BACKGROUND: Excessive pericyte coverage promotes tumor growth, and a downregulation may solve this dilemma. Due to the double-edged sword role of vascular pericytes in tumor microenvironment (TME), indiscriminately decreasing pericyte coverage by imatinib causes poor treatment outcomes. Here, we optimized the use of imatinib in a colorectal cancer (CRC) model in high pericyte-coverage status, and revealed the value of multiparametric magnetic resonance imaging (mpMRI) at 9.4T in monitoring treatment-related changes in pericyte coverage and the TME.
METHODS: CRC xenograft models were evaluated by histological vascular characterizations and mpMRI. Mice with the highest pericyte coverage were treated with imatinib or saline; then, vascular characterizations, tumor apoptosis and HIF-1α level were analyzed histologically, and alterations in the expression of Bcl-2/bax pathway were assessed through qPCR. The effects of imatinib were monitored by dynamic contrast-enhanced (DCE)-, diffusion-weighted imaging (DWI)- and amide proton transfer chemical exchange saturation transfer (APT CEST)-MRI at 9.4T.
RESULTS: The DCE- parameters provided a good histologic match the tumor vascular characterizations. In the high pericyte coverage status, imatinib exhibited significant tumor growth inhibition, necrosis increase and pericyte coverage downregulation, and these changes were accompanied by increased vessel permeability, decreased microvessel density (MVD), increased tumor apoptosis and altered gene expression of apoptosis-related Bcl-2/bax pathway. Strategically, a 4-day imatinib effectively decreased pericyte coverage and HIF-1α level, and continuous treatment led to a less marked decrease in pericyte coverage and re-elevated HIF-1α level. Correlation analysis confirmed the feasibility of using mpMRI parameters to monitor imatinib treatment, with DCE-derived Ve and Ktrans being most correlated with pericyte coverage, Ve with vessel permeability, AUC with microvessel density (MVD), DWI-derived ADC with tumor apoptosis, and APT CEST-derived MTRasym at 1 µT with HIF-1α.
CONCLUSIONS: These results provided an optimized imatinib regimen to achieve decreasing pericyte coverage and HIF-1α level in the high pericyte-coverage CRC model, and offered an ultrahigh-field multiparametric MRI approach for monitoring pericyte coverage and dynamics response of the TME to treatment.

Clinical decisions based on the test results for prostate-specific antigen often result in overdiagnosis and overtreatment. Multiparametric magnetic resonance imaging (mpMRI) can be used to identify high-grade prostate cancer (HGPCa; Gleason score ≥3 + 4); however, certain limitations remain such as inter-reader variability and false negatives. The combination of mpMRI and prostate cancer (PCa) biomarkers (prostate-specific antigen density, Proclarix, TMPRSS2:ERG gene fusion, Michigan prostate score, ExoDX prostate intelliscore, four kallikrein score, select molecular diagnosis, prostate health index, and prostate health index density) demonstrates high accuracy in the diagnosis of HGPCa, ensuring that patients avoid unnecessary prostate biopsies with a low leakage rate. This manuscript describes the characteristics and diagnostic performance of each biomarker alone and in combination with mpMRI, with the intension to provide a basis for decision-making in the diagnosis and treatment of HGPCa. Additionally, we explored the applicability of the combination protocol to the Asian population.

BACKGROUND: In this work, we compare input level, feature level and decision level data fusion techniques for automatic detection of clinically significant prostate lesions (csPCa).
METHODS: Multiple deep learning CNN architectures were developed using the Unet as the baseline. The CNNs use both multiparametric MRI images (T2W, ADC, and High b-value) and quantitative clinical data (prostate specific antigen (PSA), PSA density (PSAD), prostate gland volume & gross tumor volume (GTV)), and only mp-MRI images (n = 118), as input. In addition, co-registered ground truth data from whole mount histopathology images (n = 22) were used as a test set for evaluation.
RESULTS: The CNNs achieved for early/intermediate / late level fusion a precision of 0.41/0.51/0.61, recall value of 0.18/0.22/0.25, an average precision of 0.13 / 0.19 / 0.27, and F scores of 0.55/0.67/ 0.76. Dice Sorensen Coefficient (DSC) was used to evaluate the influence of combining mpMRI with parametric clinical data for the detection of csPCa. We compared the DSC between the predictions of CNN\'s trained with mpMRI and parametric clinical and the CNN\'s trained with only mpMRI images as input with the ground truth. We obtained a DSC of data 0.30/0.34/0.36 and 0.26/0.33/0.34 respectively. Additionally, we evaluated the influence of each mpMRI input channel for the task of csPCa detection and obtained a DSC of 0.14 / 0.25 / 0.28.
CONCLUSIONS: The results show that the decision level fusion network performs better for the task of prostate lesion detection. Combining mpMRI data with quantitative clinical data does not show significant differences between these networks (p = 0.26/0.62/0.85). The results show that CNNs trained with all mpMRI data outperform CNNs with less input channels which is consistent with current clinical protocols where the same input is used for PI-RADS lesion scoring.
BACKGROUND: The trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under proposal number Nr. 476/14 & 476/19.

BACKGROUND: Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function and negatively impact graft survival and function. There is a lack of an accurate and non-invasive tool for evaluating the degree of CIRI. Multi-parametric MRI has been widely used to detect and evaluate kidney injury. The machine learning algorithms introduced the opportunity to combine biomarkers from different MRI metrics into a single classifier.
OBJECTIVE: To evaluate the performance of multi-parametric magnetic resonance imaging for grading renal injury in a rat model of renal cold ischemia-reperfusion injury using a machine learning approach.
METHODS: Eighty male SD rats were selected to establish a renal cold ischemia -reperfusion model, and all performed multiparametric MRI scans (DWI, IVIM, DKI, BOLD, T1mapping and ASL), followed by pathological analysis. A total of 25 parameters of renal cortex and medulla were analyzed as features. The pathology scores were divided into 3 groups using K-means clustering method. Lasso regression was applied for the initial selecting of features. The optimal features and the best techniques for pathological grading were obtained. Multiple classifiers were used to construct models to evaluate the predictive value for pathology grading.
RESULTS: All rats were categorized into mild, moderate, and severe injury group according the pathologic scores. The 8 features that correlated better with the pathologic classification were medullary and cortical Dp, cortical T2*, cortical Fp, medullary T2*, ∆T1, cortical RBF, medullary T1. The accuracy(0.83, 0.850, 0.81, respectively) and AUC (0.95, 0.93, 0.90, respectively) for pathologic classification of the logistic regression, SVM, and RF are significantly higher than other classifiers. For the logistic model and combining logistic, RF and SVM model of different techniques for pathology grading, the stable and perform are both well. Based on logistic regression, IVIM has the highest AUC (0.93) for pathological grading, followed by BOLD(0.90).
CONCLUSIONS: The multi-parametric MRI-based machine learning model could be valuable for noninvasive assessment of the degree of renal injury.

In addition to focal lesions, diffusely abnormal white matter (DAWM) is seen on brain MRI of multiple sclerosis (MS) patients and may represent early or distinct disease processes. The role of MRI-observed DAWM is understudied due to a lack of automated assessment methods. Supervised deep learning (DL) methods are highly capable in this domain, but require large sets of labeled data. To overcome this challenge, a DL-based network (DAWM-Net) was trained using semi-supervised learning on a limited set of labeled data for segmentation of DAWM, focal lesions, and normal-appearing brain tissues on multiparametric MRI. DAWM-Net segmentation performance was compared to a previous intensity thresholding-based method on an independent test set from expert consensus (N = 25). Segmentation overlap by Dice Similarity Coefficient (DSC) and Spearman correlation of DAWM volumes were assessed. DAWM-Net showed DSC > 0.93 for normal-appearing brain tissues and DSC > 0.81 for focal lesions. For DAWM-Net, the DAWM DSC was 0.49 ± 0.12 with a moderate volume correlation (ρ = 0.52, p < 0.01). The previous method showed lower DAWM DSC of 0.26 ± 0.08 and lacked a significant volume correlation (ρ = 0.23, p = 0.27). These results demonstrate the feasibility of DL-based DAWM auto-segmentation with semi-supervised learning. This tool may facilitate future investigation of the role of DAWM in MS.

OBJECTIVE: 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.
CONCLUSIONS: The lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.

BACKGROUND: The study aimed to evaluate the diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters in distinguishing sinonasal lymphoma from sinonasal carcinoma.
METHODS: Forty-two participants with histologically confirmed sinonasal lymphomas and fifty-two cases of sinonasal carcinoma underwent imaging with a 3.0T MRI scanner. DCE-MRI and DWI were conducted, and various parameters including type of time-intensity curve(TIC), time to peak, peak enhancement, peak contrast enhancement, washout rate, apparent diffusion coefficient (ADC), and relative ADC were measured. Binary logistic regression and receiver operating characteristic (ROC) curve analysis were employed to assess the diagnostic capability of individual and combined indices for differentiating nasal sinus lymphoma from nasal sinus carcinoma.
RESULTS: Sinonasal lymphoma predominantly exhibited type II TIC(n = 20), whereas sinonasal carcinoma predominantly exhibited type III TIC(n = 23). Significant differences were observed in all parameters except washout ratio (p < 0.05), and ADC value emerged as the most reliable diagnostic tool in single parameter. Combined DCE-MRI parameters demonstrated superior diagnostic efficacy compared to individual parameters, with the highest efficiency (area under curve = 0.945) achieved when combining all parameters of DCE-MRI and DWI.
CONCLUSIONS: Multiparametric evaluation involving contrast-enhanced dynamic MRI and DWI holds considerable diagnostic value in distinguishing sinonasal lymphoma from sinonasal carcinoma.

OBJECTIVE: Our purpose was to evaluate the prognostic value of Vesical Imaging Reporting and Data System (VI-RADS) in bladder cancer (BCa) staging and predicting recurrence or progression.
METHODS: We retrospectively analyzed the prospectively collected data from 96 patients with bladder tumors who underwent VI-RADS-based multiparametric magnetic resonance imaging (mpMRI) before endourological treatment from April 2021 to December 2022. Diagnostic performance was evaluated by comparing mpMRI reports with final pathology, using logistic regression for muscle-invasive bladder cancer (MIBC) predictors. Follow-up until May 2023 included Kaplan-Meier and Cox regression analysis to assess VI-RADS predictive roles for recurrence-free survival (RFS) and progression-free survival (PFS).
RESULTS: A total of 96 patients (19.8% women, 80.2% men; median age 68.0 years) were included, with 71% having primary tumors and 29% recurrent BCa. Multiparametric MRI exhibited high sensitivity (92%) and specificity (79%) in predicting MIBC, showing no significant differences between primary and recurrent cancers (AUC: 0.96 vs. 0.92, P = .565). VI-RADS emerged as a key predictor for MIBC in both univariate (OR: 40.3, P < .001) and multivariate (OR: 54.6, P < .001) analyses. Primary tumors with VI-RADS ≥ 3 demonstrated significantly shorter RFS (P = .02) and PFS (P = .04).
CONCLUSIONS: In conclusion, mpMRI with VI-RADS has a high diagnostic value in predicting MIBC in both primary and recurrent BCa. A VI-RADS threshold ≥ 3 is a strong predictor for MIBC, and in primary tumors predicts early recurrence and progression.

OBJECTIVE: To investigate the diagnostic efficacy of targeted biopsy combined with regional systematic biopsy in prostate cancer (PCa) in patients with prostate imaging reporting and data system v2.1 (PI-RADS v2.1) 4-5.
METHODS: From January 2023 to October 2023, patients who underwent prostate biopsy for the first time with total prostate specific antigen (tPSA) ≤ 20 ng/mL and had a multi-parametric magnetic resonance imaging (mpMRI) PI-RADS of 4-5 in Peking University First Hospital were prospectively collected. All the patients underwent transrectal ultrasound-guided cognitive fusion targeted biopsy (3 cores) followed by systematic biopsy (12 cores). Various hypothetical biopsy schemes were defined based on different biopsy sites. The detection effectiveness of targeted biopsy combined with regional systematic biopsy and other biopsy schemes for prostate cancer were compared using Cochran\'s Q and McNemar tests.
RESULTS: A total of 255 patients were enrolled, of whom 204 (80.0%) were detected with prostate adenocarcinoma and 187 (73.3%) were clinically significant with prostate cancer (csPCa). The detection rate of PCa with targeted biopsy was significantly lower than that of targeted biopsy combined with 12-core system biopsy (77.3% vs. 80.0%, P=0.016), and 71.4% (5/7) of the missed patients was csPCa. There was no significant difference in the detection rate between targeted biopsy combined with 4-core regional system biopsy and 12-core system biopsy (P>0.999), and 1 case of csPCa and clinically insignificant prostate cancer (cisPCa) were missed. There was no significant difference in the detection rate of PCa between targeted combined regional system biopsy and targeted combined lateral or traditional 6-core system biopsy and the number of cores were reduced. Missed diagnosis of targeted biopsy was correlated with the maximum diameter of the lesion (OR=0.086, 95%CI: 0.013-0.562, P=0.010). For the patients with PI-RADS 5, only 1 case of PCa was missed in 122 cases by targeted biopsy alone. For patients with PI-RADS 4, 6 PCa cases were missed among the 133 patients with targeted biopsy alone, and 1 case of csPCa and cisPCa were missed by targeted biopsy combined with regional system biopsy. The statistics of positive core counts for different biopsy schemes indicated that targeted combined regional systematic biopsy had a higher proportion of positive cores second only to targeted biopsy alone.
CONCLUSIONS: Targeted biopsy combined with regional systematic biopsy has high diagnostic efficacy in patients with PI-RADS 4-5 and can be considered as one of the improved schemes for combined biopsy. Targeted biopsy alone is also a feasible option for patients for patients with a PI-RADS score of 5.

OBJECTIVE: To assess the rationality of the maximum lesion diameter of 15 mm in prostate imaging reporting and data system (PI-RADS) as a criterion for upgrading a lesion from category 4 to 5 and improve it to enhance the prediction of clinically significant prostate cancer (csPCa).
METHODS: In this study, the patients who underwent prostate magnetic resonance imaging (MRI) and prostate biopsy at Peking University First Hospital from 2019 to 2022 as a development cohort, and the patients in 2023 as a validation cohort were reviewed. The localization and maximum diameter of the lesion were fully evaluated. The area under the curve (AUC) and the cut-off value of the maximum diameter of the lesion to predict the detection of csPCa were calculated from the receiver operating characteristics (ROC) curve. Confounding factors were reduced by propensity score matching (PSM). Diagnostic efficacy was compared in the validation cohort.
RESULTS: Of the 589 patients in the development cohort, 358 (60.8%) lesions were located in the peripheral zone and 231 (39.2%) were located in the transition zone, and 496 (84.2%) patients detected csPCa. The median diameter of the lesions in the peripheral zone was smaller than that in the transition zone (14 mm vs. 19 mm, P < 0.001). In the ROC analysis of the maximal diameter on the csPCa prediction, there was no statistically significant difference between the peri-pheral zone (AUC=0.709) and the transition zone (AUC=0.673, P=0.585), and the cut-off values were calculated to be 11.5 mm for the peripheral zone and 16.5 mm for the migrating zone. By calcula-ting the Youden index for the cut-off values in the validation cohort, we found that the categorisation by lesion location led to better predictive results. Finally, the net reclassification index (NRI) was 0.170.
CONCLUSIONS: 15 mm as a criterion for upgrading the PI-RADS score from 4 to 5 is reasonable but too general. The cut-off value for peripheral zone lesions is smaller than that in transitional zone. In the future consideration could be given to setting separate cut-off values for lesions in different locations.