Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer

Autonomous Tumor Signature Extraction Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study

BACKGROUND

Accurate, reliable, non-invasive assessment of patients diagnosed with prostate cancer is essential for proper disease management. Quantitative assessment of multi-parametric MRI, such as through artificial intelligence or spectral/statistical approaches, can provide a non-invasive objective determination of the prostate tumor aggressiveness without side effects or potential poor sampling from needle biopsy or overdiagnosis from prostate serum antigen measurements. To simplify and expedite prostate tumor evaluation, this study examined the efficacy of autonomously extracting tumor spectral signatures for spectral/statistical algorithms for spatially registered bi-parametric MRI.

METHODS

Spatially registered hypercubes were digitally constructed by resizing, translating, and cropping from the image sequences (Apparent Diffusion Coefficient (ADC), High B-value, T2) from 42 consecutive patients in the bi-parametric MRI PI-CAI dataset. Prostate cancer blobs exceeded a threshold applied to the registered set from normalizing the registered set into an image that maximizes High B-value, but minimizes the ADC and T2 images, appearing "green" in the color composite. Clinically significant blobs were selected based on size, average normalized green value, sliding window statistics within a blob, and position within the hypercube. The center of mass and maximized sliding window statistics within the blobs identified voxels associated with tumor signatures. We used correlation coefficients (R) and p-values, to evaluate the linear regression fits of the z-score and SCR (with processed covariance matrix) to tumor aggressiveness, as well as Area Under the Curves (AUC) for Receiver Operator Curves (ROC) from logistic probability fits to clinically significant prostate cancer.

RESULTS

The highest R (R > 0.45), AUC (>0.90), and lowest p-values (<0.01) were achieved using z-score and modified registration applied to the covariance matrix and tumor signatures selected from the "greenest" parts from the selected blob.

CONCLUSIONS

The first autonomous tumor signature applied to spatially registered bi-parametric MRI shows promise for determining prostate tumor aggressiveness.

Early change in apparent diffusion coefficient as a predictor of response to neoadjuvant androgen deprivation and external beam radiation therapy for intermediate- to high-risk prostate cancer

AIM

To determine the role of serial apparent diffusion coefficient (ADC) as a biomarker for response to neoadjuvant androgen deprivation therapy (nADT) followed by external beam radiation therapy (EBRT) in intermediate- to high-risk prostate cancer (PCa) patients.

METHODS

This Health Insurance Portability and Accountability Act (HIPAA)-compliant, institutional review board (IRB)-approved prospective study included 12 patients with intermediate- to high-risk PCa patients prior to nADT and EBRT, who underwent serial serum prostate-specific antigen (PSA) and multiparametric prostate magnetic resonance imaging (mpMRI) at baseline (BL), 8-weeks after nADT initiation (time point [TP]1), 6-weeks into EBRT delivery (TP2), and 6-months after nADT initiation (TP3). Tumour volume (tVOL) and tumour and normal tissue ADC (tADC and nlADC) were determined at all TPs. tADC and nlADC dynamics were correlated with post-treatment PSA using Pearson's correlation coefficient. Paired t-tests compared pre/post-treatment ADC.

RESULTS

There was a sequential decrease in PSA at all TPs, reaching their lowest values at TP3 post-treatment completion. Mean tADC increased significantly from baseline to TP1 (917.8 ± 107.7 × 10-6 versus 1033.8 ± 139.3 × 10-6 mm2/s; p<0.01), with no subsequent change at TP2 or TP3. Both percentage and absolute change in tADC from BL to TP1 correlated with post-treatment PSA (r=-0.666, r=-0.674; p=0.02). Post-treatment PSA in good responders (<0.1 ng/ml) versus poor responders (≥ 0.1 ng/ml) was associated with a greater increase in tADC from BL to TP1 (169.2 ± 122.4 × 10-6 versus 22.9 ± 75.5 × 10-6 mm2/s, p=0.03).

CONCLUSION

This pilot study demonstrates the potential for early ADC metrics as a biomarker of response to nADT and EBRT in intermediate to high-risk PCA.

Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma

BACKGROUND

 Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric).

METHOD

 This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality.

RESULTS AND CONCLUSION

 Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers.

KEY POINTS

· Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas..

CITATION FORMAT

· Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.

Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI

Background Deep learning (DL) reconstructions can enhance image quality while decreasing MRI acquisition time. However, DL reconstruction methods combined with compressed sensing for prostate MRI have not been well studied. Purpose To use an industry-developed DL algorithm to reconstruct low-resolution T2-weighted turbo spin-echo (TSE) prostate MRI scans and compare these with standard sequences. Materials and Methods In this prospective study, participants with suspected prostate cancer underwent prostate MRI with a Cartesian standard-resolution T2-weighted TSE sequence (T2C) and non-Cartesian standard-resolution T2-weighted TSE sequence (T2NC) between August and November 2022. Additionally, a low-resolution Cartesian DL-reconstructed T2-weighted TSE sequence (T2DL) with compressed sensing DL denoising and resolution upscaling reconstruction was acquired. Image sharpness was assessed qualitatively by two readers using a five-point Likert scale (from 1 = nondiagnostic to 5 = excellent) and quantitatively by calculating edge rise distance. The Friedman test and one-way analysis of variance with post hoc Bonferroni and Tukey tests, respectively, were used for group comparisons. Prostate Imaging Reporting and Data System (PI-RADS) score agreement between sequences was compared by using Cohen κ. Results This study included 109 male participants (mean age, 68 years ± 8 [SD]). Acquisition time of T2DL was 36% and 29% lower compared with that of T2C and T2NC (mean duration, 164 seconds ± 20 vs 257 seconds ± 32 and 230 seconds ± 28; P < .001 for both). T2DL showed improved image sharpness compared with standard sequences using both qualitative (median score, 5 [IQR, 4-5] vs 4 [IQR, 3-4] for T2C and 4 [IQR, 3-4] for T2NC; P < .001 for both) and quantitative (mean edge rise distance, 0.75 mm ± 0.39 vs 1.15 mm ± 0.68 for T2C and 0.98 mm ± 0.65 for T2NC; P < .001 and P = .01) methods. PI-RADS score agreement between T2NC and T2DL was excellent (κ range, 0.92-0.94 [95% CI: 0.87, 0.98]). Conclusion DL reconstruction of low-resolution T2-weighted TSE sequences enabled accelerated acquisition times and improved image quality compared with standard acquisitions while showing excellent agreement with conventional sequences for PI-RADS ratings. Clinical trial registration no. NCT05820113 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Turkbey in this issue.

The Diagnostic Performance of Tumor Stage on MRI for Predicting Prostate Cancer-Positive Surgical Margins: A Systematic Review and Meta-Analysis

PURPOSE

Surgical margin status in radical prostatectomy (RP) specimens is an established predictive indicator for determining biochemical prostate cancer recurrence and disease progression. Predicting positive surgical margins (PSMs) is of utmost importance. We sought to perform a meta-analysis evaluating the diagnostic utility of a high clinical tumor stage (≥3) on magnetic resonance imaging (MRI) for predicting PSMs.

METHOD

A systematic search of the PubMed, Embase databases, and Cochrane Library was performed, covering the interval from 1 January 2000 to 31 December 2022, to identify relevant studies. The Quality Assessment of Diagnostic Accuracy Studies 2 method was used to evaluate the studies' quality. A hierarchical summary receiver operating characteristic plot was created depicting sensitivity and specificity data. Analyses of subgroups and meta-regression were used to investigate heterogeneity.

RESULTS

This meta-analysis comprised 13 studies with 3924 individuals in total. The pooled sensitivity and specificity values were 0.40 (95% CI, 0.32-0.49) and 0.75 (95% CI, 0.69-0.80), respectively, with an area under the receiver operating characteristic curve of 0.63 (95% CI, 0.59-0.67). The Higgins I2 statistics indicated moderate heterogeneity in sensitivity (I2 = 75.59%) and substantial heterogeneity in specificity (I2 = 86.77%). Area, prevalence of high Gleason scores (≥7), laparoscopic or robot-assisted techniques, field strength, functional technology, endorectal coil usage, and number of radiologists were significant factors responsible for heterogeneity (p ≤ 0.01).

CONCLUSIONS

T stage on MRI has moderate diagnostic accuracy for predicting PSMs. When determining the treatment modality, clinicians should consider the factors contributing to heterogeneity for this purpose.

Preoperative imaging accuracy in size determination of prostate cancer in men undergoing radical prostatectomy for clinically localised disease

OBJECTIVES

To compare the accuracy of pre-surgical prostate size measurements using mpMRI and USWE with imaging-based 3D-printed patient-specific whole-mount moulds facilitated histopathology, and to assess whether size assessment varies between clinically significant and non-significant cancerous lesions including their locations in different zones of the prostate.

METHODS

The study population included 202 men with clinically localised prostate cancer opting for radical surgery derived from two prospective studies. Protocol-based imaging data was used for measurement of size of prostate cancer in clinically localised disease using MRI (N = 106; USWE (N = 96). Forty-eight men overlapped between two studies and formed the validation cohort. The primary outcome of this study was to assess the accuracy of pre-surgical prostate cancerous size measurements using mpMRI and USWE with imaging-based 3D-printed patient-specific whole-mount moulds facilitated histopathology as a reference standard. Independent-samples T-tests were used for the continuous variables and a nonparametric Mann-Whitney U test for independent samples was applied to examine the distribution and median differences between mpMRI and USWE groups.

RESULTS

A significant number of men had underestimation of prostate cancer using both mpMRI (82.1%; 87/106) and USWE (64.6%; 62/96). On average, tumour size was underestimated by a median size of 7 mm in mpMRI, and 1 mm in USWE. There were 327 cancerous lesions (153 with mpMRI and 174 for USWE). mpMRI and USWE underestimated the majority of cancerous lesions (108/153; 70.6%) and (88/174; 50.6%), respectively. Validation cohort data confirmed these findings MRI had a nearly 20% higher underestimation rate than USWE (χ² (1, N = 327) = 13.580, p = 0.001); especially in the mid and apical level of the gland. Clinically non-significant cancers were underestimated in significantly higher numbers in comparison to clinically significant cancers.

CONCLUSIONS

Size measurement of prostate cancers on preoperative imaging utilising maximum linear extent technique, underestimated the extent of cancer. Further research is needed to confirm our observations using different sequences, methods and approaches for cancer size measurement.

Toward mechatronic MRI-guided focal laser ablation of the prostate: Robust registration for improved needle delivery

BACKGROUND

Multiparametric MRI (mpMRI) is an effective tool for detecting and staging prostate cancer (PCa), guiding interventional therapy, and monitoring PCa treatment outcomes. MRI-guided focal laser ablation (FLA) therapy is an alternative, minimally invasive treatment method to conventional therapies, which has been demonstrated to control low-grade, localized PCa while preserving patient quality of life. The therapeutic success of FLA depends on the accurate placement of needles for adequate delivery of ablative energy to the target lesion. We previously developed an MR-compatible mechatronic system for prostate FLA needle guidance and validated its performance in open-air and clinical 3T in-bore experiments using virtual targets.

PURPOSE

To develop a robust MRI-to-mechatronic system registration method and evaluate its in-bore MR-guided needle delivery accuracy in tissue-mimicking prostate phantoms.

METHODS

The improved registration multifiducial assembly houses thirty-six aqueous gadolinium-filled spheres distributed over a 7.3 × 7.3 × 5.2 cm volume. MRI-guided needle guidance accuracy was quantified in agar-based tissue-mimicking prostate phantoms on trajectories (N = 44) to virtual targets covering the mechatronic system's range of motion. 3T gradient-echo recalled (GRE) MRI images were acquired after needle insertions to each target, and the air-filled needle tracks were segmented. Needle guidance error was measured as the shortest Euclidean distance between the target point and the segmented needle trajectory, and angular error was measured as the angle between the targeted trajectory and the segmented needle trajectory. These measurements were made using both the previously designed four-sphere registration fiducial assembly on trajectories (N = 7) and compared with the improved multifiducial assembly using a Mann-Whitney U test.

RESULTS

The median needle guidance error of the system using the improved registration fiducial assembly at a depth of 10 cm was 1.02 mm with an interquartile range (IQR) of 0.42-2.94 mm. The upper limit of the one-sided 95% prediction interval of needle guidance error was 4.13 mm. The median (IQR) angular error was 0.0097 rad (0.0057-0.015 rad) with a one-sided 95% prediction interval upper limit of 0.022 rad. The median (IQR) positioning error using the previous four-sphere registration fiducial assembly was 1.87 mm (1.77-2.14 mm). This was found to be significantly different (p = 0.0012) from the median (IQR) positioning error of 0.28 mm (0.14-0.95 mm) using the new registration fiducial assembly on the same trajectories. No significant difference was detected between the medians of the angular errors (p = 0.26).

CONCLUSION

This is the first study presenting an improved registration method and validation in tissue-mimicking phantoms of our remotely actuated MR-compatible mechatronic system for delivery of prostate FLA needles. Accounting for the effects of needle deflection, the system was demonstrated to be capable of needle delivery with an error of 4.13 mm or less in 95% of cases under ideal conditions, which is a statistically significant improvement over the previous method. The system will next be validated in a clinical setting.

Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods

Prostate cancer is the second leading cause of cancer-related death in men. Its early and correct diagnosis is of particular importance to controlling and preventing the disease from spreading to other tissues. Artificial intelligence and machine learning have effectively detected and graded several cancers, in particular prostate cancer. The purpose of this review is to show the diagnostic performance (accuracy and area under the curve) of supervised machine learning algorithms in detecting prostate cancer using multiparametric MRI. A comparison was made between the performances of different supervised machine-learning methods. This review study was performed on the recent literature sourced from scientific citation websites such as Google Scholar, PubMed, Scopus, and Web of Science up to the end of January 2023. The findings of this review reveal that supervised machine learning techniques have good performance with high accuracy and area under the curve for prostate cancer diagnosis and prediction using multiparametric MR imaging. Among supervised machine learning methods, deep learning, random forest, and logistic regression algorithms appear to have the best performance.

Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features

BACKGROUND

The aim of our study was to develop a radiomic tool for the prediction of clinically significant prostate cancer.

METHODS

From September 2020 to December 2021, 91 patients who underwent magnetic resonance imaging prostate fusion biopsy at our institution were selected. Prostate cancer aggressiveness was assessed by combining the three orthogonal planes-Llocal binary pattern the 3Dgray level co-occurrence matrix, and other first order statistical features with clinical (semantic) features. The 487 features were used to predict whether the Gleason score was clinically significant (≥7) in the final pathology. A feature selection algorithm was used to determine the most predictive features, and at the end of the process, nine features were chosen through a 10-fold cross validation.

RESULTS

The feature analysis revealed a detection accuracy of 83.5%, with a clinically significant precision of 84.4% and a clinically significant sensitivity of 91.5%. The resulting area under the curve was 80.4%.

CONCLUSIONS

Radiomic analysis allowed us to develop a tool that was able to predict a Gleason score of ≥7. This new tool may improve the detection rate of clinically significant prostate cancer and overcome the limitations of the subjective interpretation of magnetic resonance imaging, reducing the number of useless biopsies.

A critical analysis of deficiencies in the quality of information contained in prostate multiparametric MRI requests and reports

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) has been increasingly recognised as an important tool in the diagnosis of prostate cancer. PI-RADSv2 guidelines recommend that important clinical information including prostate-specific antigen (PSA) levels, examination findings, and biopsy information should be included in mpMRI requests. PIRADS score and PSA density (PSAD) are both independent predictors for the presence of a clinically significant prostate cancer.

AIMS

This study aims to evaluate the quality of mpMRI requests and reports at our institution in accordance with these parameters.

METHODS

All prostate mpMRIs performed by radiology services in Galway University Hospital between 1st September 2019 and 1st March 2020 were reviewed. Exclusion criteria were applied. Requests and reports were analysed for the presence of the following parameters: PSA-results, examination findings, biopsy information, PI-RADS score, prostate volume, and PSAD.

RESULTS

A total of 586 mpMRIs were performed, and of these, 546 were included. PSA value was provided in 497 (91%) of requests, exam findings in 355 (65%), and biopsy information in 452 (82%). PIRADS score was included in 224 (41%) of reports, prostate volume in 178 (32.6%), and PSAD in 106 (19%).

CONCLUSIONS

Great variation in the quality of information contained in both requests and reports for prostate mpMRIs exists within our service. We aim to improve this by collaborating with our radiology colleagues to develop a proforma for requesting and reporting of mpMRIs for our radiology systems to ensure important clinical and radiological information is provided in future.

False-positive magnetic resonance imaging prostate cancer correlates and clinical implications

Background

False-positive (FP) multiparametric magnetic resonance imaging (MPMRI) obscures and swift needless biopsies in men with a high prostate-specific antigen.

Materials and Methods

This was a retrospective study, in which all patients who had been exposed to consecutive MP-MRI of the prostate combined with transrectal ultrasound-guided-magnetic resonance imaging fusion-guided prostate biopsy between 2017 and 2020 were involved in the study. The FP was measured as the number of biopsies that did not encompass prostate cancer divided by the whole number of biopsies.

Results

The percentage of FP cases was 51.1%, the highest percentage was found in Prostate Imaging-Reporting and Data System (PI-RADs) 3 (37.7%) and the lowest was detected in PI-RAD 5 (14.5%). Those with FP biopsies are younger, and their total prostate antigen (PSA) and PSA density (PSAD) are significantly lesser. The area under the curve PSAD, age, and total PSA are 0.76, 0.74, and 0.69, respectively. An optimum PSAD value of 0.135 was chosen as a cutoff because it showed the highest sum of sensitivity and specificity, 68% and 69%, respectively.

Conclusion

FP results of mpMRI were detected in more than half of our sample, more than one-third were presented in Pi-RAD3, improved imaging techniques to decrease FP rates are highly needed.

The Promise of Magnetic Resonance Imaging in Radiation Oncology Practice in the Management of Brain, Prostate, and GI Malignancies

Magnetic resonance imaging (MRI) has a key role to play at multiple steps of the radiotherapy (RT) treatment planning and delivery process. Development of high-precision RT techniques such as intensity-modulated RT, stereotactic ablative RT, and particle beam therapy has enabled oncologists to escalate RT dose to the target while restricting doses to organs at risk (OAR). MRI plays a critical role in target volume delineation in various disease sites, thus ensuring that these high-precision techniques can be safely implemented. Accurate identification of gross disease has also enabled selective dose escalation as a means to widen the therapeutic index. Morphological and functional MRI sequences have also facilitated an understanding of temporal changes in target volumes and OAR during a course of RT, allowing for midtreatment volumetric and biological adaptation. The latest advancement in linear accelerator technology has led to the incorporation of an MRI scanner in the treatment unit. MRI-guided RT provides the opportunity for MRI-only workflow along with online adaptation for either target or OAR or both. MRI plays a key role in post-treatment response evaluation and is an important tool for guiding decision making. In this review, we briefly discuss the RT-related applications of MRI in the management of brain, prostate, and GI malignancies.

Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings

Objective

To derive and evaluate the association of prostate shape distension descriptors from T2-weighted MRI (T2WI) with prostate cancer (PCa) biochemical recurrence (BCR) post-radical prostatectomy (RP) independently and in conjunction with texture radiomics of PCa.

Methods

This retrospective study comprised 133 PCa patients from two institutions who underwent 3T-MRI prior to RP and were followed up with PSA measurements for ≥3 years. A 3D shape atlas-based approach was adopted to derive prostate shape distension descriptors from T2WI, and these descriptors were used to train a random forest classifier (C_S) to predict BCR. Texture radiomics was derived within PCa regions of interest from T2WI and ADC maps, and another machine learning classifier (C_R) was trained for BCR. An integrated classifier C_S+R was then trained using predictions from C_S and C_R. These models were trained on D₁ (N = 71, 27 BCR+) and evaluated on independent hold-out set D₂ (N = 62, 12 BCR+). C_S+R was compared against pre-RP, post-RP clinical variables, and extant nomograms for BCR-free survival (bFS) at 3 years.

Results

C_S+R resulted in a higher AUC (0.75) compared to C_R (0.70, p = 0.04) and C_S (0.69, p = 0.01) on D₂ in predicting BCR. On univariable analysis, C_S+R achieved a higher hazard ratio (2.89, 95% CI 0.35–12.81, p < 0.01) compared to other pre-RP clinical variables for bFS. C_S+R, pathologic Gleason grade, extraprostatic extension, and positive surgical margins were associated with bFS (p < 0.05). C_S+R resulted in a higher C-index (0.76 ± 0.06) compared to CAPRA (0.69 ± 0.09, p < 0.01) and Decipher risk (0.59 ± 0.06, p < 0.01); however, it was comparable to post-RP CAPRA-S (0.75 ± 0.02, p = 0.07).

Conclusions

Radiomic shape descriptors quantifying prostate surface distension complement texture radiomics of prostate cancer on MRI and result in an improved association with biochemical recurrence post-radical prostatectomy.

Ultra-high-field MR in Prostate cancer: Feasibility and Potential

Multiparametric MRI of the prostate at clinical magnetic field strengths (1.5/3 Tesla) has emerged as a reliable noninvasive imaging modality for identifying clinically significant cancer, enabling selective sampling of high-risk regions with MRI-targeted biopsies, and enabling minimally invasive focal treatment options. With increased sensitivity and spectral resolution, ultra-high-field (UHF) MRI (≥ 7 Tesla) holds the promise of imaging and spectroscopy of the prostate with unprecedented detail. However, exploiting the advantages of ultra-high magnetic field is challenging due to inhomogeneity of the radiofrequency field and high local specific absorption rates, raising local heating in the body as a safety concern. In this work, we review various coil designs and acquisition strategies to overcome these challenges and demonstrate the potential of UHF MRI in anatomical, functional and metabolic imaging of the prostate and pelvic lymph nodes. When difficulties with power deposition of many refocusing pulses are overcome and the full potential of metabolic spectroscopic imaging is used, UHF MR(S)I may aid in a better understanding of the development and progression of local prostate cancer. Together with large field-of-view and low-flip-angle anatomical 3D imaging, 7 T MRI can be used in its full strength to characterize different tumor stages and help explain the onset and spatial distribution of metastatic spread.

Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature

Purpose

This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI).

Methods

This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1.

Results

In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases (P < 0.05) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade.

Conclusions

The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.

T2 mapping for the characterization of prostate lesions

Purpose

Purpose of this study is to evaluate the diagnostic accuracy of quantitative T2/ADC values in differentiating between PCa and lesions showing non-specific inflammatory infiltrates and atrophy, features of chronic prostatitis, as the most common histologically proven differential diagnosis.

Methods

In this retrospective, single-center cohort study, we analyzed 55 patients suspected of PCa, who underwent mpMRI (3T) including quantitative T2 maps before robot-assisted mpMRI-TRUS fusion prostate biopsy. All prostate lesions were scored according to PI-RADS v2.1. Regions of interest (ROIs) were annotated in focal lesions and normal prostate tissue. Quantitative mpMRI values from T2 mapping and ADC were compared using two-tailed t tests. Receiver operating characteristic curves (ROCs) and cutoff were calculated to differentiate between PCa and chronic prostatitis.

Results

Focal lesions showed significantly lower ADC and T2 mapping values than normal prostate tissue (p < 0.001). PCa showed significantly lower ADC and T2 values than chronic prostatitis (p < 0.001). ROC analysis revealed areas under the receiver operating characteristic curves (AUCs) of 0.85 (95% CI 0.74–0.97) for quantitative ADC values and 0.84 (95% CI 0.73–0.96) for T2 mapping. A significant correlation between ADC and T2 values was observed (r = 0.70; p < 0.001).

Conclusion

T2 mapping showed high diagnostic accuracy for differentiating between PCa and chronic prostatitis, comparable to the performance of ADC values.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00345-022-03991-8.

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

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

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

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

Non-Invasive Monitoring of Increased Fibrotic Tissue and Hyaluronan Deposition in the Tumor Microenvironment in the Advanced Stages of Pancreatic Ductal Adenocarcinoma

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease with a poor prognosis. A better understanding of the tumor microenvironment may help better treat the disease. Magnetic resonance imaging may be a great tool for monitoring the tumor microenvironment at different stages of tumor evolution. Here, we used multi-parametric magnetic resonance imaging techniques to monitor underlying pathophysiologic processes during the advanced stages of tumor development and correlated with histologic measurements.

Abstract

Pancreatic ductal adenocarcinomas are characterized by a complex and robust tumor microenvironment (TME) consisting of fibrotic tissue, excessive levels of hyaluronan (HA), and immune cells. We utilized quantitative multi-parametric magnetic resonance imaging (mp-MRI) methods at 14 Tesla in a genetically engineered KPC (Kras^LSL-G12D/+, Trp53^LSL-R172H/+, Cre) mouse model to assess the complex TME in advanced stages of tumor development. The whole tumor, excluding cystic areas, was selected as the region of interest for data analysis and subsequent statistical analysis. Pearson correlation was used for statistical inference. There was a significant correlation between tumor volume and T2 (r = −0.66), magnetization transfer ratio (MTR) (r = 0.60), apparent diffusion coefficient (ADC) (r = 0.48), and Glycosaminoglycan-chemical exchange saturation transfer (GagCEST) (r = 0.51). A subset of mice was randomly selected for histological analysis. There were positive correlations between tumor volume and fibrosis (0.92), and HA (r = 0.76); GagCEST and HA (r = 0.81); and MTR and CD31 (r = 0.48). We found a negative correlation between ADC low-b (perfusion) and Ki67 (r = −0.82). Strong correlations between mp-MRI and histology results suggest that mp-MRI can be used as a non-invasive tool to monitor the tumor microenvironment.

PCaGuard: A Software Platform to Support Optimal Management of Prostate Cancer

Background and Objective  Prostate cancer (PCa) is a severe public health issue and the most common cancer worldwide in men. Early diagnosis can lead to early treatment and long-term survival. The addition of the multiparametric magnetic resonance imaging in combination with ultrasound (mpMRI-U/S fusion) biopsy to the existing diagnostic tools improved prostate cancer detection. Use of both tools gradually increases in every day urological practice. Furthermore, advances in the area of information technology and artificial intelligence have led to the development of software platforms able to support clinical diagnosis and decision-making using patient data from personalized medicine.

Methods  We investigated the current aspects of implementation, architecture, and design of a health care information system able to handle and store a large number of clinical examination data along with medical images, and produce a risk calculator in a seamless and secure manner complying with data security/accuracy and personal data protection directives and standards simultaneously. Furthermore, we took into account interoperability support and connectivity to legacy and other information management systems. The platform was implemented using open source, modern frameworks, and development tools.

Results  The application showed that software platforms supporting patient follow-up monitoring can be effective, productive, and of extreme value, while at the same time, aiding toward the betterment medicine clinical workflows. Furthermore, it removes access barriers and restrictions to specialized care, especially for rural areas, providing the exchange of medical images and patient data, among hospitals and physicians.

Conclusion  This platform handles data to estimate the risk of prostate cancer detection using current state-of-the-art in eHealth systems and services while fusing emerging multidisciplinary and intersectoral approaches. This work offers the research community an open architecture framework that encourages the broader adoption of more robust and comprehensive systems in standard clinical practice.

Multi-resolution super learner for voxel-wise classification of prostate cancer using multi-parametric MRI

Multi-parametric MRI (mpMRI) is a critical tool in prostate cancer (PCa) diagnosis and management. To further advance the use of mpMRI in patient care, computer aided diagnostic methods are under continuous development for supporting/supplanting standard radiological interpretation. While voxel-wise PCa classification models are the gold standard, few if any approaches have incorporated the inherent structure of the mpMRI data, such as spatial heterogeneity and between-voxel correlation, into PCa classification. We propose a machine learning-based method to fill in this gap. Our method uses an ensemble learning approach to capture regional heterogeneity in the data, where classifiers are developed at multiple resolutions and combined using the super learner algorithm, and further account for between-voxel correlation through a Gaussian kernel smoother. It allows any type of classifier to be the base learner and can be extended to further classify PCa sub-categories. We introduce the algorithms for binary PCa classification, as well as for classifying the ordinal clinical significance of PCa for which a weighted likelihood approach is implemented to improve the detection of less prevalent cancer categories. The proposed method has shown important advantages over conventional modeling and machine learning approaches in simulations and application to our motivating patient data.

Visualization and Quantification of the Extracellular Matrix in Prostate Cancer Using an Elastin Specific Molecular Probe

Human prostate cancer (PCa) is a type of malignancy and one of the most frequently diagnosed cancers in men. Elastin is an important component of the extracellular matrix and is involved in the structure and organization of prostate tissue. The present study examined prostate cancer in a xenograft mouse model using an elastin-specific molecular probe for magnetic resonance molecular imaging. Two different tumor sizes (500 mm3 and 1000 mm3) were compared and analyzed by MRI in vivo and histologically and analytically ex vivo. The T1-weighted sequence was used in a clinical 3-T scanner to calculate the relative contrast enhancement before and after probe administration. Our results show that the use of an elastin-specific probe enables better discrimination between tumors and surrounding healthy tissue. Furthermore, specific binding of the probe to elastin fibers was confirmed by histological examination and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). Smaller tumors showed significantly higher signal intensity (p > 0.001), which correlates with the higher proportion of elastin fibers in the histological evaluation than in larger tumors. A strong correlation was seen between relative enhancement (RE) and Elastica-van Gieson staining (R2 = 0.88). RE was related to inductively coupled plasma-mass spectrometry data for Gd and showed a correlation (R2 = 0.78). Thus, molecular MRI could become a novel quantitative tool for the early evaluation and detection of PCa.

Focal Prostate Stereotactic Body Radiation Therapy With Correlative Pathological and Radiographic-Based Treatment Planning

Introduction

Advances in multiparametric MRI (mpMRI) combining anatomic and functional imaging can accurately identify foci of adenocarcinoma within the prostate, offering the possibility of partial gland therapy. We performed tandem prospective pilot trials to investigate the feasibility of focal prostate SBRT (f-SBRT) based on correlating diagnostic mpMRI and biopsies with confirmatory pathology in treatment planning.

Materials and Methods

Patients with pathologic focal Gleason 6–7 disease and a corresponding PIRADS 4–5 lesion on mpMRI underwent targeted and comprehensive biopsies using MRI/ultrasound fusion under electromagnetic sensor navigation. After rigorous analysis for imaging biopsy concordance, five of 18 patients were eligible to proceed to f-SBRT. Chi-squared test was used for differences from expected outcomes, and concordance was estimated with binomial distribution theory and Wilson’s method.

Results

Six patients had Gleason 6 and 12 had Gleason 3 + 4 disease (mean PSA: 5.8 ng/ml, range: 2.2–8.4). Absolute concordance was 43.8% (95% CI: 0.20, 0.64). Patterns of discordance included additional sites of ipsilateral disease, bilateral disease, and negative target. Five were upstaged to a new NCCN risk category necessitating treatment escalation. The five patients with concordant pathology completed three-fraction f-SBRT with sparing of the surrounding normal structures (including contralateral neurovascular bundle), with no reported grade 2+ toxicities and favorable PSA responses (mean: 41% decrease).

Conclusions

On our pilot trials of f-SBRT planning using rigorous imaging and pathology concordance, image-guided confirmatory biopsies frequently revealed additional disease, suggesting the need for caution in partial-gland therapy. For truly focal disease, f-SBRT provided excellent dosimetry, minimal toxicity, and encouraging biochemical response. Clinical Trial Registration: www.clinicaltrials.gov, NCT02681614; NCT02163317.

Cost-effectiveness of MRI targeted biopsy strategies for diagnosing prostate cancer in Singapore

BACKGROUND

To evaluate the cost-effectiveness of six diagnostic strategies involving magnetic resonance imaging (MRI) targeted biopsy for diagnosing prostate cancer in initial and repeat biopsy settings from the Singapore healthcare system perspective.

METHODS

A combined decision tree and Markov model was developed. The starting model population was men with mean age of 65 years referred for a first prostate biopsy due to clinical suspicion of prostate cancer. The six diagnostic strategies were selected for their relevance to local clinical practice. They comprised MRI targeted biopsy following a positive pre-biopsy multiparametric MRI (mpMRI) [Prostate Imaging - Reporting and Data System (PI-RADS) score ≥ 3], systematic biopsy, or saturation biopsy employed in different testing combinations and sequences. Deterministic base case analyses with sensitivity analyses were performed using costs from the healthcare system perspective and quality-adjusted life years (QALY) gained as the outcome measure to yield incremental cost-effectiveness ratios (ICERs).

RESULTS

Deterministic base case analyses showed that Strategy 1 (MRI targeted biopsy alone), Strategy 2 (MRI targeted biopsy ➔ systematic biopsy), and Strategy 4 (MRI targeted biopsy ➔ systematic biopsy ➔ saturation biopsy) were cost-effective options at a willingness-to-pay (WTP) threshold of US$20,000, with ICERs ranging from US$18,975 to US$19,458. Strategies involving MRI targeted biopsy in the repeat biopsy setting were dominated. Sensitivity analyses found the ICERs were affected mostly by changes to the annual discounting rate and prevalence of prostate cancer in men referred for first biopsy, ranging between US$15,755 to US$23,022. Probabilistic sensitivity analyses confirmed Strategy 1 to be the least costly, and Strategies 2 and 4 being the preferred strategies when WTP thresholds were US$20,000 and US$30,000, respectively.

LIMITATIONS AND CONCLUSIONS

This study found MRI targeted biopsy to be cost-effective in diagnosing prostate cancer in the biopsy-naïve setting in Singapore.

A systematic review and meta-analysis of the diagnostic accuracy of biparametric prostate MRI for prostate cancer in men at risk

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI), the use of three multiple imaging sequences, typically T2-weighted, diffusion weighted (DWI) and dynamic contrast enhanced (DCE) images, has a high sensitivity and specificity for detecting significant cancer. Current guidance now recommends its use prior to biopsy. However, the impact of DCE is currently under debate regarding test accuracy. Biparametric MRI (bpMRI), using only T2 and DWI has been proposed as a viable alternative. We conducted a contemporary systematic review and meta-analysis to further examine the diagnostic performance of bpMRI in the diagnosis of any and clinically significant prostate cancer.

METHODS

A systematic review of the literature from 01/01/2017 to 06/07/2019 was performed by two independent reviewers using predefined search criteria. The index test was biparametric MRI and the reference standard whole-mount prostatectomy or prostate biopsy. Quality of included studies was assessed by the QUADAS-2 tool. Statistical analysis included pooled diagnostic performance (sensitivity; specificity; AUC), meta-regression of possible covariates and head-to-head comparisons of bpMRI and mpMRI where both were performed in the same study.

RESULTS

Forty-four articles were included in the analysis. The pooled sensitivity for any cancer detection was 0.84 (95% CI, 0.80-0.88), specificity 0.75 (95% CI, 0.68-0.81) for bpMRI. The summary ROC curve yielded a high AUC value (AUC = 0.86). The pooled sensitivity for clinically significant prostate cancer was 0.87 (95% CI, 0.78-0.93), specificity 0.72 (95% CI, 0.56-0.84) and the AUC value was 0.87. Meta-regression analysis revealed no difference in the pooled diagnostic estimates between bpMRI and mpMRI.

CONCLUSIONS

This meta-analysis on contemporary studies shows that bpMRI offers comparable test accuracies to mpMRI in detecting prostate cancer. These data are broadly supportive of the bpMRI approach but heterogeneity does not allow definitive recommendations to be made. There is a need for prospective multicentre studies of bpMRI in biopsy naïve men.

Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

Background

Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel ¹H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading.

Methods

Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient.

Results

The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01).

Conclusion

The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

Improving B 1 + parametric estimation in the brain from multispin-echo sequences using a fusion bootstrap moves solver

PURPOSE

To simultaneously estimate the B 1 + field (along with the T₂ ) in the brain with multispin-echo (MSE) sequences and dictionary matching.

METHODS

T₂ mapping provides clinically relevant information such as in the assessment of brain degenerative diseases. It is commonly obtained with MSE sequences, and accuracy can be further improved by matching the MSE signal to a precomputed dictionary of echo-modulation curves. For additional T₁ quantification, transmit B 1 + field knowledge is also required. Preliminary work has shown that although simultaneous brain B 1 + estimation along with T₂ is possible, it presents a bimodal distribution with the main peak coinciding with the true value. By taking advantage of this, the B 1 + maps are expected to be spatially smooth by applying an iterative method that takes into account each pixel neighborhood known as the fusion bootstrap moves solver (FBMS). The effect of the FBMS on B 1 + accuracy and piecewise smoothness is investigated and different spatial regularization levels are compared. Total variation regularization was used for both B 1 + and T₂ simultaneous estimation because of its simplicity as an initial proof-of-concept; future work could explore non edge-preserving regularization independently for B 1 + .

RESULTS

Improvements in B 1 + accuracy (up to 45.37% and 16.81% B 1 + error decrease) and recovery of spatially homogeneous maps are shown in simulations and in vivo 3.0T brain data, respectively.

CONCLUSION

Accurate B 1 + estimated values can be obtained from widely available MSE sequences while jointly estimating T₂ maps with the use of echo-modulation curve matching and FBMS at no further cost.

Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294

A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.

Current State of Personalized Genitourinary Cancer Radiotherapy in the Era of Precision Medicine

Radiation therapy plays a crucial role for the management of genitourinary malignancies, with technological advancements that have led to improvements in outcomes and decrease in treatment toxicities. However, better risk-stratification and identification of patients for appropriate treatments is necessary. Recent advancements in imaging and novel genomic techniques can provide additional individualized tumor and patient information to further inform and guide treatment decisions for genitourinary cancer patients. In addition, the development and use of targeted molecular therapies based on tumor biology can result in individualized treatment recommendations. In this review, we discuss the advances in precision oncology techniques along with current applications for personalized genitourinary cancer management. We also highlight the opportunities and challenges when applying precision medicine principles to the field of radiation oncology. The identification, development and validation of biomarkers has the potential to personalize radiation therapy for genitourinary malignancies so that we may improve treatment outcomes, decrease radiation-specific toxicities, and lead to better long-term quality of life for GU cancer survivors.

Diagnostic efficiency of hybrid imaging using PSMA ligands, PET/CT, PET/MRI and MRI in identifying malignant prostate lesions

OBJECTIVE

The objective of this study was to assess the accuracy of 68Ga-PSMA-11 PET/MRI, 18F-PSMA-1007 PET/CT, 68Ga-PSMA-11 PET/CT, and multiparametric (mp)MRI for the delineating of dominant intraprostatic lesions (IPL).

MATERIALS AND METHODS

35 patients with organ-confined prostate cancer who were assigned to definitive radiotherapy (RT) were divided into three groups based on imaging techniques: 68Ga-PSMA-PET/MRI (n = 9), 18F-PSMA-PET/CT (n = 16) and 68Ga-PSMA-PET/CT (n = 10). All patients without PSMA-PET/MRI received an additional mpMRI. PSMA-PET-based automatic isocontours and manual contours of the dominant IPLs were generated for each modality. The biopsy results were then used to validate whether any of the prostate biopsies were positive in the marked lesion using Dice similarity coefficient (DSC), Youden index (YI), sensitivity and specificity. Factors that can predict the accuracy of IPLs contouring were analysed.

RESULTS

Diagnostic performance was significantly superior both for manual and automatic IPLs contouring using 68Ga-PSMA-PET/MRI (DSC/YI SUV70%-0.62/0.51), 18F-PSMA-PET/CT (DSC/YI SUV70%-0.67/0.53) or 68Ga-PSMA-PET/CT (DSC/YI SUV70%-0.63/0.51) compared to mpMRI (DSC/YI-0.47/0.41; p < 0.001). The accuracy for delineating IPLs was not improved by combination of PET/CT and mpMRI images compared to PET/CT alone. Significantly superior diagnostic accuracy was found for large prostate lesions (at least 15% from the prostate volume) and higher Gleason score (at least 7b) comparing to smaller lesions with lower GS.

CONCLUSION

IPL localization was significantly improved when using PSMA-imaging procedures compared to mpMRI. No significant difference for delineating IPLs was found between hybrid method PSMA-PET/MRI and PSMA-PET/CT. PSMA-based imaging technique should be considered for the diagnostics of IPLs and focal treatment modality.

Reproducibility of magnetic resonance fingerprinting-based T1 mapping of the healthy prostate at 1.5 and 3.0 T: A proof-of-concept study

Facilitating clinical translation of quantitative imaging techniques has been suggested as means of improving interobserver agreement and diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) of the prostate. One such technique, magnetic resonance fingerprinting (MRF), has significant competitive advantages over conventional mapping techniques in terms of its multi-site reproducibility, short scanning time and inherent robustness to motion. It has also been shown to improve the detection of clinically significant prostate cancer when added to standard mpMRI sequences, however, the existing studies have all been conducted on 3.0 T MRI systems, limiting the technique's use on 1.5 T MRI scanners that are still more widely used for prostate imaging across the globe. The aim of this proof-of-concept study was, therefore, to evaluate the cross-system reproducibility of prostate MRF T1 in healthy volunteers (HVs) using 1.5 and 3.0 T MRI systems. The initial validation of MRF T1 against gold standard inversion recovery fast spin echo (IR-FSE) T1 in the ISMRM/NIST MRI system revealed a strong linear correlation between phantom-derived MRF and IR-FSE T1 values was observed at both field strengths (R2 = 0.998 at 1.5T and R2 = 0.993 at 3T; p = < 0.0001 for both). In young HVs, inter-scanner CVs demonstrated marginal differences across all tissues with the highest difference of 3% observed in fat (2% at 1.5T vs 5% at 3T). At both field strengths, MRF T1 could confidently differentiate prostate peripheral zone from transition zone, which highlights the high quantitative potential of the technique given the known difficulty of tissue differentiation in this age group. The high cross-system reproducibility of MRF T1 relaxometry of the healthy prostate observed in this preliminary study, therefore, supports the technique's prospective clinical validation as part of larger trials employing 1.5 T MRI systems, which are still widely used clinically for routine mpMRI of the prostate.

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

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

An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy

Prostate cancer is a leading cause of morbidity and mortality for adult males in the US. The diagnosis of prostate carcinoma is usually made on prostate core needle biopsies obtained through a transrectal approach. These biopsies may account for a significant portion of the pathologists' workload, yet variability in the experience and expertise, as well as fatigue of the pathologist may adversely affect the reliability of cancer detection. Machine-learning algorithms are increasingly being developed as tools to aid and improve diagnostic accuracy in anatomic pathology. The Paige Prostate AI-based digital diagnostic is one such tool trained on the digital slide archive of New York's Memorial Sloan Kettering Cancer Center (MSKCC) that categorizes a prostate biopsy whole-slide image as either "Suspicious" or "Not Suspicious" for prostatic adenocarcinoma. To evaluate the performance of this program on prostate biopsies secured, processed, and independently diagnosed at an unrelated institution, we used Paige Prostate to review 1876 prostate core biopsy whole-slide images (WSIs) from our practice at Yale Medicine. Paige Prostate categorizations were compared to the pathology diagnosis originally rendered on the glass slides for each core biopsy. Discrepancies between the rendered diagnosis and categorization by Paige Prostate were each manually reviewed by pathologists with specialized genitourinary pathology expertise. Paige Prostate showed a sensitivity of 97.7% and positive predictive value of 97.9%, and a specificity of 99.3% and negative predictive value of 99.2% in identifying core biopsies with cancer in a data set derived from an independent institution. Areas for improvement were identified in Paige Prostate's handling of poor quality scans. Overall, these results demonstrate the feasibility of porting a machine-learning algorithm to an institution remote from its training set, and highlight the potential of such algorithms as a powerful workflow tool for the evaluation of prostate core biopsies in surgical pathology practices.

Texture analysis on bi-parametric MRI for evaluation of aggressiveness in patients with prostate cancer

PURPOSE

To evaluate the association between texture parameters based on bi-parametric MRI and Gleason score (GS) in patients with prostate cancer (PCa) and to evaluate diagnostic performance of any significant parameter for discriminating clinically significant cancer (CSC, GS ≥ 7) from non-CSC.

METHODS

A total of 116 patients who had been confirmed as prostate adenocarcinoma by radical prostatectomy or biopsy were divided into a training (n = 65) and a validation dataset (n = 51). All of the patients underwent preoperative 3T-MRI. Texture analysis was performed on axial T2WI and ADC maps (generated from b values, 0 and 1000 s/mm²) using dedicated software to cover the whole tumor volume. The correlation coefficient was calculated to evaluate the association between texture parameters and GS, and subsequent multiple regression analyses were applied for the significant parameters. To extract an optimal cut-off value for prediction of CSC, ROC curve analysis was performed.

RESULTS

In the training dataset, gray-level co-occurrence matrix (GLCM) entropy on ADC map was the only significant indicator for GS (coefficient of determination R², 0.4227, P = 0.0034). The AUC of GLCM entropy on ADC map was 0.825 (95% CI 0.711-0.907) with a maximum accuracy of 82%, a sensitivity of 86%, a specificity of 71%. When a cut-off value of 2.92 was applied to the validation dataset, it showed an accuracy of 92%, a sensitivity of 98%, and a specificity of 70%.

CONCLUSION

GLCM entropy on ADC map was associated with GS in patients with PCa and its estimated accuracy for discriminating CSC from non-CSC was 82%.

Effect of Echo Times on Prostate Cancer Detection on T2-Weighted Images

PURPOSE

To compare the effect of different echo times (TE) on the detection of prostate cancer (PCa) on T2-weighted MR images.

MATERIALS AND METHODS

This study recruited patients (n = 38) with histologically confirmed PCa who underwent preoperative 3T MRI. Three radiologists independently marked region on interests (ROIs) on suspected PCa lesions on T2-weighted images at different TEs: 90, 150, and 180 ms obtained with Turbo Spin Echo imaging protocol with multiple echoes. The ROIs were assigned a value 1-5 indicating the reviewer's confidence in accurately detecting PCa. These ROIs were compared to histologically confirmed PCa (n = 95) on whole mount prostatectomy sections to calculate sensitivity, positive predictive value (PPV), and confidence score.

RESULTS

Two radiologists (R1, R2) showed significantly increased sensitivity for PCa detection at 180 ms TE compared to 90 ms (R1: 43.2, 50.5, 50.5%, R2: 45.3, 44.2, 53.7% at TE of 90, 150, 180 ms, respectively) (p = 0.048, 0.033 for R1 and R2). Sensitivity was similar for radiologist 3 (45.3%-46.3%) at different TE values (p = 0.953). No significant difference in the PPV (R1: 64.1%-70.6%, R2: 46.7%-56.0%, R3: 70.5%-81.5%) and the confidence score assigned (R1: 4.6-4.8, R2: 4.6-4.8 R3: 4.3-4.4) was found for either of the radiologists.

CONCLUSION

Our results suggest improved detection of PCa with similar PPV and confidence scores when higher TE values are utilized for T2-weighted image acquisition.

Value of MRI texture analysis for predicting high-grade prostate cancer

PURPOSE

To explore the potential value of MRI texture analysis (TA) combined with prostate-related biomarkers to predict high-grade prostate cancer (HGPCa).

MATERIALS AND METHODS

Eighty-five patients who underwent MRI scanning, including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) prior to trans-rectal ultrasound (TRUS)-guided core prostate biopsy, were retrospectively enrolled. TA parameters derived from T2WI and DWI, prostate-specific antigen (PSA), and free PSA (fPSA) were compared between the HGPCa and non-high-grade prostate cancer (NHGPCa) groups using independent Student's t-test and the Mann-Whitney U test. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the predictive value for HGPCa.

RESULTS

Univariate analysis showed that PSA and entropy based on apparent diffusion coefficient (ADC) map differed significantly between the HGPCa and NHGPCa groups and showed higher diagnostic values for HGPCa (area under the curve (AUC) = 82.0% and 80.0%, respectively). Logistic regression and ROC curve analyses revealed that kurtosis, skewness and entropy derived from ADC maps had diagnostic power to predict HGPCa; when the three texture parameters were combined, the area under the ROC curve reached the maximum (AUC = 84.6%; 95% confidence interval (CI): 0.758, 0.935; P = 0.000).

CONCLUSION

TA parameters derived from ADC may be a valuable tool in predicting HGPCa. The combination of specific textural parameters extracted from ADC map may be additional tools to predict HGPCa.

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.

The role of radiomics in prostate cancer radiotherapy

"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.

Clinical utility of combined T2-weighted imaging and T2-mapping in the detection of prostate cancer: a multi-observer study

Background

To evaluate the clinical utility of combined T2-weighted imaging and T2-mapping for the detection of prostate cancer.

Methods

Forty patients underwent multiparametric magnetic resonance imaging (mpMRI) and T2-mapping of the prostate. Three readers each reviewed two sets of images: T2-weighted fast spin-echo (FSE) sequence (standard T2), and standard T2 in combination with T2-mapping. Each reader assigned probability scores for malignancy to each zone [peripheral zone (PZ) or transition zone (TZ)]. Inter-observer variability for standard T2 and combined standard T2 with T2-mapping were assessed. Diagnostic accuracy was compared between standard T2 and combined standard T2 with T2-mapping.

Results

There was fair agreement between all three readers for standard T2 [intraclass correlation coefficient (ICC) =0.56] and combined standard T2 with T2-mapping (ICC =0.58). There was no significant difference in the area under the receiver operator characteristics curve for standard T2 compared to combined standard T2 with T2-mapping (0.89 vs. 0.82, P=0.31). Sensitivity (Sn) for combined standard T2 with T2-mapping was significantly higher compared to standard T2 alone (73.0% vs. 49.2%, P=0.006). Specificity (Sp) for combined standard T2 with T2-mapping was borderline significantly lower compared to standard T2 alone (89.3% vs. 94.9%, P=0.05). There was no significant differences between the negative predictive values (NPVs) and positive predictive values (PPVs) (P=0.07, P=0.45).

Conclusions

Combination of T2-weighted imaging and T2-mapping could potentially increase Sn for prostate malignancy compared to T2-weighted imaging alone.

Computer-Aided Diagnosis in Multiparametric MRI of the Prostate: An Open-Access Online Tool for Lesion Classification with High Accuracy

Computer-aided diagnosis (CADx) approaches could help to objectify reporting on prostate mpMRI, but their use in many cases is hampered due to common-built algorithms that are not publicly available. The aim of this study was to develop an open-access CADx algorithm with high accuracy for classification of suspicious lesions in mpMRI of the prostate. This retrospective study was approved by the local ethics commission, with waiver of informed consent. A total of 124 patients with 195 reported lesions were included. All patients received mpMRI of the prostate between 2014 and 2017, and transrectal ultrasound (TRUS)-guided and targeted biopsy within a time period of 30 days. Histopathology of the biopsy cores served as a standard of reference. Acquired imaging parameters included the size of the lesion, signal intensity (T2w images), diffusion restriction, prostate volume, and several dynamic parameters along with the clinical parameters patient age and serum PSA level. Inter-reader agreement of the imaging parameters was assessed by calculating intraclass correlation coefficients. The dataset was stratified into a train set and test set (156 and 39 lesions in 100 and 24 patients, respectively). Using the above parameters, a CADx based on an Extreme Gradient Boosting algorithm was developed on the train set, and tested on the test set. Performance optimization was focused on maximizing the area under the Receiver Operating Characteristic curve (ROC_AUC). The algorithm was made publicly available on the internet. The CADx reached an ROC_AUC of 0.908 during training, and 0.913 during testing (p = 0.93). Additionally, established rule-in and rule-out criteria allowed classifying 35.8% of the malignant and 49.4% of the benign lesions with error rates of <2%. All imaging parameters featured excellent inter-reader agreement. This study presents an open-access CADx for classification of suspicious lesions in mpMRI of the prostate with high accuracy. Applying the provided rule-in and rule-out criteria might facilitate to further stratify the management of patients at risk.

Epidemiology, Staging and Management of Prostate Cancer

Prostate cancer is the second most common and fifth most aggressive neoplasm among men worldwide. It is particularly incident in high human development index (HDI) nations, with an estimated one in seven men in the US receiving a prostate cancer diagnosis in their lifetime. A rapid rise and then fall in prostate cancer incidence in the US and Europe corresponded to the implementation of widespread prostate specific antigen (PSA) testing in 1986 and then subsequent fall from favor due to high rates of false positives, overdiagnosis, and overtreatment (as many as 20-50% of men diagnosed could have remained asymptomatic in their lifetimes). Though few risk factors have been characterized, the best known include race (men of African descent are at higher risk), genetics (e.g., BRCA1/2 mutations), and obesity. The Gleason scoring system is used for histopathological staging and is combined with clinical staging for prognosis and treatment. National guidelines have grown more conservative over the past decades in management, recommending watchful waiting and observation in older men with low to intermediate risk disease. Among higher risk patients, prostatectomy (robotic is preferred) and/or external beam radiotherapy is the most common interventions, followed by ADT maintenance. Following progression on androgen deprivation therapy (ADT) (known as castration-resistance), next generation endocrine therapies like enzalutamide, often in combination with cytotoxic agent docetaxel, are standard of care. Other promising treatments include Radium-223 for bone metastases, pembrolizumab for programmed death ligand-1 (PDL1) and microsatellite instability (MSI) high disease, and poly ADP ribose polymerase (PARP) inhibitors for those with mutations in homologous recombination (most commonly BRCA2).

Utilization of Multiparametric MRI of Prostate in Patients under Consideration for or Already in Active Surveillance: Correlation with Imaging Guided Target Biopsy

This study sought to assess the value of multiparametric magnetic resonance image (mp-MRI) in patients with a prostate cancer (PCa) Gleason score of 6 or less under consideration for or already in active surveillance and to determine the rate of upgrading by target biopsy. Three hundred and fifty-four consecutive men with an initial transrectal ultrasound-guided (TRUS) biopsy-confirmed PCa Gleason score of 6 or less under clinical consideration for or already in active surveillance underwent mp-MRI and were retrospectively reviewed. One hundred and nineteen of 354 patients had cancer-suspicious regions (CSRs) at mp-MRI. Each CSR was assigned a Prostate Imaging Reporting and Data System (PI-RADS) score based on PI-RADS v2. One hundred and eight of 119 patients underwent confirmatory imaging-guided biopsy for CSRs. Pathology results including Gleason score (GS) and percentage of specimens positive for PCa were recorded. Associations between PI-RADS scores and findings at target biopsy were evaluated using logistic regression. At target biopsy, 81 of 108 patients had PCa (75%). Among them, 77 patients had upgrading (22%, 77 of 354 patients). One hundred and forty-six CSRs in 108 patients had PI-RADS 3 n = 28, 4 n = 66, and 5 n = 52. The upgraded rate for each category of CSR was for PI-RADS 3 (5 of 28, 18%), 4 (47 of 66, 71%) and 5 (49 of 52, 94%). Using logistic regression analysis, differences in PI-RADS scores from 3 to 5 are significantly associated with the probability of disease upgrade (20%, 73%, and 96% for PI-RADS score of 3, 4, and 5, respectively). Adding mp-MRI to patients under consideration for or already in active surveillance helps to identify undiagnosed PCa of a higher GS or higher volume resulting in upgrading in 22%.

Design, evaluation and comparison of endorectal coils for hybrid MR-PET imaging of the prostate

Prostate cancer is one of the most common cancers among men and its early detection is critical for its successful treatment. The use of multimodal imaging, such as MR-PET, is most advantageous as it is able to provide detailed information about the prostate. However, as the human prostate is flexible and can move into different positions under external conditions, it is important to localise the focused region-of-interest using both MRI and PET under identical circumstances. In this work, we designed five commonly used linear and quadrature radiofrequency surface coils suitable for hybrid MR-PET use in endorectal applications. Due to the endorectal design and the shielded PET insert, the outer face of the coils investigated was curved and the region to be imaged was outside the volume of the coil. The tilting angles of the coils were varied with respect to the main magnetic field direction. This was done to approximate the various positions from which the prostate could be imaged. The transmit efficiencies and safety excitation efficiencies from simulations, together with the signal-to-noise ratios from the MR images were calculated and analysed. Overall, it was found that the overlapped loops driven in quadrature were superior to the other types of coils we tested. In order to determine the effect of the different coil designs on PET, transmission scans were carried out, and it was observed that the differences between attenuation maps with and without the coils were negligible. The findings of this work can provide useful guidance for the integration of such coil designs into MR-PET hybrid systems in the future.

An Automated Two-step Pipeline for Aggressive Prostate Lesion Detection from Multi-parametric MR Sequence

A substantial percentage of prostate cancer cases are overdiagnosed and overtreated due to the challenge in deter- mining aggressiveness. Multi-parametric MR is a powerful imaging technique to capture distinct characteristics of prostate lesions that are informative for aggressiveness assessment. However, manual interpretation requires a high level of expertise, is time-consuming, and significant inter-observer variation exists for radiologists. We propose a completely automated approach to assessing pixel-level aggressiveness of prostate cancer in multi-parametric MRI. Our model efficiently combines traditional computer vision and deep learning algorithms, to remove reliance on manual features, prostate segmentation, and prior lesion detection and identified optimal combinations of MR pulse sequences for assessment. Using ADC and DWI, our proposed model achieves ROC-AUC of 0.86 and ROC-AUC of 0.88 for the diagnosis of aggressive and non-aggressive prostate lesions, respectively. In performing pixel-level clas- sification, our model's classifications are easily interpretable and allow clinicians to infer localized analyses of the lesion.

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.

Assessment of DCE Utility for PCa Diagnosis Using PI-RADS v2.1: Effects on Diagnostic Accuracy and Reproducibility

The role of dynamic contrast-enhanced-MRI (DCE-MRI) for Prostate Imaging-Reporting and Data System (PI-RADS) scoring is a controversial topic. In this retrospective study, we aimed to measure the added value of DCE-MRI in combination with T2-weighted (T2W) and diffusion-weighted imaging (DWI) using PI-RADS v2.1, in terms of reproducibility and diagnostic accuracy, for detection of prostate cancer (PCa) and clinically significant PCa (CS-PCa, for Gleason Score ≥ 7). 117 lesions in 111 patients were identified as suspicion by multiparametric MRI (mpMRI) and addressed for biopsy. Three experienced readers independently assessed PI-RADS score, first using biparametric MRI (bpMRI, including DWI and T2W), and then multiparametric MRI (also including DCE). The inter-rater and inter-method agreement (bpMRI- vs. mpMRI-based scores) were assessed by Cohen's kappa (κ). Receiver operating characteristics (ROC) analysis was performed to evaluate the diagnostic accuracy for PCa and CS-PCa detection among the two scores. Inter-rater agreement was excellent for the three pairs of readers (κ ≥ 0.83), while the inter-method agreement was good (κ ≥ 0.73). Areas under the ROC curve (AUC) showed similar high-values (0.8 ≤ AUC ≤ 0.85). The reproducibility of PI-RADS v2.1 scoring was comparable and high among readers, without relevant differences, depending on the MRI protocol used. The inclusion of DCE did not influence the diagnostic accuracy.

Locally advanced prostate cancer imaging findings and implications for treatment from the surgical perspective

The anatomy of the prostate is reviewed in the context of discussing the staging of prostate cancer and patterns of tumor spread. The utility of prostate magnetic resonance imaging along with new advancements in tumor staging are discussed specifically in locally advanced disease. What should be included in the radiology report carries a substantial weight to formulate the urologist's decision in regards to the selection of surgical candidates, preoperative planning and avoiding postoperative complications.

Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol

Prostate cancer (PCa) is a disease affecting an increasing number of men worldwide. Several efforts have been made to identify imaging biomarkers to non-invasively detect and characterize PCa, with substantial improvements thanks to multiparametric Magnetic Resonance Imaging (mpMRI). In recent years, diffusion kurtosis imaging (DKI) was proposed to be directly related to tissue physiological and pathological characteristic, while the radiomic approach was proven to be a key method to study cancer imaging phenotypes. Our aim was to compare a standard radiomic model for PCa detection, built using T2-weighted (T2W) and Apparent Diffusion Coefficient (ADC), with an advanced one, including DKI and quantitative Dynamic Contrast Enhanced (DCE), while also evaluating differences in prediction performance when using 2D or 3D lesion segmentation. The obtained results in terms of diagnostic accuracy were high for all of the performed comparisons, reaching values up to 0.99 for the area under a receiver operating characteristic curve (AUC), and 0.98 for both sensitivity and specificity. In comparison, the radiomic model based on standard features led to prediction performances higher than those of the advanced model, while greater accuracy was achieved by the model extracted from 3D segmentation. These results provide new insights into active topics of discussion, such as choosing the most convenient acquisition protocol and the most appropriate postprocessing pipeline to accurately detect and characterize PCa.

Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy

OBJECTIVE

Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach.

METHODS

We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE).

RESULTS

Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm.

CONCLUSION

SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.

A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN.