Diagnosis of transition zone prostate cancer using T2-weighted (T2W) MRI: comparison of subjective features and quantitative shape analysis

Superiority of 68Ga-PSMA-11 PET/CT over mpMRI for lateralization accuracy of diagnosing intra-glandular prostate cancer lesions: avoiding fluke targeting

OBJECTIVE

The aim of this study was to compare the diagnostic accuracy of 68Ga-PSMA-11 PET/CT and multiparametric MRI (mpMRI) in detecting unilateral and bilateral intra-glandular prostate cancer lesions.

METHODS

A retrospective analysis was conducted on 73 prostate cancer patients diagnosed via biopsy, all of whom underwent both 68Ga-PSMA-11 PET/CT and mpMRI prior to surgery. Two independent readers, blinded to each other's results and to pathology findings, evaluated the imaging modalities to make a diagnosis of unilateral (left or right) or bilateral lesions for suspected prostate lesions. Histopathological findings from a 12-core transrectal ultrasound-guided biopsy and radical prostatectomy served as reference standards. The accuracy of both imaging modalities in determining unilateral and bilateral intra-glandular prostate cancer was assessed through receiver operating characteristic curve analysis. Additionally, factors influencing diagnostic discordance between the two modalities were evaluated.

RESULTS

A total of 73 patients were included in the final analysis, comprising 34 with unilateral lesions and 39 with bilateral lesions. Among these, 35 patients underwent radical prostatectomy, revealing 22 cases of bilateral lesions and 13 cases of unilateral lesions [Kappa = 0.76 (P < 0.001)]. The lateral diagnostic accuracy of 68Ga-PSMA-11 PET/CT, based on pathological results from biopsy or prostatectomy, was 80.82% (59/73) and 82.86% (29/35), respectively. These values were significantly higher than those of mpMRI, which demonstrated an accuracy of 54.79% (40/73, P < 0.001) and 40% (14/35, P < 0.001), respectively. Concordance between 68Ga-PSMA-11 PET/CT and mpMRI for the lateralization accuracy was poor (kappa = 0.015, P < 0.05). When both imaging modalities provided consistent lateralization results (39/73), concordance with pathological findings reached 87.18% (34/39). However, concordance with pathological results was significantly higher for 68Ga-PSMA-11 PET/CT (76.47%, 26/34) compared to mpMRI (20.59%, 7/34). Further analysis revealed that an SUVmax > 3.95 for 68Ga-PSMA-11 PET/CT and a PI-RADS score ≥ 4 for mpMRI were independent factors influencing lateral diagnostic concordance.

CONCLUSION

The 68Ga-PSMA-11 PET/CT demonstrated significantly higher lateralization accuracy than mpMRI in intra-glandular prostate cancer. There was considerable inconsistency in the diagnostic outcomes between 68Ga-PSMA-11 PET/CT and mpMRI, and in cases of discordance, 68Ga-PSMA-11 PET/CT was notably more accurate. SUVmax > 3.95 and PI-RADS score ≥ 4 were critical factors influencing the correct lateralization accuracy when the results from 68Ga-PSMA-11 PET/CT and mpMRI were inconsistent.

A multi-center study: development and validation of a BpMRI focused model in transition zone PI-RADS 3 and 4 lesions to detect clinically significant prostate cancer

OBJECTIVE

To develop and validate a biparametric magnetic resonance imaging(BpMRI) focused model for detecting clinically significant prostate cancer(csPCa)( Gleason score ≥ 7) in TZ PI-RADS 3 and 4 lesions, compared to the Risk-based model (PI-RADS ≥ 3 and PSA density (PSAD) ≥ 0.15 ng/ml/cm³).

METHODS

A multi-center, retrospective cohort analysis was conducted on consecutive patients with PI-RADS 3 or 4 and eligible biopsy result. Multivariable logistic regression identified predictors of csPCa, followed by the areas under the curve(AUC) and decision curve analysis (DCA) comparisons between the Risk-based and BpMRI focused models, with external validation.

RESULTS

A total of 121 patients with 231 lesions in the development cohort(cohort 1) and 45 patients with 81 lesions the external validation cohort(cohort 2) were included between January 2020 and December 2024. The AUCs of the BpMRI-focused model were higher than those of the risk-based model in both the development cohort (0.71 [95% CI: 0.62-0.81] vs. 0.83 [95% CI: 0.74-0.92], p < 0.05) and the external validation cohort (0.75 [95% CI: 0.63-0.87] vs. 0.87 [95% CI: 0.79-0.95], p < 0.05). Furthermore, the BpMRI Focused Model significantly reduced the number of false positives for clinically significant prostate cancer compared to the Risk-Based Model [54 (23%) vs. 142 (61%), p < 0.002], while maintaining a cancer detection rate comparable to the PI-RADS ≥ 3 strategy (both p > 0.05). Additionally, the BpMRI Focused Model achieved a higher biopsy avoidance rate for csPCa [15 (6%)] compared to the Risk-Based Model [10 (4%)], though the difference was not statistically significant (p = 0.30).

CONCLUSION

In clinical decision-making, lesions in the TZ with PI-RADS 3 or 4 can be incorporated into the BpMRI focused model to reduce unnecessary biopsies.

Synthetic DWI: contrast improvement for diffusion-weighted imaging in prostate using T1 shine-through by synthesizing images with adjusted TR and TE

OBJECTIVE

To investigate whether synthetic DWI (SyDWI) calculated with short TR and zero TE can improve diffusion contrast in prostate compared to conventional DWI acquired with standard TR and TE.

MATERIALS AND METHODS

Thirty-two patients who underwent multiparametric MRI (mp-MRI) on a 3.0 T scanner were enrolled. For SyDWI, DWIs at b0 were acquired with two different TRs and TEs in addition to b1000 and b2000 images acquired with single conventional TR and TE. Contrast ratio (CR) was compared between SyDWI calculated with TR of 1000 ms and TE of 0 ms and conventional DWI acquired with TR of 6000 ms and TE of 70 ms.

RESULTS

The mean CR between prostate cancer (PCa) and normal prostate, and between PCa and benign prostatic hyperplasia (BPH), is significantly higher in SyDWI compared to conventional DWI for both b-values of 1000 and 2000 s/mm2. In addition, contrast within some lesions is now visualized, suggesting that tumour heterogeneity can be observed that is not seen with conventional DWI.

CONCLUSION

SyDWI calculated with TR of 1000 ms and TE of 0 ms significantly improves diffusion contrast between PCa and normal prostate or BPH, and within the lesion, compared to conventional DWI as a result of T1 shine-through.

Introduction to matrix-based method for analyzing hybrid multidimensional prostate MRI data

A new approach to analysis of prostate hybrid multidimensional MRI (HM-MRI) data was introduced in this study. HM-MRI data were acquired for a combination of a few echo times (TEs) and a few b-values. Naturally, there is a matrix associated with HM-MRI data for each image pixel. To process the data, we first linearized HM-MRI data by taking the natural logarithm of the imaging signal intensity. Subsequently, a hybrid symmetric matrix was constructed by multiplying the matrix for each pixel by its own transpose. The eigenvalues for each pixel could then be calculated from the hybrid symmetric matrix. In order to compare eigenvalues between patients, three b-values and three TEs were used, because this was smallest number of b-values and TEs among all patients. The results of eigenvalues were displayed as qualitative color maps for easier visualization. For quantitative analysis, the ratio (λr) of eigenvalues (λ1, λ2, λ3) was defined as λr = (λ1/λ2)/λ3 to compare region of interest (ROI) between prostate cancer (PCa) and normal tissue. The results show that the combined eigenvalue maps show PCas clearly and these maps are quite different from apparent diffusion coefficient (ADC) and T2 maps of the same prostate. The PCa has significant larger λr, smaller ADC and smaller T2 values than normal prostate tissue (p < 0.001). This suggests that the matrix-based method for analyzing HM-MRI data provides new information that may be clinically useful. The method is easy to use and could be easily implemented in clinical practice. The eigenvalues are associated with combination of ADC and T2 values, and could aid in the identification and staging of PCa.

Quantitative magnetic resonance imaging in prostate cancer: A review of current technology

Prostate cancer (PCa) imaging forms an important part of PCa clinical management. Magnetic resonance imaging is the modality of choice for prostate imaging. Most of the current imaging assessment is qualitative i.e., based on visual inspection and thus subjected to inter-observer disagreement. Quantitative imaging is better than qualitative assessment as it is more objective, and standardized, thus improving interobserver agreement. Apart from detecting PCa, few quantitative parameters may have potential to predict disease aggressiveness, and thus can be used for prognosis and deciding the course of management. There are various magnetic resonance imaging-based quantitative parameters and few of them are already part of PIRADS v.2.1. However, there are many other parameters that are under study and need further validation by rigorous multicenter studies before recommending them for routine clinical practice. This review intends to discuss the existing quantitative methods, recent developments, and novel techniques in detail.

Quantitative Prostate MRI, From the AJR Special Series on Quantitative Imaging

Prostate MRI has traditionally relied on qualitative interpretation. However, quantitative components hold the potential to markedly improve performance. The ADC from DWI is probably the most widely recognized quantitative MRI biomarker and has shown strong discriminatory value for clinically significant prostate cancer (csPCa) as well as for recurrent cancer after treatment. Advanced diffusion techniques, including intravoxel incoherent motion, diffusion kurtosis, diffusion tensor imaging, and specific implementations such as restriction spectrum imaging, purport even better discrimination, but are more technically challenging. The inherent T1 and T2 of tissue also provide diagnostic value, with more advanced techniques deriving luminal water imaging and hybrid-multidimensional MRI. Dynamic contrast-enhanced imaging, primarily using a modified Tofts model, also shows independent discriminatory value. Finally, quantitative size and shape features can be combined with the aforementioned techniques and be further refined using radiomics, texture analysis, and artificial intelligence. Which technique will ultimately find widespread clinical use will depend on validation across a myriad of platforms use-cases.

Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer

BACKGROUND

Magnetic resonance imaging (MRI) is useful in the diagnosis of clinically significant prostate cancer (csPCa). MRI-derived radiomics may support the diagnosis of csPCa.

PURPOSE

To investigate whether adding radiomics from biparametric MRI to predictive models based on clinical and MRI parameters improves the prediction of csPCa in a multisite-multivendor setting.

MATERIAL AND METHODS

Clinical information (PSA, PSA density, prostate volume, and age), MRI reviews (PI-RADS 2.1), and radiomics (histogram and texture features) were retrieved from prospectively included patients examined at different radiology departments and with different MRI systems, followed by MRI-ultrasound fusion guided biopsies of lesions PI-RADS 3-5. Predictive logistic regression models of csPCa (Gleason score ≥7) for the peripheral (PZ) and transition zone (TZ), including clinical data and PI-RADS only, and combined with radiomics, were built and compared using receiver operating characteristic (ROC) curves.

RESULTS

In total, 456 lesions in 350 patients were analyzed. In PZ and TZ, PI-RADS 4-5 and PSA density, and age in PZ, were independent predictors of csPCa in models without radiomics. In models including radiomics, PI-RADS 4-5, PSA density, age, and ADC energy were independent predictors in PZ, and PI-RADS 5, PSA density and ADC mean in TZ. Comparison of areas under the ROC curve (AUC) for the models without radiomics (PZ: AUC = 0.82, TZ: AUC = 0.80) versus with radiomics (PZ: AUC = 0.82, TZ: AUC = 0.82) showed no significant differences (PZ: P = 0.366; TZ: P = 0.171).

CONCLUSION

PSA density and PI-RADS are potent predictors of csPCa. Radiomics do not add significant information to our multisite-multivendor dataset.

Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers

Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.1 categories and to identify MRI features that could improve csPCa detection. Materials and Methods This single-center prospective study included men with suspected or known prostate cancer who underwent multiparametric MRI and MRI/US-guided biopsy from April 2019 to December 2021. MRI scans were prospectively evaluated using PI-RADS version 2.1. Atypical transition zone (TZ) nodules were upgraded to category 3 if marked diffusion restriction was present. Lesions with an International Society of Urological Pathology (ISUP) grade of 2 or higher (range, 1-5) were considered csPCa. MRI features, including three-dimensional diameter, relative lesion volume (lesion volume divided by prostate volume), sphericity, and surface to volume ratio (SVR), were obtained from lesion contours delineated by the radiologist. Univariable and multivariable analyses were conducted at the lesion and participant levels to determine features associated with csPCa. Results In total, 454 men (median age, 67 years [IQR, 62-73 years]) with 838 lesions were included. The csPCa rates for lesions categorized as PI-RADS 1 (n = 3), 2 (n = 170), 3 (n = 197), 4 (n = 319), and 5 (n = 149) were 0%, 9%, 14%, 37%, and 77%, respectively. csPCa rates of PI-RADS 4 lesions were lower than PI-RADS 5 lesions (P < .001) but higher than PI-RADS 3 lesions (P < .001). Upgraded PI-RADS 3 TZ lesions were less likely to harbor csPCa compared with their nonupgraded counterparts (4% [one of 26] vs 20% [20 of 99], P = .02). Predictors of csPCa included relative lesion volume (odds ratio [OR], 1.6; P < .001), SVR (OR, 6.2; P = .02), and extraprostatic extension (EPE) scores of 2 (OR, 9.3; P < .001) and 3 (OR, 4.1; P = .02). Conclusion The rates of csPCa differed between consecutive PI-RADS categories of 3 and higher. MRI features, including lesion volume, shape, and EPE scores of 2 and 3, predicted csPCa. Upgrading of PI-RADS category 3 TZ lesions may result in unnecessary biopsies. ClinicalTrials.gov registration no. NCT03354416 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Goh in this issue.

The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms

OBJECTIVE

To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research.

MATERIALS AND METHODS

This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons.

RESULTS

In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation.

CONCLUSION

A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.

Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate

BACKGROUND

There is an overlap comparing transition zone prostate cancer (TZ PCa) and benign prostatic hyperplasia (BPH) on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), creating additional challenges for assessment of TZ tumors on MRI.

PURPOSE

To evaluate whether amide proton transfer-weighted (APTw) imaging provides new diagnostic ideas for TZ PCa.

STUDY TYPE

Prospective.

POPULATION

A total of 51 TZ PCa patients (age, 49-89), 44 stromal BPH (age, 57-92), and 45 glandular BPH patients (age, 56-92).

FIELD STRENGTH/SEQUENCE

A 3 T; T2WI turbo spin echo (TSE), quantitative T2*-weighted imaging, DWI echo planar imaging, 3D APTw TSE.

ASSESSMENT

Differences in APTw, apparent diffusion coefficient (ADC), and T2* among three lesions were compared by one-way analysis of variance (ANOVA). Regions of interest were drawn by two radiologists (X.Q.Z. and X.Y.Q., with 21 and 15 years of experience, respectively).

STATISTICAL TESTS

Multivariable logistic regression analyses; ANOVA with post hoc testing; receiver operator characteristic curve analysis; Delong test. Significance level: P < 0.05.

RESULTS

APTw among TZ PCa, stromal BPH, and glandular BPH (3.48% ± 0.83% vs. 2.76% ± 0.49% vs. 2.72% ± 0.45%, respectively) were significantly different except between stromal BPH and glandular BPH (P > 0.99). Significant differences were found in ADC (TZ PCa 0.76 ± 0.16 × 10^-3  mm² /sec vs. stromal BPH 0.91 ± 0.14 × 10^-3  mm² /sec vs. glandular BPH 1.08 ± 0.18 × 10^-3  mm² /sec) among three lesions. APTw (OR = 12.18, 11.80, respectively) and 1/ADC (OR = 703.87, 181.11, respectively) were independent predictors of TZ PCa from BPH and stromal BPH. The combination of APTw and ADC had better diagnostic performance in the identification of TZ PCa from BPH and stromal BPH.

DATA CONCLUSION

APTw imaging has the potential to be of added value to ADC in differentiating TZ PCa from BPH and stromal BPH.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Multiparametric magnetic resonance imaging of the prostate: Lights and shadows

Prostate cancer is the second most commonly diagnosed cancer in man. Since the first MRI was performed, enormous progress has been made in diagnosis, treatment, and follow up of PCa, mainly due to multiparametric prostatic MRI (mpMRI). Although mpMRI has become the best imaging tool for identifying PCa, some limitations still exist. Prostate imaging with mpMRI is, to date, the best way to locate suspicious lesions to trigger prostate biopsy, plan active surveillance, or definitive treatment. In case of relapse, mpMRI can help detect local disease and provide specific management. It is well known that there is a subset of patients in whom mpMRI fails to depict csPCa. These missed significant cancers demand great attention. Prostate mpMRI quality depends on several factors related to equipment (including equipment vendor, magnet field and gradient strength, coil set used, software and hardware levels, sequence parameter choices), patient (medications, body habitus, motion, metal implants, rectal gas), and most importantly the radiologic interpretation of images (learning curve effects, subjectivity of observations, interobserver variations, and reporting styles). Inter-reader variability represents a huge current limitation of this method. Therefore, mpMRI remains the best imaging tool available to detect PCa, guiding diagnosis, treatment, and follow up while inter-reader variability represents the best limitation. Radiomics can help identifying imaging biomarkers to help radiologist in detecting significant PCa, reducing examination times, and costs.

Fully automated detection of prostate transition zone tumors on T2-weighted and apparent diffusion coefficient (ADC) map MR images using U-Net ensemble

PURPOSE

Accurate detection of transition zone (TZ) prostate cancer (PCa) on magnetic resonance imaging (MRI) remains challenging using clinical subjective assessment due to overlap between PCa and benign prostatic hyperplasia (BPH). The objective of this paper is to describe a deep-learning-based framework for fully automated detection of PCa in the TZ using T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images.

METHOD

This was a single-center IRB-approved cross-sectional study of men undergoing 3T MRI on two systems. The dataset consisted of 196 patients (103 with and 93 without clinically significant [Grade Group 2 or higher] TZ PCa) to train and test our proposed methodology, with an additional 168 patients with peripheral zone PCa used only for training. We proposed an ensemble of classifiers in which multiple U-Net-based models are designed for prediction of TZ PCa location on ADC map MR images, with initial automated segmentation of the prostate to guide detection. We compared accuracy of ADC alone to T2W and combined ADC+T2W MRI for input images, and investigated improvements using ensembles over their constituent models with different methods of diversity in individual models by hyperparameter configuration, loss function and model architecture.

RESULTS

Our developed algorithm reported sensitivity and precision of 0.829 and 0.617 in 56 test cases containing 31 instances of TZ PCa and in 25 patients without clinically significant TZ tumors. Patient-wise classification accuracy had an area under receiver operator characteristic curve (AUROC) of 0.974. Single U-Net models using ADC alone (sensitivity 0.829, precision 0.534) outperformed assessment using T2W (sensitivity 0.086, precision 0.081) and assessment using combined ADC+T2W (sensitivity 0.687, precision 0.489). While the ensemble of U-Nets with varying hyperparameters demonstrated the highest performance, all ensembles improved PCa detection compared to individual models, with sensitivities and precisions close to the collective best of constituent models.

CONCLUSION

We describe a deep-learning-based method for fully automated TZ PCa detection using ADC map MR images that outperformed assessment by T2W and ADC+T2W.

A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion

BACKGROUND

A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications.

PURPOSE

To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information.

STUDY TYPE

Retrospective.

POPULATION

Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases).

FIELD STRENGTH/SEQUENCE

1.5 to 3.0 Tesla, T2-weighted image pulse sequences.

ASSESSMENT

MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high-risk tumor from low-risk tumor.

STATISTICAL TESTS

To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa.

RESULTS

Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86-0.92]) and 0.78 (95% CI: [0.74-0.82]) to classify cancer vs. benign and high- vs. low-risk of prostate disease, respectively.

DATA CONCLUSION

AI-biopsy provided a data-driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI-biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag-and-drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI-assessed MR images in real time.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 2.

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.

Shape Analysis of Peripheral Zone Observations on Prostate DWI: Correlation to Histopathology Outcomes After Radical Prostatectomy

OBJECTIVE. The objective of our study was to subjectively and quantitatively assess shape features of peripheral zone (PZ) tumors at DWI compared with pathologic outcomes. MATERIALS AND METHODS. During the study period, 241 consecutive men with PZ dominant prostate tumors underwent 3-T MRI including DWI before undergoing radical prostatectomy. DW images of these patients were retrospectively assessed by two blinded radiologists. The reviewers assigned Prostate Imaging Reporting and Data System (PI-RADS) shape categories (round or oval, crescentic [i.e., conforming to PZ], linear or wedge-shaped) and segmented tumors for quantitative shape analysis. Discrepancies were resolved by consensus. Comparisons were performed with Gleason score (GS) and pathologic stage. RESULTS. Consensus review results were as follows: 63.9% (154/241) of tumors were round or oval; 22.8% (55/241), crescentic; and 13.3% (32/241), linear or wedge-shaped. Agreement for shape assessment was moderate (κ = 0.41). Round or oval tumors were higher grade (GS 6 = 1.3%, GS 7 = 78.0%, GS ≥ 8 = 20.7%) than crescentic tumors (GS 6 = 9.1%, GS 7 = 74.6%, GS ≥ 8 = 16.3%) and linear or wedge-shaped tumors (GS 6 = 6.3%, GS 7 = 78.1%, GS ≥ 8 = 15.6%) (p = 0.011). In addition, round or oval tumors had higher rates of extraprostatic extension (EPE) and seminal vesicle invasion (SVI) (EPE and SVI: 70.1% and 26.0%) than crescentic tumors (67.3% and 9.1%; p = 0.003) and linear or wedge-shaped tumors (40.6% and 9.4%; p = 0.008). Quantitatively, the shape features termed "circularity" and "roundness" were associated with EPE (p < 0.001 and p = 0.003), SVI (p < 0.001 and p = 0.029), and increasing GS (p = 0.009 and p = 0.021), but there was overlap between groups. CONCLUSION. In this study, approximately 10% of resected PZ tumors were linear or wedge-shaped on DWI. PZ tumors that were judged subjectively and evaluated quantitatively to be round or oval were associated with increased prostate cancer aggressiveness.

Direct Comparison of PI-RADS Version 2 and 2.1 in Transition Zone Lesions for Detection of Prostate Cancer: Preliminary Experience

BACKGROUND

There appears to be less agreement in the identification of cancers in the transition zone (TZ), which is not as reliable as those in peripheral zone when using the Prostate Imaging Reporting and Data System (PI-RADS) version 2 (v2). In response to such shortcomings, the updated version 2.1 was introduced, which incorporated diffusion-weighted imaging (DWI) into category 2 and clarified lexicons.

PURPOSE

To compare the diagnostic performance for the detection of clinically significant TZ prostate cancers (csPCa) and interreader agreement between PI-RADS v2.1 and v2.

STUDY TYPE

Retrospective study.

POPULATION

In all, 142 patients, 201 TZ lesions.

FIELD STRENGTH/SEQUENCE

3.0T; T₂ -weighted image and DWI.

ASSESSMENT

Lesions were scored by three independent readers using PI-RADS v2 and v2.1.

STATISTICAL TESTS

The sensitivity and specificity at category ≥3 were compared between v2 and v2.1 using the generalized estimating equation model. Detection rates for csPCa of upgraded and downgraded lesions in the use of PI-RADS v2.1 from v2 were assessed. Interreader agreement was assessed using κ statistics.

RESULTS

PI-RADS v2.1 showed a higher sensitivity and specificity (94.5% and 60.9%) than v2 (91.8% and 56.3%) for category ≥3 lesions in the detection of csPCa, although not significantly. Of eight upgraded lesions from category 2 to 3 (2 + 1) with an incorporated DWI, 50% (4/8) were csPCa. This was significantly higher than category 2 lesions (4.4%; P = 0.003). No csPCa was detected among the 22.8% (46/201) downgraded lesions. There was a moderate interreader agreement for scores ≥3 (κ = 0.565) in v2.1, which was slightly higher than that for v2 (κ = 0.534), although not significantly.

DATA CONCLUSION

PI-RADS v2.1 provides moderate and comparable interreader agreement at category ≥3 than v2 in the TZ lesions. Upgraded lesions from category 2 to 3 demonstrated a higher detection rate of csPCa than category 2 lesions in v2.1.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:577-586.

Automated segmentation of prostate zonal anatomy on T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images using U-Nets

PURPOSE

Accurate regional segmentation of the prostate boundaries on magnetic resonance (MR) images is a fundamental requirement before automated prostate cancer diagnosis can be achieved. In this paper, we describe a novel methodology to segment prostate whole gland (WG), central gland (CG), and peripheral zone (PZ), where PZ + CG = WG, from T2W and apparent diffusion coefficient (ADC) map prostate MR images.

METHODS

We designed two similar models each made up of two U-Nets to delineate the WG, CG, and PZ from T2W and ADC map MR images, separately. The U-Net, which is a modified version of a fully convolutional neural network, includes contracting and expanding paths with convolutional, pooling, and upsampling layers. Pooling and upsampling layers help to capture and localize image features with a high spatial consistency. We used a dataset consisting of 225 patients (combining 153 and 72 patients with and without clinically significant prostate cancer) imaged with multiparametric MRI at 3 Tesla.

RESULTS AND CONCLUSION

Our proposed model for prostate zonal segmentation from T2W was trained and tested using 1154 and 1587 slices of 100 and 125 patients, respectively. Median of Dice similarity coefficient (DSC) on test dataset for prostate WG, CG, and PZ were 95.33 ± 7.77%, 93.75 ± 8.91%, and 86.78 ± 3.72%, respectively. Designed model for regional prostate delineation from ADC map images was trained and validated using 812 and 917 slices from 100 and 125 patients. This model yielded a median DSC of 92.09 ± 8.89%, 89.89 ± 10.69%, and 86.1 ± 9.56% for prostate WG, CG, and PZ on test samples, respectively. Further investigation indicated that the proposed algorithm reported high DSC for prostate WG segmentation from both T2W and ADC map MR images irrespective of WG size. In addition, segmentation accuracy in terms of DSC does not significantly vary among patients with or without significant tumors.

SIGNIFICANCE

We describe a method for automated prostate zonal segmentation using T2W and ADC map MR images independent of prostate size and the presence or absence of tumor. Our results are important in terms of clinical perspective as fully automated methods for ADC map images, which are considered as one of the most important sequences for prostate cancer detection in the PZ and CG, have not been reported previously.

Clinically significant prostate cancer detection on MRI: A radiomic shape features study

PURPOSE

Prostate multiparametric MRI (mpMRI) is the imaging modality of choice for detecting clinically significant prostate cancer (csPCa). Among various parameters, lesion maximum diameter and volume are currently considered of value to increase diagnostic accuracy. Quantitative radiomics allows for the extraction of more advanced shape features. Our aim was to assess which shape features derived from MRI index lesions correlate with csPCa presence.

MATERIALS AND METHODS

We retrospectively enrolled 75 consecutive subjects, who underwent mpMRI on a 3 T scanner, divided based on MRI index lesion Gleason Score in a csPCa group (GS > 3 + 4, n = 41) and a non-csPCa one (n = 34). Ten shape features were extracted both from axial T2-weighted and ADC maps images, after lesion tridimensional segmentation. Univariable and multivariable logistic analysis were used to evaluate the relationship between shape features and csPCa. Diagnostic performance was assessed measuring the area under the curve of the receiver operating characteristic (ROC) analysis. Diagnostic accuracy, sensitivity, and specificity were determined using the best cut-off on each ROC. A P value < 0.05 was considered statistically significant.

RESULTS

Univariable analysis demonstrated that almost every shape feature was statistically significant between csPCa e non-csPCa groups. However, multivariable analysis revealed that the parameter defined as surface area to volume ratio (SAVR), especially when extracted from ADC maps is the strongest independent predictor of csPCa among tested shape features.

CONCLUSION

The radiomic shape feature SAVR, extracted from ADC maps after index lesion segmentation, appears as a promising tool for csPCa detection.