately or for all cores of the lesion in aggregate. Because of the potential for disparate grades, an optimal method for pathology reporting MRI lesion grade awaits validation. We examined our institutional experience on the concordance of biopsy grade with subsequent radical prostatectomy (RP) grade of targeted lesions when grade is determined on individual versus aggregate core basis. For 317 patients (with 367 lesions) who underwent MRI-targeted biopsy followed by RP, targeted lesion grade was assigned as (1) global Grade Group (GG), aggregated positive cores; (2) highest GG (highest grade in single biopsy core); and (3) largest volume GG (grade in the core with longest cancer linear length). The 3 biopsy grades were compared (equivalence, upgrade, or downgrade) with the final grade of the lesion in the RP, using κ and weighted κ coefficients. The biopsy global, highest, and largest GGs were the same as the final RP GG in 73%, 68%, 62% cases, respectively (weighted κ: 0.77, 0.79, and 0.71). For cases where the targeted lesion biopsy grade scores differed from each other when assigned by global, highest, and largest GG, the concordance with the targeted lesion RP GG was 69%, 52%, 31% for biopsy global, highest, and largest GGs tumors (weighted κ: 0.65, 0.68, 0.59). Overall, global, highest, and largest GG of the targeted biopsy show substantial agreement with RP-targeted lesion GG, however targeted global GG yields slightly better agreement than either targeted highest or largest GG. This becomes more apparent in nearly one third of cases when each of the 3 targeted lesion level biopsy scores differ. These results support the use of global (aggregate) GG for reporting of MRI lesion-targeted biopsies, while further validations are awaited.

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

Objective

To evaluate a combination of texture features and machine learning-based analysis of apparent diffusion coefficient (ADC) maps for the prediction of Grade Group (GG) upgrading in Gleason score (GS) ≤6 prostate cancer (PCa) (GG1) and GS 3 + 4 PCa (GG2).

Materials and methods

Fifty-nine patients who were biopsy-proven to have GG1 or GG2 and underwent MRI examination with the same MRI scanner prior to transrectal ultrasound (TRUS)-guided systemic biopsy were included. All these patients received radical prostatectomy to confirm the final GG. Patients were divided into training cohort and test cohort. 94 texture features were extracted from ADC maps for each patient. The independent sample t-test or Mann−Whitney U test was used to identify the texture features with statistically significant differences between GG upgrading group and GG non-upgrading group. Texture features of GG1 and GG2 were compared based on the final pathology of radical prostatectomy. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter features. Four supervised machine learning methods were employed. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The statistical comparison between AUCs was performed.

Results

Six texture features were selected for the machine learning models building. These texture features were significantly different between GG upgrading group and GG non-upgrading group (P < 0.05). The six features had no significant difference between GG1 and GG2 based on the final pathology of radical prostatectomy. All machine learning methods had satisfactory predictive efficacy. The diagnostic performance of nearest neighbor algorithm (NNA) and support vector machine (SVM) was better than random forests (RF) in the training cohort. The AUC, sensitivity, and specificity of NNA were 0.872 (95% CI: 0.750−0.994), 0.967, and 0.778, respectively. The AUC, sensitivity, and specificity of SVM were 0.861 (95%CI: 0.732−0.991), 1.000, and 0.722, respectively. There had no significant difference between AUCs in the test cohort.

Conclusion

A combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.

Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions

Despite the key-role of the Prostate Imaging and Reporting and Data System (PI-RADS) in the diagnosis and characterization of prostate cancer (PCa), this system remains to be affected by several limitations, primarily associated with the interpretation of equivocal PI-RADS 3 lesions and with the debated role of Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), which is only used to upgrade peripheral PI-RADS category 3 lesions to PI-RADS category 4 if enhancement is focal. We aimed at investigating the usefulness of radiomics for detection of PCa lesions (Gleason Score ≥ 6) in PI-RADS 3 lesions and in peripheral PI-RADS 3 upgraded to PI-RADS 4 lesions (upPI-RADS 4). Multiparametric MRI (mpMRI) data of patients who underwent prostatic mpMRI between April 2013 and September 2018 were retrospectively evaluated. Biopsy results were used as gold standard. PI-RADS 3 and PI-RADS 4 lesions were re-scored according to the PI-RADS v2.1 before and after DCE-MRI evaluation. Radiomic features were extracted from T2-weighted MRI (T2), Apparent diffusion Coefficient (ADC) map and DCE-MRI subtracted images using PyRadiomics. Feature selection was performed using Wilcoxon-ranksum test and Minimum Redundancy Maximum Relevance (mRMR). Predictive models were constructed for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions using at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. 41 PI-RADS 3 and 32 upPI-RADS 4 lesions were analyzed. Among 293 radiomic features, the top selected features derived from T2 and ADC. For PI-RADS 3 stratification, second order model showed higher performances (Area Under the Receiver Operating Characteristic Curve-AUC- = 80%), while for upPI-RADS 4 stratification, first order model showed higher performances respect to superior order models (AUC = 89%). Our results support the significant role of T2 and ADC radiomic features for PCa detection in lesions scored as PI-RADS 3 and upPI-RADS 4. Radiomics models showed high diagnostic efficacy in classify PI-RADS 3 and upPI-RADS 4 lesions, outperforming PI-RADS v2.1 performance.

Cellular density of low-grade transition zone prostate cancer: A limiting factor to correlate restricted diffusion with tumor aggressiveness

OBJECTIVE

To compare the mean apparent diffusion coefficient (ADCmean) and glandular density of Gleason score (GS) 3 + 3 transition zone prostate cancers (TZ-PCa) with those of the peripheral zone (PZ-PCa).

MATERIAL & METHODS

Seventy-nine men (mean age: 65 ± 6 [SD] years; range: 52-81 years) with 37 TZ-PCa (37/79; 53 %) and 42 PZ-PCa (42/79; 47 %) had prostate MRI before radical prostatectomy. Glandular cell density was semi-quantitatively evaluated in all tumors. ADCmean and glandular cell density of GS3 + 3 TZ-PCa were compared to those of PZ-PCa. ADCmean was correlated to GS in each zone.

RESULTS

ADCmean of GS 3 + 3 tumors was significantly lower in the TZ (728 × 10^-6±52 [SD] mm²/s; range: 670-1060mm²/s) than in the PZ (865 × 10^-6 ±121 [SD] mm²/s; range: 670-1120mm²/s) (p = 0.0007), related to a significantly higher glandular density involving more than 50 % of the tumor in 58 % (7/12) of patients in GS3 + 3 TZ-PCa versus 7.6 % (1/13) in PZ-PCa (p = 0.03). ADCmean of GS3 + 3 TZ-PCa was not significantly different from that of GS 3 + 4 (p = 0.14) or GS>3 + 4 Ca (p = 0.9), whatever the zone of origin. In the PZ, ADCmean of GS 3 + 3-PCa was higher than that of Gleason>3 + 4 PZ-PCa (p = 0.02) and similar to that of GS 3 + 4 PZ-PCa (p = 0.24). Correlation between ADCmean and GS was weak for TZ-PCa (ρ = 0.32; p = 0.04) and moderate for PZ-PCa (ρ = 0.45; p = 0.003).

CONCLUSION

ADCmean of GS 3 + 3 TZ-PCa is significantly lower than that of GS 3 + 3 PZ-PCa, related to a unique dense histological pattern and reaches that of higher-grade PCa, whatever the zone of origin.

Determination of Gleason score discrepancy for risk stratification in magnetic resonance-ultrasound fusion prostate biopsy

BACKGROUND

Magnetic resonance imaging (MRI)-ultrasound (US) fusion biopsy remains challenging and highlights the need towards standardization.

PURPOSE

To characterize the clinical and MRI features of clinically significant prostate cancer (csPCa) with discrepant Gleason score (GS) in MRI-US fusion biopsy.

MATERIAL AND METHODS

A total of 400 consecutive patients with suspected cancer lesions who underwent MRI-US fusion biopsy and subsequent prostatectomy were included. In the comparison of biopsy GS with pathology GS, matched lesions were defined as a GS, and discrepant lesions were defined as an upgrade of the GS. Descriptive statistics were used to define clinical characteristics, including age, prostate-specific antigen (PSA), PSA density, and maximal cancer core length (MCCL). Differences between lesions with matched and discrepant GS were determined considering the location and PI-RADS v2 score. A paired comparison of the volumes between the two groups was performed.

RESULTS

There were 130 lesions with discrepant GS in 124 patients. There was no significant difference in the age, PSA, and PSA density between the two groups, except for the MCCL (P = 0.028). The lesions were distributed in the peripheral (n = 88) and transition (n = 42) zones; 33, 50, and 47 lesions were at the apex, mid-gland, and base levels, respectively. PI-RADS scores were as follows: 2 (n = 5), 3 (n = 8), 4 (n = 68), and 5 (n = 39). In comparison with matched lesions, discrepant lesions had significantly smaller multiparametric MRI-measured cancer volumes (P < 0.05).

CONCLUSION

Knowledge of discrepant GS in MRI-US fusion biopsy is important, and a careful approach is needed to reduce this discrepancy.

Prediction of prostate cancer aggressiveness using 18F-Fluciclovine (FACBC) PET and multisequence multiparametric MRI

The aim of this prospective single-institution clinical trial (NCT02002455) was to evaluate the potential of advanced post-processing methods for ¹⁸F-Fluciclovine PET and multisequence multiparametric MRI in the prediction of prostate cancer (PCa) aggressiveness, defined by Gleason Grade Group (GGG). 21 patients with PCa underwent PET/CT, PET/MRI and MRI before prostatectomy. DWI was post-processed using kurtosis (ADC_k, K), mono- (ADC_m), and biexponential functions (f, D_p, D_f) while Logan plots were used to calculate volume of distribution (V_T). In total, 16 unique PET (V_T, SUV) and MRI derived quantitative parameters were evaluated. Univariate and multivariate analysis were carried out to estimate the potential of the quantitative parameters and their combinations to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross validation (LPOCV) scheme and recursive feature elimination technique applied for feature selection. The second order rotating frame imaging (RAFF), monoexponential and kurtosis derived parameters had LPOCV AUC in the range of 0.72 to 0.92 while the corresponding value for V_T was 0.85. _The best performance for GGG prediction was achieved by K parameter of kurtosis function followed by quantitative parameters based on DWI, RAFF and ¹⁸F-FACBC PET. No major improvement was achieved using parameter combinations with or without feature selection. Addition of ¹⁸F-FACBC PET derived parameters (V_T, SUV) to DWI and RAFF derived parameters did not improve LPOCV AUC.

Quantitative Prostate MRI

Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T₁ and T₂ relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Combining molecular and imaging metrics in cancer: radiogenomics

Background

Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing.

Main body

In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis.

Conclusion

Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow.

Application of texture analysis based on T2-weighted magnetic resonance images in discriminating Gleason scores of prostate cancer

OBJECTIVE

To investigate the value of texture analysis in magnetic resonance images for the evaluation of Gleason scores (GS) of prostate cancer.

METHODS

Sixty-six prostate cancer patients are retrospective enrolled, which are divided into five groups namely, GS = 6, 3 + 4, 4 + 3, 8 and 9-10 according to postoperative pathological results. Extraction and analysis of texture features in T2-weighted MR imaging defined tumor region based on pathological specimen after operation are performed by texture software OmniKinetics. The values of texture are analyzed by single factor analysis of variance (ANOVA), and Spearman correlation analysis is used to study the correlation between the value of texture and Gleason classification. Receiver operating characteristic (ROC) curve is then used to assess the ability of applying texture parameters to predict Gleason score of prostate cancer.

RESULTS

Entropy value increases and energy value decreases as the elevation of Gleason score, both with statistical difference among five groups (F = 10.826, F = 2.796, P < 0.05). Energy value of group GS = 6 is significantly higher than that of groups GS = 8 and 9-10 (P < 0.005), which is similar between three groups (GS = 3 + 4, 8 and 9-10). The entropy and energy values correlate with GS (r = 0.767, r = -0.692, P < 0.05). Areas under ROC curves (AUC) of combination of entropy and energy are greater than that of using energy alone between groups GS = 6 and ≥7. Analogously, AUC of combination of entropy and energy are significantly higher than that of using entropy alone between groups GS≤3 + 4 and ≥4 + 3, as well as between groups GS≤4 + 3 and ≥8.

CONCLUSION

Texture analysis on T2-weighted images of prostate cancer can evaluate Gleason score, especially using the combination of entropy and energy rendering better diagnostic efficiency.

Effectiveness of Bi-Parametric MR/US Fusion Biopsy for Detecting Clinically Significant Prostate Cancer in Prostate Biopsy Naïve Men

PURPOSE

To explore the effect of bi-parametric MRI-ultrasound (MR/US) fusion prostate biopsy on the detection of overall cancer and significant prostate cancer (sPCa).

MATERIALS AND METHODS

We examined 140 patients with suspected prostate cancer lesions on MRI from August 2016 to March 2018. All patients had undergone 3T pre-biopsy bi-parametric (T2 weighted and diffusion-weighted) prostate MRI (bpMRI), and their MRI images were evaluated with Prostate Imaging Reporting and Data System (PI-RADS) version 2.0. MR/US fusion targeted prostate biopsy was performed for lesions with a PI-RADS score ≥3 before systemic biopsy. The results of targeted and systemic biopsy were evaluated in regards to detection rate according to PI-RADS score.

RESULTS

Of the patients (mean age=67.2 years, mean prostate-specific antigen level=8.1 ng/mL), 66 (47.1%) and 37 (26.4%) patients were diagnosed with cancer and significant prostate cancer, respectively. The rate of positive targeted biopsy increased with higher PI-RADS score (3: 40.4%, 4: 56.7%, 5: 90.0%). The proportion of significant prostate cancer among positive target lesions was 65.3% (32/49).

CONCLUSION

bpMRI is a feasible tool with which to identify sPCa. MR/US fusion biopsy, rather than systemic biopsy, can help identify sPCa. We recommend using supplemental tools to increase prostate cancer detection in patients with PI-RADS 3 lesions.

Diagnostic Accuracy of a MR Protocol Acquired with and without Endorectal Coil for Detection of Prostate Cancer: A Multicenter Study

Introduction

The purpose of this study was to compare diagnostic accuracy of a prostate multiparametric magnetic resonance imaging (mpMRI) protocol for detection of prostate cancer between images acquired with and without en-dorectal coil (ERC).

Materials

This study was approved by the regional ethics committee. Between 2014 and 2015, 33 patients (median age 51.3 years; range 42.1-77.3 years) who underwent prostate-MRI at 3T scanners at 2 different institutions, acquired with (mpMRI_ERC) and without (mpMRI_PPA) ERC and who received radical prostatectomy, were included in this retrospective study. Two expert readers (R1, R2) attributed a PI-RADS version 2 score for the most suspect (i. e. index) lesion for mpMRI_PPA and mpMRI_ERC. Sensitivity and positive predictive value for detection of index lesions were assessed using 2 × 2 contingency tables. Differences between groups were tested using the McNemar test. Whole-mount histopathology served as reference standard.

Results

On a quadrant-basis cumulative sensitivity ranged between 0.61-0.67 and 0.76-0.88 for mpMRI_PPA and mpMRI_ERC protocols, respectively (p > 0.05). Cumulative positive predictive value ranged between 0.80-0.81 and 0.89-0.91 for mpMRI_PPA and mpMRI_ERC protocols, respectively. The differences were not statistically significant for R1 (p = 0.267) or R2 (p = 0.508).

Conclusion

Our results suggest that there may be no significant differences for detection of prostate cancer between images acquired with and without an ERC.

Prostate cancer heterogeneity: texture analysis score based on multiple magnetic resonance imaging sequences for detection, stratification and selection of lesions at time of biopsy

OBJECTIVE

To undertake an early proof-of-concept study on a novel, semi-automated texture-based scoring system in order to enhance the association between magnetic resonance imaging (MRI) lesions and clinically significant prostate cancer (SPCa).

PATIENTS AND METHODS

With ethics approval, 536 imaging volumes were generated from 20 consecutive patients who underwent multiparametric MRI (mpMRI) at time of biopsy. Volumes of interest (VOIs) included zonal anatomy segmentation and suspicious MRI lesions for cancer (Likert Scale score >2). Entropy (E), measuring heterogeneity, was computed from VOIs and plotted as a multiparametric score defined as the entropy score (ES) = E ADC + E K^trans + E Ve + E T2WI. The reference test that was used to define the ground truth comprised systematic saturation biopsies coupled with MRI-targeted sampling. This generated 422 cores in all that were individually labelled and oriented in three-dimensions. Diagnostic accuracy for detection of SPCa, defined as Gleason score ≥3 + 4 or >3 mm of any grade of cancer on a single core, was assessed using receiver operating characteristics, correlation, and descriptive statistics. The proportion of cancerous lesions detected by ES and visual scoring (VS) were statistically compared using the paired McNemar test.

RESULTS

Any cancer (Gleason score 6-8) was found in 12 of the 20 (60%) patients, with a median PSA level of 8.22 ng/mL. SPCa (mean [95% confidence interval, CI] ES = 17.96 [0.72] NATural information unit [NAT]) had a significantly higher ES than non-SPCa (mean [95% CI] ES = 15.33 [0.76] NAT). The ES correlated with Gleason score (r_s = 0.568, P = 0.033) and maximum cancer core length (ρ = 0.781; P < 0.001). The area under the curve for the ES (0.89) and VS (0.91) were not significantly different (P = 0.75) for the detection of SPCa amongst MRI lesions. Best ES estimated numerical threshold of 16.61 NAT led to a sensitivity of 100% and negative predictive value of 100%. The proportion of MRI lesions that were found to be positive for SPCa using this ES threshold (54%) was significantly higher (P < 0.001) than using the VS (24% of score 3, 4, 5) in a paired analysis using the McNemar test. In all, 53% of MRI lesions would have avoided biopsy sampling without missing significant disease.

CONCLUSION

Capturing heterogeneity of prostate cancer across multiple MRI sequences with the ES yielded high performances for the detection and stratification of SPCa. The ES outperformed the VS in predicting positivity of lesions, holding promise in the selection of targets for biopsy and calling for further understanding of this association.

Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer

PURPOSE

To compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard.

METHODS

We retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as "low risk," a PI-RADSv2 score ≥ 4 as "high risk" for clinically significant PCa. Mean tumor ADC (ADCT), ADC of adjacent normal tissue (ADCN), and ADCratio (ADCT/ADCN) were calculated. Stepwise regression analysis using tumor location, ADCT and ADCratio, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3.

RESULTS

119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADCratio was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score ("low" vs. "high") was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADCT (p = 0.03) and ADCratio (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively.

CONCLUSIONS

ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.

Repeatability of diffusion-weighted MRI of the prostate using whole lesion ADC values, skew and histogram analysis

PURPOSE

To investigate the repeatability of diffusion-weighted imaging parameter including ADC-derived histogram values in prostate cancer.

METHODS

10 patients with prostate cancer were prospectively recruited to a retest cohort. 3 T diffusion-weighted MRI of the prostate was acquired consecutively with patient getting off the scanner between studies. Prostatectomy-histopathology defined tumour regions-of-interest were outlined on ADC maps and diffusion-weighted metrics including histograms were calculated. The coefficient of reproducibility (CoR) and Bland-Altman plots were used to assess repeatability.

RESULTS

10th centile, 90th centile, and median ADC showed good repeatability with mean difference ranging from -0.005 to -0.025 × 103 mm2s-1, and CoR ranging from 0.271-0.294 × 103 mm2s-1 of scan 1 mean). Two measures of heterogeneity and simplified texture, IQR and mean local range, had only moderate repeatability. IQR had a mean difference of -0.032 × 103 mm2s-1 between scans with CoR 0.181 × 103 mm2s-1 (56% of scan 1 mean). Mean local range had a mean difference -0.008 × 103 mm2s-1 between scans (37% of scan 1 mean). Bland-Altman plots showed good repeatability for test and re-test analysis for median, percentile and mean range values. All ADC values had good reliability regardless of whether the tumour border was included in quantitative analysis. ADC histogram skew had poor repeatability, CoR 0.78 × 103 mm2s-1 (373% of scan 1 mean).

CONCLUSION

10th and 90th centile ADC demonstrated sufficient repeatability for clinical use. However, more advanced measures of heterogeneity such as histogram skew, IQR, or mean local range may be limited by their repeatability.

New prostate cancer prognostic grade group (PGG): Can multiparametric MRI (mpMRI) accurately separate patients with low-, intermediate-, and high-grade cancer?

PURPOSE

Our objective is to determine the accuracy of multiparametric MRI (mpMRI) in predicting pathologic grade of prostate cancer (PCa) after radical prostatectomy (RP) using simple apparent diffusion coefficient metrics and, specifically, whether mpMRI can accurately separate disease into one of two risk categories (low vs. higher grade) or one of three risk categories (low, intermediate, or high grade) corresponding to the new prognostic grade group (PGG) criteria.

METHODS

This retrospective, HIPAA-compliant, IRB-approved study included 140 patients with PCa who underwent 3 T mpMRI with endorectal coil and transrectal ultrasound-guided (TRUS-G) biopsy before RP. MpMRI was used to classify lesions using a two-tier (low-grade/PGG 1 vs. high-grade/PGG 2-5) or a three-tier system (low-grade/PGG 1 vs. intermediate-grade/PGG 2 vs. high-grade/PGG 3-5). Accuracy of mpMRI was compared against RP for each system.

RESULTS

The predictive accuracy of mpMRI using the two-tier system is higher than when using three-tier system (0.77 and 0.45, respectively). There were similar rates of undergrading between mpMRI and TRUS-G biopsy compared to RP (16% & 21%; respectively); rate of overgrading was higher for mpMRI vs. TRUS-G biopsy compared to RP (42% & 17%, respectively). When mpMRI and TRUS-G biopsy are combined, rate of undergrading is 1.4% and overgrading is 11%.

CONCLUSIONS

MpMRI predictive accuracy is higher when using a two-tier vs. a three-tier system, suggesting that advanced metrics may be necessary to delineate intermediate- from high-grade disease. Rates of under- and overgrading decreased when mpMRI and TRUS-G biopsy are combined, suggesting that these techniques may be complementary in predicting tumor grade.

Microstructure Characterization of Bone Metastases from Prostate Cancer with Diffusion MRI: Preliminary Findings

PURPOSE

To examine the usefulness of rich diffusion protocols with high b-values and varying diffusion time for probing microstructure in bone metastases. Analysis techniques including biophysical and mathematical models were compared with the clinical apparent diffusion coefficient (ADC).

METHODS

Four patients were scanned using 13 b-values up to 3,000 s/mm2 and diffusion times ranging 18-52 ms. Data were fitted to mono-exponential ADC, intravoxel incoherent motion (IVIM), Kurtosis and Vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) models. Parameters from the models were compared using correlation plots.

RESULTS

ADC and IVIM did not fit the data well, failing to capture the signal at high b-values. The Kurtosis model best explained the data in many voxels, but in voxels exhibiting a more time-dependent signal, the VERDICT model explained the data best. The ADC correlated significantly (p < 0.004) with the intracellular diffusion coefficient (r = 0.48), intracellular volume fraction (r = -0.21), and perfusion fraction (r = 0.46) parameters from VERDICT, suggesting that these factors all contribute to ADC contrast. The mean kurtosis correlated with the intracellular volume fraction parameter (r = 0.26) from VERDICT, consistent with the hypothesis that kurtosis relates to cellularity, but also correlated weakly with the intracellular diffusion coefficient (r = 0.18) and cell radius (r = 0.16) parameters, suggesting that it may be difficult to attribute physical meaning to kurtosis.

CONCLUSION

Both Kurtosis and VERDICT explained the diffusion signal better than ADC and IVIM, primarily due to poor fitting at high b-values in the latter two models. The Kurtosis and VERDICT models captured information at high b using parameters (Kurtosis or intracellular volume fraction and radius) that do not have a simple relationship with ADC and that may provide additional microstructural information in bone metastases.

Background, current role, and potential applications of radiogenomics

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.

Comparison of Prostate Imaging Reporting and Data System versions 1 and 2 for the Detection of Peripheral Zone Gleason Score 3 + 4 = 7 Cancers

OBJECTIVE

The objective of our study was to compare Prostate Imaging Reporting and Data System version 1 (PI-RADSv1) and Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) for the detection of peripheral zone (PZ) Gleason score 3 + 4 = 7 cancers.

MATERIALS AND METHODS

Forty-seven consecutive patients with 52 PZ Gleason score 3 + 4 = 7 cancers that were 0.5 cm³ or larger underwent radical prostatectomy (RP) and 3-T MRI between 2012 and 2015. Two blinded radiologists (readers 1 and 2) retrospectively assigned PI-RADSv1 sequence (T2-weighted imaging, DWI, dynamic contrast-enhanced MRI [DCE-MRI]) and sum scores and PI-RADSv2 assessment categories. A third blinded radiologist (reader 3) measured apparent diffusion coefficient (ADC) ratio (ADC of tumor / ADC of normal PZ) using RP-MRI maps. Sensitivity, false-positive rate, and overall accuracy were compared using McNemar test. Pearson correlation was performed.

RESULTS

Using PI-RADSv1, reader 1 detected 86.5% (45/52) of the cancers and reader 2, 76.9% (40/52) of the cancers. Using PI-RADSv2, reader 1 detected 78.9% (41/52) and reader 2, 67.3% (35/52). Reader 1 detected 7.7% (4/52) and reader 2 detected 9.6% (5/52) more tumors using PI-RADSv1 due to T2-weighted imaging score ≥ 4 or DCE-MRI score ≥ 3. Sensitivity was higher for PI-RADSv1 (p = 0.01 and 0.03, readers 1 and 2). False-positive rates were higher with PI-RADSv1 than with PI-RADSv2 (1.8% vs 0.9% for reader 1; 3.6% vs 1.8% for reader 2) without significant differences in false-positive rate (p = 0.41 and 0.25) or overall accuracy (p = 0.06 and 0.23). PI-RADSv1 sum scores correlated strongly with PI-RADSv2 categories (B = 0.78-0.93, p < 0.0001). The mean ADC ratio was 0.61 ± 0.14 mm²/s with no difference between visible and nonvisible tumors (p = 0.06-0.5). Interobserver agreement was moderate for PI-RADSv2 (κ = 0.41) and ranged from slight to substantial for PI-RADSv1 (T2-weighted imaging, κ = 0.32; DWI, κ = 0.52; DCE-MRI, κ = 0.13).

CONCLUSION

There was no difference in overall detection of cancers comparing PI-RADSv1 and PI-RADSv2; however, PI-RADSv1 sequence scores on T2-weighted imaging and DCE-MRI detected approximately 10% more tumors that were otherwise underestimated on DWI and using PI-RADSv2 decision-tree rules.

Apparent diffusion coefficient in the analysis of prostate cancer: determination of optimal b-value pair to differentiate normal from malignant tissue

PURPOSE

Determining optimal b-value pair for differentiation between normal and prostate cancer (PCa) tissues.

METHODS

Forty-three patients with diagnosis or PCa symptoms were included. Apparent diffusion coefficient (ADC) was estimated using minimum and maximum b-values of 0, 50, 100, 150, 200, 500s/mm2 and 500, 800, 1100, 1400, 1700 and 2000s/mm2, respectively. Diagnostic performances were evaluated when Area-under-the-curve (AUC)>95%.

RESULTS

15 of the 35 b-values pair surpassed this AUC threshold. The pair (50, 2000s/mm2) provided the highest AUC (96%) with ADC cutoff 0.89×10-³mm²/s, sensitivity 95.5%, specificity 93.2% and accuracy 94.4%.

CONCLUSIONS

The best b-value pair was b=50, 2000s/mm2.

The role of whole-lesion apparent diffusion coefficient analysis for predicting outcomes of prostate cancer patients on active surveillance

PURPOSE

To explore the role of whole-lesion apparent diffusion coefficient (ADC) analysis for predicting outcomes in prostate cancer patients on active surveillance.

METHODS

This study included 72 prostate cancer patients who underwent MRI-ultrasound fusion-targeted biopsy at the initiation of active surveillance, had a visible MRI lesion in the region of tumor on biopsy, and underwent 3T baseline and follow-up MRI examinations separated by at least one year. Thirty of the patients also underwent an additional MRI-ultrasound fusion-targeted biopsy after the follow-up MRI. Whole-lesion ADC metrics and lesion volumes were computed from 3D whole-lesion volumes-of-interest placed on lesions on the baseline and follow-up ADC maps. The percent change in lesion volume on the ADC map between the serial examinations was computed. Statistical analysis included unpaired t tests, ROC analysis, and Fisher's exact test.

RESULTS

Baseline mean ADC, ADC_{0-10th-percentile}, ADC_{10-25th-percentile}, and ADC_{25-50th-percentile} were all significantly lower in lesions exhibiting ≥50% growth on the ADC map compared with remaining lesions (all P ≤ 0.007), with strongest difference between lesions with and without ≥50% growth observed for ADC_{0-10th-percentile} (585 ± 308 vs. 911 ± 336; P = 0.001). ADC_{0-10th-percentile} achieved highest performance for predicting ≥50% growth (AUC = 0.754). Mean percent change in tumor volume on the ADC map was 62.3% ± 26.9% in patients with GS ≥ 3 + 4 on follow-up biopsy compared with 3.6% ± 64.6% in remaining patients (P = 0.050).

CONCLUSION

Our preliminary results suggest a role for 3D whole-lesion ADC analysis in prostate cancer active surveillance.

Magnetic resonance imaging for prostate cancer before radical and salvage radiotherapy: What radiation oncologists need to know

External beam radiotherapy (EBRT) is one of the principal curative treatments for patients with prostate cancer (PCa). Risk group classification is based on prostate-specific antigen (PSA) level, Gleason score, and T-stage. After risk group determination, the treatment volume and dose are defined and androgen deprivation therapy is prescribed, if appropriate. Traditionally, imaging has played only a minor role in T-staging due to the low diagnostic accuracy of conventional imaging strategies such as transrectal ultrasound, computed tomography, and morphologic magnetic resonance imaging (MRI). As a result, a notable percentage of tumours are understaged, leading to inappropriate and imprecise EBRT. The development of multiparametric MRI (mpMRI), an imaging technique that combines morphologic studies with functional diffusion-weighted sequences and dynamic contrast-enhanced imaging, has revolutionized the diagnosis and management of PCa. As a result, mpMRI is now used in staging PCa prior to EBRT, with possible implications for both risk group classification and treatment decision-making for EBRT. mpMRI is also being used in salvage radiotherapy (SRT), the treatment of choice for patients who develop biochemical recurrence after radical prostatectomy. In the clinical context of biochemical relapse, it is essential to accurately determine the site of recurrence - pelvic (local, nodal, or bone) or distant - in order to select the optimal therapeutic management approach. Studies have demonstrated the value of mpMRI in detecting local recurrences - even in patients with low PSA levels (0.3-0.5 ng/mL) - and in diagnosing bone and nodal metastasis. The main objective of this review is to update the role of mpMRI prior to radical EBRT or SRT. We also consider future directions for the use and development of MRI in the field of radiation oncology.

Whole-volume apparent diffusion coefficient-based entropy parameters for assessment of gastric cancer aggressiveness

PURPOSE

To explore the role of whole-volume apparent diffusion coefficient (ADC)-based entropy parameters in the preoperative assessment of gastric cancer's aggressiveness.

MATERIALS AND METHODS

In all, 64 patients with gastric cancers who underwent 3.0T magnetic resonance imaging (MRI) were retrospectively included. Regions of interest were drawn manually using in-house software, around gastric cancer lesions on each slice of the diffusion-weighted images and ADC maps. Entropy-related parameters based on ADC maps were calculated automatically: (1) first-order entropy; (2-5) second-order entropies, including entropy(H)₀ , entropy(H)₄₅ , entropy(H)₉₀ , and entropy(H)₁₃₅ ; (6) entropy(H)_mean ; and (7) entropy(H)_range . Correlations between entropy-related parameters and pathological characteristics were analyzed with the Spearman correlation test. The parameters were compared among different pathological characteristics with independent-samples Kruskal-Wallis or Mann-Whitney U-test. Additionally, diagnostic performances of parameters in differentiating different pathological characteristics were analyzed by receiver operating characteristic (ROC) curve analysis.

RESULTS

All the entropy-related parameters significantly correlated with T, N, and overall stages, especially the first-order entropy (r = 0.588, 0.585, and 0.677, respectively, all P < 0.05). All the entropy-related parameters showed significant differences in gastric cancers at different T, N, and overall stages, as well as at different status of vascular invasion (P < 0.001-0.027). And four parameters, including entropy, entropy(H)₀ , entropy(H)₄₅ , and entropy(H)₉₀ , showed significant differences between gastric cancers with and without perineural invasion (P 0.006-0.040).

CONCLUSION

Entropy-related parameters derived from whole-volume ADC texture analysis could help assess the aggressiveness of gastric cancers via analyzing intratumoral heterogeneity quantitatively, especially the first-order entropy.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:168-175.

Role of MRI in the Risk Assessment of Primary Prostate Cancer

A successful paradigm shift toward personalized management strategies for patients with prostate cancer (PCa) is heavily dependent on the availability of noninvasive diagnostic tools capable of accurately establishing the true extent of disease at the time of diagnosis and estimating the risk of subsequent disease progression and related mortality. Although there is still considerable scope for improvement in its diagnostic, predictive, and prognostic capabilities, multiparametric prostate magnetic resonance imaging (MRI) is currently regarded as the imaging modality of choice for local staging of PCa. A negative MRI, that is, the absence of any MRI-visible intraprostatic lesion, has a high negative predictive value for the presence of clinically significant PCa and can substantiate the consideration of active surveillance as a preferred initial management approach. MRI-derived quantitative and semi-quantitative parameters can be utilized to noninvasively characterize MRI-visible prostate lesions and identify those patients who are most likely to benefit from radical treatment, and differentiate them from patients with benign or indolent prostate pathology that may also be visible on MRI. This literature review summarizes current strategies how MRI can be used to determine a tailored management strategy for an individual patient.

Evaluation of MRI for diagnosis of extraprostatic extension in prostate cancer

PURPOSE

To assess the ability of magnetic resonance imaging (MRI) to diagnose extraprostatic extension (EPE) in prostate cancer.

MATERIALS AND METHODS

With Institutional Review Board (IRB) approval, 149 men with 170 ≥0.5 mL tumors underwent preoperative 3T MRI followed by radical prostatectomy (RP) between 2012-2015. Two blinded radiologists (R1/R2) assessed tumors using Prostate Imaging Reporting and Data System (PI-RADS) v2, subjectively evaluated for the presence of EPE, measured tumor size, and length of capsular contact (LCC). A third blinded radiologist, using MRI-RP-maps, measured whole-lesion: apparent diffusion coefficient (ADC) mean/centile and histogram features. Comparisons were performed using chi-square, logistic regression, and receiver operator characteristic (ROC) analysis.

RESULTS

The subjective EPE assessment showed high specificity (SPEC = 75.4/91.3% [R1/R2]), low sensitivity (SENS = 43.3/43.6% [R1/R2]), and area-under (AU) ROC curve = 0.67 (confidence interval [CI] 0.61-0.73) R1 and 0.61 (CI 0.53-0.70) R2; (k = 0.33). PI-RADS v2 scores were strongly associated with EPE (P < 0.001 / P = 0.008; R1/R2) with AU-ROC curve = 0.72 (0.64-0.79) R1 and 0.61 (0.53-0.70) R2; (k = 0.44). Tumors with EPE were larger (18.8 ± 7.8 [median 17, range 6-51] vs. 18.8 ± 4.9 [12, 6-28] mm) and had greater LCC (21.1 ± 14.9 [16, 1-85] vs. 13.6 ± 6.1 [11.5, 4-30] mm); P < 0.001 and 0.002, respectively. AU-ROC for size was 0.73 (0.64-0.80) and LCC was 0.69 (0.60-0.76), respectively. Optimal SENS/SPEC for diagnosis of EPE were: size ≥15 mm = 67.7/66.7% and LCC ≥11 mm = 84.9/44.8%. 10^th -centile ADC and ADC entropy were both associated with EPE (P = 0.02 and < 0.001), with AU-ROC = 0.56 (0.47-0.65) and 0.76 (0.69-0.83), respectively. Optimal SENS/SPEC for diagnosis of EPE with entropy ≥6.99 was 63.3/75.0%. 25^th -centile ADC trended towards being significantly lower with EPE (P = 0.06) with no difference in other ADC metrics (P = 0.25-0.88). Size, LCC, and ADC entropy improved sensitivity but reduced specificity compared with subjective analysis with no difference in overall accuracy (P = 0.38).

CONCLUSION

Measurements of tumor size, capsular contact, and ADC entropy improve sensitivity but reduce specificity for diagnosis of EPE compared to subjective assessment.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:176-185.

Detection of Clinically Significant Prostate Cancer: Short Dual-Pulse Sequence versus Standard Multiparametric MR Imaging-A Multireader Study

Purpose To compare the diagnostic performance of a short dual-pulse sequence magnetic resonance (MR) imaging protocol versus a standard six-pulse sequence multiparametric MR imaging protocol for detection of clinically significant prostate cancer. Materials and Methods This HIPAA-compliant study was approved by the regional ethics committee. Between July 2013 and March 2015, 63 patients from a prospectively accrued study population who underwent MR imaging of the prostate including transverse T1-weighted; transverse, coronal, and sagittal T2-weighted; diffusion-weighted; and dynamic contrast material-enhanced MR imaging with a 3-T imager at a single institution were included in this retrospective study. The short MR imaging protocol image set consisted of transverse T2-weighted and diffusion-weighted images only. The standard MR imaging protocol image set contained images from all six pulse sequences. Three expert readers from different institutions assessed the likelihood of prostate cancer on a five-point scale. Diagnostic performance on a quadrant basis was assessed by using areas under the receiver operating characteristic curves, and differences were evaluated by using 83.8% confidence intervals. Intra- and interreader agreement was assessed by using the intraclass correlation coefficient. Transperineal template saturation biopsy served as the standard of reference. Results At histopathologic evaluation, 84 of 252 (33%) quadrants were positive for cancer in 38 of 63 (60%) men. There was no significant difference in detection of tumors larger than or equal to 0.5 mL for any of the readers of the short MR imaging protocol, with areas under the curve in the range of 0.74-0.81 (83.8% confidence interval [CI]: 0.64, 0.89), and for readers of the standard MR imaging protocol, areas under the curve were 0.71-0.77 (83.8% CI: 0.62, 0.86). Ranges for sensitivity were 0.76-0.95 (95% CI: 0.53, 0.99) and 0.76-0.86 (95% CI: 0.53, 0.97) and those for specificity were 0.84-0.90 (95% CI: 0.79, 0.94) and 0.82-0.90 (95% CI: 0.77, 0.94) for the short and standard MR protocols, respectively. Ranges for interreader agreement were 0.48-0.60 (83.8% CI: 0.41, 0.66) and 0.49-0.63 (83.8% CI: 0.42, 0.68) for the short and standard MR imaging protocols. Conclusion For the detection of clinically significant prostate cancer, no difference was found in the diagnostic performance of the short MR imaging protocol consisting of only transverse T2-weighted and diffusion-weighted imaging pulse sequences compared with that of a standard multiparametric MR imaging protocol. ^© RSNA, 2017 Online supplemental material is available for this article.

Prostate Imaging Reporting and Data System, Version 2, Assessment Categories and Pathologic Outcomes in Patients With Gleason Score 3 + 4 = 7 Prostate Cancer Diagnosed at Biopsy

OBJECTIVE

The purpose of this study is to assess associations between Prostate Imaging Reporting and Data System, version 2 (PI-RADSv2), categories and the presence of a tumor with a Gleason score (GS) of 4 + 3 = 7 or greater or the presence of extraprostatic extension (EPE) at radical prostatectomy (RP) in patients with a GS 3 + 4 = 7 tumor at biopsy.

MATERIALS AND METHODS

A total of 81 men with GS 3 + 4 = 7 prostate cancer diagnosed by transrectal ultrasound-guided biopsy underwent multiparametric MRI and RP between 2012 and 2015. Two blinded radiologists assessed multiparametric MR images and assigned PI-RADSv2 assessment categories (categories 1-5) with the use of sector maps, which were compared with regard to the location of the tumor, the GS, and the presence of EPE at RP. Comparisons were performed between groups with the use of chi-square and multivariate analysis. Diagnostic accuracy was assessed using ROC curve analysis, and localization was compared using the Fisher exact test.

RESULTS

A total of 53.1% of men (43/81) had EPE, and 21.0% (17/81) had GS 4 + 3 = 7 prostate cancer after RP, whereas 2.5% of men (2/81) had their tumors downgraded to GS 3 + 3 = 6. No statistically significant difference in patient age, prostate specific antigen level, or clinical stage existed between groups (p > 0.05). PI-RADSv2 assessment categories were significantly higher for GS 4 + 3 = 7 tumors (p = 0.03). PI-RADSv2 showed moderate accuracy for the diagnosis of GS 4 + 3 = 7 tumors (AUC, 0.65; 95% CI, 0.54-0.77), with a category of 4 or higher having a sensitivity and specificity for diagnosis of 94.1% and 23.4%, respectively. No patient with a PI-RADSv2 category lower than 3 had a GS 4 + 3 = 7 tumor. Accuracy of tumor localization ranged from 86.4% to 92.6%, with 88.2% of errors (15/17) occurring in GS 3 + 3 = 6 or GS 3 + 4 = 7 tumors (p = 0.30). PI-RADSv2 categories were noted to be higher when EPE was present (p < 0.001). Interobserver agreement was moderate (κ = 0.43).

CONCLUSION

For GS 3 + 4 = 7 cancers detected at transrectal ultrasound-guided biopsy, higher PI-RADSv2 assessment categories are associated with upgrading to GS 4 + 3 = 7 cancer and with the presence of EPE after RP. A PI-RADSv2 score of 3 or higher was 100% sensitive for diagnosing GS 4 + 3 = 7 tumors.

Diffusion-weighted endorectal MR imaging at 3T for prostate cancer: correlation with tumor cell density and percentage Gleason pattern on whole mount pathology

OBJECTIVE

To determine if tumor cell density and percentage of Gleason pattern within an outlined volumetric tumor region of interest (TROI) on whole-mount pathology (WMP) correlate with apparent diffusion coefficient (ADC) values on corresponding TROIs outlined on pre-operative MRI.

METHODS

Men with biopsy-proven prostate adenocarcinoma undergoing multiparametric MRI (mpMRI) prior to prostatectomy were consented to this prospective study. WMP and mpMRI images were viewed using 3D Slicer and each TROI from WMP was contoured on the high b-value ADC maps (b0, 1400). For each TROI outlined on WMP, TCD (tumor cell density) and the percentage of Gleason pattern 3, 4, and 5 were recorded. The ADC_mean, ADC_{10th percentile}, ADC_{90th percentile}, and ADC_ratio were also calculated in each case from the ADC maps using 3D Slicer.

RESULTS

Nineteen patients with 21 tumors were included in this study. ADC_mean values for TROIs were 944.8 ± 327.4 vs. 1329.9 ± 201.6 mm²/s for adjacent non-neoplastic prostate tissue (p < 0.001). ADC_mean, ADC_{10th percentile}, and ADC_ratio values for higher grade tumors were lower than those of lower grade tumors (mean 809.71 and 1176.34 mm²/s, p = 0.014; 10th percentile 613.83 and 1018.14 mm²/s, p = 0.009; ratio 0.60 and 0.94, p = 0.005). TCD and ADC_mean (ρ = -0.61, p = 0.005) and TCD and ADC_{10th percentile} (ρ = -0.56, p = 0.01) were negatively correlated. No correlation was observed between percentage of Gleason pattern and ADC values.

CONCLUSION

DWI MRI can characterize focal prostate cancer using ADC_ratio, ADC_{10th percentile}, and ADC_mean, which correlate with pathological tumor cell density.

T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results

PURPOSE

To evaluate the diagnostic relevance of T2-weighted (T2W) MRI-derived textural features relative to quantitative physiological parameters derived from diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI in Gleason score (GS) 3+4 and 4+3 prostate cancers.

MATERIALS AND METHODS

3T multiparametric-MRI was performed on 23 prostate cancer patients prior to prostatectomy. Textural features [angular second moment (ASM), contrast, correlation, entropy], apparent diffusion coefficient (ADC), and DCE pharmacokinetic parameters (K^trans and V_e) were calculated from index tumours delineated on the T2W, DW, and DCE images, respectively. The association between the textural features and prostatectomy GS and the MRI-derived parameters, and the utility of the parameters in differentiating between GS 3+4 and 4+3 prostate cancers were assessed statistically.

RESULTS

ASM and entropy correlated significantly (p < 0.05) with both GS and median ADC. Contrast correlated moderately with median ADC. The textural features correlated insignificantly with K^trans and V_e. GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, K^trans, and V_e. The combined texture-MRI parameters yielded higher classification accuracy (91%) than the individual parameter sets.

CONCLUSION

T2W MRI-derived textural features could serve as potential diagnostic markers, sensitive to the pathological differences in prostate cancers.

KEY POINTS

• T2W MRI-derived textural features correlate significantly with Gleason score and ADC. • T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers. • T2W image textural features could augment tumour characterization.

Whole-Lesion Apparent Diffusion Coefficient-Based Entropy-Related Parameters for Characterizing Cervical Cancers: Initial Findings

RATIONALE AND OBJECTIVES

This study aimed to develop whole-lesion apparent diffusion coefficient (ADC)-based entropy-related parameters of cervical cancer to preliminarily assess intratumoral heterogeneity of this lesion in comparison to adjacent normal cervical tissues.

MATERIALS AND METHODS

A total of 51 women (mean age, 49 years) with cervical cancers confirmed by biopsy underwent 3-T pelvic diffusion-weighted magnetic resonance imaging with b values of 0 and 800 s/mm² prospectively. ADC-based entropy-related parameters including first-order entropy and second-order entropies were derived from the whole tumor volume as well as adjacent normal cervical tissues. Intraclass correlation coefficient, Wilcoxon test with Bonferroni correction, Kruskal-Wallis test, and receiver operating characteristic curve were used for statistical analysis.

RESULTS

All the parameters showed excellent interobserver agreement (all intraclass correlation coefficients  > 0.900). Entropy, entropy(H)₀, entropy(H)₄₅, entropy(H)₉₀, entropy(H)₁₃₅, and entropy(H)_mean were significantly higher, whereas entropy(H)_range and entropy(H)_std were significantly lower in cervical cancers compared to adjacent normal cervical tissues (all P <.0001). Kruskal-Wallis test showed that there were no significant differences among the values of various second-order entropies including entropy(H)_0, entropy(H)₄₅, entropy(H)₉₀, entropy(H)₁₃₅, and entropy(H)_mean. All second-order entropies had larger area under the receiver operating characteristic curve than first-order entropy in differentiating cervical cancers from adjacent normal cervical tissues. Further, entropy(H)₄₅, entropy(H)₉₀, entropy(H)₁₃₅, and entropy(H)_mean had the same largest area under the receiver operating characteristic curve of 0.867.

CONCLUSION

Whole-lesion ADC-based entropy-related parameters of cervical cancers were developed successfully, which showed initial potential in characterizing intratumoral heterogeneity in comparison to adjacent normal cervical tissues.

Comparison of image quality and patient discomfort in prostate MRI: pelvic phased array coil vs. endorectal coil

PURPOSE

To compare image quality (IQ) and patient discomfort during prostate MRI using a pelvic phased array (PPA) coil and an endorectal (ER) coil.

MATERIALS AND METHODS

Ninety-eight patients (median age, 65.7; range 42.1-78.1) underwent prostate MRI on a 3T scanner including T2w and DWI acquired with PPA and an ER coil within the same exam. Acquisition time was kept similar for both acquisitions. Two radiologists evaluated aspects of IQ on a 5-point Likert scale and classified image artifacts. All patients completed a questionnaire on discomfort/pain regarding the ER coil using a visual analogue scale from 1 to 10.

RESULTS

There was no significant difference in overall IQ for T2w images for both readers (reader 1, 3.27 ± 0.91 and 3.07 ± 0.84, p = 0.057; reader 2, 3.70 ± 0.75 and 3.77 ± 0.81, p = 0.555) for PPA and ER coils, respectively. Overall IQ for DWI acquired with PPA and ER coils was rated similar by reader 1 (3.03 ± 1.10 and 3.08 ± 0.80, respectively, (p = 0.67)), while reader 2 preferred ER coil images (3.27 ± 0.81 and 3.66 ± 0.85 (p < 0.05)). Susceptibility artifacts were more frequent in ER than in PPA coil images (109 vs. 75). Discomfort and pain experienced during insertion of the ER coil was low altogether (VAS score, 3.5 ± 2.1 for "discomfort" and 2.4 ± 2.4 for "pain").

CONCLUSION

T2-weighted images may be acquired with comparable IQ using a PPA coil as compared to an ER coil, while DWI images showed better IQ using the ER coil for one of two readers. The insertion of the ER coil caused low to moderate discomfort and pain in patients.

A model describing diffusion in prostate cancer

PURPOSE

Quantitative diffusion MRI has frequently been studied as a means of grading prostate cancer. Interpretation of results is complicated by the nature of prostate tissue, which consists of four distinct compartments: vascular, ductal lumen, epithelium, and stroma. Current diffusion measurements are an ill-defined weighted average of these compartments. In this study, prostate diffusion is analyzed in terms of a model that takes explicit account of tissue compartmentalization, exchange effects, and the non-Gaussian behavior of tissue diffusion.

METHOD

The model assumes that exchange between the cellular (ie, stromal plus epithelial) and the vascular and ductal compartments is slow. Ductal and cellular diffusion characteristics are estimated by Monte Carlo simulation and a two-compartment exchange model, respectively. Vascular pseudodiffusion is represented by an additional signal at b = 0. Most model parameters are obtained either from published data or by comparing model predictions with the published results from 41 studies. Model prediction error is estimated using 10-fold cross-validation.

RESULTS

Agreement between model predictions and published results is good. The model satisfactorily explains the variability of ADC estimates found in the literature.

CONCLUSION

A reliable model that predicts the diffusion behavior of benign and cancerous prostate tissue of different Gleason scores has been developed. Magn Reson Med 78:316-326, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

The expanding landscape of diffusion-weighted MRI in prostate cancer

The added value of diffusion-weighted magnetic resonance imaging (DW-MRI) for the detection, localization, and staging of primary prostate cancer has been extensively reported in original studies and meta-analyses. More recently, DW-MRI and related techniques have been used to noninvasively assess prostate cancer aggressiveness and estimate its biological behavior. The present article aims to summarize the potential applications of DW-MRI for noninvasive optimization of pretherapeutic risk assessment, patient management decisions, and evaluation of treatment response.

Prostate Cancer: Utility of Whole-Lesion Apparent Diffusion Coefficient Metrics for Prediction of Biochemical Recurrence After Radical Prostatectomy

OBJECTIVE

The purpose of this study was to investigate the additional value of whole-lesion histogram apparent diffusion coefficient (ADC) metrics, when combined with standard pathologic features, in prediction of biochemical recurrence (BCR) after radical prostatectomy for prostate cancer.

MATERIALS AND METHODS

The study included 193 patients (mean age, 61 ± 7 years) who underwent 3-T MRI with DWI (b values, 50 and 1000 s/mm(2)) before prostatectomy. Histogram metrics were derived from 3D volumes of interest encompassing the entire lesion on ADC maps. Pathologic features from radical prostatectomy and subsequent BCR were recorded for each patient. The Fisher exact test and Mann-Whitney test were used to compare ADC-based metrics and pathologic features between patients with and patients without BCR. Stepwise logistic regression analysis was used to construct multivariable models for prediction of BCR, which were assessed by ROC analysis.

RESULTS

BCR occurred in 16.6% (32/193) of patients. Variables significantly associated with BCR included primary Gleason grade, Gleason score, extraprostatic extension, seminal vesicle invasion, positive surgical margin, preoperative prostate-specific antigen level, MRI tumor volume, mean whole-lesion ADC, entropy ADC, and mean ADC of the bottom 10th, 10-25th, and 25-50th percentiles (p ≤ 0.019). Significant independent predictors of BCR at multivariable analysis were primary Gleason grade, extraprostatic extension, mean of the bottom 10th percentile ADC, and entropy ADC (p = 0.002-0.037). The AUC of this multivariable model was 0.94 for prediction of BCR; the AUC of pathologic features alone was 0.89 (p = 0.001).

CONCLUSION

A model integrating whole-lesion ADC metrics had significantly higher performance for prediction of BCR than did standard pathologic features alone and may help guide postoperative prognostic assessments and decisions regarding adjuvant therapy.

Whole-Tumor Quantitative Apparent Diffusion Coefficient Histogram and Texture Analysis to Predict Gleason Score Upgrading in Intermediate-Risk 3 + 4 = 7 Prostate Cancer

OBJECTIVE

The objective of our study was to evaluate whole-lesion quantitative apparent diffusion coefficient (ADC) for the prediction of Gleason score (GS) upgrading in 3 + 4 = 7 prostate cancer.

MATERIALS AND METHODS

Fifty-four patients with GS 3 + 4 = 7 prostate cancer diagnosed at systematic transrectal ultrasound (TRUS)-guided biopsy underwent 3-T MRI and radical prostatectomy (RP) between 2012 and 2014. A blinded radiologist contoured dominant tumors on ADC maps using histopathologic correlation. The whole-lesion mean ADC, ADC ratio (normalized to peripheral zone), ADC histogram, and texture analysis were compared between tumors with GS upgrading and those without GS upgrading using multivariate ROC analyses and logistic regression modeling.

RESULTS

Tumors were upgraded to GS 4 + 3 = 7 after RP in 26% (n = 14) of the 54 patients, and tumors were downgraded after RP in none of the patients. The mean ADC, ADC ratio, 10th-centile ADC, 25th-centile ADC, and 50th-centile ADC were similar between patients with GS 3 + 4 = 7 tumors (0.99 ± 0.22, 0.58 ± 0.15, 0.77 ± 0.31, 0.94 ± 0.28, and 1.15 ± 0.24, respectively) and patients with upgraded GS 4 + 3 = 7 tumors (1.02 ± 0.18, 0.55 ± 0.11, 0.71 ± 0.26, 0.89 ± 0.20, and 1.11 ± 0.16) (p > 0.05). Regression models combining texture features improved the prediction of GS upgrading. The combination of kurtosis, entropy, and skewness yielded an AUC of 0.76 (SE = 0.07) (p < 0.001), a sensitivity of 71%, and a specificity of 73%. The combination of kurtosis, heterogeneity, entropy, and skewness yielded an AUC of 0.77 (SE = 0.07) (p < 0.001), a sensitivity of 71%, and a specificity of 78%.

CONCLUSION

In this study, whole-lesion mean ADC, ADC ratio, and ADC histogram analysis were not predictive of pathologic upgrading of GS 3 + 4 = 7 prostate cancer after RP. ADC texture analysis improved accuracy.

Body Diffusion-weighted MR Imaging in Oncology: Imaging at 3 T

Advances in hardware and software enable high-quality body diffusion-weighted images to be acquired for oncologic assessment. 3.0 T affords improved signal/noise for higher spatial resolution and smaller field-of-view diffusion-weighted imaging (DWI). DWI at 3.0 T can be applied as at 1.5 T to improve tumor detection, disease characterization, and the assessment of treatment response. DWI at 3.0 T can be acquired on a hybrid PET-MR imaging system, to allow functional MR information to be combined with molecular imaging.