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

Histogram analysis of MR quantitative parameters: are they correlated with prognostic factors in prostate cancer?

PURPOSE

To investigate the correlation between quantitative MR parameters and prognostic factors in prostate cancer (PCa).

METHOD

A total of 186 patients with pathologically confirmed PCa who underwent preoperative multiparametric MRI (mpMRI), including synthetic MRI (SyMRI), were enrolled from two medical centers. The histogram metrics of SyMRI [T1, T2, proton density (PD)] and apparent diffusion coefficient (ADC) values were extracted. The Mann‒Whitney U test or Student's t test was employed to determine the association between these histogram features and the prognostically relevant factors. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the differentiation performance. Spearman's rank correlation coefficients were calculated to determine the correlations between histogram parameters and the International Society of Urological Pathology (ISUP) grade group as well as pathological T stage.

RESULTS

Significant correlations were found between the histogram parameters and the ISUP grade as well as pathological T stage of PCa. Among these histogram parameters, ADC_minimum had the strongest correlation with the ISUP grade (r = - 0.481, p < 0.001), and ADC_Median showed the strongest association with pathological T stage (r = - 0.285, p = 0.008). The ADC_10th percentile exhibited the highest performance in identifying clinically significant prostate cancer (csPCa) (AUC 0.833; 95% CI 0.771-0.883). When discriminating between the status of different prognostically relevant factors, a significant difference was observed between extraprostatic extension-positive and -negative cancers with regard to histogram parameters of the ADC map (10th percentile, 90th percentile, mean, median, minimum) and T1 map (minimum) (p = 0.002-0.032). Moreover, histogram parameters of the ADC map (90th percentile, maximum, mean, median), T2 map (10th percentile, median), and PD map (10th percentile, median) were significantly lower in PCa with perineural invasion (p = 0.009-0.049). The T2 values were significantly lower in patients with seminal vesicle invasion (minimum, p = 0.036) and positive surgical margin (10th percentile, 90th percentile, mean, median, and minimum, p = 0.015-0.025).

CONCLUSION

Quantitative histogram parameters derived from synthetic MRI and ADC maps may have great potential for predicting the prognostic features of PCa.

Incremental prognostic value of ADC histogram analysis in patients with high-risk prostate cancer receiving adjuvant hormonal therapy after radical prostatectomy

Purpose

To investigate the incremental prognostic value of preoperative apparent diffusion coefficient (ADC) histogram analysis in patients with high-risk prostate cancer (PCa) who received adjuvant hormonal therapy (AHT) after radical prostatectomy (RP).

Methods

Sixty-two PCa patients in line with the criteria were enrolled in this study. The 10^th, 50^th, and 90^th percentiles of ADC (ADC₁₀, ADC₅₀, ADC₉₀), the mean value of ADC (ADC_mean), kurtosis, and skewness were obtained from the whole-lesion ADC histogram. The Kaplan-Meier method and Cox regression analysis were used to analyze the relationship between biochemical recurrence-free survival (BCR-fs) and ADC parameters and other clinicopathological factors. Prognostic models were constructed with and without ADC parameters.

Results

The median follow-up time was 53.4 months (range, 41.1-79.3 months). BCR was found in 19 (30.6%) patients. Kaplan-Meier curves showed that lower ADC_mean, ADC₁₀, ADC₅₀, and ADC₉₀ and higher kurtosis could predict poorer BCR-fs (all p<0.05). After adjusting for clinical parameters, ADC₅₀ and kurtosis remained independent prognostic factors for BCR-fs (HR: 0.172, 95% CI: 0.055-0.541, p=0.003; HR: 7.058, 95% CI: 2.288-21.773, p=0.001, respectively). By adding ADC parameters to the clinical model, the C index and diagnostic accuracy for the 24- and 36-month BCR-fs were improved.

Conclusion

ADC histogram analysis has incremental prognostic value in patients with high-risk PCa who received AHT after RP. Combining ADC₅₀, kurtosis and clinical parameters can improve the accuracy of BCR-fs prediction.

Can Pre-treatment Quantitative Multi-parametric MRI Predict the Outcome of Radiotherapy in Patients with Prostate Cancer?

RATIONALE AND OBJECTIVES

To investigate whether pre-treatment quantitative multiparametric MRI can predict biochemical outcome of prostate cancer (PCa) patients treated with primary radiotherapy (RT).

MATERIALS AND METHODS

Fifty-one patients with biopsy confirmed PCa underwent prostate multiparametric MRI on 3T MR scanner prior to RT. Thirty-seven men (73%) were treated with external beam RT alone, 12 men (24%) were treated with brachytherapy monotherapy, and two men (4%) were treated with external beam RT with brachytherapy boost. The index lesion was outlined by a radiologist and quantitative apparent diffusion coefficient (ADC), T2 and DCE parameters were measured. Biochemical failure was defined using the Phoenix criteria.

RESULTS

After a median follow-up of 65 months, seven patients had biochemical failure. ADC had an area under the receiver operating characteristic curve of 0.71 for predicting RT outcome with significantly lower ADC (0.78 ± 0.17 vs 0.96 ± 0.26 µm²/ms, p = 0.04) of the index lesion in men with biochemical failure. Ideal ADC cutoff point (Youdens index) was 0.96 µm²/ms which had a sensitivity of 100% and specificity of 48% for predicting biochemical failure. Kaplan-Meier analysis showed that lower ADC values were associated with significantly lower freedom from biochemical failure (FFBF, p = 0.03, no failures out of 20 men if ADC ≥ 0.96 µm²/ms; seven of 31 with failures if ADC < 0.96 µm²/ms). On multivariable analysis, ADC was associated with FFBF (HR 0.96 per increase in ADC of 0.01 um²/ms [95% CI, 0.92-1.00]; p = 0.042) after accounting for National Comprehensive Cancer Network risk category (p = 0.064) and receipt of androgen deprivation therapy (p = 0.141). Quantitative T2 and DCE parameters were not associated with biochemical outcome.

CONCLUSION

Our results suggest that quantitative ADC values of the index lesion may predict biochemical failure following primary radiotherapy in patients with PCa. Lower ADC values were associated with inferior biochemical control.

Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging

Background

To explore the usefulness of analyzing histograms and textures of apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) images to differentiate prostatic cancer (PCa) from benign prostatic hyperplasia (BPH) using histopathology as the reference.

Methods

Ninety patients with PCa and 112 patients with BPH were included in this retrospective study. Differences in whole-lesion histograms and texture parameters of ADC maps and T2W images between PCa and BPH patients were evaluated using the independent samples t-test. The diagnostic performance of ADC maps and T2W images in being able to differentiate PCa from BPH was assessed using receiver operating characteristic (ROC) curves.

Results 

The mean, median, 5^th, and 95^th percentiles of ADC values in images from PCa patients were significantly lower than those from BPH patients (p < 0.05). Significant differences were observed in the means, standard deviations, medians, kurtosis, skewness, and 5^th percentile values of T2W image between PCa and BPH patients (p < 0.05). The ADC_5th showed the largest AUC (0.906) with a sensitivity of 83.3 % and specificity of 89.3 %. The diagnostic performance of the T2W image histogram and texture analysis was moderate and had the largest AUC of 0.634 for T2W_Kurtosis with a sensitivity and specificity of 48.9% and 79.5 %, respectively. The diagnostic performance of the combined ADC_5th & T2W_Kurtosis parameters was also similar to that of the ADC_5th & ADC_{Diff−Variance}.

Conclusions

Histogram and texture parameters derived from the ADC maps and T2W images for entire prostatic lesions could be used as imaging biomarkers to differentiate PCa and BPH biologic characteristics, however, histogram parameters outperformed texture parameters in the diagnostic performance.

Diffusion-weighted imaging in prostate cancer

Diffusion-weighted imaging (DWI), a key component in multiparametric MRI (mpMRI), is useful for tumor detection and localization in clinically significant prostate cancer (csPCa). The Prostate Imaging Reporting and Data System versions 2 and 2.1 (PI-RADS v2 and PI-RADS v2.1) emphasize the role of DWI in determining PIRADS Assessment Category in each of the transition and peripheral zones. In addition, several recent studies have demonstrated comparable performance of abbreviated biparametric MRI (bpMRI), which incorporates only T2-weighted imaging and DWI, compared with mpMRI with dynamic contrast-enhanced MRI. Therefore, further optimization of DWI is essential to achieve clinical application of bpMRI for efficient detection of csPC in patients with elevated PSA levels. Although DWI acquisition is routinely performed using single-shot echo-planar imaging, this method suffers from such as susceptibility artifact and anatomic distortion, which remain to be solved. In this review article, we will outline existing problems in standard DWI using the single-shot echo-planar imaging sequence; discuss solutions that employ newly developed imaging techniques, state-of-the-art technologies, and sequences in DWI; and evaluate the current status of quantitative DWI for assessment of tumor aggressiveness in PC.

Quantitative diffusion-weighted imaging and dynamic contrast-enhanced MR imaging for assessment of tumor aggressiveness in prostate cancer at 3T

PURPOSE

To compare diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MR imaging (DCE-MRI) for characterization of prostate cancer (PC).

METHODS

104 PC patients who underwent prostate multiparametric MRI at 3T including DWI and DCE-MRI before MRI-guided biopsy or radical prostatectomy. Apparent diffusion coefficient (ADC) with histogram analysis (mean, 0-25th percentile, skewness, and kurtosis), intravoxel incoherent motion model including D and f; stretched exponential model including distributed diffusion coefficient (DDC) and a; and permeability parameters including K^trans, K_ep, and V_e were obtained from a region of interest placed on the dominant tumor of each patient.

RESULTS

ADC_mean, ADC_0-25, D, DDC, and V_e were significantly lower and K_ep was significantly higher in GS ≥ 3 + 4 tumors (n = 89) than in GS = 3 + 3 tumors (n = 15), and also in GS ≥ 4 + 3 tumors (n = 57) than in GS ≤ 3 + 4 tumors (n = 47) (P < 0.001 to P = 0.040). f was significantly lower in GS ≥ 4 + 3 tumors than in GS ≤ 3 + 4 tumors (P = 0.022), but there was no significant difference between GS = 3 + 3 tumors and GS ≥ 3 + 4 tumors, or between the remaining metrics in both comparisons. In metrics with area under the curve (AUC) >0.80, there was a significant difference in AUC between ADC_0-25 and D, and DDC for separating GS ≤ 3 + 4 tumors from GS ≥ 4 + 3 tumors (P = 0.040 and P = 0.022, respectively). There were no significant differences between metrics with AUC > 0.80 for separating GS = 3 + 3 tumors from GS ≥ 3 + 4 tumors. ADC_0-25 had the highest correlation with Gleason grade (ρ = -0.625, P < 0.001).

CONCLUSIONS

DWI and DCE-MRI showed no apparent clinical superiority of non-Gaussian models or permeability MRI over the mono-exponential model for assessment of tumor aggressiveness in PC.

Prognostic Features of Biochemical Recurrence of Prostate Cancer Following Radical Prostatectomy Based on Multiparametric MRI and Immunohistochemistry Analysis of MRI-guided Biopsy Specimens

Background Although prostate MRI is routinely used for the detection and staging of localized prostate cancer, imaging-based assessment and targeted molecular sampling for risk stratification are an active area of research. Purpose To evaluate features of preoperative MRI and MRI-guided biopsy immunohistochemistry (IHC) findings associated with biochemical recurrence (BCR) of prostate cancer after surgery. Materials and Methods In this retrospective case-control study, patients underwent multiparametric MRI before MRI-guided biopsy followed by radical prostatectomy between 2008 and 2016. Lesions were retrospectively scored with the Prostate Imaging Reporting and Data System (PI-RADS) (version 2) by radiologists who were blinded to the clinical-pathologic results. The IHC staining, including stains for the ETS-related gene, phosphatase and tensin homolog, androgen receptor, prostate specific antigen, and p53, was performed with targeted biopsy specimens of the index lesion (highest suspicion at MRI and pathologic grade) and scored by pathologists who were blinded to clinical-pathologic outcomes. Cox proportional hazards regression analysis was used to evaluate associations with recurrence-free survival (RFS). Results The median RFS was 31.7 months (range, 1-101 months) for 39 patients (median age, 62 years; age range, 47-76 years) without BCR and 14.6 months (range, 1-61 months) for 40 patients (median age, 59 years; age range, 47-73 years) with BCR. MRI features that showed a significant relationship with the RFS interval included an index lesion with a PI-RADS score of 5 (hazard ratio [HR], 2.10; 95% CI: 1.05, 4.21; P = .04); index lesion burden, defined as ratio of index lesion volume to prostate volume (HR, 1.55; 95% CI: 1.2, 2.1; P = .003); and suspicion of extraprostatic extension (EPE) (HR, 2.18; 95% CI: 1.1, 4.2; P = .02). Presurgical multivariable analysis indicated that suspicion of EPE at MRI (adjusted HR, 2.19; 95% CI: 1.1, 4.3; P = .02) and p53 stain intensity (adjusted HR, 2.22; 95% CI: 1.0, 4.7; P = .04) were significantly associated with RFS. Conclusion MRI features, including Prostate Imaging Reporting and Data System score, index lesion burden, extraprostatic extension, and preoperative guided biopsy p53 immunohistochemistry stain intensity are associated with biochemical relapse of prostate cancer after surgery. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Costa in this issue.

Baseline Volumetric T2 Relaxation Time Histogram Analysis: Can It Be Used to Predict the Response to Intravenous Methylprednisolone Therapy in Patients With Thyroid-Associated Ophthalmopathy?

OBJECTIVE

Prediction of therapy response to intravenous methylprednisolone pulses (ivMP) is crucial for thyroid-associated ophthalmopathy (TAO). Image histograms may offer sensitive imaging biomarkers for therapy effect prediction. This study aimed to investigate whether pretherapeutic, multiparametric T2 relaxation time(T2RT) histogram features of extraocular muscles (EOMs) can be used to predict therapy response.

MATERIALS AND METHODS

Forty-five active and moderate-severe TAO patients, who were treated with standard ivMP and underwent orbital MRI before therapy, were retrospectively included in this study. The patients were divided into responsive (n = 24, 48 eyes) and unresponsive group(n = 21, 42 eyes) according to clinical evaluation. Baseline clinical features of patients and histogram-derived T2RT parameters of the EOMs were analyzed and compared. Logistic regression model was conducted to determine independent predictors, and a histogram features nomogram was formulated for personalized prediction.

RESULTS

Responsive group displayed lower values for 5^th, 10^th percentiles (P < 0.050, respectively), and higher values for 75^th, 90^th, and 95^th percentiles, skewness, entropy, and inhomogeneity (P < 0.050, respectively) than unresponsive group. Multivariate logistic regression analysis showed that 95^th percentile of >88.1 [odds ratio (OR) = 12.078; 95% confidence interval (CI) = 3.98-36.655, p < 0.001], skewness of >0.31 (OR = 3.935; 95% CI = 2.28-6.788, p < 0.001) and entropy of >3.41 (OR = 4.375; 95% CI = 2.604-7.351, p < 0.001) were independent predictors for favorable response. The nomogram integration of three independent predictors demonstrated optimal predictive efficiency, with a C-index of 0.792.

CONCLUSIONS

Pre-treatment volumetric T2RT histogram features of EOMs could function to predict the response to ivMP in patients with TAO. The nomogram based on histogram features facilitates the selection of patients who will derive maximal benefit from ivMP.

Histogram analysis of T2 mapping for detecting early involvement of extraocular muscles in patients with thyroid-associated ophthalmopathy

Using histogram analysis of T2 values to detect early involvement of extraocular muscles (EOMs) in patients with thyroid-associated ophthalmopathy (TAO). Five EOMs of each orbit were analyzed for 45 TAO patients and 22 healthy controls (HCs). Patients’ EOMs were grouped into involved or normal-appearing EOMs (NAEOMs). Histogram parameters and signal intensity ratios (SIRs) of EOMs were compared; receiver operating characteristic (ROC) curve analysis was performed to differentiate NAEOMs from EOMs of HCs. 24 patients were reassessed following immunosuppressive treatment. For SIRs, involved muscles showed higher values than those of NAEOMs and HCs (p < 0.05); there were no differences between NAEOMs and HCs (p = 0.26). Parameters of involved muscles showed no different from those of NAEOMs excluding 25th, 50th percentiles, and standard deviation (SD) (p < 0.05). NAEOMs displayed higher values of 90th, 95th percentiles, SD, skewness, inhomogeneity, and entropy than HCs (p < 0.05). ROC curve analysis of entropy yielded the best area under the ROC curve (AUC; 0.816) for differentiating NAEOMs and HCs. After treatment, histogram parameters including 5th, 75th, 90th, and 95th percentiles, SD, kurtosis, inhomogeneity, and entropy were reduced in NAEOMs (p < 0.05). T2 histogram analysis could detect early involvement of EOMs in TAO prior to detection on conventional orbital MRI.

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.

Extracapsular extension on MRI indicates a more aggressive cell cycle progression genotype of prostate cancer

PURPOSE

To explore associations between magnetic resonance imaging (MRI) features of prostate cancer and expression levels of cell cycle genes, as assessed by the Prolaris^® test.

MATERIALS AND METHODS

Retrospective analysis of 118 PCa patients with genetic testing of biopsy specimen and prostate MRI from 08/2013 to 11/2015. Associations between the cell cycle risk (CCR) score and MRI features [i.e., PI-RADSv2 score, extracapsular extension (ECE), quantitative metrics] were analyzed with Fisher's exact test, nonparametric tests, and Spearman's correlation coefficient. In 41 patients (34.7%), test results were compared to unfavorable features on prostatectomy specimen (i.e., Gleason group ≥ 3, ECE, lymph node metastases).

RESULTS

Fifty-four (45.8%), 60 (50.8%), and 4 (3.4%) patients had low-, intermediate-, and high-risk cancers according to American Urological Association scoring system. Patients with ECE on MRI had significantly higher mean CCR scores (reader 1: 3.9 vs. 3.2, p = 0.015; reader 2: 3.6 vs. 3.2, p = 0.045). PI-RADSv2 scores and quantitative MRI features were not associated with CCR scores. In the prostatectomy subset, ECE on MRI (p = < 0.001-0.001) and CCR scores (p = 0.049) were significantly associated with unfavorable histopathologic features.

CONCLUSION

The phenotypic trait of ECE on MRI indicates a more aggressive genotype of prostate cancer.

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.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported

OBJECTIVE

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use.

METHODS

A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error, and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risks.

RESULTS

Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorized as high risk of error; however, this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured.

CONCLUSION

Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions.

Effects of Echo Time on IVIM Quantification of the Normal Prostate

The two-compartment intravoxel incoherent motion (IVIM) theory assumes that the transverse relaxation time is the same in both compartments. However, blood and tissue have different T2 values, and echo time (TE) may thus have an effect on the quantitative parameters of IVIM. The purpose of this study was to investigate the effects of TE on IVIM-DWI-derived parameters of the prostate. In total, 17 healthy volunteers underwent two repeat examinations. IVIM-DWI data were scanned 6 times with variable TE values of 60, 70, 80, 90, 100, and 120 ms. The ADC of a mono-exponential model and the D, D*, and f parameters of the IVIM model were calculated separately for each TE. Repeat measures were assessed by calculating the coefficient of variation and Bland-Altman limits of agreement for each parameter. Spearman's rho test was used to analyse relationships between IVIM indices and TE. Our results showed that TE had an effect on IVIM quantification, which should be kept constant in the examination protocol at each individual institution. Alternatively, an extended IVIM could be used to eliminate the effect of the TE value on the quantitative parameters of IVIM. This may be helpful for guiding clinical research, especially for longitudinal studies.

Pathological and 3 Tesla Volumetric Magnetic Resonance Imaging Predictors of Biochemical Recurrence after Robotic Assisted Radical Prostatectomy: Correlation with Whole Mount Histopathology

PURPOSE

We sought to identify the clinical and magnetic resonance imaging variables predictive of biochemical recurrence after robotic assisted radical prostatectomy in patients who underwent multiparametric 3 Tesla prostate magnetic resonance imaging.

MATERIALS AND METHODS

We performed an institutional review board approved, HIPAA (Health Insurance Portability and Accountability Act) compliant, single arm observational study of 3 Tesla multiparametric magnetic resonance imaging prior to robotic assisted radical prostatectomy from December 2009 to March 2016. Clinical, magnetic resonance imaging and pathological information, and clinical outcomes were compiled. Biochemical recurrence was defined as prostate specific antigen 0.2 ng/cc or greater. Univariate and multivariate regression analysis was performed.

RESULTS

Biochemical recurrence had developed in 62 of the 255 men (24.3%) included in the study at a median followup of 23.5 months. Compared to the subcohort without biochemical recurrence the subcohort with biochemical recurrence had a greater proportion of patients with a high grade biopsy Gleason score, higher preoperative prostate specific antigen (7.4 vs 5.6 ng/ml), intermediate and high D'Amico classifications, larger tumor volume on magnetic resonance imaging (0.66 vs 0.30 ml), higher PI-RADS® (Prostate Imaging-Reporting and Data System) version 2 category lesions, a greater proportion of intermediate and high grade radical prostatectomy Gleason score lesions, higher pathological T3 stage (all p <0.01) and a higher positive surgical margin rate (19.3% vs 7.8%, p = 0.016). On multivariable analysis only tumor volume on magnetic resonance imaging (adjusted OR 1.57, p = 0.016), pathological T stage (adjusted OR 2.26, p = 0.02), positive surgical margin (adjusted OR 5.0, p = 0.004) and radical prostatectomy Gleason score (adjusted OR 2.29, p = 0.004) predicted biochemical recurrence.

CONCLUSIONS

In this cohort tumor volume on magnetic resonance imaging and pathological variables, including Gleason score, staging and positive surgical margins, significantly predicted biochemical recurrence. This suggests an important new imaging biomarker.

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.

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.

Assessment of prostate cancer aggressiveness using apparent diffusion coefficient values: impact of patient race and age

PURPOSE

To assess the impact of patient race and age on the performance of apparent diffusion coefficient (ADC) values for assessment of prostate cancer aggressiveness.

MATERIALS AND METHODS

457 prostate cancer patients who underwent 3T phased-array coil prostate MRI including diffusion-weighted imaging (DWI; maximal b-value 1000 s/mm²) before prostatectomy were included. Mean ADC of a single dominant lesion was measured in each patient, using histopathologic findings from the prostatectomy specimen as reference. In subsets defined by race and age, ADC values were compared between Gleason score (GS) ≤ 3 + 4 and GS ≥ 4 + 3 tumors.

RESULTS

81% of patients were Caucasian, 12% African-American, 7% Asian-American. 13% were <55 years, 42% 55-64 years, 41% 65-74 years, and 4% ≥75 years. 63% were GS ≤ 3 + 4, 37% GS ≥ 4 + 3. ADC was significantly lower in GS ≥ 4 + 3 tumors than in GS ≤ 3 + 4 tumors in the entire cohort, as well as in Caucasian, African-American, and all four age groups (P ≤ 0.015). AUC for differentiation of GS ≤ 3 + 4 and GS ≥ 4 + 3 as well as optimal ADC threshold was Caucasian: 0.73/≤848; African-American: 0.76/≤780; Asian-American: 0.66/≤839: <55 years, 0.73/≤830; 55-64 years, 0.71/≤800; 65-74 years, 0.74/≤872; ≥75 years, 0.79/≤880. A race-optimized ADC threshold resulted in higher specificity in African-American than Caucasian men (84.9% vs. 67.1%, P = 0.045); age-optimized ADC threshold resulted in higher sensitivity in patients aged ≥75 years than <55 years or 55-64 years (100.0% vs. 53.6%-73.3%; P < 0.001).

CONCLUSION

Patients' race and age may impact the diagnostic performance and optimal threshold when applying ADC values for evaluation of prostate cancer aggressiveness.

Prostate Cancer: Diffusion-weighted MR Imaging for Detection and Assessment of Aggressiveness-Comparison between Conventional and Kurtosis Models

Purpose To compare standard diffusion-weighted (DW) imaging and diffusion kurtosis (DK) imaging for prostate cancer (PC) detection and characterization in a large patient cohort, with attention to the potential added value of DK imaging. Materials and Methods This retrospective institutional review board-approved study received a waiver of informed consent. Two hundred eighty-five patients with PC underwent 3.0-T phased-array coil prostate magnetic resonance (MR) imaging, including a DK imaging sequence (b values 0, 500, 1000, 1500, and 2000 sec/mm²) before prostatectomy. Maps of apparent diffusion coefficient (ADC) and diffusional kurtosis (K) were derived by using maximal b values of 1000 and 2000 sec/mm², respectively. Mean ADC and K were obtained from volumes of interest (VOIs) placed on each patient's dominant tumor and benign prostate tissue. Metrics were compared between benign and malignant tissue, between Gleason score (GS) ≤ 3 + 3 and GS ≥ 3 + 4 tumors, and between GS ≤ 3 + 4 and GS ≥ 4 + 3 tumors by using paired t tests, analysis of variance, receiver operating characteristic (ROC) analysis, and exact tests. Results ADC and K showed significant differences for benign versus tumor tissues, GS ≤ 3 + 3 versus GS ≥ 3 + 4 tumors, and GS ≤ 3 + 4 versus GS ≥ 4 + 3 tumors (P < .001 for all). ADC and K were highly correlated (r = -0.82; P < .001). Area under the ROC curve was significantly higher (P = .002) for ADC (0.921) than for K (0.902) for benign versus malignant tissue but was similar for GS ≤ 3 + 3 versus GS ≥ 3 + 4 tumors (0.715-0.744) and GS ≤ 3 + 4 versus GS ≥ 4 + 3 tumors (0.694-0.720) (P > .15). ADC and K were concordant for these various outcomes in 80.0%-88.6% of patients; among patients with discordant results, ADC showed better performance than K for GS ≤ 3 + 4 versus GS ≥ 4 + 3 tumors (P = .016) and was similar to K for other outcomes (P > .136). Conclusion ADC and K were highly correlated, had similar diagnostic performance, and were concordant for the various outcomes in the large majority of cases. These observations did not show a clear added value of DK imaging compared with standard DW imaging for clinical PC evaluation. ^© RSNA, 2017 Online supplemental material is available for this article.

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.

Quantified analysis of histological components and architectural patterns of gleason grades in apparent diffusion coefficient restricted areas upon diffusion weighted MRI for peripheral or transition zone cancer locations

PURPOSE

To quantify and compare the histological components and architectural patterns of Gleason grades in cancerous areas with restriction on apparent diffusion coefficient (ADC) maps.

MATERIALS AND METHODS

Twelve consecutive cases with 14 separate ADC restriction areas, positive for cancer in the peripheral zone (PZ) and transition zone (TZ) were included. All had 3 Tesla MRI and radical prostatectomy. Ten regions of interest (ROIs) within and outside the 14 ADC restriction areas positive for cancer were selected. For each ROI, we performed quantitative analysis of (a) prostate benign and malignant histological component surface ratios, including stroma, glands, epithelium, lumen, cellular nuclei; (b) percent of Gleason grades and measures of ADC values. Means of histological components according to ADC restriction for cancerous area were compared with analyses of variance with repeated measures.

RESULTS

Independent predictors of the probability of cancer were median epithelium/ROI ratio (P = 0.001) and nuclei/ROI ratio (P = 0.03). Independent predictors of the probability of ADC restriction were malignant glands/ROI and luminal space/ROI (P < 0.0001). Effect of malignant glands/ROI area was different according to the localization of the ROI (P = 0.03). We observed an overall difference between the means for all of the histological components for the comparison of true positive and false negative (P < 0.0001), except for the percent of Gleason grade 4 (P = 0.18). In TZ cancers, a predominant grade 3 pattern was associated with low ADC values. In PZ cancers, a predominant grade 4 pattern was associated with low ADC values.

CONCLUSION

Determinants of low ADC were high ratio of malignant glands/ROI area which may be seen in Gleason grades 3 or 4 cancers.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1786-1796.

Utility of whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs)

PURPOSE

To evaluate whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs), including in comparison with conventional MRI features.

METHODS

Eighteen branch-duct IPMNs underwent MRI with DWI prior to resection (n = 16) or FNA (n = 2). A blinded radiologist placed 3D volumes-of-interest on the entire IPMN on the ADC map, from which whole-lesion histogram metrics were generated. The reader also assessed IPMN size, mural nodularity, and adjacent main-duct dilation. Benign (low-to-intermediate grade dysplasia; n = 10) and malignant (high-grade dysplasia or invasive adenocarcinoma; n = 8) IPMNs were compared.

RESULTS

Whole-lesion ADC histogram metrics demonstrating significant differences between benign and malignant IPMNs were: entropy (5.1 ± 0.2 vs. 5.4 ± 0.2; p = 0.01, AUC = 86%); mean of the bottom 10th percentile (2.2 ± 0.4 vs. 1.6 ± 0.7; p = 0.03; AUC = 81%); and mean of the 10-25th percentile (2.8 ± 0.4 vs. 2.3 ± 0.6; p = 0.04; AUC = 79%). The overall mean ADC, skewness, and kurtosis were not significantly different between groups (p ≥ 0.06; AUC = 50-78%). For entropy (highest performing histogram metric), an optimal threshold of >5.3 achieved a sensitivity of 100%, a specificity of 70%, and an accuracy of 83% for predicting malignancy. No significant difference (p = 0.18-0.64) was observed between benign and malignant IPMNs for cyst size ≥3 cm, adjacent main-duct dilatation, or mural nodule. At multivariable analysis of entropy in combination with all other ADC histogram and conventional MRI features, entropy was the only significant independent predictor of malignancy (p = 0.004).

CONCLUSION

Although requiring larger studies, ADC entropy obtained from 3D whole-lesion histogram analysis may serve as a biomarker for identifying the malignant potential of IPMNs, independent of conventional MRI features.

Detection of prostate cancer local recurrence following radical prostatectomy: assessment using a continuously acquired radial golden-angle compressed sensing acquisition

PURPOSE

To compare image quality and diagnostic performance for detecting local recurrence (LR) of prostate cancer after radical prostatectomy (RP) between standard dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and a high spatiotemporal resolution, continuously acquired Golden-angle RAdial Sparse Parallel acquisition employing compressed sensing reconstruction ("GRASP").

METHODS

A search was conducted for prostate MRI examinations performed in patients with PSA ≥0.2 ng/mL after RP in whom follow-up evaluation allowed classification as positive (≥50% PSA reduction after pelvic radiation or positive biopsy) or negative (<50% PSA reduction after pelvic radiation; spontaneous PSA normalization) for LR, yielding 13 patients with standard DCE (11 LR+) and 12 with GRASP (10 LR+). Standard DCE had voxel size 3.0 × 1.9 × 1.9 mm and temporal resolution 5.5 s. GRASP had voxel size 1.0 × 1.1 × 1.1 cm and was retrospectively reconstructed at 2.3 s resolution. Two radiologists evaluated DCE sequences for image quality measures (1-5 scale) and the presence of LR.

RESULTS

GRASP achieved higher scores than standard DCE from both readers (p < 0.001-0.136) for anatomic clarity (R1: 4.4 ± 0.8 vs. 2.8 ± 0.67 R2: 4.8 ± 0.5 vs. 3.2 ± 0.6), sharpness (3.6 ± 0.9 vs. 2.5 ± 0.7; 4.6 ± 0.5 vs. 2.6 ± 0.5), confidence in interpretation (3.8 ± 0.8 vs. 3.1 ± 0.9; 3.8 ± 1.0 vs. 3.1 ± 1.2), and conspicuity of detected lesions (4.7 ± 0.5 vs. 3.8 ± 1.1; 4.5 ± 0.5 vs. 3.8 ± 1.0). For detecting LR, GRASP also achieved higher sensitivity (70% vs. 36%; 80% vs. 45%), specificity (R1 and R2: 100% vs. 50%), and accuracy (75% vs. 38%; 83% vs. 46%) for both readers.

CONCLUSION

Although requiring larger studies, high spatiotemporal resolution GRASP achieved substantially better image quality and diagnostic performance than standard DCE for detecting LR in patients with elevated PSA after prostatectomy.

Apparent diffusion coefficient histogram shape analysis for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy

Background

To explore the role of apparent diffusion coefficient (ADC) histogram shape related parameters in early assessment of treatment response during the concurrent chemo-radiotherapy (CCRT) course of advanced cervical cancers.

Methods

This prospective study was approved by the local ethics committee and informed consent was obtained from all patients. Thirty-two patients with advanced cervical squamous cell carcinomas underwent diffusion weighted magnetic resonance imaging (b values, 0 and 800 s/mm²) before CCRT, at the end of 2nd and 4th week during CCRT and immediately after CCRT completion. Whole lesion ADC histogram analysis generated several histogram shape related parameters including skewness, kurtosis, s-sD_av, width, standard deviation, as well as first-order entropy and second-order entropies. The averaged ADC histograms of 32 patients were generated to visually observe dynamic changes of the histogram shape following CCRT.

Results

All parameters except width and standard deviation showed significant changes during CCRT (all P < 0.05), and their variation trends fell into four different patterns. Skewness and kurtosis both showed high early decline rate (43.10 %, 48.29 %) at the end of 2nd week of CCRT. All entropies kept decreasing significantly since 2 weeks after CCRT initiated. The shape of averaged ADC histogram also changed obviously following CCRT.

Conclusions

ADC histogram shape analysis held the potential in monitoring early tumor response in patients with advanced cervical cancers undergoing CCRT.

The role of MRI in prostate cancer diagnosis and management

Multiparametric MRI of the prostate demonstrates strong potential to address many limitations of traditional prostate cancer diagnosis and management strategies. Recent evidence supports roles for prostate MRI in prebiopsy risk stratification, guidance of targeted biopsy and preoperative disease staging. Prostate MRI may also assist the planning and follow-up of investigational partial gland ablative therapies. This article reviews the impact of prostate MRI on such diagnostic and therapeutic paradigms in contemporary prostate cancer management.

Risk stratification of prostate cancer: integrating multiparametric MRI, nomograms and biomarkers

Accurate risk stratification of prostate cancer is achieved with a number of existing tools to ensure the identification of at-risk patients, characterization of disease aggressiveness, prediction of cancer burden and extrapolation of treatment outcomes for appropriate management of the disease. Statistical tables and nomograms using classic clinicopathological variables have long been the standard of care. However, the introduction of multiparametric MRI, along with fusion-guided targeted prostate biopsy and novel biomarkers, are being assimilated into clinical practice. The majority of studies to date present the outcomes of each in isolation. The current review offers a critical and objective assessment regarding the integration of multiparametric MRI and fusion-guided prostate biopsy with novel biomarkers and predictive nomograms in contemporary clinical practice.

Prostate cancer risk stratification with magnetic resonance imaging

In recent years, multiparametric magnetic resonance imaging (mpMRI) has shown promise for prostate cancer (PCa) risk stratification. mpMRI, often followed by targeted biopsy, can be used to confirm low-grade disease before enrollment in active surveillance. In patients with intermediate or high-risk PCa, mpMRI can be used to inform surgical management. mpMRI has sensitivity of 44% to 87% for detection of clinically significant PCa and negative predictive value of 63% to 98% for exclusion of significant disease. In addition to tumor identification, mpMRI has also been shown to contribute significant incremental value to currently used clinical nomograms for predicting extraprostatic extension. In combination with conventional clinical criteria, accuracy of mpMRI for prediction of extraprostatic extension ranges from 92% to 94%, significantly higher than that achieved with clinical criteria alone. Supplemental sequences, such as diffusion-weighted imaging and dynamic contrast-enhanced imaging, allow quantitative evaluation of cancer-suspicious regions. Apparent diffusion coefficient appears to be an independent predictor of PCa aggressiveness. Addition of apparent diffusion coefficient to Epstein criteria may improve sensitivity for detection of significant PCa by as much as 16%. Limitations of mpMRI include variability in reporting, underestimation of PCa volume and failure to detect clinically significant disease in a small but significant number of cases.