BACKGROUND: Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation. The diagnosis of FCD is challenging. We generated a radiomics nomogram based on multiparametric magnetic resonance imaging (MRI) to diagnose FCD and identify laterality early.
METHODS: Forty-three patients treated between July 2017 and May 2022 with histopathologically confirmed FCD were retrospectively enrolled. The contralateral unaffected hemispheres were included as the control group. Therefore, 86 ROIs were finally included. Using January 2021 as the time cutoff, those admitted after January 2021 were included in the hold-out set (n = 20). The remaining patients were separated randomly (8:2 ratio) into training (n = 55) and validation (n = 11) sets. All preoperative and postoperative MR images, including T1-weighted (T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and combined (T1w + T2w + FLAIR) images, were included. The least absolute shrinkage and selection operator (LASSO) was used to select features. Multivariable logistic regression analysis was used to develop the diagnosis model. The performance of the radiomic nomogram was evaluated with an area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration and clinical utility.
RESULTS: The model-based radiomics features that were selected from combined sequences (T1w + T2w + FLAIR) had the highest performances in all models and showed better diagnostic performance than inexperienced radiologists in the training (AUCs: 0.847 VS. 0.664, p = 0.008), validation (AUC: 0.857 VS. 0.521, p = 0.155), and hold-out sets (AUCs: 0.828 VS. 0.571, p = 0.080). The positive values of NRI (0.402, 0.607, 0.424) and IDI (0.158, 0.264, 0.264) in the three sets indicated that the diagnostic performance of Model-Combined improved significantly. The radiomics nomogram fit well in calibration curves (p > 0.05), and decision curve analysis further confirmed the clinical usefulness of the nomogram. Additionally, the contrast (the radiomics feature) of the FCD lesions not only played a crucial role in the classifier but also had a significant correlation (r = -0.319, p < 0.05) with the duration of FCD.
CONCLUSIONS: The radiomics nomogram generated by logistic regression model-based multiparametric MRI represents an important advancement in FCD diagnosis and treatment.

BACKGROUND: The landscape of prostate cancer (PCa) segmentation within multiparametric magnetic resonance imaging (MP-MRI) was fragmented, with a noticeable lack of consensus on incorporating background details, culminating in inconsistent segmentation outputs. Given the complex and heterogeneous nature of PCa, conventional imaging segmentation algorithms frequently fell short, prompting the need for specialized research and refinement.
OBJECTIVE: This study sought to dissect and compare various segmentation methods, emphasizing the role of background information and gland masks in achieving superior PCa segmentation. The goal was to systematically refine segmentation networks to ascertain the most efficacious approach.
METHODS: A cohort of 232 patients (ages 61-73 years old, prostate-specific antigen: 3.4-45.6 ng/mL), who had undergone MP-MRI followed by prostate biopsies, was analyzed. An advanced segmentation model, namely Attention-Unet, which combines U-Net with attention gates, was employed for training and validation. The model was further enhanced through a multiscale module and a composite loss function, culminating in the development of Matt-Unet. Performance metrics included Dice Similarity Coefficient (DSC) and accuracy (ACC).
RESULTS: The Matt-Unet model, which integrated background information and gland masks, outperformed the baseline U-Net model using raw images, yielding significant gains (DSC: 0.7215 vs. 0.6592; ACC: 0.8899 vs. 0.8601, p < 0.001).
CONCLUSIONS: A targeted and practical PCa segmentation method was designed, which could significantly improve PCa segmentation on MP-MRI by combining background information and gland masks. The Matt-Unet model showcased promising capabilities for effectively delineating PCa, enhancing the precision of MP-MRI analysis.

OBJECTIVE: To investigate whether longitudinal changes in multiparametric MRI can predict early response to neoadjuvant chemotherapy (NAC) for HER2-positive breast cancer (BC) and to further establish quantitative models based on these features.
METHODS: A total of 164 HER2-positive BC patients from three centers were included. MRI was performed at baseline and after two cycles of NAC (early post-NAC). Clinicopathological characteristics were enrolled. MRI features were evaluated at baseline and early post-NAC, as well as longitudinal changes in multiparametric MRI, including changes in the largest diameter (LD) of the tumor (ΔLD), apparent diffusion coefficient (ADC) values (ΔADC), and time-signal intensity curve (TIC) (ΔTIC). The patients were divided into a training set (n = 95), an internal validation set (n = 31), and an independent external validation set (n = 38). Univariate and multivariate logistic regression analyses were used to identify the independent indicators of pCR, which were then used to establish the clinicopathologic model and combined model. The AUC was used to evaluate the predictive power of the different models and calibration curves were used to evaluate the consistency of the prediction of pCR in different models. Additionally, decision curve analysis (DCA) was employed to determine the clinical usefulness of the different models.
RESULTS: Two models were enrolled in this study, including the clinicopathologic model and the combined model. The LD at early post-NAC (OR=0.913, 95 % CI=0.953-0.994 p = 0.026), ΔADC (OR=1.005, 95 % CI=1.005-1.008, p = 0.007), and ΔTIC (OR=3.974, 95 % CI=1.276-12.358, p = 0.017) were identified as the best predictors of NAC response. The combined model constructed by the combination of LD at early post-NAC, ΔADC, and ΔTIC showed good predictive performance in the training set (AUC=0.87), internal validation set (AUC=0.78), and external validation set (AUC=0.79), which performed better than the clinicopathologic model in all sets.
CONCLUSIONS: The changes in multiparametric MRI can predict early treatment response for HER2-positive BC and may be helpful for individualized treatment planning.

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.

Aims/Background Prostate cancer stands out as one of the most prevalent malignant tumours among males. The non-invasive identification of clinically significant prostate cancer via magnetic resonance imaging plays a critical role in circumventing unnecessary biopsies and determining suitable treatment strategies for patients. Our study aimed to evaluate the potential improvement in predictive accuracy for clinically significant prostate cancer by incorporating perfusion data obtained from dynamic contrast-enhanced magnetic resonance imaging acquisition protocols into multiparametric magnetic resonance imaging parameters. Methods Radiomics extracted from perfusion parameters (Ktrans, Kep, Ve) of dynamic contrast-enhanced magnetic resonance imaging were analysed in patients suspected of prostate cancer who underwent 3T multiparametric magnetic resonance imaging between January 2017 and June 2023 in this retrospective study. The pathological findings obtained from biopsy or therapy were categorised into groups based on the Gleason sum score as either clinically significant prostate cancer (Gleason sum score > 7) or non-clinically significant prostate cancer (Gleason sum score ≤ 6). Diagnostic models were constructed using logistic regression analysis, incorporating prostate imaging reporting and data system V2.1 scores and clinical data, with or without radiomics extracted from dynamic contrast-enhanced. The area under curve (AUC) values were compared using the DeLong test. Results Overall, 214 men (clinically significant prostate cancer [n=78] and non-clinically significant prostate cancer [n=136]) were included. The clinical-prostate imaging reporting and data system model demonstrated an AUC of 0.89 (95% confidence interval: 0.84-0.95) in the training cohort and 0.91 (95% confidence interval: 0.84-0.98) in the test cohort. For the clinical-prostate imaging reporting and data system-radscore model, the AUC values were 0.97 (95% confidence interval: 0.95-0.99) for Ktrans, 0.98 (95% confidence interval: 0.96-1.00) for Ve, and 0.96 (95% confidence interval: 0.93-0.98) for Kep in the training cohort, and 0.97 (95% confidence interval: 0.94-1.00) for Ktrans, 0.95 (95% confidence interval: 0.91-1.00) for Ve, and 0.97 (95% confidence interval: 0.94-1.00) for Kep in the test cohort. Radiomics based on perfusion parameters exhibited good diagnostic performance in predicting clinically significant prostate cancer. The clinical-prostate imaging reporting and data system-radscore model demonstrated superior diagnostic capability compared to perfusion-based radiomics or clinical-prostate imaging reporting and data system models alone. Conclusion The application of radiomics, which involves extracting perfusion parameters from dynamic contrast-enhanced imaging, has the potential to enhance diagnostic accuracy for clinically significant prostate cancer.

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.

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: 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.