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In the United States (US), urological guidelines recommend active surveillance (AS) for patients with low-risk prostate cancer (PCa) and endorse it as an option for those with favorable intermediate-risk PCa with a > 10-year life expectancy. Multiparametric magnetic resonance imaging (mpMRI) is being increasingly used in the screening, monitoring, and staging of PCa and involves the combination of T2-weighted, diffusion-weighted, and dynamic contrast-enhanced T1-weighted imaging. The American Urological Association (AUA) guidelines provide recommendations about the use of mpMRI in the confirmatory setting for AS patients but do not discuss the timing of follow-up mpMRI in AS. The National Comprehensive Cancer Network (NCCN) discourages using it more frequently than every 12 months. Finally, guidelines state that mpMRI can be used to augment risk stratification but should not replace periodic surveillance biopsy. In this review, we discuss the current literature regarding the use of mpMRI for patients with AS, with a particular focus on the approach in the US. Although AS shows a benefit to the addition of mpMRI to diagnostic, confirmatory, and follow-up biopsy, there is no strong evidence to suggest that mpMRI can safely replace biopsy for most patients and thus it must be incorporated into a multimodal approach. CLINICAL RELEVANCE STATEMENT: According to the US guidelines, regular follow-ups are important for men with prostate cancer on active surveillance, and prostate MRI is a valuable tool that should be utilized, in combination with PSA kinetics and biopsies, for monitoring prostate cancer. KEY POINTS: According to the US guidelines, the addition of MRI improves the detection of clinically significant prostate cancer. Timing interval imaging of patients on active surveillance remains unclear and has not been specifically addressed. MRI should trigger further work-ups, but not replace periodic follow-up biopsies, in men on active surveillance.

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RATIONALE AND OBJECTIVES: This meta-analysis aimed to assess the diagnostic accuracy of multiparametric MRI (mpMRI) in detecting suspected prostate cancer (PCa) in biopsy-naive men. MATERIALS AND METHODS: PubMed, Scopus, and the Cochrane Library databases were systematically searched for studies published from January 2013 to April 2024. Sixteen studies comprising 4973 patients met the inclusion criteria. Data were extracted to construct 2×2 contingency tables for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A random-effects model was used for pooled estimation, and subgroup analyses were conducted. Summary receiver operating characteristic (SROC) curves were generated to summarize overall diagnostic performance. RESULTS: The overall detection rate of PCa across studies was 57.3%. For detecting any PCa, mpMRI showed pooled sensitivity of 82% (95% CI, 80-83%) and specificity of 62% (95% CI, 60-64%), with positive likelihood ratio (LR) of 1.97 (95% CI, 1.71-2.26), negative LR of 0.28 (95% CI, 0.24-0.34), and diagnostic odds ratio (DOR) of 7.34 (95% CI, 5.60-9.63), and an area under the SROC curve of 0.81. For clinically significant PCa (csPCa), mpMRI had pooled sensitivity of 88% (95% CI, 87-90%) and specificity of 64% (95% CI, 63-66%), with positive LR of 2.49 (95% CI, 2.03-3.05), negative LR of 0.20 (95% CI, 0.16-0.25), DOR of 13.83 (95% CI, 9.14-20.9), and area under the curve of 0.90. CONCLUSION: This meta-analysis suggests that mpMRI is effective in detecting PCa in biopsy-naive patients, particularly for csPCa. It can help reduce unnecessary biopsies and lower the risk of missing clinically significant cases, thereby guiding informed biopsy decisions.

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Objectives: This study aimed to evaluate the performance of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) in comparison to multiparametric magnetic resonance imaging (mpMRI) for detecting biochemical recurrence of prostate cancer (PCa). Materials and methods: We conducted a comprehensive search for articles published in PubMed, Web of Science, Embase, and the Cochrane Library, spanning the inception of the database until October 26, 2022, which included head-to-head comparisons of PSMA PET/CT and mpMRI for assessing the biochemical recurrence of PCa. Results: A total of 5 studies including 228 patients were analyzed. The overall positivity rates of PSMA PET/CT and mpMRI for detecting biochemical recurrence of PCa after final treatment were 0.68 (95% confidence interval [CI], 0.52-0.89) and 0.56 (95% CI, 0.36-0.88), respectively. The positivity rates of PSMA PET/CT and mpMRI for detecting local recurrence, lymph node metastasis, and bone metastases were 0.37 (95% CI, 0.30-0.47) and 0.38 (95% CI, 0.22-0.67), 0.44 (95% CI, 0.35-0.56) and 0.25 (95% CI, 0.17-0.35), and 0.19 (95% CI, 0.11-0.31) and 0.12 (95% CI, 0.05-0.25), respectively. Compared with mpMRI, PSMA PET/CT exhibited a higher positivity rate for detecting biochemical recurrence and lymph node metastases, and no significant difference in the positivity rate of local recurrence was observed between these 2 imaging modalities. Conclusions: Compared with mpMRI, PSMA PET/CT appears to have a higher positivity rate for detecting biochemical recurrence of PCa. Although both imaging methods showed similar positivity rates of detecting local recurrence, PSMA PET/CT outperformed PSMA PET/CT in detecting lymph node involvement and overall recurrence.

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Purpose: Glioblastoma (GBM) is the most common and aggressive primary adult brain tumor. The standard treatment approach is surgical resection to target the enhancing tumor mass, followed by adjuvant chemoradiotherapy. However, malignant cells often extend beyond the enhancing tumor boundaries and infiltrate the peritumoral edema. Traditional supervised machine learning techniques hold potential in predicting tumor infiltration extent but are hindered by the extensive resources needed to generate expertly delineated regions of interest (ROIs) for training models on tissue most and least likely to be infiltrated. Approach: We developed a method combining expert knowledge and training-based data augmentation to automatically generate numerous training examples, enhancing the accuracy of our model for predicting tumor infiltration through predictive maps. Such maps can be used for targeted supra-total surgical resection and other therapies that might benefit from intensive yet well-targeted treatment of infiltrated tissue. We apply our method to preoperative multi-parametric magnetic resonance imaging (mpMRI) scans from a subset of 229 patients of a multi-institutional consortium (Radiomics Signatures for Precision Diagnostics) and test the model on subsequent scans with pathology-proven recurrence. Results: Leave-one-site-out cross-validation was used to train and evaluate the tumor infiltration prediction model using initial pre-surgical scans, comparing the generated prediction maps with follow-up mpMRI scans confirming recurrence through post-resection tissue analysis. Performance was measured by voxel-wised odds ratios (ORs) across six institutions: University of Pennsylvania (OR: 9.97), Ohio State University (OR: 14.03), Case Western Reserve University (OR: 8.13), New York University (OR: 16.43), Thomas Jefferson University (OR: 8.22), and Rio Hortega (OR: 19.48). Conclusions: The proposed model demonstrates that mpMRI analysis using deep learning can predict infiltration in the peri-tumoral brain region for GBM patients without needing to train a model using expert ROI drawings. Results for each institution demonstrate the model's generalizability and reproducibility.

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BACKGROUND: The diagnosis of myocarditis by CMR requires the use of T2 and T1 weighted imaging, ideally incorporating parametric mapping. Current 2D mapping sequences are acquired sequentially and involve multiple breath-holds resulting in prolonged scan times and anisotropic image resolution. We developed an isotropic free-breathing 3D whole-heart sequence which allows simultaneous T1 and T2 mapping and validated it in patients with suspected acute myocarditis. METHODS: Eighteen healthy volunteers and 28 patients with suspected myocarditis underwent conventional 2D T1 and T2 mapping with whole heart coverage and 3D joint T1/T2 mapping on a 1.5T scanner. Acquisition time, image quality, and diagnostic performance were compared. Qualitative analysis was performed using a 4-point Likert scale. Bland-Altman plots were used to assess the quantitative agreement between 2D and 3D sequences. RESULTS: The 3D T1/T2 sequence was acquired in 8mins 26s under free breathing, whereas 2D T1 and T2 sequences were acquired with breath holds in 11mins 44s (p=0.0001). All 2D images were diagnostic. For 3D images, 89% of T1 and 96% of T2 images were diagnostic with no significant difference in the proportion of diagnostic images for the 3D and 2D T1 (p=0.2482) and T2 maps (p=1.0000). Systematic bias in T1 was noted with biases of 102ms, 115ms, and 152ms for basal-apical segments, with a larger bias for higher T1 values. Good agreement between T2 values for 3D and 2D techniques was found (bias of 1.8ms, 3.9ms, and 3.6ms for basal-apical segments). The sensitivity and specificity of the 3D sequence for diagnosing acute myocarditis was 74% (95% confidence interval [CI] 49-91%) and 83% (36-100%) respectively, with an estimated c-statistic (95% CI) of 0.85 (0.79-0.91) and no statistically significant difference between the 2D and 3D sequences for the detection of acute myocarditis for T1 (p=0.2207) or T2 (p=1.0000). CONCLUSION: Free-breathing whole heart 3D joint T1/T2 mapping was comparable to 2D mapping sequences with respect to diagnostic performance, but with the added advantages of free-breathing, and shorter scan times. Further work is required to address the bias noted at high T1 values, but this did not significantly impact on diagnostic accuracy.

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BACKGROUND: This study aimed to validate a previously published risk model (RM) which combines clinical and multiparametric MRI (mpMRI) parameters to predict extraprostatic extension (EPE) of prostate cancer (PC) prior to radical prostatectomy (RP). MATERIALS AND METHODS: A previously published RM combining clinical with mpMRI parameters including European Society of Urogenital Radiology (ESUR) classification for EPE was retrospectively evaluated in a cohort of two urological university hospitals in Germany. Consecutive patients (n = 205, January 2015 -June 2021) with available preoperative MRI images, clinical information including PSA, prostate volume, ESUR classification for EPE, histopathological results of MRI-fusion biopsy and RP specimen were included. Validation was performed by receiver operating characteristic analysis and calibration plots. The RM's performance was compared to ESUR criteria. RESULTS: Histopathological T3 stage was detected in 43% of the patients (n = 89); 45% at Essen and 42% at Düsseldorf. Discrimination performance between pT2 and pT3 of the RM in the entire cohort was AUC = 0.86 (AUC = 0.88 at site 1 and AUC = 0.85 at site 2). Calibration was good over the entire probability range. The discrimination performance of ESUR classification alone was comparable (AUC = 0.87). CONCLUSIONS: The RM showed good discriminative performance to predict EPE for decision-making for RP as a patient-tailored risk stratification. However, when experienced MRI reading is available, standardized MRI reading with ESUR scoring is comparable regarding information outcome. A main limitation is the potentially limited transferability to other populations because of the high prevalence of EPE in our subgroups.

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OBJECTIVE: To comprehensively evaluate the renal structure and function of patients with renal artery stenosis (RAS) using multiparametric magnetic resonance imaging (MRI), and analyze the correlation between magnetic resonance (MR) parameters and renal function. MATERIALS AND METHODS: Renal multiparametric MRI was conducted on 62 patients with RAS utilizing a Philips Ingenia CX 3.0 T MRI system. The scanning protocols encompassed arterial spin labeling, phase contrast MRI, diffusion weighted imaging, T1 mapping, and blood oxygen level-dependent MRI. All patients underwent radionuclide renal dynamic imaging to calculate the glomerular filtration rate (GFR) for assessing renal function. RESULTS: Most MR parameters were correlated with GFR: renal parenchymal volume (R = 0.603), whole kidney renal blood flow (RBF) (R = 0.192), renal cortical RBF (R = 0.294), renal artery mean velocity (R = 0.593), stroke volume (R = 0.599), mean flux (R = 0.629), renal cortical apparent diffusion coefficient (ADC) (R = 0.466), medullary ADC (R = 0.332), cortical T1 value (R = - 0.206), corticomedullary T1 difference (R = 0.204), cortical T2* value (R = 0.448), and medullary T2* value (R = 0.272). The best prediction model for GFR using multiparametric MRI was obtained, including renal PV, whole kidney RBF, cortical RBF, mean velocity, mean flux, and CMD T1. CONCLUSION: Multiparametric MRI is a novel noninvasive examination method that can effectively and comprehensively assess the renal structure and function of RAS.

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Undifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft tissue sarcomas. This study determined the value of perfusion-weighted imaging with dynamic-contrast-enhancement (PWI/DCE) morphologic, qualitative, and semiquantitative features for predicting UPS pathology-assessed treatment effect (PATE). This retrospective study included 33 surgically excised extremity UPS patients with pre-surgical MRI. Volumetric tumor segmentation from PWI/DCE was obtained at Baseline (BL), Post-Chemotherapy (PC), and Post-Radiation Therapy (PRT). The surgical specimens' PATE separated cases into Responders (R) (≥ 90%, 16 patients), Partial-Responders (PR) (89 - 31%, 10 patients), and Non-Responders (NR) (≤ 30%, seven patients). Seven semiquantitative kinetic parameters and maps were extracted from time-intensity curves (TICs), and 107 radiomic features were derived. Statistical analyses compared R vs. PR/NR. At PRT, 79% of R displayed a "Capsular" morphology (P = 1.49 × 10-7), and 100% demonstrated a TIC-type II (P = 8.32 × 10-7). 80% of PR showed "Unipolar" morphology (P = 1.03 × 10-5), and 60% expressed a TIC-type V (P = 0.06). Semiquantitative wash-in rate (WiR) was able to separate R vs. PR/NR (P = 0.0078). The WiR radiomics displayed significant differences in the first_order_10 percentile (P = 0.0178) comparing R vs. PR/NR at PRT. The PWI/DCE TIC-type II curve, low WiR, and "Capsular" enhancement represent PRT patterns typically observed in successfully treated UPS and demonstrate potential for UPS treatment response assessment.

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OBJECTIVE: Prostate cancer, the second most diagnosed cancer among men, requires precise diagnostic techniques to ensure effective treatment. This review explores the technological advancements, optimal application conditions, and benefits of targeted prostate biopsies facilitated by multiparametric magnetic resonance imaging (mpMRI). METHODS: A systematic literature review was conducted to compare traditional 12-core systematic biopsies guided by transrectal ultrasound with targeted biopsy techniques using mpMRI. We searched electronic databases including PubMed, Scopus, and Web of Science from January 2015 to December 2024 using keywords such as "targeted prostate biopsy", "fusion prostate biopsy", "cognitive prostate biopsy", "MRI-guided biopsy", and "transrectal ultrasound prostate biopsy". Studies comparing various biopsy methods were included, and data extraction focused on study characteristics, patient demographics, biopsy techniques, diagnostic outcomes, and complications. CONCLUSION: mpMRI-guided targeted biopsies enhance the detection of clinically significant prostate cancer while reducing unnecessary biopsies and the detection of insignificant cancers. These targeted approaches preserve or improve diagnostic accuracy and patient outcomes, minimizing the risks associated with overdiagnosis and overtreatment. By utilizing mpMRI, targeted biopsies allow for precise targeting of suspicious regions within the prostate, providing a cost-effective method that reduces the number of biopsies performed. This review highlights the importance of integrating advanced imaging techniques into prostate cancer diagnosis to improve patient outcomes and quality of life.

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(1) Background: The basophil activation test (BAT) is a functional whole blood-based ex vivo assay to quantify basophil activation after allergen exposure by flow cytometry. One of the most important prerequisites for the use of the BAT in the routine clinical diagnosis of allergies is a reliable, standardized and reproducible data analysis workflow. (2) Methods: We re-analyzed a public mass cytometry dataset from peanut (PN) allergic patients (n = 6) and healthy controls (n = 3) with our binning approach "pattern recognition of immune cells" (PRI). Our approach enabled a comprehensive analysis of the dataset, evaluating 30 markers to achieve optimal basophil identification and activation through multi-parametric analysis and visualization. (3) Results: We found FcεRIα/CD32 (FcγRII) as a new marker couple to identify basophils and kept CD63 as an activation marker to establish a modified BAT in combination with our PRI analysis approach. Based on this, we developed an algorithm for automated raw data processing, which enables direct data analysis and the intuitive visualization of the test results including controls and allergen stimulations. Furthermore, we discovered that the expression pattern of CD32 correlated with FcεRIα, anticorrelated with CD63 and was detectable in both the re-analyzed public dataset and our own flow cytometric results. (4) Conclusions: Our improved BAT, combined with our PRI procedure (bin-BAT), provides a reliable test with a fully reproducible analysis. The advanced bin-BAT enabled the development of an automated workflow with an intuitive visualization to discriminate allergic patients from non-allergic individuals.

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BACKGROUND: Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases. METHOD: A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation. RESULTS: All the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%. CONCLUSIONS: The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.

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OBJECTIVE: This study aims to analyse multiparametric MRI (mpMRI) characteristics of patients diagnosed with ISUP grade group (GG) 1 prostate cancer (PC) on initial target plus systematic MRI/TRUS fusion-guided biopsy and investigate histopathological progression during follow-up. METHODS: A retrospective single-centre cohort analysis was conducted on consecutive patients with mpMRI visible lesions (PI-RADS ≥ 3) and detection of ISUP-1-PC at the time of initial biopsy. The study assessed clinical, mpMRI, and histopathological parameters. Subcohorts were analysed with (1) patients who had confirmed ISUP-1-PC and (2) patients who experienced histopathological upgrading to ISUP ≥ 2 PC during follow-up either at re-biopsy or radical prostatectomy (RP). RESULTS: A total of 156 patients (median age 65 years) between March 2014 and August 2021 were included. Histopathological upgrading to ISUP ≥ 2 was detected in 55% of patients during a median follow-up of 9.5 months (IQR 2.2-16.4). When comparing subgroups with an ISUP upgrade and sustained ISUP 1 PC, they differed significantly in contact length of the index lesion to the pseudocapsule, ADC value, PI-RADS category, and the MRI grading group (mGG) (p < 0.05). In the ISUP GG ≥ 2 subgroup, 91% of men had PI-RADS category 4 or 5 and 82% exhibited the highest mGG (mGG3). In multivariate analysis, mGG was the only independent parameter for predicting ISUP ≥ 2-PC in these patients. CONCLUSIONS: MRI reveals important information about PC aggressiveness and should be incorporated into clinical decision-making when ISUP-1-PC is diagnosed. In cases of specific MRI characteristics adverse to the histopathology, early re-biopsy might be considered. CLINICAL RELEVANCE STATEMENT: In cases with clear MRI characteristics for clinically significant prostate cancer (e.g., mGG 3 and/or PI-RADS 5, cT3, or clear focal PI-RADS 4 lesions on MRI) and ISUP GG 1 PC diagnosed on initial prostate biopsy, MRI findings should be incorporated into clinical decision-making and early re-biopsy (e.g., within 6 months) might be considered. KEY POINTS: MRI reveals important information about prostate cancer (PC) aggressiveness. MRI should be incorporated into clinical decision-making when ISUP GG 1 PC is diagnosed on initial prostate biopsy. In cases of specific MRI characteristics adverse to the histopathology, early re-biopsy might be considered.

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Significance: Uterine fibroids (UFs) can pose a serious health risk to women. UFs are benign tumors that vary in clinical presentation from asymptomatic to causing debilitating symptoms. UF management is limited by our inability to predict UF growth rate and future morbidity. Aim: We aim to develop a predictive model to identify UFs with increased growth rates and possible resultant morbidity. Approach: We retrospectively analyzed 44 expertly outlined UFs from 20 patients who underwent two multi-parametric MR imaging exams as part of a prospective study over an average of 16 months. We identified 44 initial features by extracting quantitative magnetic resonance imaging (MRI) features plus morphological and textural radiomics features from DCE, T2, and apparent diffusion coefficient sequences. Principal component analysis reduced dimensionality, with the smallest number of components explaining over 97.5% of the variance selected. Employing a leave-one-fibroid-out scheme, a linear discriminant analysis classifier utilized these components to output a growth risk score. Results: The classifier incorporated the first three principal components and achieved an area under the receiver operating characteristic curve of 0.80 (95% confidence interval [0.69; 0.91]), effectively distinguishing UFs growing faster than the median growth rate of 0.93 cm 3 / year / fibroid from slower-growing ones within the cohort. Time-to-event analysis, dividing the cohort based on the median growth risk score, yielded a hazard ratio of 0.33 [0.15; 0.76], demonstrating potential clinical utility. Conclusion: We developed a promising predictive model utilizing quantitative MRI features and principal component analysis to identify UFs with increased growth rates. Furthermore, the model's discrimination ability supports its potential clinical utility in developing tailored patient and fibroid-specific management once validated on a larger cohort.

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PURPOSE: To develop a model-based motion correction (MoCo) method that does not need an analytical signal model to improve the quality of cardiac multi-parametric mapping. METHODS: The proposed method constructs a hybrid loss that includes a dictionary-matching loss and a signal low-rankness loss, where the former registers the multi-contrast original images to a set of motion-free synthetic images and the latter forces the deformed images to be spatiotemporally coherent. We compared the proposed method with non-MoCo, a pairwise registration method (Pairwise-MI), and a groupwise registration method (pTVreg) via a free-breathing Multimapping dataset of 15 healthy subjects, both quantitatively and qualitatively. RESULTS: The proposed method achieved the lowest contour tracking errors (epicardium: 2.00 ± 0.39 mm vs 4.93 ± 2.29 mm, 3.50 ± 1.26 mm, and 2.61 ± 1.00 mm, and endocardium: 1.84 ± 0.34 mm vs 4.93 ± 2.40 mm, 3.43 ± 1.27 mm, and 2.55 ± 1.09 mm for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01) and the lowest dictionary matching errors among all methods. The proposed method also achieved the highest scores on the visual quality of mapping (T1: 4.74 ± 0.33 vs 2.91 ± 0.82, 3.58 ± 0.87, and 3.97 ± 1.05, and T2: 4.48 ± 0.56 vs 2.59 ± 0.81, 3.56 ± 0.93, and 4.14 ± 0.80 for the proposed method, non-MoCo, Pairwise-MI, and pTVreg, respectively; all p < 0.01). Finally, the proposed method had similar T1 and T2 mean values and SDs relative to the breath-hold reference in nearly all myocardial segments, whereas all other methods led to significantly different T1 and T2 measures and increases of SDs in multiple segments. CONCLUSION: The proposed method significantly improves the motion correction accuracy and mapping quality compared with non-MoCo and alternative image-based methods.

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PURPOSE: To establish the incidence, size, zonal location and Gleason Score(GS)/Gleason Grade Group(GG) of sparse versus dense prostate cancer (PCa) lesions and to identify the imaging characteristics of sparse versus dense cancers on multiparametric MRI (mpMRI). METHODS: Seventy-six men with untreated PCa were scanned prior to prostatectomy with endorectal-coil 3 T MRI including T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced MRI. Cancerous regions were outlined and graded on the whole-mount, processed specimens, with tissue compositions estimated. Regions with cancer comprising <50 % and ≥ 50 % of the tissue were considered sparse and dense respectively. Regions of interest (ROI) were manually drawn on T2-weighted MRI. Within each patient, area-weighted ROI averages were calculated for each imaging measure for each tissue type, GS/GG, and sparse/dense composition. RESULTS: A large number of cancer regions were identified on histopathology (n = 1193: 939 (peripheral zone (PZ)) and 254 (transition zone (TZ))). Thirty-seven percent of these lesions were sparse. Sparse lesions were primarily low-grade with the majority of PZ and 100 % of TZ sparse lesions ≤GS3 + 3/GG1. Dense lesions were significantly larger than sparse lesions in both PZ and TZ, p < 0.0001. On imaging, 246/45 PZ and 109/8 TZ dense/sparse 2D cancerous ROIs were drawn. Sparse GS3 + 3 and sparse ≥GS3 + 4 cancers did not have significantly different MRI intensities to dense GS3 + 3 cancers, while sparse GS3 + 3/GG1 cancers differed from benign, p < 0.05. CONCLUSION: Histopathologically identified prostate cancer lesions were sparse in 37 % of cases. Sparse cancers were entirely low grade in TZ and predominantly low-grade in PZ and generally small, thus likely posing lower risk for spread and progression than dense lesions. Sparse lesions were not distinguishable from dense lesions on mpMRI, but could be distinguished from benign tissues.

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AuroLase® Therapy-a nanoparticle-enabled focal therapy-has the potential to safely and effectively treat localized prostate cancer (PCa), preserving baseline functionality. This article presents a detailed case of localized PCa treated with AuroLase, providing insight on expectations from the diagnosis of PCa to one year post-treatment. AuroLase Therapy is a two-day treatment consisting of a systemic infusion of gold nanoshells (~150-nm hydrodynamic diameter) on Day 1, and sub-ablative laser treatment on Day 2. Multiparametric MRI (mpMRI) was used for tumor visualization, treatment planning, and therapy response assessment. The PCa was targeted with a MR/Ultrasound-fusion (MR/US) transperineal approach. Successful treatment was confirmed at 6 and 12 months post-treatment by the absence of disease in MR/US targeted biopsies. On the mpMRI, confined void space was evident, an indication of necrotic tissues encompassing the treated lesion, which was completely resolved at 12 months, forming a band-like scar with no evidence of recurrent tumor. The patient's urinary and sexual functions were unchanged. During the one-year follow-up, changes on the DCE sequence and in the Ktrans and ADC values assist in qualitatively and quantitatively evaluating tissue changes. The results highlight the potential of gold-nanoparticle-enabled sub-ablative laser treatment to target and control localized PCa, maintain quality of life, and preserve baseline functionality.

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Background: Multiparametric magnetic resonance imaging (mpMRI) is a commonly used method to diagnose pelvic lymph node metastasis (PLNM) in prostate cancer (PCa) patients, but there are few comparative studies on mpMRI and 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) in locally advanced PCa (LAPC) patients. Therefore, we designed a retrospective study to compare the diagnostic value of 68Ga-PSMA PET/CT and mpMRI for PLNM of LAPC. Methods: A retrospective study was performed on 50 patients with LAPC who underwent radical prostatectomy (RP) in Tongji Hospital from 2021 to 2023. All patients underwent PET/CT and mpMRI examination, and were diagnosed as LAPC before surgery, followed by robot-assisted laparoscopic prostatectomy or laparoscopic RP and extended pelvic lymph node dissection (ePLND). Routine postoperative pathological examination was performed. According to the results, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT and mpMRI for the diagnosis of PLNM of LAPC were compared. Results: Among the 50 patients, the mean age was 65.5±10.3 years, the preoperative total serum prostate-specific antigen (PSA) was 30.7±12.3 ng/mL, and the Gleason score was 7 [7, 8]. The difference in diagnostic efficacy between 68Ga-PSMA PET/CT and mpMRI in the preoperative diagnosis of PLNM of PCa was determined by postoperative pathological results. Based on the number of patients who developed PLNM, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT were as follows: 93.75%, 100.00%, 100.00%, 97.14%, and 68.75%, 97.06%, 91.67%, 86.84% for mpMRI, respectively. Based on the number of pelvic metastatic lymph nodes, the sensitivity, specificity, positive predictive value, and negative predictive value of 68Ga-PSMA PET/CT were 95.24%, 100.00%, 100.00%, 99.48%, and 65.08%, 99.13%, 89.13%, 96.30% for mpMRI, respectively. It turned out that PET/CT was more sensitive than mpMRI in detecting PLNM of PCa, and the difference was statistically significant. Conclusions: 68Ga-PSMA PET/CT is more sensitive than mpMRI in the detection of PLNM in patients with LAPC. It is a promising method in the diagnosis and preoperative assessment of PLNM in LAPC.

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Neurofibromatosis (NF) type I is a neuroectodermal and mesodermal dysplasia caused by a mutation of the neurofibromin tumor suppressor gene. Phenotypic features of NF1 vary, and patients develop benign peripheral nerve sheath tumors and malignant neoplasms, such as malignant peripheral nerve sheath tumor, malignant melanoma, and astrocytoma. Multiparametric whole-body MR imaging (WBMRI) plays a critical role in disease surveillance. Multiparametric MRI, typically used in prostate imaging, is a general term for a technique that includes multiple sequences, i.e. anatomic, diffusion, and Dixon-based pre- and post-contrast imaging. This article discusses the value of multiparametric WBMRI and illustrates the spectrum of whole-body lesions of NF1 in a single imaging setting. Examples of lesions include those in the skin (tumors and axillary freckling), soft tissues (benign and malignant peripheral nerve sheath tumors, visceral plexiform, and diffuse lesions), bone and joints (nutrient nerve lesions, non-ossifying fibromas, intra-articular neurofibroma, etc.), spine (acute-angled scoliosis, dural ectasia, intraspinal tumors, etc.), and brain/skull (optic nerve glioma, choroid plexus xanthogranuloma, sphenoid wing dysplasia, cerebral hamartomas, etc.). After reading this article, the reader will gain knowledge of the variety of lesions encountered with NF1 and their WBMRI appearances. Timely identification of such lesions can aid in accurate diagnosis and appropriate patient management.

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PURPOSE: There is an unmet clinical need for non-invasive imaging biomarkers that could replace liver biopsy in the management of patients with autoimmune hepatitis (AIH). In this study, we sought to evaluate the diagnostic accuracy of a simple uncorrected, non-contrast T1 mapping for detecting fibrosis and inflammation in AIH patients using histopathology as a reference standard. MATERIAL AND METHODS: Over 3 years, 33 patients with AIH were prospectively studied using a multiparametric liver MRI protocol which included T1 mapping. Biopsies were performed up to 3 months before imaging, and a standardized histopathological score for fibrosis (F0-F4) and inflammatory activity (PPA0-4) was used as a reference. Statistical analysis included independent t test, Mann-Whitney U-test, and ROC (receiver operating characteristic) analysis. RESULTS: T1 mapping values were significantly higher in patients with advanced fibrosis (F0-2 vs. F3-4; p < 0.015), significant fibrosis (F0-1 vs. F2-4; p < 0.005), and significant inflammatory activity (PPA 0-1 vs. PPA 2-4 p = 0.048). Moreover, the technique demonstrated a good diagnostic performance in detecting significant (AUC 0.856) and advanced fibrosis (AUC 0.835), as well as significant inflammatory activity (AUC 0.763). CONCLUSION: A rapid, simple, uncorrected, non-contrast T1 mapping sequence showed satisfactory diagnostic performance in comparison with histopathology for detecting significant tissue inflammation and fibrosis in AIH patients, being a potential non-invasive imaging biomarker for monitoring disease activity in such individuals.

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BACKGROUND: Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance. PURPOSE: To investigate whether integrating multiparametric MRI (mp-MRI) with clinical factors improves NMIBC 5-year recurrence risk assessment. STUDY TYPE: Retrospective. POPULATION: One hundred ninety-one patients (median age, 65 years; age range, 54-73 years; 27 females) underwent mp-MRI between 2011 and 2017, and received ≥5-year follow-ups. They were divided into a training cohort (N = 115) and validation/testing cohorts (N = 38 in each). Recurrence rates were 23.5% (27/115) in the training cohort and 23.7% (9/38) in both validation and testing cohorts. FIELD STRENGTH/SEQUENCE: 3-T, fast spin echo T2-weighted imaging (T2WI), single-shot echo planar diffusion-weighted imaging (DWI), and volumetric spoiled gradient echo dynamic contrast-enhanced (DCE) sequences. ASSESSMENT: Radiomics and deep learning (DL) features were extracted from the combined region of interest (cROI) including intratumoral and peritumoral areas on mp-MRI. Four models were developed, including clinical, cROI-based radiomics, DL, and clinical-radiomics-DL (CRDL) models. STATISTICAL TESTS: Student's t-tests, DeLong's tests with Bonferroni correction, receiver operating characteristics with the area under the curves (AUCs), Cox proportional hazard analyses, Kaplan-Meier plots, SHapley Additive ExPlanations (SHAP) values, and Akaike information criterion for clinical usefulness. A P-value <0.05 was considered statistically significant. RESULTS: The cROI-based CRDL model showed superior performance (AUC 0.909; 95% CI: 0.792-0.985) compared to other models in the testing cohort for assessing 5-year recurrence in NMIBC. It achieved the highest Harrell's concordance index (0.804; 95% CI: 0.749-0.859) for estimating recurrence-free survival. SHAP analysis further highlighted the substantial role (22%) of the radiomics features in NMIBC recurrence assessment. DATA CONCLUSION: Integrating cROI-based radiomics and DL features from preoperative mp-MRI with clinical factors could improve 5-year recurrence risk assessment in NMIBC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.