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Background PSMA-PET 1 has shown good concordance with histology, but there is a need to investigate the ability of PSMA-PET to delineate DIL 2 boundaries for guided biopsy and focal therapy planning. Objective To determine threshold and margin combinations that satisfy the following criteria: ≥95% sensitivity with max specificity and ≥95% specificity with max sensitivity. Design, setting and participants We registered pathologist-annotated whole-mount mid-gland prostatectomy histology sections cut in 4.4 mm intervals from 12 patients to pre-surgical PSMA-PET/MRI by mapping histology to ex-vivo imaging to in-vivo imaging. We generated PET-derived tumor volumes using boundaries defined by thresholded PET volumes from 1–100% of SUV 3 max in 1% intervals. At each interval, we applied margins of 0–30 voxels in one voxel increments, giving 3000 volumes/patient. Outcome measurements Mean and standard deviation of sensitivity and specificity for cancer detection within the 2D oblique histologic planes that intersected with the 3D PET volume for each patient. Results and limitations A threshold of 67% SUV max with an 8.4 mm margin achieved a (mean ± std.) sensitivity of 95.0 ± 7.8% and specificity of 76.4 ± 14.7%. A threshold of 81% SUV max with a 5.1 mm margin achieved sensitivity of 65.1 ± 28.4% and specificity of 95.1 ± 5.2%. Conclusions Preliminary evidence of thresholding and margin expansion of PSMA-PET images targeted at DILs validated with histopathology demonstrated excellent mean sensitivity and specificity in the setting of focal therapy/boosting and guided biopsy. These parameters can be used in a larger validation study supporting clinical translation.

Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction. We employ a probabilistic-temporal framework, namely, hidden Markov models (HMMs), for modeling TeUS data obtained from PCa patients. We distinguish malignant from benign tissue by comparing the respective log-likelihood estimates generated by the HMMs. We analyze 1100 TeUS signals acquired from 12 patients. Our results show improved malignancy identification compared to previous results, demonstrating over 85% accuracy and AUC of 0.95. Incorporating temporal information directly into the models leads to improved tissue differentiation in PCa. We expect our method to generalize and be applied to other types of cancer in which temporal-ultrasound can be recorded.

Rebuilding the local vasculature is central to restoring the health of muscles subjected to ischemic injury. Arteriogenesis yields remodeled collateral arteries that circumvent the obstruction, and angiogenesis produces capillaries to perfuse the regenerating myofibers. However, the vital intervening network of arterioles that feed the regenerated capillaries is poorly understood and is an investigative challenge. We used machine learning and automated micromorphometry to quantify the arteriolar landscape in distal hindlimb muscles in mice that have regenerated after femoral artery excision. Assessment of 1,546 arteriolar sections revealed a striking (2-fold) increase in arteriolar density in regenerated muscle 14 and 28 days after ischemic injury. Lumen caliber was initially similar to that of control arterioles but after 4 wk lumen area was reduced by 46%. In addition, the critical smooth muscle layer was attenuated throughout the arteriolar network, across a 150- to 5-µm diameter range. To understand the consequences of the reshaped distal hindlimb arterioles, we undertook computational flow modeling, which revealed blunted flow augmentation. Moreover, impaired flow reserve was confirmed in vivo by laser-Doppler analyses of flow in response to directly applied sodium nitroprusside. Thus, in hindlimb muscles regenerating after ischemic injury, the arteriolar network is amplified, inwardly remodels, and is diffusely undermuscularized. These defects and the associated flow restraints could contribute to the deleterious course of peripheral artery disease and merit attention when considering therapeutic innovations.

Multi-parametric magnetic resonance imaging (mp-MRI) is emerging as a useful tool for prostate cancer (PCa) detection but currently has unaddressed limitations. Computer aided diagnosis (CAD) systems have been developed to address these needs, but many approaches used to generate and validate the models have inherent biases.All clinically significant PCa on histology was mapped to mp-MRI using a previously validated registration algorithm. Shape and size matched non-PCa regions were selected using a proposed sampling algorithm to eliminate biases towards shape and size. Further analysis was performed to assess biases regarding inter-zonal variability.A 5-feature Naïve-Bayes classifier produced an area under the receiver operating characteristic curve (AUC) of 0.80 validated using leave-one-patient-out cross-validation. As mean inter-class area mismatch increased, median AUC trended towards positively biasing classifiers to producing higher AUCs. Classifiers were invariant to differences in shape between PCa and non-PCa lesions (AUC: 0.82 vs 0.82). Performance for models trained and tested only in the peripheral zone was found to be lower than in the central gland (AUC: 0.75 vs 0.95).We developed a radiomics based machine learning system to classify PCa vs non-PCa tissue on mp-MRI validated on accurately co-registered mid-gland histology with a measured target registration error. Potential biases involved in model development were interrogated to provide considerations for future work in this area.

Purpose Localized prostate cancer (PCa) in patients is characterized by a dominant focus in the gland (dominant intraprostatic lesion, DIL). Accurate DIL identification may enable more accurate diagnosis and therapy through more precise targeting of biopsy, radiotherapy and focal ablative therapies. The goal of this study is to validate the performance of [18F]DCFPyL PET and CT perfusion (CTP) for detecting and localizing DIL against digital histopathological images. Methods Multi-modality image sets: in vivo T2-weighted (T2w)-MRI, 22-min dynamic [18F]DCFPyL PET/CT, CTP, and 2-h post-injection PET/MR were acquired in patients prior to radical prostatectomy. The explanted gland with implanted fiducial markers was imaged with T2w-MRI. All images were co-registered to the pathologist-annotated digital images of whole-mount mid-gland histology sections using fiducial markers and anatomical landmarks. Regions of interest encompassing DIL and non-DIL tissue were drawn on the digital histopathological images and superimposed on PET and CTP parametric maps. Logistic regression with backward elimination of parameters was used to select the most sensitive parameter set to distinguish DIL from non-DIL voxels. Leave-one-patient-out cross-validation was performed to determine diagnostic performance. Results [18F]DCFPyL PET and CTP parametric maps of 15 patients were analyzed. SUVLate and a model combining Ki and k4 of [18F]DCFPyL achieved the most accurate performance distinguishing DIL from non-DIL voxels. Both detection models achieved an AUC of 0.90 and an error rate of i and k4 model and 0.7 for SUVLate. Conclusions We have validated using co-registered digital histopathological images that parameters from kinetic analysis of 22-min dynamic [18F]DCFPyL PET can accurately localize DILs in PCa for targeting of biopsy, radiotherapy, and focal ablative therapies. Short-duration dynamic [18F]DCFPyL PET was not inferior to SUVLate in this diagnostic task. Clinical trial registration number: NCT04009174 (ClinicalTrials.gov).

A statistical contrast-enhanced ultrasound (CEUS) method, which describes the change in the histogram of image intensity during microbubble wash-in, was developed previously in our lab as a means of characterizing the spatial heterogeneity of tumor perfusion. This study tests whether that statistical CEUS method should be preferred to conventional mean-intensity-based CEUS analysis for classification of anti-angiogenic treatment responses in a preclinical tumor model. Seven perfusion parameters from conventional and statistical CEUS were fed into logistic regression and support vector machine learning models to classify control (modeling resistant) and treated (modeling sensitive) tumors engrafted on a chicken embryo angiogenic assay. Learning models combining features from both conventional and statistical CEUS analysis more accurately classified anti-angiogenic response than models using either statistical or conventional features alone. Therefore, the statistical CEUS method is best used as a supplement to conventional CEUS analysis.

Multiparametric magnetic resonance imaging (mpMRI) has demonstrated the ability to localize intraprostatic lesions. It is our goal to determine how to optimally target the underlying histopathological cancer within the setting of high-dose-rate brachytherapy (HDR-BT).Ten prostatectomy patients had pathologist-annotated mid-gland histology registered to pre-procedural mpMRI, which were interpreted by four different observers. Simulated HDR-BT plans with realistic catheter placements were generated by registering the mpMRI lesions and corresponding histology annotations to previously performed clinical HDR-BT implants. Inverse treatment planning was used to generate treatment plans that treated the entire gland to a single dose of 15 Gy, as well as focally targeted plans that aimed to escalate dose to the mpMRI lesions to 20.25 Gy. Three margins to the lesion were explored: 0 mm, 1 mm, and 2 mm. The analysis compared the dose that would have been delivered to the corresponding histologically-defined cancer with the different treatment planning techniques.mpMRI-targeted plans delivered a significantly higher dose to the histologically-defined cancer (p0.001), in comparison to the standard treatment plans. Additionally, adding a 1 mm margin resulted in significantly higher D98, and D90 to the histologically-defined cancer in comparison to the 0 mm margin targeted plans (p = 0.019p = 0.0026). There was no significant difference between plans using 1 mm and 2 mm margins.Adding a 1 mm margin to intraprostatic mpMRI lesions significantly increased the dose to histologically-defined cancer, in comparison applying no margin. No significant effect was observed by further expanding the margins.

PSMA-PETTo determine threshold and margin combinations that satisfy the following criteria: ≥95% sensitivity with max specificity and ≥95% specificity with max sensitivity.We registered pathologist-annotated whole-mount mid-gland prostatectomy histology sections cut in 4.4 mm intervals from 12 patients to pre-surgical PSMA-PET/MRI by mapping histology to ex-vivo imaging to in-vivo imaging. We generated PET-derived tumor volumes using boundaries defined by thresholded PET volumes from 1-100% of SUVMean and standard deviation of sensitivity and specificity for cancer detection within the 2D oblique histologic planes that intersected with the 3D PET volume for each patient.A threshold of 67% SUV max with an 8.4 mm margin achieved a (mean ± std.) sensitivity of 95.0 ± 7.8% and specificity of 76.4 ± 14.7%. A threshold of 81% SUV max with a 5.1 mm margin achieved sensitivity of 65.1 ± 28.4% and specificity of 95.1 ± 5.2%.Preliminary evidence of thresholding and margin expansion of PSMA-PET images targeted at DILs validated with histopathology demonstrated excellent mean sensitivity and specificity in the setting of focal therapy/boosting and guided biopsy. These parameters can be used in a larger validation study supporting clinical translation.

INTRODUCTION AND OBJECTIVE:Prostate specific membrane antigen positron emission tomography (PSMA PET) has shown good concordance with histology in involved sextants, but there is a need, addressed ...

BACKGROUND Overtreatment of prostate cancer (PCa) is a healthcare issue. Development of noninvasive imaging tools for improved characterization of prostate lesions might reduce overtreatment. PURPOSE To measure the distribution of tissue sodium concentration (TSC), proton T2 -weighted signal, and apparent diffusion coefficient (ADC) values in human PCa and to test the presence of a correlation between regional differences in imaging metrics and the Gleason grade of lesions determined from histopathology. STUDY TYPE Cross-sectional. SUBJECTS Ten men with biopsy-proven PCa. SEQUENCES/FIELD STRENGTH Sodium, proton T2 -weighted, and diffusion-weighted MRI data were acquired using Broad-Band 3D-Fast-Gradient-Recalled, 3D Cube (Isotropic 3D-Fast-Turbo-Spin-Echo acquisition) and 2D Spin-Echo sequences, respectively, with a 3.0T MR scanner. ASSESSMENT All imaging data were coregistered to Gleason-graded postprostatectomy histology, as the standard for prostate cancer lesion characterization. Regional TSC and T2 data were assessed using percent changes from healthy tissue of the same patient (denoted ΔTSC, ΔT2 ). STATISTICS Differences in ΔTSC, ADC, and ΔT2 as a function of Gleason score were analyzed for each imaging contrast using a one-way analysis of variance or a nonparametric t-test. Correlations between imaging data measures and Gleason score were assessed using a Spearman's ranked correlation. RESULTS Evaluation of the correlation of ΔTSC, ADC, and ΔT2 datasets with Gleason scoring revealed that only the correlation between ΔTSC and Gleason score was statistically significant (rs = 0.791, p

PURPOSE Multiparametric MRI (mpMRI) has shown promise in the detection and localization of prostate cancer foci. Although techniques have been previously introduced to delineate lesions from mpMRI, these techniques were evaluated in datasets with T2 maps available. The generation of T2 map is not included in the clinical prostate mpMRI consensus guidelines; the acquisition of which requires repeated T2-weighted (T2W) scans and would significantly lengthen the scan time currently required for the clinically recommended acquisition protocol, which includes T2W, diffusion-weighted (DW), and dynamic contrast-enhanced (DCE) imaging. The goal of this study is to develop and evaluate an algorithm that provides pixel-accurate lesion delineation from images acquired based on the clinical protocol. METHODS Twenty-five pixel-based features were extracted from the T2-weighted (T2W), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) images. The pixel-wise classification was performed on the reduced space generated by locality alignment discriminant analysis (LADA), a version of linear discriminant analysis (LDA) localized to patches in the feature space. Postprocessing procedures, including the removal of isolated points identified and filling of holes inside detected regions, were performed to improve delineation accuracy. The segmentation result was evaluated against the lesions manually delineated by four expert observers according to the Prostate Imaging-Reporting and Data System (PI-RADS) detection guideline. RESULTS The LADA-based classifier (60 ± 11%) achieved a higher sensitivity than the LDA-based classifier (51 ± 10%), thereby demonstrating, for the first time, that higher classification performance was attained on the reduced space generated by LADA than by LDA. Further sensitivity improvement (75 ± 14%) was obtained after postprocessing, approaching the sensitivities attained by previous mpMRI lesion delineation studies in which nonclinical T2 maps were available. CONCLUSION The proposed algorithm delineated lesions accurately and efficiently from images acquired following the clinical protocol. The development of this framework may potentially accelerate the clinical uses of mpMRI in prostate cancer diagnosis and treatment planning.

An ongoing prospective study is acquiring preoperative imaging data for men with prostate cancer (PCa) using the molecular imaging agent [ F]-DCFPyL targeted against prostate-specific membrane antigen (PSMA). To date, six men (of a planned accrual of 24) with clinically localized, biopsy-proven PCa have undergone preoperative [ F]-DCFPyL positron emission tomography (PET) imaging and multiparametric magnetic resonance imaging acquired using a hybrid PET/MRI system. Lesions identified by [ F]-DCFPyL uptake on PET/MRI were characterized in terms of maximum standardized uptake value (SUV ) and volume using a boundary threshold of 40% SUV . Following surgery, all prostatectomy specimens were processed using a whole-mount technique for accurate deformable co-registration and correlation with PCa foci defined on digitized pathology images. Well-defined intraprostatic dominant lesions were identified by [ F]-DCFPyL PET/MRI (mean SUV 11.4 ± 8.25; mean volume 2.2 ± 2.4 cm ) in all six men. Co-registered digitized whole-mount pathology for the first case revealed that intense [ F]-DCFPyL uptake (SUV 27 ± 1.1 cm ) and multiparametric MRI changes (Prostate Imaging Reporting and Data System score of 4) were highly correlated with a 0.5-cm dominant (largest) lesion with Gleason pattern 4 PCa in the right mid peripheral zone. A smaller focus (0.01 cm ) of lower-grade PCa (Gleason pattern 3) had much lower uptake (SUV 2.7). These early prospective data show that dominant intraprostatic lesions could be identified in all six men using [ F]-DCFPyL as an imaging probe. Trial accrual will continue to quantify in terms of spatial concordance the ability of [ F]-DCFPyL to identify the location and characterize the grade of intraprostatic cancer foci in clinically localized PCa. Patient summary: Positron emission tomography using a novel probe called [ F]-DCFPyL directed against the prostate-specific membrane antigen protein was able to identify locations of prostate cancer in the prostate glands of men undergoing imaging before surgery. In the future, such imaging may allow better targeting of treatment to the portion of the prostate containing the most aggressive components of cancer rather than treating the whole prostate in a uniform fashion. [ F]-DCFPyL positron emission tomography/magnetic resonance imaging shows promise for identifying high-grade intraprostatic foci of prostate cancer in a spatially accurate fashion to facilitate targeted biopsy and treatment. 18 18 18 18 3 18 3 3 3 18 18 18 18 max max max max

PURPOSE: To investigate, in the setting of stereotactic ablative radiation therapy (SABR) for early-stage non-small cell lung cancer, the incidence and patterns of change in high-risk radiologic features (HRFs) in patients known to have no local recurrence.METHODS AND MATERIALS: Computed tomography (CT) scans of patients treated using volumetric modulated arc therapy SABR between 2008 and 2013 were eligible if follow-up scans were available for 2 years and no local recurrences were diagnosed. All scans were reviewed at a workstation using an add-on tool for ClearCanvas (Synaptive Medical). Five clinicians who were blinded to clinical outcomes scored the presence of HRFs: enlarging opacity (EO), sequential enlarging opacity, enlarging opacity after 12 months (EO12), bulging margin, loss of linear margins, cranio-caudal growth, and loss of air bronchogram. After each review, clinicians recommended follow-up procedures based on published recommendations.RESULTS: A total of 88 patients (747 CT scans) were evaluated. The HRFs most frequently recorded by ≥3 observers on at least 1 follow-up scan were EO (64.8%), EO12 (50.0%), and sequential enlarging opacity (13.6%). Fifty-six patients developed EO within the first year after SABR, and of these, 46 also developed subsequent EO (EO12). In 76 patients who developed EO after 1 year of follow-up, 30 had not manifested EO previously. Three or more HRFs have been associated with recurrences, and this was observed on CT scan in 22.7% of patients. In their routine care, 6 patients had undergone a positron emission tomography scan because of a suspected local recurrence, and 4 underwent an attempt at biopsy.CONCLUSIONS: More than 50% of patients without a local recurrence after SABR develop HRFs. Because ≥3 HRFs were present in nearly 25% of patients, further refinement of follow-up recommendations are necessary.

In-vivo imaging of the prostate has shown to be useful for prostate cancer (PCa) localization especially during biopsy procedures. Multi-parametric MRI (mp-MRI) is gaining rapid popularity amongst clinicians but is complex and difficult to interpret by even expert radiologists. Prostate specific membrane antigen positron emission tomography (PSMA PET) is emerging as a new tool for PCa detection and has shown promising results towards lesion identification. Both imaging procedures suffer from intra- and inter- observer variability in PCa detection. Computer-aided diagnosis (CAD) systems have been developed as a solution to mitigate observer variability and have shown to boost diagnostic accuracy. There are currently no studies published that assessed the benefit of incorporating PSMA PET imaging and mp-MRI into a CAD system for PCa detection. We compared the accuracy of CAD models trained and tested on features from mp-MRI+PSMA PET, mp-MRI and PSMA PET by training on 1-10 features chosen from three feature selection methods for 10 different classifiers for each of the three experiments. We found that models trained on mp-MRI provided lower overall error and greater specificity, and models trained on mp-MRI+PSMA PET and PSMA PET provided greater sensitivity to lesions in the central gland, which is a known area of difficulty for mp-MRI. Further validation using a larger dataset is required to prove the added benefit of PSMA PET imaging as a second modality to PCa CAD systems. Once fully validated, these results will demonstrate the added benefit of incorporating PSMA PET imaging into CAD models towards PCa detection.

Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods In this prospective study (ClinicalTrials.gov: NCT02723474; conducted from January 2010 to February 2017), participants were randomized to optimization (n = 1), training (n = 67), and testing (n = 27) data sets. Hyperpolarized (HP) helium 3 (3He) MRI ventilation maps were co-registered with thoracic CT to provide ground truth labels, and 87 quantitative imaging features were extracted and normalized to lung averages to generate 174 features. The volume-of-interest dimension and the training data sampling method were optimized to maximize the area under the receiver operating characteristic curve (AUC). Forward feature selection was performed to reduce the number of features; logistic regression, linear support vector machine, and quadratic support vector machine classifiers were trained through fivefold cross validation. The highest-performing classification model was applied to the test data set. Pearson coefficients were used to determine the relationships between the model, MRI, and pulmonary function measurements. Results The quadratic support vector machine performed best in training and was applied to the test data set. Model-predicted ventilation maps had an accuracy of 88% (95% confidence interval [CI]: 88%, 88%) and an AUC of 0.82 (95% CI: 0.82, 0.83) when the HP 3He MRI ventilation maps were used as the reference standard. Model-predicted ventilation defect percentage (VDP) was correlated with VDP at HP 3He MRI (r = 0.90, P < .001). Both model-predicted and HP 3He MRI VDP were correlated with forced expiratory volume in 1 second (FEV1) (model: r = -0.65, P < .001; MRI: r = -0.70, P < .001), ratio of FEV1 to forced vital capacity (model: r = -0.73, P < .001; MRI: r = -0.75, P < .001), diffusing capacity (model: r = -0.69, P < .001; MRI: r = -0.65, P < .001), and quality-of-life score (model: r = 0.59, P = .001; MRI: r = 0.65, P < .001). Conclusion Model-predicted ventilation maps generated by using CT textures and machine learning were correlated with MRI ventilation maps (r = 0.90, P < .001). © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Fain in this issue.

Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading prostate cancer on digital histopathology images have been reported using machine learning techniques. However, the importance and applicability of those methods have not been fully investigated. We computed three-class tissue component maps (TCMs) from the images, where each pixel was labeled as nuclei, lumina, or other. We applied seven different machine learning approaches: three non-deep learning classifiers with features extracted from TCMs, and four deep learning, using transfer learning with the 1) TCMs, 2) nuclei maps, 3) lumina maps, and 4) raw images for cancer detection and grading on whole-mount RP tissue sections. We performed leave-one-patient-out cross-validation against expert annotations using 286 whole-slide images from 68 patients. For both cancer detection and grading, transfer learning using TCMs performed best. Transfer learning using nuclei maps yielded slightly inferior overall performance, but the best performance for classifying higher-grade cancer. This suggests that 3-class TCMs provide the major cues for cancer detection and grading primarily using nucleus features, which are the most important information for identifying higher-grade cancer.

Immunohistochemical tissue staining enhances microvasculature characteristics, including the smooth muscle in the medial layer of the vessel walls that is responsible for regulation of blood flow. The vasculature can be imaged in a comprehensive fashion using whole-slide scanning. However, since each such image potentially contains hundreds of small vessels, manual vessel delineation and quantification is not practically feasible. In this work, we present a fully automatic segmentation and vasculature quantification algorithm for whole-slide images. We evaluated its performance on tissue samples drawn from the hind limbs of wild-type mice, stained for smooth muscle using 3,3'-Diaminobenzidine (DAB) immunostain. The algorithm was designed to be robust to vessel fragmentation due to staining irregularity, and artefactual staining of nonvessel objects. Colour deconvolution was used to isolate the DAB stain for detection of vessel wall fragments. Complete vessels were reconstructed from the fragments by joining endpoints of topological skeletons. Automatic measures of vessel density, perimeter, wall area and local wall thickness were taken. The segmentation algorithm was validated against manual measures, resulting in a Dice similarity coefficient of 89%. The relationships observed between these measures were as expected from a biological standpoint, providing further reinforcement of the accuracy of this system. This system provides a fully automated and accurate means of measuring the arteriolar and venular morphology of vascular smooth muscle.

The study aimed to determine the relationship between T2-weighted magnetic resonance imaging (MRI) signal and histologic sub-patterns in prostate cancer areas with different Gleason grades.MR images of prostates (n = 25) were obtained prior to radical prostatectomy. These were processed as whole-mount specimens with tumors and the peripheral zone was annotated digitally by two pathologists. Gleason grade 3 was the most prevalent grade and was subdivided into packed, intermediate, and sparse based on gland-to-stroma ratio. Large cribriform, intraductal carcinoma, and small cribriform glands (grade 4 group) were separately annotated but grouped together for statistical analysis. The log MRI signal intensity for each contoured region (n = 809) was measured, and pairwise comparisons were performed using the open-source software R version 3.0.1.Packed grade 3 sub-pattern has a significantly lower MRI intensity than the grade 4 group (P 0.00001). Sparse grade 3 has a significantly higher MRI intensity than the packed grade 3 sub-pattern (P 0.0001). No significant difference in MRI intensity was observed between the Gleason grade 4 group and the sparse sub-pattern grade 3 group (P = 0.54). In multivariable analysis adjusting for peripheral zone, the P values maintained significance (packed grade 3 group vs grade 4 group, P 0.001; and sparse grade 3 sub-pattern vs packed grade 3 sub-pattern, P 0.001).This study demonstrated that T2-weighted MRI signal is dependent on histologic sub-patterns within Gleason grades 3 and 4 cancers, which may have implications for directed biopsy sampling and patient management.