Pretreatment multiparametric MRI radiomics-integrated clinical hematological biomarkers can predict early rapid metastasis in patients with nasopharyngeal carcinoma.

BACKGROUND: To establish and validate a predictive model combining pretreatment multiparametric MRI-based radiomic signatures and clinical characteristics for the risk evaluation of early rapid metastasis in nasopharyngeal carcinoma (NPC) patients. METHODS: The cutoff time was used to randomly assign 219 consecutive patients who underwent chemoradiation treatment to the training group (n = 154) or the validation group (n = 65). Pretreatment multiparametric magnetic resonance (MR) images of individuals with NPC were employed to extract 428 radiomic features. LASSO regression analysis was used to select radiomic features related to early rapid metastasis and develop the Rad-score. Blood indicators were collected within 1 week of pretreatment. To identify independent risk variables for early rapid metastasis, univariate and multivariate logistic regression analyses were employed. Finally, multivariate logistic regression analysis was applied to construct a radiomics and clinical prediction nomogram that integrated radiomic features and clinical and blood inflammatory predictors. RESULTS: The NLR, T classification and N classification were found to be independent risk indicators for early rapid metastasis by multivariate logistic regression analysis. Twelve features associated with early rapid metastasis were selected by LASSO regression analysis, and the Rad-score was calculated. The AUC of the Rad-score was 0.773. Finally, we constructed and validated a prediction model in combination with the NLR, T classification, N classification and Rad-score. The area under the curve (AUC) was 0.936 (95% confidence interval (95% CI): 0.901-0.971), and in the validation cohort, the AUC was 0.796 (95% CI: 0.686-0.905). CONCLUSIONS: A predictive model that integrates the NLR, T classification, N classification and MR-based radiomics for distinguishing early rapid metastasis may serve as a clinical risk stratification tool for effectively guiding individual management.

Prediction of false-positive PI-RADS 5 lesions on prostate multiparametric MRI: development and internal validation of a clinical-radiological characteristics based nomogram.

BACKGROUND: To develop a risk model including clinical and radiological characteristics to predict false-positive The Prostate Imaging Reporting and Data System (PI-RADS) 5 lesions. METHODS: Data of 612 biopsy-naïve patients who had undergone multiparametric magnetic resonance imaging (mpMRI) before prostate biopsy were collected. Clinical variables and radiological variables on mpMRI were adopted. Lesions were divided into the training and validation cohort randomly. Stepwise multivariate logistic regression analysis with backward elimination was performed to screen out variables with significant difference. A diagnostic nomogram was developed in the training cohort and further validated in the validation cohort. Calibration curve and receiver operating characteristic (ROC) analysis were also performed. RESULTS: 296 PI-RADS 5 lesions in 294 patients were randomly divided into the training and validation cohort (208 : 88). 132 and 56 lesions were confirmed to be clinically significant prostate cancer in the training and validation cohort respectively. The diagnostic nomogram was developed based on prostate specific antigen density, the maximum diameter of lesion, zonality of lesion, apparent diffusion coefficient minimum value and apparent diffusion coefficient minimum value ratio. The C-index of the model was 0.821 in the training cohort and 0.871 in the validation cohort. The calibration curve showed good agreement between the estimation and observation in the two cohorts. When the optimal cutoff values of ROC were 0.288 in the validation cohort, the sensitivity, specificity, PPV, and NPV were 90.6%, 67.9%, 61.7%, and 92.7% in the validation cohort, potentially avoiding 9.7% unnecessary prostate biopsies. CONCLUSIONS: We developed and validated a diagnostic nomogram by including 5 factors. False positive PI-RADS 5 lesions could be distinguished from clinically significant ones, thus avoiding unnecessary prostate biopsy.

Radio-anatomical evaluation of clinical and radiomic profile of multi-parametric magnetic resonance imaging of de novo glioblastoma multiforme.

BACKGROUND: Glioblastoma (GBM) is a fatal, fast-growing, and aggressive brain tumor arising from glial cells or their progenitors. It is a primary malignancy with a poor prognosis. The current study aims at evaluating the neuroradiological parameters of de novo GBM by analyzing the brain multi-parametric magnetic resonance imaging (mpMRI) scans acquired from a publicly available database analysis of the scans. METHODS: The dataset used was the mpMRI scans for de novo glioblastoma (GBM) patients from the University of Pennsylvania Health System, called the UPENN-GBM dataset. This was a collection from The Cancer Imaging Archive (TCIA), a part of the National Cancer Institute. The MRIs were reviewed by a single diagnostic radiologist, and the tumor parameters were recorded, wherein all recorded data was corroborated with the clinical findings. RESULTS: The study included a total of 58 subjects who were predominantly male (male:female ratio of 1.07:1). The mean age with SD was 58.49 (11.39) years. Mean survival days with SD were 347 (416.21) days. The left parietal lobe was the most commonly found tumor location with 11 (18.96%) patients. The mean intensity for T1, T2, and FLAIR with SD was 1.45E + 02 (20.42), 1.11E + 02 (17.61), and 141.64 (30.67), respectively (p = < 0.001). The tumor dimensions of anteroposterior, transverse, and craniocaudal gave a z-score (significance level = 0.05) of - 2.53 (p = 0.01), - 3.89 (p < 0.001), and 1.53 (p = 0.12), respectively. CONCLUSION: The current study takes a third-party database and reduces physician bias from interfering with study findings. Further prospective and retrospective studies are needed to provide conclusive data.

Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

[Multiparametric magnetic resonance imaging for hepatocellular carcinoma, part 1 : Morphology and dynamic perfusion imaging in primary diagnostics and treatment monitoring]. / Multiparametrische MRT-Bildgebung beim hepatozellulären Karzinom, Teil 1 : Morphologie und dynamische Perfusionsbildgebung in Primärdiagnostik und Therapieverlaufskontrolle.

Radiology plays a key role in the diagnosis and monitoring of hepatocellular carcinoma (HCC). Ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) are used to identify HCC lesions. Multiparametric MRI provides detailed insights into the tumor biology through the analysis of morphology, perfusion and diffusion. In this way preoperative decisions can be optimized. The guidelines recommend using contrast-enhanced MRI or ultrasound for the diagnosis of HCC. The preferred method is MRI due to its superiority in the detection of small lesions The treatment response is evaluated using modified response evaluation criteria for solid tumors (RECIST) and the European Association for the Study of the Liver (EASL) criteria. The use of multiparametric MRI in conjunction with the liver imaging reporting and data system (LI-RADS) plays overall a central role in the precise diagnosis and monitoring of the treatment of HCC.

Elucidating the need for prostate cancer risk calculators in conjunction with mpMRI in initial risk assessment before prostate biopsy at a tertiary prostate cancer center.

OBJECTIVE: Utilizing personalized risk assessment for clinically significant prostate cancer (csPCa) incorporating multiparametric magnetic resonance imaging (mpMRI) reduces biopsies and overdiagnosis. We validated both multi- and univariate risk models in biopsy-naïve men, with and without the inclusion of mpMRI data for csPCa detection. METHODS: N = 565 men underwent mpMRI-targeted prostate biopsy, and the diagnostic performance of risk calculators (RCs), mpMRI alone, and clinical measures were compared using receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). Subgroups were stratified based on mpMRI findings and quality. RESULTS: csPCa was detected in 56.3%. PI-RADS score achieved the highest area under the curve (AUC) when comparing univariate risk models (AUC 0.82, p < 0.001). Multivariate RCs showed only marginal improvement in csPCa detection compared to PI-RADS score alone, with just one of four RCs showing significant superiority. In mpMRI-negative cases, the non-MRI-based RC performed best (AUC 0.80, p = 0.016), with the potential to spare biopsies for 23%. PSA-density and multivariate RCs demonstrated comparable performance for PI-RADS 3 constellation (AUC 0.65 vs. 0.60-0.65, p > 0.5; saved biopsies 16%). In men with suspicious mpMRI, both mpMRI-based RCs and the PI-RADS score predicted csPCa excellently (AUC 0.82-0.79 vs. 0.80, p > 0.05), highlighting superior performance compared to non-MRI-based models (all p < 0.002). Quality-assured imaging consistently improved csPCa risk stratification across all subgroups. CONCLUSION: In tertiary centers serving a high-risk population, high-quality mpMRI provides a simple yet effective way to assess the risk of csPCa. Using multivariate RCs reduces multiple biopsies, especially in mpMRI-negative and PI-RADS 3 constellation.

Myocardial Strain for the Differentiation of Myocardial Involvement in the Post-Acute Sequelae of COVID-19-A Multiparametric Cardiac MRI Study.

Myocardial involvement was shown to be associated with an unfavorable prognosis in patients with COVID-19, which could lead to fatal outcomes as in myocardial injury-induced arrhythmias and sudden cardiac death. We hypothesized that magnetic resonance imaging (MRI) myocardial strain parameters are sensitive markers for identifying subclinical cardiac dysfunction associated with myocardial involvement in the post-acute sequelae of COVID-19 (PASC). This study evaluated 115 subjects, including 65 consecutive COVID-19 patients, using MRI for the assessment of either post-COVID-19 myocarditis or other cardiomyopathies. Subjects were categorized, based on the results of the MRI exams, as having either 'suspected' or 'excluded' myocarditis. A control group of 50 matched individuals was studied. Along with parameters of global cardiac function, the MRI images were analyzed for measurements of the myocardial T1, T2, extracellular volume (ECV), strain, and strain rate. Based on the MRI late gadolinium enhancement and T1/T2/ECV mappings, myocarditis was suspected in 7 out of 22 patients referred due to concern of myocarditis and in 9 out of 43 patients referred due to concern of cardiomyopathies. The myocardial global longitudinal, circumferential, and radial strains and strain rates in the suspected myocarditis group were significantly smaller than those in the excluded myocarditis group, which in turn were significantly smaller than those in the control group. The results showed significant correlations between the strain, strain rate, and global cardiac function parameters. In conclusion, this study emphasizes the value of multiparametric MRI for differentiating patients with myocardial involvement in the PASC based on changes in the myocardial contractility pattern and tissue structure.

Comparison of Magnetic Resonance Imaging-Based Risk Calculators to Predict Prostate Cancer Risk.

Importance: Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms. Objective: To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America. Design, Setting, and Participants: This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy. Interventions: Prostate MRI followed by prostate biopsy. Main Outcomes and Measures: The primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated. Results: A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort. Conclusions and Relevance: In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.

The Value of 68Ga-PSMA PET/MRI for Classifying Patients with PI-RADS 3 Lesions on Multiparametric MRI: A Prospective Single-Center Study.

Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions remain a diagnostic challenge for detecting clinically significant prostate cancer (csPCa). This article evaluates the added value of 68Ga-labeled prostate-specific membrane antigen-11 (68Ga-PSMA) PET/MRI in classifying PI-RADS 3 lesions to avoid unnecessary biopsies. Methods: Sixty biopsy-naïve men with PI-RADS 3 lesions on multiparametric MRI were prospectively enrolled between February 2020 and October 2022. In all, 56 participants underwent 68Ga-PSMA PET/MRI and prostate systematic biopsy. 68Ga-PSMA PET/MRI was independently evaluated and reported by the 5-level PRIMARY score developed within the PRIMARY trial. Receiver-operating-characteristic curve analysis was used to estimate the diagnostic performance. Results: csPCa was detected in 8 of 56 patients (14.3%). The proportion of patients with csPCa and a PRIMARY score of 1, 2, 3, 4, and 5 was 0% (0/12), 0% (0/13), 6.3% (1/16), 38.5% (5/13), and 100% (2/2), respectively. The estimated area under the curve of the PRIMARY score was 0.91 (95% CI, 0.817-0.999). For a PRIMARY score of 4-5 versus a PRIMARY score of 1-3, the sensitivity, specificity, positive predictive value, and negative predictive value were 87.5%, 83.3%, 46.7%, and 97.5%, respectively. With a PRIMARY score of at least 4 to make a biopsy decision in men with PI-RADS 3 lesions, 40 of 48 patients (83.3%) could avoid unnecessary biopsies, at the expense of missing 1 of 8 (12.5%) csPCa cases. Conclusion: 68Ga-PSMA PET/MRI has great potential to classify patients with PI-RADS 3 lesions and help avoid unnecessary biopsies.

Evaluation of renal tubular function by multiparametric functional MRI in early diabetes.

Purpose To evaluate the tubular function in an alloxan-induced type 1 diabetes mellitus (DM) rabbit model measured by renal oxygenation (R2*), oxygen extraction fraction (OEF), and renal blood flow (RBF) using blood oxygenation level dependent, asymmetric spin echo, and arterial spin labeling MRI. Methods Twenty-six rabbits were randomized into the 3-day DM group (n = 13) and the 7-day DM group (n = 13). We performed pairs of multiparametric MRIs (before and after furosemide injection) at baseline and 3/7 days post-DM, and scored pathological kidney injury. We performed statistical analyses using non-parametric, chi-square, and Spearman correlation tests. Results At baseline, medullary R2* significantly decreased by 24.97% and 16.74% in the outer and inner stripes of the outer medulla (OS and IS, p = 0.006 and 0.003, respectively) after furosemide administration. While the corresponding OEF decreased by 15.91% for OS and 16.67% for IS (both p = 0.003), and no significant change in medullary RBF was observed (p > 0.05). In the 3-day DM group, the decrease of medullary R2* and OEF post-furosemide became unremarkable, suggesting tubular dysfunction. We noticed similar changes in the 7-day DM group. Correlation analysis showed pathological tubular injury score significantly correlated with medullary ∆R2* (post-furosemide - pre-furosemide difference, r = 0.82 for OS and 0.82 for IS) and ∆OEF (r = 0.82 for OS and 0.82 for IS) (p < 0.001, respectively). Conclusion: The combination of medullary OEF and R2* in response to furosemide could detect renal tubular dysfunction in early DM.

Detection of abdominopelvic lymph nodes in multi-parametric MRI.

Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies. Multiple sequences are acquired in mpMRI studies, including T2 fat suppressed (T2FS) and diffusion weighted imaging (DWI) sequences among others; consequently, the sizing of LNs is rendered challenging due to the variety of signal intensities in these sequences. Furthermore, radiologists can miss potentially metastatic LNs during a busy clinical day. To lighten these imaging and workflow challenges, we propose a computer-aided detection (CAD) pipeline to detect both benign and malignant LNs in the body for their subsequent measurement. We employed the recently proposed Dynamic Head (DyHead) neural network to detect LNs in mpMRI studies that were acquired using a variety of scanners and exam protocols. The T2FS and DWI series were co-registered, and a selective augmentation technique called Intra-Label LISA (ILL) was used to blend the two volumes with the interpolation factor drawn from a Beta distribution. In this way, ILL diversified the samples that the model encountered during the training phase, while the requirement for both sequences to be present at test time was nullified. Our results showed a mean average precision (mAP) of 53.5% and a sensitivity of ∼78% with ILL at 4 FP/vol. This corresponded to an improvement of ≥10% in mAP and ≥12% in sensitivity at 4FP (p ¡ 0.05) respectively over current LN detection approaches evaluated on the same dataset. We also established the out-of-distribution robustness of the DyHead model by training it on data acquired by a Siemens Aera scanner and testing it on data from the Siemens Verio, Siemens Biograph mMR, and Philips Achieva scanners. Our pilot work represents an important first step towards automated detection, segmentation, and classification of lymph nodes in mpMRI.

The impact of the relationship between lesion diameter and total core length on the detection rate of clinically significant prostate cancer for PI-RADS 3 lesions.

BACKGROUND: The aim of our study was to determine the effect of total core length (TCL) for prostate imaging reporting and data system (PI-RADS) 3 lesions to facilitate clinically significant prostate cancer (csPCa) detection based on the lesion diameter. MATERIALS AND METHODS: A total of 149 patients with at least 1 lesion with a PI-RADS 3 were evaluated retrospectively. The lesions with diameters of < 1 cm were categorized as small lesions and lesions of ≥ 1 cm were categorized as large lesions. The lengths of biopsy cores from PI-RADS 3 lesions were summed for each lesion separately, and TCL was calculated. The relationship between TCL and csPCa was analyzed separately for the small and large groups with multiple logistic regression analyses. RESULTS: A total of 208 lesions were detected by multiparametric magnetic resonance imaging (MpMRI) in 149 males included in the study. The mean TCL was 44.68 mm (26-92) and the mean lesion diameter was 10.73 mm (4-27) in PIRADS 3 lesions. For small diameter lesions (< 1 cm), the odds of finding clinically insignificant prostate cancer (ciPCa) increase by 1.67 times if TCL increases by one unit. Hence, increasing TCL for small lesions only increases the odds of ciPCa detection. For large diameter lesions (≥ 1 cm), if TCL increases by one unit, the odds of finding ciPCa increase 1.13 times and the odds of finding csPCa increases1.16 times. Accordingly, large lesions are more likely to have both csPCa and ciPCa as TCL increases. CONCLUSIONS: Our study showed that for PI-RADS 3 lesions, both more csPCa and more ciPCa were detected as TCL increased. However, in lesions with a size of < 1 cm, only ciPCa was detected more frequently as TCL increased. In conclusion, taking more and longer biopsy cores in PI-RADS 3 lesions below 1 cm does not contribute to the detection of csPCa.

AtPCa-Net: anatomical-aware prostate cancer detection network on multi-parametric MRI.

Multi-parametric MRI (mpMRI) is widely used for prostate cancer (PCa) diagnosis. Deep learning models show good performance in detecting PCa on mpMRI, but domain-specific PCa-related anatomical information is sometimes overlooked and not fully explored even by state-of-the-art deep learning models, causing potential suboptimal performances in PCa detection. Symmetric-related anatomical information is commonly used when distinguishing PCa lesions from other visually similar but benign prostate tissue. In addition, different combinations of mpMRI findings are used for evaluating the aggressiveness of PCa for abnormal findings allocated in different prostate zones. In this study, we investigate these domain-specific anatomical properties in PCa diagnosis and how we can adopt them into the deep learning framework to improve the model's detection performance. We propose an anatomical-aware PCa detection Network (AtPCa-Net) for PCa detection on mpMRI. Experiments show that the AtPCa-Net can better utilize the anatomical-related information, and the proposed anatomical-aware designs help improve the overall model performance on both PCa detection and patient-level classification.

Multiparametric Magnetic Resonance Imaging in the follow-up of non-muscle-invasive bladder tumors after intravesical instillations: a promising tool.

PURPOSE: The standard follow-up for non-muscle-invasive bladder cancer is based on cystoscopy. Unfortunately, post-instillation inflammatory changes can make the interpretation of this exam difficult, with lower specificity. This study aimed to evaluate the interest of bladder MRI in the follow-up of patients following intravesical instillation. METHODS: Data from patients who underwent cystoscopy and bladder MRI in a post-intravesical instillation setting between February 2020 and March 2023 were retrospectively collected. Primary endpoint was to evaluate and compare the diagnostic performance of cystoscopy and bladder MRI in the overall cohort (n = 67) using the pathologic results of TURB as a reference. The secondary endpoint was to analyze the diagnostic accuracy of cystoscopy and bladder MRI according to the appearance of the lesion on cystoscopy [flat (n = 40) or papillary (n = 27)]. RESULTS: The diagnostic performance of bladder MRI was better than that of cystoscopy, with a specificity of 47% (vs. 6%, p < 0.001), a negative predictive value of 88% (vs. 40%, p = 0.03), and a positive predictive value of 66% (vs. 51%, p < 0.001), whereas the sensitivity did not significantly differ between the two exams. In patients with doubtful cystoscopy and negative MRI findings, inflammatory changes were found on TURB in most cases (17/19). The superiority in MRI bladder performance prevailed for "flat lesions", while no significant difference was found for "papillary lesions". CONCLUSIONS: In cases of doubtful cystoscopy after intravesical instillations, MRI appears to be relevant with good performance in differentiating post-therapeutic inflammatory changes from recurrent tumor lesions and could potentially allow avoiding unnecessary TURB.

Comparison of Ga-PSMA PET MRI with mpMRI in localization and regional staging of prostate cancer.

PURPOSE: To compare diagnostic accuracy in localization and detection of extraprostatic extension (EPE), seminal vesicle invasion (SVI), lymph node involvement (LNI) between PSMA PET MRI and multiparametric MRI (mpMRI) in carcinoma prostate. METHODS: We did a prospective study of consecutive men with biopsy-proven prostate cancer who underwent radical prostatectomy between July'2020 and Dec'2021 at our institution. Patients underwent PSMA PET MRI imaging. MpMRI findings were inferred separately by another radiologist who was blinded to the PSMA PET findings. PIRADS > 2 and any standardized uptake value (SUV) were considered positive. Findings were mapped to a 30-region anatomical grid and compared with pathology. The uro-pathologist also marked the presence of the tumor onto the same anatomical grid. The presence of EPE, SVI, and LVI was noted. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The significance in difference: McNemar test. SUVmax and Gleason score: Kruskal-Wallis test. RESULTS: Seventy-five men (mean age 65) with an average PSA of 21.5 ng/ml were included. The sensitivity of PSMA PET MRI for localization was higher [63.6 vs 41.9] (p < 0.001) while specificity was similar [81.5 vs 83.2] (p 0.103). The former had a higher sensitivity to detect SVI [85.7 vs 57.10] (p = 0.03). No difference in the detection of EPE or LNI was noted. SUVmax > 7 was associated with high-risk disease (Gleason score >/= 7). LIMITATIONS: non-randomized nature, higher risk population. CONCLUSION: Ga-PSMA PET MRI improved the localization of prostate cancer and better detection of SVI. Further studies are required. It can act as a single-stop investigation for the primary staging of prostate cancer.

MRI-guided Focused Ultrasound Focal Therapy for Intermediate-Risk Prostate Cancer: Final Results from a 2-year Phase II Clinical Trial.

Background MRI-guided focal therapy (FT) allows for accurate targeting of localized clinically significant prostate cancer (csPCa) while preserving healthy prostate tissue, but the long-term outcomes of this approach require more study. Purpose To assess the 2-year oncological and functional outcomes of men with intermediate-risk prostate cancer (PCa) treated with targeted FT. Materials and Methods In this single-center prospective phase II trial, men with localized unifocal intermediate-risk PCa underwent transrectal MRI-guided focused ultrasound between July 2016 and July 2019. Planned ablation volumes included 10-mm margins when possible. Data regarding adverse events were collected and quality-of-life questionnaires were completed by participants at 6 weeks and at 5, 12, 18, and 24 months after treatment. Multiparametric MRI and targeted and systematic biopsies were performed at 24 months. Ablation volumes were determined by manual contouring of nonperfused volumes on immediate contrast-enhanced images. Generalized estimating equations were used to model trends in quality-of-life measures. Results Treatment was successfully completed in the 44 participants (median age, 67 years; IQR, 62-70 years; 36 patients with grade group [GG] 2; eight patients with GG 3). No major adverse events from treatment were recorded. One participant refused biopsy at 24 months. After 2 years, 39 of 43 participants (91%) had no csPCa at the treatment site and 36 of 43 (84%) had no cancer in the entire gland. No changes in International Index of Erectile Function-15 score or International Prostate Symptom Score were observed during 2-year follow-up (P = .73 and .39, respectively). Conclusion The majority of men treated with MRI-guided focused ultrasound for intermediate risk PCa had negative results for csPCa at biopsy 2 years after treatment. Additionally, there was no significant decline in quality of life per the validated questionnaires. Clinical trial registration no. NCT02968784 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Woodrum in this issue.

A rapid multi-parametric quantitative MR imaging method to assess Parkinson's disease: a feasibility study.

BACKGROUND: MULTIPLEX is a single-scan three-dimensional multi-parametric MRI technique that provides 1 mm isotropic T1-, T2*-, proton density- and susceptibility-weighted images and the corresponding quantitative maps. This study aimed to investigate its feasibility of clinical application in Parkinson's disease (PD). METHODS: 27 PD patients and 23 healthy control (HC) were recruited and underwent a MULTIPLEX scanning. All image reconstruction and processing were automatically performed with in-house C + + programs on the Automatic Differentiation using Expression Template platform. According to the HybraPD atlas consisting of 12 human brain subcortical nuclei, the region-of-interest (ROI) based analysis was conducted to extract quantitative parameters, then identify PD-related abnormalities from the T1, T2* and proton density maps and quantitative susceptibility mapping (QSM), by comparing patients and HCs. RESULTS: The ROI-based analysis revealed significantly decreased mean T1 values in substantia nigra pars compacta and habenular nuclei, mean T2* value in subthalamic nucleus and increased mean QSM value in subthalamic nucleus in PD patients, compared to HCs (all p values < 0.05 after FDR correction). The receiver operating characteristic analysis showed all these four quantitative parameters significantly contributed to PD diagnosis (all p values < 0.01 after FDR correction). Furthermore, the two quantitative parameters in subthalamic nucleus showed hemicerebral differences in regard to the clinically dominant side among PD patients. CONCLUSIONS: MULTIPLEX might be feasible for clinical application to assist in PD diagnosis and provide possible pathological information of PD patients' subcortical nucleus and dopaminergic midbrain regions.

Annual mpMRI surveillance: PI-RADS upgrading and increasing trend correlated with patients who harbor clinically significant disease.

INTRODUCTION: Prostate cancer screening has routinely identified men with very low- or low-risk disease, per the National Comprehensive Cancer Network guidelines. Current literature has demonstrated that the most appropriate management strategy for these patients is active surveillance (AS). The mainstay of AS includes periodic biopsies and biannual prostate-specific antigen tests. However, multiparametric magnetic resonance imaging (mpMRI) is uniquely posed to improve patient surveillance. This study aimed to evaluate the utility of an annual mpMRI in patients on AS, focusing on radiologic upgrading and Prostate Imaging-Reporting and Data System (PI-RADS) trends as indicators of clinically significant disease. METHODS: This prospective, single intuition, study enrolled 208 patients on AS who had at least two biopsies and 1 mpMRI with a median follow-up of 5.03 years. The main outcome variable was time to Gleason grade (GG) reclassification. RESULTS: After delineating patients on their initial PI-RADS score, men with score 3 and 5 lesions at first MRI had comparable GG reclassification-free survival to their counterparts. Conversely, men with initial PI-RADS 4 lesions showed a lower 5-year GG reclassification-free survival compared to those with PI-RADS score 1-2. The cohort was then subset to 70 patients who obtained ≥2 mpMRIs on protocol. Men experiencing uptrending mpMRI scores had an increased risk of GG reclassification, with a 35.4% difference in 5 year GG reclassification-free survival probability on the Kaplan-Meier curve analysis. CONCLUSION: In conclusion, this study demonstrates that for men on AS with stable recapitulated disease, an annual MRI may replace repeat biopsies after confirmatory sampling has been obtained. On the other hand, men who initiate AS with PI-RADS 4 and/or who display uptrending mpMRI scores require periodic biopsies along with repeat imaging. This study highlights the utility of integrating an annual MRI into AS protocols, thus promising a more effective approach to management.

Deep learning model for the detection of prostate cancer and classification of clinically significant disease using multiparametric MRI in comparison to PI-RADs score.

BACKGROUND: The Prostate Imaging Reporting and Data System (PI-RADS) is an established reporting scheme for multiparametric magnetic resonance imaging (mpMRI) to distinguish clinically significant prostate cancer (csPCa). Deep learning (DL) holds great potential for automating csPCa classification on mpMRI. METHOD: To compare the performance between a DL algorithm and PI-RADS categorization in PCa detection and csPCa classification, we included 1,729 consecutive patients who underwent radical prostatectomy or biopsy in Tongji hospital. We developed DL models by integrating individual mpMRI sequences and employing an ensemble approach for distinguishing between csPCa and CiSPCa (specifically defined as PCa with a Gleason group 1 or benign prostate disease, training cohort: 1,285 patients vs. external testing cohort: 315 patients). RESULTS: DL-based models exhibited higher csPCa detection rates than PI-RADS categorization (area under the curve [AUC]: 0.902; sensitivity: 0.728; specificity: 0.906 vs. AUC: 0.759; sensitivity: 0.761; specificity: 0.756) (P < 0.001) Notably, DL networks exhibited significant strength in the prostate-specific antigen (PSA) arm < 10 ng/ml compared with PI-RADS assessment (AUC: 0.788; sensitivity: 0.588; specificity: 0.883 vs. AUC: 0.618; sensitivity: 0.379; specificity: 0.763) (P = 0.041). CONCLUSIONS: We developed DL-based mpMRI ensemble models for csPCa classification with improved sensitivity, specificity, and accuracy compared with clinical PI-RADS assessment. In the PSA-stratified condition, the DL ensemble model performed better than PI-RADS in the detection of csPCa in both the high PSA group and the low PSA group.