Correlation of diffusion-weighted magnetic resonance data with cellularity in prostate cancer

Partial Fourier in the presence of respiratory motion in prostate diffusion-weighted echo planar imaging

PURPOSE

To investigate the effect of respiratory motion in terms of signal loss in prostate diffusion-weighted imaging (DWI), and to evaluate the usage of partial Fourier in a free-breathing protocol in a clinically relevant b-value range using both single-shot and multi-shot acquisitions.

METHODS

A controlled breathing DWI acquisition was first employed at 3 T to measure signal loss from deep breathing patterns. Single-shot and multi-shot (2-shot) acquisitions without partial Fourier (no pF) and with partial Fourier (pF) factors of 0.75 and 0.65 were employed in a free-breathing protocol. The apparent SNR and ADC values were evaluated in 10 healthy subjects to measure if low pF factors caused low apparent SNR or overestimated ADC.

RESULTS

Controlled breathing experiments showed a difference in signal coefficient of variation between shallow and deep breathing. In free-breathing single-shot acquisitions, the pF 0.65 scan showed a significantly (p < 0.05) higher apparent SNR than pF 0.75 and no pF in the peripheral zone (PZ) of the prostate. In the multi-shot acquisitions in the PZ, pF 0.75 had a significantly higher apparent SNR than 0.65 pF and no pF. The single-shot pF 0.65 scan had a significantly lower ADC than single-shot no pF.

CONCLUSION

Deep breathing patterns can cause intravoxel dephasing in prostate DWI. For single-shot acquisitions at a b-value of 800 s/mm2, any potential risks of motion-related artefacts at low pF factors (pF 0.65) were outweighed by the increase in signal from a lower TE, as shown by the increase in apparent SNR. In multi-shot acquisitions however, the minimum pF factor should be larger, as shown by the lower apparent SNR at low pF factors.

A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model

Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer.

SIGNIFICANCE

Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.

Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment

Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.

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.

The prognostic value of mean apparent diffusion coefficient measured with diffusion-weighted magnetic resonance image in patients with prostate cancer treated with definitive radiotherapy

PURPOSE

To assess the correlation between initial tumor apparent diffusion coefficient (ADC) values and clinicopathological parameters in prostate cancer (PCa) patients treated with definitive radiotherapy (RT). Additionally, the prognostic factors for freedom from biochemical failure (FFBF) and progression-free survival (PFS) in this patient cohort were analyzed.

MATERIALS AND METHODS

The clinical data of 503 patients with biopsy-confirmed PCa were evaluated retrospectively. All patients had clearly evident tumors on diffusion-weighted magnetic resonance imaging (DW-MRI) for ADC values. Univariable and multivariable analyses were used to determine prognostic factors for FFBF and PFS.

RESULTS

The median follow-up was 72.9 months. The 5-year FFBF and PFS rates were 93.2% and 86.2%, respectively. Significantly lower ADC values were found in patients with a high PSA level; advanced clinical stage; higher ISUP score, and higher risk group than their counterparts. Receiver operating characteristic (ROC) curve analysis revealed an ADC cut-off value of 0.737 × 10^-3 mm²/sec for tumor recurrence. Patients who progressed had a lower mean ADC value than those who did not (0.712±0.158 vs. 1.365±0.227 × 10^-3 mm²/sec; p<0.001). There was a significant difference in 5-year FFBF (96.3% vs. 90%; p<0.001) and PFSrates (83.8% vs. 73.5%; p=0.002) between patients with higher and lower mean ADC values. The FFBF and PFS were found to be correlated with tumor ADC value and ISUP grades in multivariable analysis. Additionally, older age was found to be a significant predictor of worse PFS.

CONCLUSIONS

Lower ADC values were found in patients with high-risk characteristics such as a high serum PSA level, stage or grade of tumor, or high-risk disease, implying that ADC values could be used to predict prognosis. Lower ADC values and higher ISUP grades were associated with an increased risk of BF and progression, implying that treatment intensification may be required in these patients.

Using IVIM Parameters to Differentiate Prostate Cancer and Contralateral Normal Tissue through Fusion of MRI Images with Whole-Mount Pathology Specimen Images by Control Point Registration Method

The intravoxel incoherent motion (IVIM) model may enhance the clinical value of multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer (PCa). However, while past IVIM modeling studies have shown promise, they have also reported inconsistent results and limitations, underscoring the need to further enhance the accuracy of IVIM modeling for PCa detection. Therefore, this study utilized the control point registration toolbox function in MATLAB to fuse T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) MRI images with whole-mount pathology specimen images in order to eliminate potential bias in IVIM calculations. Sixteen PCa patients underwent prostate MRI scans before undergoing radical prostatectomies. The image fusion method was then applied in calculating the patients’ IVIM parameters. Furthermore, MRI scans were also performed on 22 healthy young volunteers in order to evaluate the changes in IVIM parameters with aging. Among the full study cohort, the f parameter was significantly increased with age, while the D* parameter was significantly decreased. Among the PCa patients, the D and ADC parameters could differentiate PCa tissue from contralateral normal tissue, while the f and D* parameters could not. The presented image fusion method also provided improved precision when comparing regions of interest side by side. However, further studies with more standardized methods are needed to further clarify the benefits of the presented approach and the different IVIM parameters in PCa characterization.

Daily Intravoxel Incoherent Motion (IVIM) In Prostate Cancer Patients During MR-Guided Radiotherapy-A Multicenter Study

PURPOSE

Daily quantitative MR imaging during radiotherapy of cancer patients has become feasible with MRI systems integrated with linear accelerators (MR-linacs). Quantitative images could be used for treatment response monitoring. With intravoxel incoherent motion (IVIM) MRI, it is possible to acquire perfusion information without the use of contrast agents. In this multicenter study, daily IVIM measurements were performed in prostate cancer patients to identify changes that potentially reflect response to treatment.

MATERIALS AND METHODS

Forty-three patients were included, treated with 20 fractions of 3 Gy on a 1.5 T MR-linac. IVIM measurements were performed on each treatment day. The diffusion coefficient (D), perfusion fraction (f), and pseudo-diffusion coefficient (D*) were calculated based on the median signal intensities in the non-cancerous prostate and the tumor. Repeatability coefficients (RCs) were determined based on the first two treatment fractions. Separate linear mixed-effects models were constructed for the three IVIM parameters.

RESULTS

In total, 726 fractions were analyzed. Pre-treatment average values, measured on the first fraction before irradiation, were 1.46 × 10-3 mm2/s, 0.086, and 28.7 × 10-3 mm2/s in the non-cancerous prostate and 1.19 × 10-3 mm2/s, 0.088, and 28.9 × 10-3 mm2/s in the tumor, for D, f, and D*, respectively. The repeatability coefficients for D, f, and D* in the non-cancerous prostate were 0.09 × 10-3 mm2/s, 0.05, and 15.3 × 10-3 mm2/s. In the tumor, these values were 0.44 × 10-3 mm2/s, 0.16, and 76.4 × 10-3 mm2/s. The mixed effects analysis showed an increase in D of the tumors over the course of treatment, while remaining stable in the non-cancerous prostate. The f and D* increased in both the non-cancerous prostate and tumor.

CONCLUSIONS

It is feasible to perform daily IVIM measurements on an MR-linac system. Although the repeatability coefficients were high, changes in IVIM perfusion parameters were measured on a group level, indicating that IVIM has potential for measuring treatment response.

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

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

Statistical Evaluation of Different Mathematical Models for Diffusion Weighted Imaging of Prostate Cancer Xenografts in Mice

Purpose

To evaluate fitting quality and repeatability of four mathematical models for diffusion weighted imaging (DWI) during tumor progression in mouse xenograft model of prostate cancer.

Methods

Human prostate cancer cells (PC-3) were implanted subcutaneously in right hind limbs of 11 immunodeficient mice. Tumor growth was followed by weekly DWI examinations using a 7T MR scanner. Additional DWI examination was performed after repositioning following the fourth DWI examination to evaluate short term repeatability. DWI was performed using 15 and 12 b-values in the ranges of 0-500 and 0-2000 s/mm², respectively. Corrected Akaike information criteria and F-ratio were used to evaluate fitting quality of each model (mono-exponential, stretched exponential, kurtosis, and bi-exponential).

Results

Significant changes were observed in DWI data during the tumor growth, indicated by ADC_m, ADC_s, and ADC_k. Similar results were obtained using low as well as high b-values. No marked changes in model preference were present between the weeks 1−4. The parameters of the mono-exponential, stretched exponential, and kurtosis models had smaller confidence interval and coefficient of repeatability values than the parameters of the bi-exponential model.

Conclusion

Stretched exponential and kurtosis models showed better fit to DWI data than the mono-exponential model and presented with good repeatability.

Multiparametric Magnetic Resonance Imaging in Evaluation of Clinically Significant Prostate Cancer

Aim  In this prospective study, we evaluate the role of multiparametric magnetic resonance imaging (mp-MRI) in the assessment of clinically significant prostate cancer at 1.5 T without endorectal coil (ERC).

Materials and Methods  Forty-five men with clinical suspicion of prostate cancer (prostate-specific antigen [PSA] level > 4 ng/mL, hard prostate on digital rectal examination, and suspicious area at transrectal ultrasound [TRUS]) were evaluated using the mp-MRI protocol over a period of 24 months. All cases were interpreted using the Prostate Imaging Reporting and Data System (PI-RADS) version 2 guidelines and correlated with histopathology.

Statistical Analysis Used  A chi-squared test was used for analysis of nominal/categorical variables and receiver operating characteristic (ROC) curve and one-way analysis of variance (ANOVA) test for continuous variables.

Results  The mean age was 67 years and the mean PSA was 38.2 ng/mL. Eighty percent had prostate cancer and 20% were benign (11% benign prostatic hyperplasia [BPH] and 9% chronic prostatitis). Eighty-six percent of all malignancies were in the peripheral zone. The PI-RADS score for T2-weighted (T2W) imaging showed good sensitivity (81%) but low specificity (67%). The PI-RADS score for diffusion weighted imaging (DWI) with sensitivity of 92% and specificity of 78% had a better accuracy overall than T2W imaging alone. The mean apparent diffusion coefficient (ADC) value (×10 ^–6 mm ² /s) was 732 ± 160 in prostate cancer, 1,009 ± 161 in chronic prostatitis, 1,142 ± 82 in BPH, and 663 in a single case of granulomatous prostatitis. Low ADC values (<936) have shown good correlation (area under curve [AUC]: 0.87) with the presence of cancer foci. Inverse correlation was observed between Gleason scores and ADC values. Dynamic contrast-enhanced (DCE) imaging has shown 100% sensitivity/negative predictive value (NPV), but moderate specificity (67%) in predicting malignancy. The final PI-RADS score had 100% sensitivity and NPV with good overall positive predictive value (PPV) of 95%.

Conclusions  T2W imaging and DWI remain the mainstays in diagnosis of prostate cancer with mp-MRI. DCE-MRI can be a problem-solving tool in case of equivocal findings. Because assessment with mp-MRI can be subjective, use of the newly developed PI-RADS version 2 scoring system is helpful in accurate interpretation.

Single-cell Spatial Proteomic Revelations on the Multiparametric MRI Heterogeneity of Clinically Significant Prostate Cancer

PURPOSE

Multiparametric MRI (mpMRI) has become an indispensable radiographic tool in diagnosing prostate cancer. However, mpMRI fails to visualize approximately 15% of clinically significant prostate cancer (csPCa). The molecular, cellular, and spatial underpinnings of such radiographic heterogeneity in csPCa are unclear.

EXPERIMENTAL DESIGN

We examined tumor tissues from clinically matched patients with mpMRI-invisible and mpMRI-visible csPCa who underwent radical prostatectomy. Multiplex immunofluorescence single-cell spatial imaging and gene expression profiling were performed. Artificial intelligence-based analytic algorithms were developed to examine the tumor ecosystem and integrate with corresponding transcriptomics.

RESULTS

More complex and compact epithelial tumor architectures were found in mpMRI-visible than in mpMRI-invisible prostate cancer tumors. In contrast, similar stromal patterns were detected between mpMRI-invisible prostate cancer and normal prostate tissues. Furthermore, quantification of immune cell composition and tumor-immune interactions demonstrated a lack of immune cell infiltration in the malignant but not in the adjacent nonmalignant tissue compartments, irrespective of mpMRI visibility. No significant difference in immune profiles was detected between mpMRI-visible and mpMRI-invisible prostate cancer within our patient cohort, whereas expression profiling identified a 24-gene stromal signature enriched in mpMRI-invisible prostate cancer. Prostate cancer with strong stromal signature exhibited a favorable survival outcome within The Cancer Genome Atlas prostate cancer cohort. Notably, five recurrences in the 8 mpMRI-visible patients with csPCa and no recurrence in the 8 clinically matched patients with mpMRI-invisible csPCa occurred during the 5-year follow-up post-prostatectomy.

CONCLUSIONS

Our study identified distinct molecular, cellular, and structural characteristics associated with mpMRI-visible csPCa, whereas mpMRI-invisible tumors were similar to normal prostate tissue, likely contributing to mpMRI invisibility.

Biparametric (bp) and multiparametric (mp) magnetic resonance imaging (MRI) approach to prostate cancer disease: a narrative review of current debate on dynamic contrast enhancement

Prostate cancer is the most common malignancy in male population. Over the last few years, magnetic resonance imaging (MRI) has proved to be a robust clinical tool for identification and staging of clinically significant prostate cancer. Though suggestions by the European Society of Urogenital Radiology to use complete multiparametric (mp) T2-weighted/diffusion weighted imaging (DWI)/dynamic contrast enhancement (DCE) acquisition for all prostate MRI examinations, the real advantage of functional DCE remains a matter of debate. Recent studies demonstrate that biparametric (bp) and mp approaches have similar accuracy, but controversial evidences remain, and the specific potential benefits of contrast medium administration are still poorly discussed in literature. The bp approach is in fact sufficient in most cases to adequately identify a negative test, or to accurately define the degree of aggressiveness of a lesion, especially if larger or with major characteristics of malignancy. This feature would give the DCE a secondary role, probably limited to a second evaluation of the lesion location, for detecting small cancer or in case of controversy. However, DCE has proved to increase the sensitivity of prostate MRI, though a less specificity. Therefore, an appropriate decision algorithm is needed to standardize the MRI approach. Aim of this review study was to provide a schematic description of bpMRI and mpMRI approaches in the study of prostatic anatomy, focusing on comparative validity and current DCE application. Additional theoretical considerations on prostate MRI are provided.

Round table: arguments in supporting abbreviated or biparametric MRI of the prostate protocol

Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 update, in the attempt to improve clinical guidelines for multiparametric magnetic resonance imaging (mpMRI) of the prostate, has clear limitations. The role of dynamic contrast-enhanced sequences is not defined, precise guidance on the clinical management (biopsy or clinical surveillance) for score 3 lesions [equivocal for clinical significant prostate cancer (sPCa)] is not offered and criteria for lesions interpretation remain difficult and subjective. We report criteria and arguments in supporting the use of abbreviated or biparametric prostate MRI protocol in clinical practice for detection and management of PCa.

Prostate MRI: practical guidelines for interpreting and reporting according to PI-RADS version 2.1

The increasing precision of multiparametric magnetic resonance imaging of the prostate, together with greater experience and standardization in its interpretation, has given this technique an important role in the management of prostate cancer, the most prevalent non-cutaneous cancer in men. This article reviews the concepts in PI-RADS version 2.1 for estimating the probability and zonal location of significant tumors of the prostate, using a practical approach that includes current considerations about the prerequisites for carrying out the test and recommendations for interpreting the findings. It emphasizes benign findings that can lead to confusion and the criteria for evaluating the probability of local spread, which must be included in the structured report.

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

RATIONALE AND OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Biomarkers of pituitary macroadenomas aggressive behaviour: a conventional MRI and DWI 3T study

OBJECTIVE

Pituitary macroadenomas (PAs) are usually defined as benign intracranial tumors. However, they may present local aggressive course. High Ki67 labelling index (LI) values have been related to an aggressive tumor behavior. A recent clinicopathological classification of PA based on local invasiveness and proliferation indexes, divided them in groups with different prognosis. We evaluated the utility of conventional MRI (cMRI) and diffusion-weighted imaging (DWI), in predicting the Ki67- LI according the clinicopathological classification.

METHODS

17 patients (12 M and 5 F) who underwent surgical removal of a PA were studied. cMRI features, quantification of T₁W and T₂W signal intensity, degree of contrast uptake (enhancement ratio, ER) and apparent diffusion coefficient (ADC) values were evaluated by using a 3 T scan. Statistics included Mann-Whitney test, Spearman's test, and receiver operating characteristic analysis. A value of p ≤ 0.05 was considered significant for all the tests.

RESULTS

Negative correlations were observed between Ki-67 LI, ADCm (ρ = - 0.67, p value = 0.005) and ER values (ρ = -0.62; p = 0.008). ER values were significantly lower in the proliferative PA group (p = 0.028; p = 0.017). ADCm showed sensitivity and specificity of 90 and 85% respectively into predict Ki67-LI value. A value of ADCm ≤0, 711 x 10-6 mm² emerged as a cut-off of a value of Ki67-LI ≥ 3%.

CONCLUSION

Adding quantitative measures of ADC values to cMRI could be used routinely as a non-invasive marker of specific predictive biomarker of the proliferative activity of PA.

ADVANCES IN KNOWLEDGE

Routinely use of DWI on diagnostic work-up of pituitary adenomas may help in establish the likely biological aggressive lesions.

Evaluation of relationships between the final Gleason score, PI-RADS v2 score, ADC value, PSA level, and tumor diameter in patients that underwent radical prostatectomy due to prostate cancer

INTRODUCTION

This study aimed to investigate the relationship between the serum PSA level, Gleason score (GS), PI-RADS v2 score, tumor ADC_min value, and the largest tumor diameter in patients that underwent radical prostatectomy (RP) due to prostate cancer (PCa) and to comparatively evaluate the variables of these parameters in clinically significant and insignificant PCa groups.

MATERIALS AND METHODS

The mpMRI examinations of the patients who underwent RP due to PCa were retrospectively evaluated. According to the final GS, the lesions were divided into two groups as clinically significant (GS ≥ 7) and insignificant (GS ≤ 6). The PSA value, tumor ADC_min value, tumor diameter, and PI-RADS score were compared between the clinically significant and nonsignificant PCa groups using Student's t-test. The correlations between the serum PSA level, GS, PI-RADS v2 score, tumor ADC_min value, and tumor diameter were evaluated separately (Pearson's correlation analysis was used for peripheral gland tumors, and Spearman's correlation analysis for central gland tumors). A ROC analysis was undertaken to evaluate the efficacy of the tumor ADC_min, diameter and PSA values in differentiating clinically significant and nonsignificant tumors.

RESULTS

In both central and peripheral gland tumors, there was a correlation between the PSA level, tumor diameter, PI-RADS score, ADC_min value, and GS at various levels (poor, moderate, and high). In central gland tumors, there was no significant difference between the two groups in terms of the PSA value and PI-RADS scores (p > 0.05), but the ADC_min value and diameter of the tumor significantly differed (p < 0.05). For peripheral gland tumors, significant differences were observed in all parameters (p < 0.05). The cut-off values for the peripheral and central gland tumors are as follows: lesion diameter, 13.5 mm and 19 mm; tumor ADC_min, 0.709 × 10^-3 mm²/s and 0.874 × 10^-3 mm²/s; and PSA level, 8.47 ng/ml and 11.10 ng/ml, respectively.

CONCLUSION

The current PI-RADS v2 scoring system can be inadequate in distinguishing clinically significant and insignificant groups in central gland tumors. A separate cut-off value of the tumor diameter should be determined for central and peripheral gland tumors. Tumor ADC_min values can be used as a predictive parameter. The PSA cut-off value should be kept lower in peripheral gland tumors.

Current status and future prospective of focal therapy for localized prostate cancer: development of multiparametric MRI, MRI-TRUS fusion image-guided biopsy, and treatment modalities

Multiparametric magnetic resonance imaging (mpMRI) has been increasingly used to diagnose clinically significant prostate cancer (csPC) because of its usefulness in combination with anatomic and functional data. MRI-targeted biopsy, such as MRI-transrectal ultrasound (TRUS) fusion image-guided prostate biopsy, has high accuracy in the detection and localization of csPC. This novel diagnostic technique contributes to the development of tailor-made medicine as focal therapy, which cures the csPC while preserving the anatomical structures related to urinary and sexual function. In the early days of focal therapy, TRUS-guided systematic biopsy was used for patient selection, and treatment was performed for patients with low-risk PC. With the introduction of mpMRI and mapping biopsy, the treatment range is now determined based on individualized cancer localization. In recent prospective studies, 87.4% of treated patients had intermediate- and high-risk PC. However, focal therapy has two main limitations. First, a randomized controlled trial would be difficult to design because of the differences in pathological features between patients undergoing focal therapy and radical treatment. Therefore, pair-matched studies and/or historical controlled studies have been performed to compare focal therapy and radical treatment. Second, no long-term (≥ 10-year) follow-up study has been performed. However, recent prospective studies have encouraged the use of focal therapy as a treatment strategy for localized PC because it contributes to high preservation of continence and erectile function.

Rapid perceptual processing in two- and three-dimensional prostate images

Radiologists can identify whether a radiograph is abnormal or normal at above chance levels in breast and lung images presented for half a second or less. This early perceptual processing has only been demonstrated in static two-dimensional images (e.g., mammograms). Can radiologists rapidly extract the "gestalt" from more complex imaging modalities? For example, prostate multiparametric magnetic resonance imaging (mpMRI) displays a series of images as a virtual stack and comprises multiple imaging sequences: anatomical information from the T2-weighted (T2W) sequence, functional information from diffusion-weighted imaging, and apparent diffusion coefficient sequences. We first tested rapid perceptual processing in static T2W images then among the two functional sequences. Finally, we examined whether this rapid radiological perception could be observed using T2W multislice imaging. Readers with experience in prostate mpMRI could detect and localize lesions in all sequences after viewing a 500-ms static image. Experienced prostate readers could also detect and localize lesions when viewing multislice image stacks presented as brief movies, with image slices presented at either 48, 96, or 144 ms. The ability to quickly extract the perceptual gestalt may be a general property of expert perception, even in complex imaging modalities.

Radiological semantics discriminate clinically significant grade prostate cancer

Background

Identification of imaging traits to discriminate clinically significant prostate cancer is challenging due to the multi focal nature of the disease. The difficulty in obtaining a consensus by the Prostate Imaging and Data Systems (PI-RADS) scores coupled with disagreements in interpreting multi-parametric Magnetic Resonance Imaging (mpMRI) has resulted in increased variability in reporting findings and evaluating the utility of this imaging modality in detecting clinically significant prostate cancer. This study assess the ability of radiological traits (semantics) observed on multi-parametric Magnetic Resonance images (mpMRI) to discriminate clinically significant prostate cancer.

Methods

We obtained multi-parametric MRI studies from 103 prostate cancer patients with 167 targeted biopsies from a single institution. The study was approved by our Institutional Review Board (IRB) for retrospective analysis. The biopsy location had been identified and marked by a clinical radiologist for targeted biopsy based on initial study interpretation. Using the target locations, two study radiologists independently re-evaluated the scans and scored 16 semantic traits on a point scale (up to 5 levels) based on mpMRI images. The semantic traits describe size, shape, and border characteristics of the prostate lesion, as well as presence of disease around lymph nodes (lymphadenopathy). We built a linear classifier model on these semantic traits and related to pathological outcome to identify clinically significant tumors (Gleason Score ≥ 7). The discriminatory ability of the predictors was tested using cross validation method randomly repeated and ensemble values were reported. We then compared the performance of semantic predictors with the PI-RADS predictors.

Results

We found several semantic features individually discriminated high grade Gleason score (ADC-intensity, Homogeneity, early-enhancement, T2-intensity and extraprostatic extention), these univariate predictors had an average area under the receiver operator characteristics (AUROC) ranging from 0.54 to 0.68. Multivariable semantic predictors with three features (ADC-intensity; T2-intensity, enhancement homogenicity) had an average AUROC of 0.7 [0.43, 0.94]. The PI-RADS based predictor had average AUROC of 0.6 [0.47, 0.75].

Conclusion

We find semantics traits are related to pathological findings with relatively higher reproducibility between radiologists. Multivariable predictors formed on these traits shows higher discriminatory ability compared to PI-RADS scores.

Predictive Values of MRI and PET Derived Quantitative Parameters for Patterns of Failure in Both p16+ and p16- High Risk Head and Neck Cancer

Purpose: FDG-PET adds to clinical factors, such tumor stage and p16 status, in predicting local (LF), regional (RF), and distant failure (DF) in poor prognosis locally advanced head and neck cancer (HNC) treated with chemoradiation. We hypothesized that MRI-based quantitative imaging (QI) metrics could add to clinical predictors of treatment failure more significantly than FDG-PET metrics. Materials and methods: Fifty four patients with poor prognosis HNCs who were enrolled in an IRB approved prospective adaptive chemoradiotherapy trial were analyzed. MRI-derived gross tumor volume (GTV), blood volume (BV), and apparent diffusion coefficient (ADC) pre-treatment and mid-treatment (fraction 10), as well as pre-treatment FDG PET metrics, were analyzed in primary and individual nodal tumors. Cox proportional hazards models for prediction of LRF and DF free survival were used to test the additional value of QI metrics over dominant clinical predictors. Results: The mean ADC pre-RT and its change rate mid-treatment were significantly higher and lower in p16- than p16+ primary tumors, respectively. A Cox model identified that high mean ADC pre-RT had a high hazard for LF and RF in p16- but not p16+ tumors (p = 0.015). Most interesting, persisting subvolumes of low BV (TV_bv) in primary and nodal tumors mid-treatment had high-risk for DF (p < 0.05). Also, total nodal GTV mid-treatment, mean/max SUV of FDG in all nodal tumors, and total nodal TLG were predictive for DF (p < 0.05). When including clinical stage (T4/N3) and total nodal GTV in the model, all nodal PET parameters had a p-value of >0.3, and only TV_bv of primary tumors had a p-value of 0.06. Conclusion: MRI-defined biomarkers, especially persisting subvolumes of low BV, add predictive value to clinical variables and compare favorably with FDG-PET imaging markers. MRI could be well-integrated into the radiation therapy workflow for treatment planning, response assessment, and adaptive therapy.

Prostate diffusion MRI with minimal echo time using eddy current nulled convex optimized diffusion encoding

BACKGROUND

Prostate diffusion-weighted imaging (DWI) using monopolar encoding is sensitive to eddy-current-induced distortion artifacts. Twice-refocused bipolar encoding suppresses eddy current artifacts, but increases echo time (TE), leading to lower signal-to-noise ratio (SNR). Optimization of the diffusion encoding might improve prostate DWI.

PURPOSE

To evaluate eddy current nulled convex optimized diffusion encoding (ENCODE) for prostate DWI with minimal TE.

STUDY TYPE

Prospective cohort study.

POPULATION

A diffusion phantom, an ex vivo prostate specimen, 10 healthy male subjects (27 ± 3 years old), and five prostate cancer patients (62 ± 7 years old).

FIELD STRENGTH/SEQUENCE

3T; single-shot spin-echo echoplanar DWI.

ASSESSMENT

Eddy-current artifacts, TE, SNR, apparent diffusion coefficient (ADC), and image quality scores from three independent readers were compared between monopolar, bipolar, and ENCODE prostate DWI for standard-resolution (1.6 × 1.6 mm² , partial Fourier factor [pF] = 6/8) and higher-resolution protocols (1.6 × 1.6 mm² , pF = off; 1.0 × 1.0 mm² , pF = 6/8).

STATISTICAL TESTING

SNR and ADC differences between techniques were tested with Kruskal-Wallis and Wilcoxon signed-rank tests (P < 0.05 considered significant).

RESULTS

Eddy current suppression with ENCODE was comparable to bipolar encoding (mean coefficient of variation across three diffusion directions of 9.4% and 9%). For a standard-resolution protocol, ENCODE achieved similar TE as monopolar and reduced TE by 14 msec compared to bipolar, resulting in 27% and 29% higher mean SNR in prostate transition zone (TZ) and peripheral zone (PZ) (P < 0.05) compared to bipolar, respectively. For higher-resolution protocols, ENCODE achieved the shortest TE (67 msec), with 17-21% and 58-70% higher mean SNR compared to monopolar (TE = 77 msec) and bipolar (TE = 102 msec) in PZ and TZ (P < 0.05). No significant differences were found in mean TZ (P = 0.91) and PZ ADC (P = 0.94) between the three techniques. ENCODE achieved similar or higher image quality scores than bipolar DWI in patients, with mean intraclass correlation coefficient of 0.77 for overall quality between three independent readers.

DATA CONCLUSION

ENCODE minimizes TE (improves SNR) and reduces eddy-current distortion for prostate DWI compared to monopolar and bipolar encoding.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1526-1539.

Histological differences in cancer cells, stroma, and luminal spaces strongly correlate with in vivo MRI-detectability of prostate cancer

The current study aimed to investigate the plausible histopathological factors that affect the detectability of prostate cancers on multiparametric magnetic resonance imaging (MP-MRI). This retrospective study included 59 consecutive patients who had undergone MP-MRI and subsequent radical prostatectomy. The cases were standardized according to the tumor size ranging from 10 to 20 mm on the final pathological diagnosis. Histopathological review and semi-automated imaging analysis were performed to evaluate the relative area fractions of the histological components, including cancer cells, stroma, and luminal spaces. Among the 59 prostatectomy specimens, no case showed two or more foci of cancer that matched the size criteria. Of the 59 lesions, 35 were MRI-detectable [Prostate Imaging Reporting and Data System (PIRADS) score of 3 or greater] and 24 were MRI-undetectable (PIRADS score of 2 or less). No significant differences were observed in Gleason Grade Group, percentage of Gleason pattern 4, and predominant subtype of Gleason pattern 4 between MRI-detectable and MRI-undetectable cancers. On the other hand, significantly higher mean area fraction of cancer cells (60.9% vs. 42.7%, P < 0.0001) and lower mean area fractions of stroma (33.8% vs. 45.1%, P = 0.00089) and luminal spaces (5.2% vs. 12.2%, P < 0.0001) were observed in MRI-detectable cancers than in MRI-undetectable cancers. In a multivariable analysis performed upon exclusion of area fraction of stroma due to its multicollinearity with that of cancer cells, area fractions of cancer cells (P = 0.0031) and luminal space (P = 0.0035) demonstrated strong positive and negative correlation with MRI-detectability, respectively. Changes in cancer cells, stroma, and luminal spaces, rather than conventional histological parameters, could be considered one of the best predictors to clinical, in vivo MRI-detectability of prostate cancer.

Radiomics and radiogenomics of prostate cancer

Radiomics and radiogenomics are attractive research topics in prostate cancer. Radiomics mainly focuses on extraction of quantitative information from medical imaging, whereas radiogenomics aims to correlate these imaging features to genomic data. The purpose of this review is to provide a brief overview summarizing recent progress in the application of radiomics-based approaches in prostate cancer and to discuss the potential role of radiogenomics in prostate cancer.

A Clinical Approach to Diffusion-Weighted Magnetic Resonance Imaging in Evaluating Chest Wall Invasion of Breast Tumors

Objective:

The purpose of this study is to evaluate diffusion weighted magnetic rsonance imaging (MRI) acquisitions in delineating posterior extent of breast tumors and in predicting chest wall invasion prior to treatment. To our knowledge, there has not been any literature specifically evaluating the utility of diffusion-weighted acquisitions in chest wall invasion of breast tumors.

Materials and Methods:

A retrospective review of our breast imaging database for keywords “chest wall invasion” and “breast MRI” was performed over the last 14 years. Diffusion sequences, T1 sequences (pre and post contrast), and T2 sequences were evaluated. Apparent diffusion coefficient (ADC) values in tumor and chest wall were assessed. Imaging findings were correlated with surgical pathology.

Results:

23 patients met inclusion criteria. All 23 had loss of fat plane on T2 sequences. 22/23 had loss of fat plane on postcontrast T1 sequences. Pectoralis muscle enhancement was present in 19/23 (83%) tumors and chest wall enhancement was present 9/23 (39%) tumors. Qualitative restricted diffusion within the pectoralis muscle was present in 18/23 (71%) tumors and in the chest wall was present in 8/23 (35%) tumors. Mean ADC values were 1.15 s/mm² in the tumor and 1.29 s/mm² in the chest wall. Sensitivity, specificity, positive predictive value and negative predictive value were 100%, 36%, 63%, and 100% for chest wall enhancement respectively and 69%, 36%, 61%, and 80% for chest wall diffusion-weighted imaging restriction respectively.

Conclusion:

Diffusion weighted sequences can be helpful in characterizing chest wall invasion of breast tumors.

Multiparametric MRI and radiomics in prostate cancer: a review

Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.

Correlations between Apparent Diffusion Coefficient and Gleason Score in Prostate Cancer: A Systematic Review

BACKGROUND

Reported data regarding the associations between apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) and Gleason score in prostate cancer (PC) are inconsistent.

OBJECTIVE

The aim of the present systematic review was to analyze relationships between ADC and Gleason score in PC.

DESIGN, SETTING, AND PARTICIPANTS

MEDLINE library, SCOPUS, and EMBASE databases were screened for relationships between ADC and Gleason score in PC up to April 2018. Overall, 39 studies with 2457 patients were identified. Data on the following parameters were extracted from the literature: number of patients, cancer localization, and correlation coefficients between ADC and Gleason score.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Associations between ADC and Gleason score were analyzed by the Spearman's correlation coefficient.

RESULTS AND LIMITATIONS

In overall sample, the pooled correlation coefficient between ADC and Gleason score was -0.45 (95% confidence interval [CI]=[-0.50; -0.40]). In PC in the transitional zone, the pooled correlation coefficient was -0.22 (95% CI=[-0.47; 0.03]). In PC in the peripheral zone, the pooled correlation coefficient was -0.48 (95% CI=[-0.54; -0.42]).

CONCLUSIONS

In PC located in the peripheral zone, ADC correlated moderately with Gleason score. In PC located in the transitional zone, ADC correlated weakly with Gleason score.

PATIENT SUMMARY

We reviewed studies using apparent diffusion coefficient for the prediction of Gleason score in prostate cancer patients.

Magnetic resonance imaging-transrectal ultrasound fusion image-guided prostate biopsy: Current status of the cancer detection and the prospects of tailor-made medicine of the prostate cancer

Multi-parametric magnetic resonance imaging (mpMRI) has been increasingly used to diagnose clinically significant prostate cancer (csPCa) because of its growing availability and its ability to combine anatomical and functional data. Magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) fusion imaging provides MRI information with TRUS images for prostate biopsies. This technique combines the superior sensitivity of MRI for targeting suspicious lesions with the practicality and familiarity of TRUS. MRI-TRUS fusion image-guided prostate biopsy is performed with different types of image registration (rigid vs. elastic) and needle tracking methods (electromagnetic tracking vs. mechanical position encoders vs. image-based software tracking). A systematic review and meta-analysis showed that MRI-targeted biopsy detected csPCa at a significantly higher rate than did TRUS-guided biopsy, while it detected significantly fewer cases of insignificant PCas. In addition to the high accuracy of MRI-targeted biopsy for csPCa, localization of csPCa is accurate. The ability to choose the route of biopsy (transperineal vs. transrectal) is required, depending on the patients' risk and the location and size of suspicious lesions on mpMRI. Fusion image-guided prostate biopsy has the potential to allow precise management of prostate cancer, including active surveillance, radical treatment, and focal therapy.

Changes in apparent diffusion coefficient radiomics features during dose-painted radiotherapy and high dose rate brachytherapy for prostate cancer

Background and purpose

Dose escalation has improved cancer outcomes for patients with localized prostate cancer. Targeting subprostatic tumor regions for dose intensification may further improve outcomes. Apparent Diffusion Coefficient (ADC) maps may enable early radiation response assessment and dose adaptation. This study was a proof-of-principle investigation of early changes in ADC radiomics features for patients undergoing radiotherapy with dose escalation to the gross tumor volume (GTV).

Materials and methods

Fifty-nine patients were enrolled on a prospective tumor dose-escalation trial. Multi-parametric MRI was performed at baseline and week six, corresponding to the time of peak ADC change. GTV and prostate contours were deformably registered between baseline and week six T2-weighted images, and applied to ADC maps, to account for diminished image contrast post-EBRT and possible differences in prostate gland volume, shape, and orientation. A total of 101 radiomics features were tested for significant change post-EBRT using two-tailed Student's t-test. All ADC features of the prostate and GTV volumes were correlated using Pearson's coefficient (p < 0.00008, based on Bonferroni correction).

Results

ADC feature extraction was insensitive to b = 0 s/mm² exclusion, and to gradient non-linearity bias. GTV presented predominant changes in first-order features, particularly 10Percentile, and prostate volumes presented predominant changes in second-order features. Changes in both first and second-order features of GTV and prostate ROIs were strongly correlated.

Conclusions

Our data confirmed significant changes in numerous GTV and prostate features assessed from ADC and T2-weighted images during radiotherapy; all of which may be potential biomarkers of early radiation response.

Voxel-wise prostate cell density prediction using multiparametric magnetic resonance imaging and machine learning

BACKGROUND

There are currently no methods to estimate cell density in the prostate. This study aimed to develop predictive models to estimate prostate cell density from multiparametric magnetic resonance imaging (mpMRI) data at a voxel level using machine learning techniques.

MATERIAL AND METHODS

In vivo mpMRI data were collected from 30 patients before radical prostatectomy. Sequences included T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging. Ground truth cell density maps were computed from histology and co-registered with mpMRI. Feature extraction and selection were performed on mpMRI data. Final models were fitted using three regression algorithms including multivariate adaptive regression spline (MARS), polynomial regression (PR) and generalised additive model (GAM). Model parameters were optimised using leave-one-out cross-validation on the training data and model performance was evaluated on test data using root mean square error (RMSE) measurements.

RESULTS

Predictive models to estimate voxel-wise prostate cell density were successfully trained and tested using the three algorithms. The best model (GAM) achieved a RMSE of 1.06 (± 0.06) × 10³ cells/mm² and a relative deviation of 13.3 ± 0.8%.

CONCLUSION

Prostate cell density can be quantitatively estimated non-invasively from mpMRI data using high-quality co-registered data at a voxel level. These cell density predictions could be used for tissue classification, treatment response evaluation and personalised radiotherapy.

Characterization of prostate microstructure using water diffusion and NMR relaxation

For many pathologies, early structural tissue changes occur at the cellular level, on the scale of micrometers or tens of micrometers. Magnetic resonance imaging (MRI) is a powerful non-invasive imaging tool used for medical diagnosis, but its clinical hardware is incapable of reaching the cellular length scale directly. In spite of this limitation, microscopic tissue changes in pathology can potentially be captured indirectly, from macroscopic imaging characteristics, by studying water diffusion. Here we focus on water diffusion and NMR relaxation in the human prostate, a highly heterogeneous organ at the cellular level. We present a physical picture of water diffusion and NMR relaxation in the prostate tissue, that is comprised of a densely-packed cellular compartment (composed of stroma and epithelium), and a luminal compartment with almost unrestricted water diffusion. Transverse NMR relaxation is used to identify fast and slow T ₂ components, corresponding to these tissue compartments, and to disentangle the luminal and cellular compartment contributions to the temporal evolution of the overall water diffusion coefficient. Diffusion in the luminal compartment falls into the short-time surface-to-volume (S/V) limit, indicating that only a small fraction of water molecules has time to encounter the luminal walls of healthy tissue; from the S/V ratio, the average lumen diameter averaged over three young healthy subjects is measured to be 217.7±188.7 μm. Conversely, the diffusion in the cellular compartment is highly restricted and anisotropic, consistent with the fibrous character of the stromal tissue. Diffusion transverse to these fibers is well described by the random permeable barrier model (RPBM), as confirmed by the dynamical exponent ϑ = 1/2 for approaching the long-time limit of diffusion, and the corresponding structural exponent p = -1 in histology. The RPBM-derived fiber diameter and membrane permeability were 19.8±8.1 μm and 0.044±0.045 μm/ms, respectively, in agreement with known values from tissue histology and membrane biophysics. Lastly, we revisited 38 prostate cancer cases from a recently published study, and found the same dynamical exponent ϑ = 1/2 of diffusion in tumors and benign regions. Our results suggest that a multi-parametric MRI acquisition combined with biophysical modeling may be a powerful non-invasive complement to prostate cancer grading, potentially foregoing biopsies.

Diffusion weighted MRI as an early predictor of tumor response to hypofractionated stereotactic boost for prostate cancer

We evaluated the feasibility of using the kinetic of diffusion-weighted MRI (DWI) and the normalized apparent coefficient diffusion (ADC) map value as an early biomarker in patients treated by external beam radiotherapy (EBRT). Twelve patients were included within the frame of a multicenter phase II trial and treated for intermediate risk prostate cancer (PCa). Multiparametric MRI was performed before treatment (M0) and every 6 months until M24. Association between nADC and PSA or PSA kinetic was evaluated using the test of nullity of the Spearman correlation coefficient. The median rates of PSA at the time of diagnosis, two years and four years after EBRT were 9.29 ng/ml (range from 5.26 to 17.67), 0.68 ng/ml (0.07–2.7), 0.47 ng/ml (0.09–1.39), respectively. Median nADC increased from 1.14 × 10⁻³ mm²/s to 1.59 × 10⁻³ mm²/s between M0 and M24. Only one patient presented a decrease of nADC (1.35 × 10⁻³ mm²/s and 1.11 × 10⁻³ mm²/s at M0 and M12 respectively). The increase in nADC at M6 was correlated with PSA decrease at M18, M24 and M30 (p < 0.05). The increase in nADc at M12 was correlated with PSA decrease at M36 (p = 0.019). Early nADC variation were correlated with late PSA decrease for patients with PCa treated by EBRT.

Can Apparent Diffusion Coefficient Values Assist PI-RADS Version 2 DWI Scoring? A Correlation Study Using the PI-RADSv2 and International Society of Urological Pathology Systems

OBJECTIVE

The purposes of this study were to assess correlation of apparent diffusion coefficient (ADC) and normalized ADC (ratio of tumor to nontumor tissue) with the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and updated International Society of Urological Pathology (ISUP) categories and to determine how to optimally use ADC metrics for objective assistance in categorizing lesions within PI-RADSv2 guidelines.

MATERIALS AND METHODS

In this retrospective study, 100 patients (median age, 62 years; range, 44-75 years; prostate-specific antigen level, 7.18 ng/mL; range, 1.70-84.56 ng/mL) underwent 3-T multiparametric MRI of the prostate with an endorectal coil. Mean ADC was extracted from ROIs based on subsequent prostatectomy specimens. Histopathologic analysis revealed 172 lesions (113 peripheral, 59 transition zone). Two radiologists blinded to histopathologic outcome assigned PI-RADSv2 categories. Kendall tau was used to correlate ADC metrics with PI-RADSv2 and ISUP categories. ROC curves were used to assess the utility of ADC metrics in differentiating each reader's PI-RADSv2 DWI category 4 or 5 assessment in the whole prostate and by zone.

RESULTS

ADC metrics negatively correlated with ISUP category in the whole prostate (ADC, τ = -0.21, p = 0.0002; normalized ADC, τ = -0.21, p = 0.0001). Moderate negative correlation was found in expert PI-RADSv2 DWI categories (ADC, τ = -0.34; normalized ADC, τ = -0.31; each p < 0.0001) maintained across zones. In the whole prostate, AUCs of ADC and normalized ADC were 87% and 82% for predicting expert PI-RADSv2 DWI category 4 or 5. A derived optimal cutoff ADC less than 1061 and normalized ADC less than 0.65 achieved positive predictive values of 83% and 84% for correct classification of PI-RADSv2 DWI category 4 or 5 by an expert reader. Consistent relations and predictive values were found by an independent novice reader.

CONCLUSION

ADC and normalized ADC inversely correlate with PI-RADSv2 and ISUP categories and can serve as quantitative metrics to assist with assigning PI-RADSv2 DWI category 4 or 5.

Multiparametric (mp) MRI of prostate cancer

Prostate cancer (PCa) is one of the most prevalent cancers in men. A large number of men are detected with PCa; however, the clinical behavior ranges from low-grade indolent tumors that never develop into a clinically significant disease to aggressive, invasive tumors that may rapidly progress to metastatic disease. The challenges in clinical management of PCa are at levels of screening, diagnosis, treatment, and follow-up after treatment. Magnetic resonance imaging (MRI) methods have shown a potential role in detection, localization, staging, assessment of aggressiveness, targeting biopsies, etc. in PCa patients. Multiparametric MRI (mpMRI) is emerging as a better option compared to the individual imaging methods used in the evaluation of PCa. There are attempts to improve the reproducibility and reliability of mpMRI by using an objective scoring system proposed in the prostate imaging reporting and data system (PIRADS) for standardized reporting. Prebiopsy mpMRI may be used to detect PCa in men with elevated prostate-specific antigen or abnormal digital rectal examination and to enable targeted biopsies. mpMRI can also be used to decide on clinical management of patients, for example active surveillance, and may help in detecting only the pathology that requires detection. It can potentially not only guide patient selection for initial and repeat biopsy but also reduce false-negative biopsies. This review presents a description of the MR methods most commonly applied for investigations of prostate. The anatomical, functional and metabolic parameters obtained from these MR methods are discussed with regard to their physical basis and their contribution to mpMRI investigations of PCa.

Histogram analysis of stretched-exponential and monoexponential diffusion-weighted imaging models for distinguishing low and intermediate/high gleason scores in prostate carcinoma

BACKGROUND

Noninvasive measures to evaluate the aggressiveness of prostate carcinoma (PCa) may benefit patients.

PURPOSE

To assess the value of stretched-exponential and monoexponential diffusion-weighted imaging (DWI) for predicting the aggressiveness of PCa.

STUDY TYPE

Retrospective study.

SUBJECTS

Seventy-five patients with PCa.

FIELD STRENGTH

3T DWI examinations were performed using b-values of 0, 500, 1000, and 2000 s/mm² .

ASSESSMENT

The research were based on entire-tumor histogram analysis and the reference standard was radical prostectomy.

STATISTICAL TESTS

The correlation analysis was programmed with Spearman's rank-order analysis between the histogram variables and Gleason grade group (GG). Receiver operating characteristic (ROC) regression was used to analyze the ability of these histogram variables to differentiate low-grade (LG) from intermediate/high-grade (HG) PCa.

RESULTS

The percentiles and mean of apparent diffusion coefficient (ADC) and distributed diffusion coefficient (DDC) were correlated with GG (ρ: 0.414-0.593), while there was no significant relation among α value, skewnesses, and kurtosises with GG (ρ:0.034-0.323). HG tumors (ADC:484 ± 136, 592 ± 139, 670 ± 144, 788 ± 146, 895 ± 141 mm² /s; DDC: 410 ± 142, 532 ± 172, 666 ± 193, 786 ± 196, 914 ± 181 mm² /s) had lower values in the 10^th , 25^th , 50^th , 75^th percentiles and means than LG tumors (ADC: 644 ± 779, 737 ± 84, 836 ± 83, 919 ± 82, 997 ± 107 mm² /s; DDC: 552 ± 82, 680 ± 94, 829 ± 112, 931 ± 106, 1045 ± 100 mm² /s). However, there was no difference between LG and HG tumors in α value (0.671 ± 0.041 vs. 0.633 ± 0.114), kurtosises (ADC 0.09 vs. 0.086; DDC -0.033 vs. -0.317), or skewnesses (ADC -0.036 vs. 0.073; DDC -0.063 vs. 0.136). The above statistics were P < 0.01. ADC10 with AUC = 0.840 and DDC10 with AUC = 0.799 were similar in discriminating between LG and HG PCa at P < 0.05.

DATA CONCLUSION

Histogram variables of DDC and ADC may predict the aggressiveness of PCa, while α value does not. The abilities of ADC10 and DDC10 to discriminate LG from HG tumors were similar, and both better than their respective means.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2018;48:491-498.

Multiparametric Magnetic Resonance Imaging of Prostate Cancer Bone Disease: Correlation With Bone Biopsy Histological and Molecular Features

OBJECTIVES

The aim of this study was to correlate magnetic resonance imaging (MRI) of castration-resistant prostate cancer (CRPC) bone metastases with histological and molecular features of bone metastases.

MATERIALS AND METHODS

Forty-three bone marrow biopsies from 33 metastatic CRPC (mCRPC) patients with multiparametric MRI and documented bone metastases were evaluated. A second cohort included 10 CRPC patients with no bone metastases. Associations of apparent diffusion coefficient (ADC), normalized b900 diffusion-weighted imaging (nDWI) signal, and signal-weighted fat fraction (swFF) with bone marrow biopsy histological parameters were evaluated using Mann-Whitney U test and Spearman correlations. Univariate and multivariate logistic regression models were analyzed.

RESULTS

Median ADC and nDWI signal was significantly higher, and median swFF was significantly lower, in bone metastases than nonmetastatic bone (P < 0.001). In the metastatic cohort, 31 (72.1%) of 43 biopsies had detectable cancer cells. Median ADC and swFF were significantly lower and median nDWI signal was significantly higher in biopsies with tumor cells versus nondetectable tumor cells (898 × 10 mm/s vs 1617 × 10 mm/s; 11.5% vs 62%; 5.3 vs 2.3, respectively; P < 0.001). Tumor cellularity inversely correlated with ADC and swFF, and positively correlated with nDWI signal (P < 0.001). In serial biopsies, taken before and after treatment, changes in multiparametric MRI parameters paralleled histological changes.

CONCLUSIONS

Multiparametric MRI provides valuable information about mCRPC bone metastases. These data further clinically qualify DWI as a response biomarker in mCRPC.

Diffusion kurtosis imaging of a human nasopharyngeal carcinoma xenograft model: Initial experience with pathological correlation

PURPOSE

The aim of this study was to investigate the relationship between diffusion kurtosis imaging (DKI)-related parameters and pathological measures using human nasopharyngeal carcinoma (NPC) xenografts in a nude mouse model.

MATERIALS AND METHODS

Twenty-six BALB/c-nu nude mice were divided into two groups that were injected with two different nasopharyngeal squamous cell carcinoma cell lines (CNE1 and CNE2). DK magnetic resonance (MR) imaging was performed on a 3.0 Tesla MR scanner. DWI and DKI-related parameters, including apparent diffusion coefficient (ADC), mean diffusivity (MD) and mean kurtosis (MK) were measured. Mice were euthanatized when the maximum diameter of the primary tumor reached 1.5cm after MR scanning. Tumor tissues were then processed for hematoxylin and eosin staining. The pathological images were analyzed using a computer-aided pixel-wise clustering method to evaluate tumor cellular density, nuclei portion, cytoplasm portion, extracellular space portion, the ratio of nuclei to cytoplasm and the ratio of nuclei to extracellular space. The relationships between DWI and DKI-related parameters and pathological features were analyzed statistically.

RESULTS

The ADC and MD values of the CNE1 group (1.16±0.24×10^-3mm²/s, 2.28±0.29×10^-3mm²/s) was higher than that of the CNE2 group (0.82±0.14×10^-3mm²/s, 1.53±0.24×10^-3mm²/s, P<0.001), but the MK values between the two groups were not significantly different (CNE1: 0.55±0.14; CNE2: 0.47±0.23; P>0.05). A Pearson test showed that the ADC and MD values were significantly correlated with cellular density, nuclei portion, extracellular space portion and the ratio of nuclei to extracellular space (r=-0.861; -0.909, P<0.001; r=-0.487; 0.591, P<0.05; r=0.567; 0.625, P<0.05; r=-0.645; -0.745, P<0.001, respectively). The MK values were significantly correlated with nuclei portion, cytoplasm portion and the ratio of nuclei to cytoplasm (r=-0.475, P<0.05; r=0.665, P<0.001; r=-0.494, P<0.05, respectively).

CONCLUSION

The preliminary animal results suggest that DKI findings can provide valuable bio-information for NPC tissue characterization. DKI imaging might be utilized as a surrogate biomarker for the non-invasive assessment of tumor microstructures.

Biparametric MRI of the prostate

Biparametric Magnetic Resonance Imaging (bpMRI) of the prostate combining both morphologic T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) is emerging as an alternative to multiparametric MRI (mpMRI) to detect, to localize and to guide prostatic targeted biopsy in patients with suspicious prostate cancer (PCa). BpMRI overcomes some limitations of mpMRI such as the costs, the time required to perform the study, the use of gadolinium-based contrast agents and the lack of a guidance for management of score 3 lesions equivocal for significant PCa. In our experience the optimal and similar clinical results of the bpMRI in comparison to mpMRI are essentially related to the DWI that we consider the dominant sequence for detection suspicious PCa both in transition and in peripheral zone. In clinical practice, the adoption of bpMRI standardized scoring system, indicating the likelihood to diagnose a clinically significant PCa and establishing the management of each suspicious category (from 1 to 4), could represent the rationale to simplify and to improve the current interpretation of mpMRI based on Prostate Imaging and Reporting Archiving Data System version 2 (PI-RADS v2). In this review article we report and describe the current knowledge about bpMRI in the detection of suspicious PCa and a simplified PI-RADS based on bpMRI for management of each suspicious PCa categories to facilitate the communication between radiologists and urologists.