To evaluate the repeatability and reproducibility of amide proton transfer (APT) and glutamate chemical exchange saturation transfer (GluCEST) imaging at 5 T and to investigate their clinical value in patients with brain tumors. Phantom experiments under varying pH conditions and bovine serum albumin (BSA) concentrations were performed to assess the within-session repeatability, between-session and between-day reproducibility of APT and GluCEST measurements using intraclass correlation coefficients (ICCs), mean absolute difference, coefficient of repeatability (CoR) and coefficient of variation (CV). In vivo APT and GluCEST imaging was performed in 96 patients with brain tumors. Intra- and inter-observer agreements of APTmean and GluCESTmean measurements were evaluated using ICCs and Bland–Altman analysis across tumor, peritumoral edema, and normal brain tissue regions. Quantitative tumor-region APT and GluCEST metrics (APT/GluCESTmean, APT/GluCEST difference, and APT/GluCEST change ratio) were compared among major tumor subtypes using the Kruskal-Wallis test, followed by post hoc pairwise comparisons using Dunn’s test with Bonferroni correction. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance for differentiating high-grade gliomas from low-grade gliomas. In phantom studies, both APT and GluCEST demonstrated robust repeatability and reproducibility with high ICCs (> 0.96) and low mean absolute difference, CoR and CV. Both APT and GluCEST-weighted signals showed nonlinear pH-dependent changes and increased with higher BSA concentrations. In patients, intra- and inter-observer agreements were excellent for APTmean and GluCESTmean in tumor, peritumoral edema, and normal brain regions (ICCs > 0.93). Tumor and peritumoral edema regions showed higher APT and GluCEST values than normal brain tissue (p < 0.05). After multiple-comparison correction, high-grade gliomas showed significantly higher APTmean, APT difference, GluCESTmean, GluCEST difference and GluCEST change ratio than low-grade gliomas. GluCEST-derived metrics showed favorable performance for glioma grading, with GluCEST change ratio achieving the highest area under the curve (AUC) of 0.846, followed by GluCEST difference (AUC = 0.838) and GluCESTmean (AUC = 0.836). APT and GluCEST imaging at 5 T showed excellent repeatability and reproducibility in phantom experiments and robust measurement agreement in patients with brain tumors. APT and GluCEST measurements demonstrated subtype-related differences, and GluCEST-derived metrics showed favorable performance for differentiating high-grade from low-grade gliomas.

Objectives: To establish a glutamine metabolism (GM)-based classification for glioblastoma (GBM) and evaluate its prognostic and immunotherapeutic implications. Methods: A total of 237 GBM patients from the Chinese Glioma Genome Atlas (CGGA) database were included as the training set, and 219 patients from the Gene Expression Omnibus (GEO) database served as the validation set. Consensus clustering was performed based on the expression profiles of 13 GM-associated genes to identify robust subgroups. Differences between clusters were analyzed using clinical indices, genomic and transcriptomic biomarkers. Tumor response to immune checkpoint inhibitors (ICIs) was predicted using the tumor immune dysfunction and exclusion (TIDE) algorithm, tumor microenvironment (TME) score, T cell inflammation score, and SubMap algorithm. A GM-based classifier was subsequently developed and validated. Results: Consensus clustering of the training set revealed 2 distinct subgroups (cluster 1 and cluster 2) with significant prognostic differences; cluster 2 exhibited poorer overall survival. Immunotherapy response prediction indicated that cluster 2 had a lower likelihood of benefiting from ICIs. The newly developed GM-based classifier demonstrated high accuracy (AUC > 0.9) and maintained strong consistency with the original clustering in terms of subtype classification and immunotherapy prediction across both datasets. Conclusion: This study establishes a robust classification system for GBM based on glutamine metabolism-related genes, which effectively stratifies patients into prognostic subgroups and predicts immunotherapy response. The GM-based classifier offers a valuable tool for guiding clinical prognosis and treatment decisions in GBM.

Purpose: This study aimed to develop a knowledge-guided radiomics model integrating prior transcriptomic knowledge to predict overall survival (OS) in glioma patients by identifying hypoxia-related imaging features. Methods: A total of 471 patients from two real-world centers (n=319) and the CGGA database (n=152) were enrolled. Hypoxia enrichment scores were calculated using ssGSEA. A knowledge-guided workflow was implemented: (1) extracting radiomic features from multi-sequence MRI (T1, T2, CE); (2) screening features via correlation removal and ANOVA; (3) categorizing features into hypoxia-related and others, followed by penalty-weighted LASSO regression; (4) constructing a prognostic model. Five machine learning classifiers were compared to identify hypoxia status. Model performance was evaluated via time-dependent ROC, Kaplan-Meier, and calibration curves. Biological validation involved functional enrichment and immune infiltration analyses. Results: High hypoxia levels were significantly associated with worse survival (P < 0.001). Logistic regression achieved the best performance in predicting hypoxia status (AUC = 0.94). The knowledge-guided model effectively stratified patients into risk groups, demonstrating robust OS prediction (3-year AUCs: 0.926 in Center 1, 0.833 in Center 2) with excellent calibration. Transcriptomic analysis revealed that high-risk patients exhibited increased stromal fibroblast infiltration and distinct immune profiles, with radiomic features correlating strongly with both innate and adaptive immunity markers. Conclusion: The knowledge-guided radiomics model, incorporating transcriptomic priors into a penalty-weighted framework, provides accurate and biologically interpretable OS prediction in glioma, offering a non-invasive tool for personalized clinical management.

Purpose: To evaluate the reproducibility of amide proton transfer (APT) and glutamate chemical exchange saturation transfer (GluCEST) imaging at 5 T and to investigate their feasibility and clinical value in patients with brain tumors. Methods: Phantom experiments under varying pH conditions and bovine serum albumin (BSA) concentrations were performed to assess the within-session, between-session, and between-day reproducibility of amide proton transfer (APT) and glutamate chemical exchange saturation transfer (GluCEST) measurements using intraclass correlation coefficients (ICCs) and coefficients of variation (CVs). In vivo APT and GluCEST imaging was performed in 96 patients with brain tumors. Intra- and inter-observer agreement of APTmean and Glumean measurements was evaluated using ICCs and Bland–Altman analysis across tumor, peritumoral edema, and normal brain tissue regions. Quantitative tumor-region APT and GluCEST metrics (APT/Glumean, APT/Glu difference, and APT/Glu change ratio) were compared among major tumor subtypes using the Kruskal-Wallis test, followed by Bonferroni-corrected pairwise comparisons. Results: In phantom studies, both APT and GluCEST demonstrated excellent reproducibility across all experimental conditions, with ICCs consistently exceeding 0.96 and most CVs below 5%. Both contrasts showed clear pH- and concentration-dependent signal changes, with GluCEST exhibiting greater sensitivity to pH variations than APT. In patients, intra- and inter-observer agreement for APTmean and Glumean was high across tumor, peritumoral edema, and normal brain regions (ICCs > 0.93). APT and GluCEST values in tumor and peritumoral edema regions were significantly higher than those in normal brain tissue (p < 0.05) and significant subtype-related differences were observed in tumor regions. After multiple-comparison correction, GluCEST-derived difference and change ratio remained significantly different between high-grade and low-grade gliomas, whereas most APT-derived differences did not. Conclusion: APT and GluCEST imaging at 5 T are reproducible and clinically feasible. GluCEST demonstrates greater sensitivity to glioma-related biochemical differences and shows promise as a complementary imaging biomarker for glioma grading and brain tumor characterization.

Objectives: To investigate the diagnostic performance of texture analysis using multi-parameter MRI in distinguishing between benign and malignant lesions with ovarian-adnexal magnetic resonance imaging report and data system (O-RADS MRI) score 4. Methods: A retrospective analysis was conducted of 57 lesions with an O-RADS MRI score of 4, of which 26 were benign and 31 were malignant. Based on the T2WI, ADC, and CE_T1WI, the textural features of the entire lesion were extracted. The minimum redundancy maximum relevance (mRMR) method was used to select features, and the random forest (RF) algorithm was used to construct four prediction models: T2WI, ADC, CE_T1WI, and the combined models. Ten-fold cross-validation was used to verify the model prediction performance, and receiver operating characteristic (ROC) analysis was used to evaluate the model performance, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: 3474 texture features were extracted from the ADC, T2WI, and CE_T1WI images. ADC, T2WI, CE_T1WI, and combined models were constructed. Each model contained ten texture features. The AUC of the ADC, T2WI, CE_T1WI, and combined models were 0.749 (95% CI: 0.621-0.876), 0.671 (95% CI: 0.524-0.818), 0.786 (95% CI: 0.662-0.909), and 0.860 (95% CI: 0.76-0.959), respectively. The AUC of the combined model was significantly higher than those of the other three groups. The accuracy, sensitivity, specificity, PPV, and NPV of the combined model in distinguishing benign and malignant lesions with an O-RADS MRI score of 4 were 75.9%, 77.8%, 74.1%, 72.4%, and 79.3%, respectively. Conclusion: Texture analysis of multi-parameter MRI can improve the diagnostic efficiency of distinguishing benign and malignant lesions with an O-RADS MRI score of 4 and provide some help in clinical decision-making.

Background: Anaplastic thyroid carcinoma (ATC) represents a rare yet highly malignant histotype of thyroid cancer. Cancer-associated fibroblasts (CAFs) play a pivotal role in tumor cell invasion, migration, and angiogenesis and present a potential target for cancer treatment. We aimed to investigate the effects of modulating specific subsets of CAFs on the proliferation, invasion, and migration of ATC. Methods: We developed nanosystems, platelet-derived growth factor receptor (PDGFR-beta) targeted-polypeptide-modified poly (beta-amino ester) (pBAE) (T-pBAE)/siB7-H3 nanoparticles (NPs), targeting PDGFR-beta+ CAFs and featuring B7-H3 knockdown. We evaluated both the targeting efficacy and gene silencing performance of T-pBAE/siB7-H3 NPs, as well as the functional contribution of B7-H3 to CAFs-driven ATC progression. Results: T-pBAE/siB7-H3 NPs were efficiently internalized by CAFs, achieving targeted knockdown of B7-H3 expression. Silencing B7-H3 significantly suppressed the expression of cell division cycle 27 and other cell cycle-related genes, thereby inhibiting CAFs' proliferation. Consequently, CAFs-secreted cytokines (e.g., CCL1 and CCL4) were altered. Through modulation of cytokine receptor activation on ATC cells, this process reduced ATC cell proliferation, invasion, and migration. In mice ATC subcutaneous tumor models, local injection of T-pBAE/siB7-H3 NPs reduced tumor volume. Moreover, the expression of invasive proliferation-related markers (PDGFR-beta, Ki-67, CD31), immune evasion-related marker CD163, and chemoresistance-related marker ATP-binding cassette subfamily G member 2 was remarkably downregulated in tumor tissues. Conclusion: This study demonstrates that PDGFR-beta polypeptide-modified pBAE could successfully deliver B7-H3 siRNA to CAFs. After knockdown of B7-H3 within CAFs, ATC proliferation, invasion, and migration were inhibited. Overall, our findings revealed that B7-H3 can be a promising therapeutic target for ATC.

Abstract Objectives Exploring the value of adding correlation analysis (radiomic features (RFs) of pelvic metastatic lymph nodes and primary lesions) to screen RFs of primary lesions in the feature selection process of establishing prediction model. Methods A total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM. Results The AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively. Conclusion The lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.

Purpose To determine the ablation efficacy of transabdominal ultrasound- and laparoscopy-guided percutaneous microwave ablation (PMWA), to investigate whether the risk of damage to adjacent organs and endometrium due to this technique can be reduced or even avoided. We also evaluated the clinical efficacy of this technique in the treatment of uterine fibroids of different sizes and at different locations over a 24-month follow-up period. Methods This study included 50 patients with uterine fibroids who underwent transabdominal ultrasound- and laparoscopy-guided PMWA from August 2018 to July 2020. Lesions were confirmed by pathology. The technical efficacy and complications of PMWA were assessed. The lesion diameter, lesion volume, lesion location, and contrast-enhanced ultrasound (CEUS) features before PMWA and within 24 h after PMWA were recorded. Magnetic resonance imaging (MRI) was used for follow-up at 3 and 6 months after PMWA. Transvaginal ultrasound was used for follow-up at 24 months after PMWA. Results A total of 50 patients with uterine fibroids received treatment. The median ablation rate of uterine fibroids was 97.21%. The mean lesion volume reduction rates were 32.63%, 57.26%, and 92.64% at 3, 6, and 24 months after treatment, respectively. The size and location of uterine fibroids did not significantly affect the ablation rate and the rate of lesion volume reduction. No major complication was found during and after the procedure. Conclusion Transabdominal ultrasound- and laparoscopy-guided PMWA can be utilized to safely enhance the ablation rate while minimizing ablation time and avoiding harm to adjacent organs and the endometrium. This technique is applicable for treating uterine fibroids of different sizes and at varying locations. Trial registration number ChiCTR-IPR-17011910, and date of trial registration: 08/07/2017.

Rationale and Objectives: Perineural invasion (PNI) is an important prognostic biomarker for prostate cancer (PCa). This study aimed to develop and validate a predictive model integrating biparametric MRI-based deep learning radiomics and clinical characteristics for the non-invasive prediction of PNI in patients with PCa. Materials and Methods: In this prospective study, 557 PCa patients who underwent preoperative MRI and radical prostatectomy were recruited and randomly divided into the training and the validation cohorts at a ratio of 7:3. Clinical model for predicting PNI was constructed by univariate and multivariate regression analyses on various clinical indicators, followed by logistic regression. Radiomics and deep learning methods were used to develop different MRI-based radiomics and deep learning models. Subsequently, the clinical, radiomics, and deep learning signatures were combined to develop the integrated deep learning-radiomics-clinical model (DLRC). The performance of the models was assessed by plotting the receiver operating characteristic (ROC) curves and precision-recall (PR) curves, as well as calculating the area under the ROC and PR curves (ROC-AUC and PR-AUC). The calibration curve and decision curve were used to evaluate the model's goodness of fit and clinical benefit. Results: The DLRC model demonstrated the highest performance in both the training and the validation cohorts, with ROC-AUCs of 0.914 and 0.848, respectively, and PR-AUCs of 0.948 and 0.926, respectively. The DLRC model showed good calibration and clinical benefit in both cohorts. Conclusion: The DLRC model, which integrated clinical, radiomics, and deep learning signatures, can serve as a robust tool for predicting PNI in patients with PCa, thus aiding in developing effective treatment strategies. (c) 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Purpose The O-RADS MRI score stratifies adnexal mass risk with characteristics of T1-weighted, T2-weighed and dynamic contrast-enhanced (DCE) images. We explored a modified approach to evaluate the value of incorporation DWI/ADC with non-DCE-MRI in ORADS scoring system, and to assess the diagnostic performance and interreader consistency in differentiating ovarian neoplasm with different types. Methods This retrospective study included 218 women who underwent pelvic MRI with 221 ovarian tumors between January 2017 and December 2021. Two radiologists independently assessed each lesion using the original and modified O-RADS approach (incorporating DWI/ADC with non-DCE). Cohen's weighted-kappa and ROC analyses were employed to assess interreader consistency and diagnostic efficiency across all lesions and three ovarian neoplasms categories. Results The area under the ROC curve (AUC) of the original protocol was 0.945 for all lesions and 0.947, 0.992, and 0.758 for the epithelial cell, germ cell and sex cord-stromal neoplasms. The modified approach achieved AUCs of 0.959 for all lesions and 0.962, 0.997, and 0.837 for the three categories. The interreader agreement was ‘excellent’ for all lesions and ‘good’ for the subgroups with the original protocol, improving to ‘excellent’ for all categories with the modified approach. Conclusion A modified O-RADS incorporating DWI/ADC with non-DCE MRI yields high diagnostic performance in differentiation of different types of ovarian neoplasms. It further improves consistency in subgroup interpretation. The modified approach can serve as an effective diagnostic tool without DCE, further promoting its adoption in primary hospitals.

Rationale and Objectives To establish a multimodal deep learning nomogram for predicting clinically significant prostate cancer in patients with gray-zone PSA levels. Methods This retrospective study enrolled 303 patients with pathological results between January 2018 and December 2022. Clinical variables and the PI-RADS v2.1 score were used to construct a clinical model. Radiomics and deep learning features from bp-MRI were used to develop a radiomics model with SVM and a deep learning model, respectively. A hybrid fusion approach was used to integrate the multimodal data and construct combined models (Comb.Rad.model and Comb.DL.model). The robustness of the radiomics model with XGBoost was validated and compared. Model efficacy was assessed through ROC curve and decision curve analysis. A nomogram was developed based on the best-performing model. Results The clinical model had AUCs of 0.845 and 0.779 in the training and testing set. The radiomics model with SVM and the deep learning model achieved AUCs of 0.825 and 0.933 in the training set and 0.811 and 0.907 in the testing set, respectively. The diagnostic performance of the combined models was significantly improved, with Comb.DL.model having a higher AUC than Comb.Rad.model in both the training (0.986 vs. 0.924, P = 0.008) and testing (0.965 vs. 0.859, P = 0.005) set. The diagnostic efficiency of both the radiomics model and Comb.Rad.model with XGBoost were comparable to that of SVM, confirming the robustness of the established model. Conclusion The integrated nomogram combining deep learning features, PI-RADS score, and clinical variables significantly outperformed the traditional radiomics and clinical models.

PurposeNoninvasively assessing the tumor biology and microenvironment before treatment is greatly important, and glypican-3 (GPC-3) is a new-generation immunotherapy target for hepatocellular carcinoma (HCC). This study investigated the application value of a nomogram based on LI-RADS features, quantitative contrast-enhanced MRI parameters and clinical indicators in the noninvasive preoperative prediction of GPC-3 expression in HCC.Methods and materialsWe retrospectively reviewed 127 patients with pathologically confirmed solitary HCC who underwent Gd-EOB-DTPA MRI examinations and related laboratory tests. Quantitative contrast-enhanced MRI parameters and clinical indicators were collected by an abdominal radiologist, and LI-RADS features were independently assessed and recorded by three trained intermediate- and senior-level radiologists. The pathological and immunohistochemical results of HCC were determined by two senior pathologists. All patients were divided into a training cohort (88 cases) and validation cohort (39 cases). Univariate analysis and multivariate logistic regression were performed to identify independent predictors of GPC-3 expression in HCC, and a nomogram model was established in the training cohort. The performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC) and the calibration curve in the training cohort and validation cohort, respectively.ResultsBlood products in mass, nodule-in-nodule architecture, mosaic architecture, contrast enhancement ratio (CER), transition phase lesion-liver parenchyma signal ratio (TP-LNR), and serum ferritin (Fer) were independent predictors of GPC-3 expression, with odds ratios (ORs) of 5.437, 10.682, 5.477, 11.788, 0.028, and 1.005, respectively. Nomogram based on LI-RADS features (blood products in mass, nodule-in-nodule architecture and mosaic architecture), quantitative contrast-enhanced MRI parameters (CER and TP-LNR) and clinical indicators (Fer) for predicting GPC-3 expression in HCC was established successfully. The nomogram showed good discrimination (AUC of 0.925 in the training cohort and 0.908 in the validation cohort) and favorable calibration. The diagnostic sensitivity and specificity were 76.9% and 92.3% in the training cohort, 76.8% and 93.8% in the validation cohort respectively.ConclusionThe nomogram constructed from LI-RADS features, quantitative contrast-enhanced MRI parameters and clinical indicators has high application value, can accurately predict GPC-3 expression in HCC and may help noninvasively identify potential patients for GPC-3 immunotherapy.

目的 建立基于多参数MRI影像组学结合PI-RADS v2.1 和临床指标的新型列线图,评价其预测临床显著性前列腺癌(csPCa)的价值.方法 回顾性分析204 例患者的资料,进行PI-RADS v2.1 评分和影像组学分析.应用受试者工作特征曲线和临床决策曲线评估临床模型、PI-RADS模型、影像组学模型及各联合模型诊断csPCa的效能和临床获益,基于效能最优模型建立列线图并验证.结果 影像组学模型诊断效能显著优于临床模型和PI-RADS评分模型,差异具有统计学意义(P<0.05).在临床模型或PI-RADS模型中增加影像组学的特征,其联合诊断效能会显著提高(P<0.05).结论 基于多参数MRI影像组学结合PI-RADS v2.1 和临床指标的联合模型所建立的列线图为术前预测csPCa提供了一种无创性的新方法.

PurposeTo compare the performance of radiomics to that of the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 scoring system in the detection of clinically significant prostate cancer (csPCa) based on biparametric magnetic resonance imaging (bpMRI) vs. multiparametric MRI (mpMRI).MethodsA total of 204 patients with pathological results were enrolled between January 2018 and December 2019, with 142 patients in the training cohort and 62 patients in the testing cohort. The radiomics model was compared with the PI-RADS v2.1 for the diagnosis of csPCa based on bpMRI and mpMRI by using receiver operating characteristic (ROC) curve analysis.ResultsThe radiomics model based on bpMRI and mpMRI signatures showed high predictive efficiency but with no significant differences (AUC = 0.975 vs 0.981, p=0.687 in the training cohort, and 0.953 vs 0.968, p=0.287 in the testing cohort, respectively). In addition, the radiomics model outperformed the PI-RADS v2.1 in the diagnosis of csPCa regardless of whether bpMRI (AUC = 0.975 vs. 0.871, p= 0.030 for the training cohort and AUC = 0.953 vs. 0.853, P = 0.024 for the testing cohort) or mpMRI (AUC = 0.981 vs. 0.880, p= 0.030 for the training cohort and AUC = 0.968 vs. 0.863, P = 0.016 for the testing cohort) was incorporated.ConclusionsOur study suggests the performance of bpMRI- and mpMRI-based radiomics models show no significant difference, which indicates that omitting DCE imaging in radiomics can simplify the process of analysis. Adding radiomics to PI-RADS v2.1 may improve the performance to predict csPCa.

Abstract Background To evaluate the diagnostic performance of multiparametric transrectal ultrasound (TRUS) and to design diagnostic scoring systems based on four modes of TRUS to predict peripheral zone prostate cancer (PCa) and clinically significant prostate cancer (csPCa). Methods A development cohort involved 124 nodules from 116 patients, and a validation cohort involved 72 nodules from 67 patients. Predictors for PCa and csPCa were extracted to construct PCa and csPCa models based on regression analysis of the development cohort. An external validation was performed to assess the performance of models using area under the curve (AUC). Then, PCa and csPCa diagnostic scoring systems were established to predict PCa and csPCa. The diagnostic accuracy was compared between PCa and csPCa scores and PI-RADS V2, using receiver operating characteristics (ROC) and decision curve analysis (DCA). Results Regression models were established as follows: PCa = − 8.284 + 4.674 × Margin + 1.707 × Adler grade + 3.072 × Enhancement patterns + 2.544 × SR; csPCa = − 7.201 + 2.680 × Margin + 2.583 × Enhancement patterns + 2.194 × SR. The PCa score ranged from 0 to 6 points, and the csPCa score ranged from 0 to 3 points. A PCa score of 5 or higher and a csPCa score of 3 had the greatest diagnostic performance. In the validation cohort, the AUC for the PCa score and PI-RADS V2 in diagnosing PCa were 0.879 (95% confidence interval [CI] 0.790–0.967) and 0.873 (95%CI 0.778–0.969). For the diagnosis of csPCa, the AUC for the csPCa score and PI-RADS V2 were 0.806 (95%CI 0.700–0.912) and 0.829 (95%CI 0.727–0.931). Conclusions The multiparametric TRUS diagnostic scoring systems permitted better identifications of peripheral zone PCa and csPCa, and their performances were comparable to that of PI-RADS V2.

Background This study attempted to develop a nomogram for predicting clinically significant prostate cancer (cs-PCa) in the transition zone (TZ) with the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) score based on biparametric magnetic resonance imaging (bp-MRI) and clinical indicators. Methods We retrospectively reviewed 383 patients with suspicious prostate lesions in the TZ as a training cohort and 128 patients as the validation cohort from January 2015 to March 2020. Multivariable logistic regression analysis was performed to determine independent predictors for building a nomogram, and the performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve and decision curve. Results The PI-RADS v2.1 score and prostate-specific antigen density (PSAD) were independent predictors of TZ cs-PCa. The prediction model had a significantly higher AUC (0.936) than the individual predictors (0.914 for PI-RADS v2.1 score, P=0.045, 0.842 for PSAD, P<0.001). The nomogram showed good discrimination (AUC of 0.936 in the training cohort and 0.963 in the validation cohort) and favorable calibration. When the PI-RADS v2.1 score was combined with PSAD, the diagnostic sensitivity and specificity were 80.7% and 93.8%, respectively, which were better than those of the PI-RADS v2.1 score (sensitivity, 74.2%; specificity, 92.5%) and PSAD (sensitivity, 66.1%; specificity, 88.2%). Conclusions The newly constructed nomogram exhibits satisfactory predictive accuracy and consistency for TZ cs-PCa. PI-RADS v2.1 based on bp-MRI is a strong predictor in the detection of TZ cs-PCa. Adding PSAD to PI-RADS v2.1 could improve its diagnostic performance, thereby avoiding unnecessary biopsies.

Background Combining targeted biopsy (TB) with systematic biopsy (SB) is currently recommended as the first-line biopsy method by the European Association of Urology (EAU) guidelines in patients diagnosed with prostate cancer (PCa) with an abnormal magnetic resonance imaging (MRI). The combined SB and TB indeed detected an additional number of patients with clinically significant prostate cancer (csPCa); however, it did so at the expense of a concomitant increase in biopsy cores. Our study aimed to evaluate if ipsilateral SB (ipsi-SB) + TB or contralateral SB (contra-SB) + TB could achieve almost equal csPCa detection rates as SB + TB using fewer cores based on a different csPCa definition. Methods Patients with at least one positive prostate lesion were prospectively diagnosed by MRI. The combination of TB and SB was conducted in all patients. We compared the csPCa detection rates of the following four hypothetical biopsy sampling schemes with those of SB + TB: SB, TB, ipsi-SB + TB, and contra-SB + TB. Results The study enrolled 279 men. The median core of SB, TB, ipsi-SB + TB, and contra-SB + TB was 10, 2, 7 and 7, respectively (P < 0.001). ipsi-SB + TB detected significantly more patients with csPCa than contra-SB + TB based on the EAU guidelines (P = 0.042). They were almost equal on the basis of the Epstein criteria (P = 1.000). Compared with SB + TB, each remaining method detected significantly fewer patients with csPCa regardless of the definition (P < 0.001) except ipsi-SB + TB on the grounds of D1 (P = 0.066). Ten additional subjects were identified with a higher Gleason score (GS) on contra-SB + TB, and only one was considered as significantly upgraded (GS = 6 on ipsi-SB + TB to a GS of 8 on contra-SB + TB). Conclusions Ipsi-SB + TB could acquire an almost equivalent csPCa detection value to SB + TB using significantly fewer cores when csPCa was defined according to the EAU guidelines. Given that there was only one significantly upgrading patient on contra-SB, our results suggested that contra-SB could be avoided.

Objective:To compare the diagnostic value between prostate imaging reporting and data system version 2 (PI-RADS V2) and version 2.1 (PI-RADS V2.1) for clinically significant prostate cancer (csPCa).Methods:The imaging, pathological and clinical data of 837 patients with prostatic multiparametric MRI in Second Affiliated Hospital of Soochow University from May 2015 to August 2019 were retrospectively analyzed. According to the pathological results of systematic biopsy, the prostate cancer with Gleason score (GS) ≥3+4 was csPCa. A total of 25% of the patients (209 cases) were selected using a simple random sampling, and the index lesions were scored by 2 radiologists with PI-RADS V2 and V2.1, respectively. The weighted Kappa test was used to evaluate the consistency of the scores interpreted between the 2 radiologists. The remaining cases were scored by one of the radiologists using the 2 scoring system respectively. The ROC curve was used to evaluate the diagnostic performance of the 2 scoring system for csPCa in total lesions, peripheral lesions and transitional lesions. Z test was used to investigate whether there was any difference in the detection efficiency between the 2 scoring system. Results:There were 251 patients with csPCa, including 163 patients in peripheral zone and 88 patients in transitional zone. The weighted Kappa value of total lesions, transitional lesions, peripheral lesions was 0.757, 0.653, 0.748 for PI-RADS V2 and 0.794, 0.707, 0.759 for PI-RADS V2.1, respectively. In total lesions, transitional lesions and peripheral lesions, the area under the ROC curve of csPCa detected by PI-RADS V2.1 was 0.922, 0.932, 0.854 and 0.902, 0.905, 0.817 by PI-RADS V2, respectively, and all the differences were statistically significant ( Z=4.104, P<0.001; Z=2.538, P=0.011; Z=3.350, P<0.001). Conclusion:PI-RADS V2.1 has a slightly higher consistent weighted Kappa value in evaluating prostate lesions than PI-RADS V2, and the detection efficiency of csPCa was higher than PI-RADS V2.

To evaluate the diagnostic values of shear wave elastography (SWE) alone and in combination with the Toronto clinical scoring system (TCSS) on diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM). The study included 41 DPN patients, 42 non-DPN patients, and 21 healthy volunteers. Conventional ultrasonography and SWE were performed on the 2 sides of the tibial nerves, and cross-sectional area (CSA) and nerve stiffness were measured. TCSS was applied to all patients. A receiver operating characteristic curve analysis was performed. The stiffness of the tibial nerve, as measured as mean, minimum or maximum elasticity, was significantly higher in patients in the DPN group than the other groups (P < .05). The tibial nerve of subjects in the non-DPN group was significantly stiffer compared to the control group (P < .05). There was no significant difference of the tibial nerve CSA among the 3 groups (P > .05). Mean elasticity of the tibial nerve with a cutoff of 71.3 kPa was the most sensitive (68.3%) and had a higher area under the curve (0.712; 0.602-0.806) among the 3 shear elasticity indices for diagnosing DPN when used alone. When combining SWE with TCSS in diagnosing DPN, the most effective parameter was the EMax, which yielded a sensitivity of 100.00% and a specificity of 95.24%. SWE is a better diagnostic tool for DPN than the conventional ultrasonic parameter CSA, and a higher diagnostic value is attained when combining SWE with TCSS.

BACKGROUND. PI-RADS version 2.1 (v2.1) introduced a number of key changes to the assessment of transition zone (TZ) lesions. OBJECTIVE. The purpose of this study was to evaluate interobserver agreement and diagnostic accuracy for detecting TZ prostate cancer (PCa) and clinically significant PCa (csPCa) by use of PI-RADS v2 and PI-RADS v2.1 among radiologists with different levels of experience. METHODS. This retrospective study included 355 biopsy-naïve patients who from January 2017 to March 2020 underwent prostate MRI that showed a TZ lesion and underwent subsequent biopsy. PCa was diagnosed in 93 patients (International Society of Urological Pathology [ISUP] grade group 1, n = 34; ISUP grade group ≥ 2, n = 59) and non-cancerous lesions in 262 patients. Five radiologists with varying experience in prostate MRI scored lesions using PI-RADS v2 and PI-RADS v2.1 in sessions separated by at least 4 weeks. Interobserver agreement was evaluated with kappa and Kendall W statistics. ROC curve analysis was used to evaluate performance in detection of TZ PCa and csPCa. RESULTS. Interobserver agreement among all readers was higher for PI-RADS v2.1 than for PI-RADS v2 (mean weighted κ = 0.700 vs 0.622; Kendall W = 0.805 vs 0.728; p = .03). The pooled AUC values for detecting TZ PCa and csPCa were higher among all readers using PI-RADS v2.1 (0.866 vs 0.827 for TZ PCa; 0.929 vs 0.899 for TZ csPCa; p < .001). For detecting TZ PCa, the pooled sensitivity, specificity, and accuracy were 86.9%, 79.4%, and 75.4% among all readers for PI-RADS v2.1 compared with 79.4%, 71.8%, and 73.8% for PI-RADS v2. For detecting TZ csPCa, the pooled sensitivity, specificity, and accuracy were 84.8%, 90.9%, and 89.9% among all readers for PI-RADS v2.1 compared with 81.4%, 89.9%, and 88.5% for PI-RADS v2. Reader 1, who had the least experience, had the lowest sensitivity, specificity, and accuracy (78.0%, 89.2%, and 87.3%). Reader 5, who had the most experience, had the highest sensitivity, specificity, and accuracy (88.1%, 92.9%, and 92.1%) in detecting csPCa. CONCLUSION. PI-RADS v2.1 had better interobserver agreement and diagnostic accuracy than PI-RADS v2 for evaluating TZ lesions. Reader experience continues to affect the performance of prostate MRI interpretation with PI-RADS v2.1. CLINICAL IMPACT. PI-RADS v2.1 is more accurate and reproducible than PI-RADS v2 for the diagnosis of TZ PCa.