A poor prognostic male choriocarcinoma with multiple systemic metastases: a case report and the literature review.

Background: Non-gestational choriocarcinoma, also known as primary choriocarcinoma, is extremely rare in men, manifesting with specific signs such as breast feminization, testicular atrophy, and loss of libido. The presentation typically includes elevated serum ß-hCG levels, widespread metastatic disease, and a rapid progression of the condition. Case report: We present a rare case of a 41-year-old man diagnosed with choriocarcinoma, exhibiting a unique combination of multiple metastases, including lung, brain, bone, and retroperitoneal lymph node metastases, as confirmed by 18F-FDG PET/CT imaging. The patient was treated with aggressive chemotherapy and pembrolizumab, and the prognosis remained poor. The patient's overall survival was a mere 5 months following diagnosis. Conclusion: Non-gestational choriocarcinoma represents a rare entity in clinical practice and should be considered in young men presenting with gynaecomastia and elevated ß-hCG levels alongside normal gonads. Thus, we advocate for a more comprehensive inquiry into medical history and a systematic examination. The 18F-FDG PET/CT examination not only visually delineates the lesion's location and extent but also serves as a cornerstone for clinical tumor staging, providing valuable support for treatment monitoring and subsequent follow-up.

Natalizumab reduces loss of gray matter and thalamic volume in patients with relapsing-remitting multiple sclerosis: A post hoc analysis from the randomized, placebo-controlled AFFIRM trial.

BACKGROUND: Loss of brain gray matter fractional volume predicts multiple sclerosis (MS) progression and is associated with worsening physical and cognitive symptoms. Within deep gray matter, thalamic damage is evident in early stages of MS and correlates with physical and cognitive impairment. Natalizumab is a highly effective treatment that reduces disease progression and the number of inflammatory lesions in patients with relapsing-remitting MS (RRMS). OBJECTIVE: To evaluate the effect of natalizumab on gray matter and thalamic atrophy. METHODS: A combination of deep learning-based image segmentation and data augmentation was applied to MRI data from the AFFIRM trial. RESULTS: This post hoc analysis identified a reduction of 64.3% (p = 0.0044) and 64.3% (p = 0.0030) in mean percentage gray matter volume loss from baseline at treatment years 1 and 2, respectively, in patients treated with natalizumab versus placebo. The reduction in thalamic fraction volume loss from baseline with natalizumab versus placebo was 57.0% at year 2 (p < 0.0001) and 41.2% at year 1 (p = 0.0147). Similar findings resulted from analyses of absolute gray matter and thalamic fraction volume loss. CONCLUSION: These analyses represent the first placebo-controlled evidence supporting a role for natalizumab treatment in mitigating gray matter and thalamic fraction atrophy among patients with RRMS. CLINICALTRIALS.GOV IDENTIFIER: NCT00027300URL: https://clinicaltrials.gov/ct2/show/NCT00027300.

Study on AEMs with Excellent Comprehensive Performance Prepared by Covalently Cross-Linked p-Triphenyl with SEBS Remotely Grafted Piperidine Cations.

A series of SEBS-C6-PIP-yPTP (y = 0-15%) AEMs with good mechanical and chemical stability were prepared by combining the strong rigidity of p-triphenyl, good toughness of SEBS, and excellent stability of PIP cations. After the introduction of a p-triphenyl polymer into the main chain, a clear hydrophilic-hydrophobic phase separation structure was constructed within the membrane, forming a continuous and interconnected ion transport channel to improve ion transport efficiency. Moreover, the molecular chains of the cross-linked AEMs change from chain-like to network-like, and the tighter binding between each molecule increases the tensile strength. The special structure of the six-membered ring makes PIP have a significant constraint effect; when nucleophilic substitution and Hoffman elimination occur at the α and ß positions, the required transition state potential energy increases, making the reaction difficult to occur and improving the alkaline stability of the polymer membrane. The SEBS-C6-PIP-15%PTP membrane has the best mechanical properties (Ts = 38.79 MPa, Eb = 183.09% at 80 °C, 100% RH), the highest ion conductivity (102.02 mS. cm-1 at 80 °C), and the best alkaline stability (6.23% degradation at 80 °C in a 2 M NaOH solution for 1400 h). It can be seen that organic-organic covalent cross-linking is an effective means to improve the comprehensive performance of AEMs.

Immune cholangitis related to anti-programmed cell death and programmed cell death ligand agents for the treatment of intrahepatic cholangiocarcinoma.

Anti-programmed cell death (anti-PD1) and anti-programmed cell death ligand (anti-PDL1) agents represent a burgeoning field of immunotherapy with an expanding array of indications. In this report, we present the observation of a patient with intrahepatic cholangiocarcinoma exhibiting features of immune-related cholangitis.

Large-scale screening of clinical assessments to distinguish between states in the Integrated HD Progression Model (IHDPM).

Background: Understanding the sensitivity and utility of clinical assessments across different HD stages is important for study/trial endpoint selection and clinical assessment development. The Integrated HD Progression Model (IHDPM) characterizes the complex symptom progression of HD and separates the disease into nine ordered disease states. Objective: To generate a temporal map of discriminatory clinical measures across the IHDPM states. Methods: We applied the IHDPM to all HD individuals in an integrated longitudinal HD dataset derived from four observational studies, obtaining disease state assignment for each study visit. Using large-scale screening, we estimated Cohen's effect sizes to rank the discriminative power of 2,472 clinical measures for separating observations in disease state pairs. Individual trajectories through IHDPM states were examined. Discriminative analyses were limited to individuals with observations in both states of the pairs compared (N = 3,790). Results: Discriminative clinical measures were heterogeneous across the HD life course. UHDRS items were frequently identified as the best state pair discriminators, with UHDRS Motor items - most notably TMS - showing the highest discriminatory power between the early-disease states and early post-transition period states. UHDRS functional items emerged as strong discriminators from the transition period and on. Cognitive assessments showed good discriminative power between all state pairs examined, excepting state 1 vs. 2. Several non-UHDRS assessments were also flagged as excellent state discriminators for specific disease phases (e.g., SF-12). For certain state pairs, single assessment items other than total/summary scores were highlighted as having excellent discriminative power. Conclusion: By providing ranked quantitative scores indicating discriminatory ability of thousands of clinical measures between specific pairs of IHDPM states, our results will aid clinical trial designers select the most effective outcome measures tailored to their study cohort. Our observations may also assist in the development of end points targeting specific phases in the disease life course, through providing specific conceptual foci.

Using an artificial intelligence model to detect and localize visible clinically significant prostate cancer in prostate magnetic resonance imaging: a multicenter external validation study.

Background: An increasing number of patients with suspected clinically significant prostate cancer (csPCa) are undergoing prostate multiparametric magnetic resonance imaging (mpMRI). The role of artificial intelligence (AI) algorithms in interpreting prostate mpMRI needs to be tested with multicenter external data. This study aimed to investigate the diagnostic efficacy of an AI model in detecting and localizing visible csPCa on mpMRI a multicenter external data set. Methods: The data of 2,105 patients suspected of having prostate cancer from four hospitals were retrospectively collected to develop an AI model to detect and localize suspicious csPCa. The lesions were annotated based on pathology records by two radiologists. Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values were used as the input for the three-dimensional U-Net framework. Subsequently, the model was validated using an external data set comprising the data of 557 patients from three hospitals. Sensitivity, specificity, and accuracy were employed to evaluate the diagnostic efficacy of the model. Results: At the lesion level, the model had a sensitivity of 0.654. At the overall sextant level, the model had a sensitivity, specificity, and accuracy of 0.846, 0.884, and 0.874, respectively. At the patient level, the model had a sensitivity, specificity, and accuracy of 0.943, 0.776, and 0.849, respectively. The AI-predicted accuracy for the csPCa patients (231/245, 0.943) was significantly higher than that for the non-csPCa patients (242/312, 0.776) (P<0.001). The lesion number and tumor volume were greater in the correctly diagnosed patients than the incorrectly diagnosed patients (both P<0.001). Among the positive patients, those with lower average ADC values had a higher rate of correct diagnosis than those with higher average ADC values (P=0.01). Conclusions: The AI model exhibited acceptable accuracy in detecting and localizing visible csPCa at the patient and sextant levels. However, further improvements need to be made to enhance the sensitivity of the model at the lesion level.

Esophago-pulmonary artery fistula caused by fish bone impaction.

We present a case of a 65-year-old male who experienced posterior sternal pain, accompanied by a week-long fever following the consumption of fish. Computed tomography (CT) examination revealed a fish bone in the middle esophageal, along with a small amount of gas in the mediastinum. A focal pseudoaneurysm formation was observed in the posterior wall of the left pulmonary artery trunk, accompanied by the presence of gas and septic emboli in the main trunk of the left pulmonary artery and some of its branches. Furthermore, distal pulmonary tissue infarction with associated infection was observed (Figure 1A-F). Clinical diagnosis: Esophago-pulmonary artery fistula caused by fish bone impaction. Reports of esophago-pulmonary artery fistulas without involvement of the trachea or bronchi are rare.

Humoral immune response after Ad26.COV2.S vaccination in patients with multiple sclerosis treated with natalizumab.

The immunomodulatory effects of disease-modifying therapies for multiple sclerosis might affect the immune response to vaccines for severe acute respiratory syndrome coronavirus 2. We analyzed the severe acute respiratory syndrome coronavirus 2-specific antibody response and lymphocyte profile before and after Ad26.COV2.S (Johnson & Johnson) vaccination in natalizumab-treated patients with multiple sclerosis. There was a 72-fold increase in mean anti-severe acute respiratory syndrome coronavirus 2 spike immunoglobulin G levels 4 weeks after vaccination and a 137-fold increase after 6 months. Other immune signals were within normal ranges. Natalizumab-treated patients with multiple sclerosis had a robust immune response to Ad26.COV2.S vaccine, and other immune signals were not significantly affected.

Single-Cell RNA-Sequencing Analysis of Colonic Lamina Propria Immune Cells Reveals the Key Immune Cell-Related Genes of Ulcerative Colitis.

Background: Ulcerative colitis (UC) is a severe threat to humans worldwide. Single-cell RNA sequencing (scRNA-seq) can be used to screen gene expression patterns of each cell in the intestine, provide new insights into the potential mechanism of UC, and analyze the development of immune cell changes. These findings can provide new ideas for the diagnosis and treatment of intestinal diseases. In this study, bioinformatics analysis combined with experiments applied in dextran sulfate sodium (DSS)-induced colitis mice was used to explore new diagnostic genes for UC and their potential relationship with immune cells. Methods: We downloaded microarray datasets (GSE75214, GSE87473, GSE92415) from the Gene Expression Omnibus and used these datasets to screen differentially expressed genes (DEGs) and conduct Weighted Gene Co-expression Network Analysis (WGCNA) after quality control. The hub genes were screened, and ROC curves were drawn to verify the reliability of the results in both training set (GSE75214, GSE87473, GSE92415) and validation cohort (GSE87466). Also, we explored the relation of diagnostic genes and immune cells by CIBERSORT algorithm and single-cell analysis. Finally, the expression of hub genes and their relation with immune cells were verified in DSS-induced colitis mice. Results: Diagnostic genes (ANXA5, MMP7, NR1H4, CYP3A4, ABCG2) were identified. In addition, we found these five genes firmly related to immune infiltration. The DSS-induced colitis mice confirm that the expression of ANXA5 mainly increased in the intestinal macrophages and had a strong negative correlation with M2 macrophages, which indicated its possible influence on the polarization of macrophages in UC patients. Conclusion: We identified ANXA5, MMP7, NR1H4, CYP3A4, and ABCG2 as diagnostic genes of UC that are closely related to immune infiltration and ANXA5 maintains a negative correlation with M2 macrophages which indicated its possible influence on the polarization of macrophage in UC patients.

Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms.

Purpose: To automatically evaluate renal masses in CT images by using a cascade 3D U-Net- and ResNet-based method to accurately segment and classify focal renal lesions. Material and Methods: We used an institutional dataset comprising 610 CT image series from 490 patients from August 2009 to August 2021 to train and evaluate the proposed method. We first determined the boundaries of the kidneys on the CT images utilizing a 3D U-Net-based method to be used as a region of interest to search for renal mass. An ensemble learning model based on 3D U-Net was then used to detect and segment the masses, followed by a ResNet algorithm for classification. Our algorithm was evaluated with an external validation dataset and kidney tumor segmentation (KiTS21) challenge dataset. Results: The algorithm achieved a Dice similarity coefficient (DSC) of 0.99 for bilateral kidney boundary segmentation in the test set. The average DSC for renal mass delineation using the 3D U-Net was 0.75 and 0.83. Our method detected renal masses with recalls of 84.54% and 75.90%. The classification accuracy in the test set was 86.05% for masses (<5 mm) and 91.97% for masses (≥5 mm). Conclusion: We developed a deep learning-based method for fully automated segmentation and classification of renal masses in CT images. Testing of this algorithm showed that it has the capability of accurately localizing and classifying renal masses.

A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI.

BACKGROUND: AI-based software may improve the performance of radiologists when detecting clinically significant prostate cancer (csPCa). This study aims to compare the performance of radiologists in detecting MRI-visible csPCa on MRI with and without AI-based software. MATERIALS AND METHODS: In total, 480 multiparametric MRI (mpMRI) images were retrospectively collected from eleven different MR devices, with 349 csPCa lesions in 180 (37.5%) cases. The csPCa areas were annotated based on pathology. Sixteen radiologists from four hospitals participated in reading. Each radiologist was randomly assigned to 30 cases and diagnosed twice. Half cases were interpreted without AI, and the other half were interpreted with AI. After four weeks, the cases were read again in switched mode. The mean diagnostic performance was compared using sensitivity and specificity on lesion level and patient level. The median reading time and diagnostic confidence were assessed. RESULTS: On lesion level, AI-aided improved the sensitivity from 40.1% to 59.0% (18.9% increased; 95% confidence interval (CI) [11.5, 26.1]; p < .001). On patient level, AI-aided improved the specificity from 57.7 to 71.7% (14.0% increase, 95% CI [6.4, 21.4]; p < .001) while preserving the sensitivity (88.3% vs. 93.9%, p = 0.06). AI-aided reduced the median reading time of one case by 56.3% from 423 to 185 s (238-s decrease, 95% CI [219, 260]; p < .001), and the median diagnostic confidence score was increased by 10.3% from 3.9 to 4.3 (0.4-score increase, 95% CI [0.3, 0.5]; p < .001). CONCLUSIONS: AI software improves the performance of radiologists by reducing false positive detection of prostate cancer patients and also improving reading times and diagnostic confidence. CLINICAL RELEVANCE STATEMENT: This study involves the process of data collection, randomization and crossover reading procedure.

Analysis of interreader agreement in structured reports of pelvic multiparametric magnetic resonance imaging using the METastasis Reporting and Data System for Prostate Cancer guidelines.

PURPOSE: To evaluate interreader agreement on pelvic multiparametric magnetic resonance imaging (mpMRI) interpretation among radiologists using a structured reporting tool based on the METastasis Reporting and Data System for Prostate Cancer (MET-RADS-P) guidelines. METHODS: A structured report for follow-up pelvic mpMRI for advanced prostate cancer (APC) patients was formulated based on MET-RADS-P guidelines. In total, 163 paired pelvic mpMRI examinations were performed from December 2017 to February 2021 on 105 patients with APC. These were retrospectively reviewed by two senior and two junior radiologists for metastatic lesion detection and were categorized by these readers using primary/secondary response assessment categories (RACs), with and without the structured report. Interreader agreement regarding metastasis detection and RAC scores was evaluated with Cohen's kappa and weighted Cohen's kappa statistics (K), respectively. RESULTS: The two senior radiologists showed higher agreement with the reference standard for metastasis detection using the structured report (S1: K = 0.83; S2: K = 0.73) compared with the conventional report (S1: K = 0.72; S2: K = 0.61). Junior radiologists showed similar results (J1: 0.66 vs. 0.59; J2: 0.65 vs. 0.57). The overall agreement between the two senior radiologists was excellent for the primary RAC pattern using the structured reports (K = 0.81) and was substantial for secondary RAC categorization (K = 0.75). The interreader agreement of the two junior radiologists was substantial for both primary and secondary RAC values (K = 0.76, 0.68). CONCLUSION: Good interreader agreement was found for the follow-up assessment of APC patients between radiologists, where the pelvic mpMRI was reported using MET-RADS-P guidelines. This improvement applied to both metastatic lesion detection and qualitative RAC assessment.

Long-Term Effectiveness and Safety of Natalizumab in African American and Hispanic/Latino Patients with Early Relapsing-Remitting Multiple Sclerosis: STRIVE Data Analysis.

INTRODUCTION: In STRIVE, natalizumab treatment demonstrated effectiveness in clinical, magnetic resonance imaging (MRI), and patient-reported outcomes (PROs) in patients with early relapsing-remitting multiple sclerosis (RRMS). This post hoc analysis examined the effectiveness and safety of natalizumab in patients who self-identified as either Black/African American (AA) or Hispanic/Latino. METHODS: Clinical, MRI, and PROs were assessed for the Black/AA subgroup (n = 40) and compared with the non-Hispanic White subgroup (n = 158). As a result of the very small sample size, outcomes for the Hispanic/Latino subgroup (n = 18) were assessed separately, including a sensitivity analysis with Hispanic/Latino patients who completed the 4-year study on natalizumab. RESULTS: Clinical, MRI, and PROs were comparable between the Black/AA and non-Hispanic White subgroups except for MRI outcomes at year 1. A higher proportion of non-Hispanic White than Black/AA patients achieved MRI no evidence of disease activity (NEDA; 75.4% vs. 50.0%, p = 0.0121) and no new or newly enlarging T2 lesions (77.6% vs. 50.0%, p = 0.0031) at year 1; these differences were not observed in years 2-4 of the study. For the Hispanic/Latino subgroup in the intent-to-treat population, 46.2% and 55.6% achieved NEDA at years 1 and 2; 66.7% and 90.0% achieved clinical NEDA at years 3 and 4. Annualized relapse rate was reduced by 93.0% at year 1 versus the year before natalizumab initiation; this reduction was maintained throughout the study. Over 4 years, 37.5-50.0% of patients had a clinically meaningful improvement in their Symbol Digit Modalities Test score, and 81.8-100.0% and 90.9-100.0% had stable/improved Multiple Sclerosis Impact Scale-29 physical and psychological scores, respectively. Similar results were observed in the sensitivity analysis with Hispanic/Latino subgroup of the 4-year natalizumab completers. CONCLUSION: These results highlight the effectiveness and safety of natalizumab in patients with early RRMS who self-identified as Black/AA or Hispanic/Latino. CLINICALTRIALS: GOV: NCT01485003.

Deep-Learning Models for Detection and Localization of Visible Clinically Significant Prostate Cancer on Multi-Parametric MRI.

BACKGROUND: Deep learning for diagnosing clinically significant prostate cancer (csPCa) is feasible but needs further evaluation in patients with prostate-specific antigen (PSA) levels of 4-10 ng/mL. PURPOSE: To explore diffusion-weighted imaging (DWI), alone and in combination with T2-weighted imaging (T2WI), for deep-learning-based models to detect and localize visible csPCa. STUDY TYPE: Retrospective. POPULATION: One thousand six hundred twenty-eight patients with systematic and cognitive-targeted biopsy-confirmation (1007 csPCa, 621 non-csPCa) were divided into model development (N = 1428) and hold-out test (N = 200) datasets. FIELD STRENGTH/SEQUENCE: DWI with diffusion-weighted single-shot gradient echo planar imaging sequence and T2WI with T2-weighted fast spin echo sequence at 3.0-T and 1.5-T. ASSESSMENT: The ground truth of csPCa was annotated by two radiologists in consensus. A diffusion model, DWI and apparent diffusion coefficient (ADC) as input, and a biparametric model (DWI, ADC, and T2WI as input) were trained based on U-Net. Three radiologists provided the PI-RADS (version 2.1) assessment. The performances were determined at the lesion, location, and the patient level. STATISTICAL TESTS: The performance was evaluated using the areas under the ROC curves (AUCs), sensitivity, specificity, and accuracy. A P value <0.05 was considered statistically significant. RESULTS: The lesion-level sensitivities of the diffusion model, the biparametric model, and the PI-RADS assessment were 89.0%, 85.3%, and 90.8% (P = 0.289-0.754). At the patient level, the diffusion model had significantly higher sensitivity than the biparametric model (96.0% vs. 90.0%), while there was no significant difference in specificity (77.0%. vs. 85.0%, P = 0.096). For location analysis, there were no significant differences in AUCs between the models (sextant-level, 0.895 vs. 0.893, P = 0.777; zone-level, 0.931 vs. 0.917, P = 0.282), and both models had significantly higher AUCs than the PI-RADS assessment (sextant-level, 0.734; zone-level, 0.863). DATA CONCLUSION: The diffusion model achieved the best performance in detecting and localizing csPCa in patients with PSA levels of 4-10 ng/mL. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Effect of AI-assisted software on inter- and intra-observer variability for the X-ray bone age assessment of preschool children.

BACKGROUND: With the rapid development of deep learning algorithms and the rapid improvement of computer hardware in the past few years, AI-assisted diagnosis software for bone age has achieved good diagnostic performance. The purpose of this study was to investigate the effect of AI-assisted software on residents' inter-observer agreement and intra-observer reproducibility for the X-ray bone age assessment of preschool children. METHODS: This prospective study was approved by the Institutional Ethics Committee. Six board-certified residents interpreted 56 bone age radiographs ranging from 3 to 6 years with structured reporting by the modified TW3 method. The images were interpreted on two separate occasions, once with and once without the assistance of AI. After a washout period of 4 weeks, the radiographs were reevaluated by each resident in the same way. The reference bone age was the average bone age results of the three experts. Both TW3-RUS and TW3-Carpal were evaluated. The root mean squared error (RMSE), mean absolute difference (MAD) and bone age accuracy within 0.5 years and 1 year were used as metrics of accuracy. Interobserver agreement and intraobserver reproducibility were evaluated using intraclass correlation coefficients (ICCs). RESULTS: With the assistance of bone age AI software, the accuracy of residents' results improved significantly. For interobserver agreement comparison, the ICC results with AI assistance among 6 residents were higher than the results without AI assistance on the two separate occasions. For intraobserver reproducibility comparison, the ICC results with AI assistance were higher than results without AI assistance between the 1st reading and 2nd reading for each resident. CONCLUSIONS: For preschool children X-ray bone age assessment, in addition to improving diagnostic accuracy, bone age AI-assisted software can also increase interobserver agreement and intraobserver reproducibility. AI-assisted software can be an effective diagnostic tool for residents in actual clinical settings.

Prediction of pelvic lymph node metastasis in prostate cancer using radiomics based on T2-weighted imaging. / 基于T2åŠ æƒæˆåƒçš„å½±åƒç»„å­¦åˆ—çº¿å›¾æ¨¡åž‹é¢„æµ‹å‰åˆ—è ºç™Œç›†è ”æ·‹å·´ç»“è½¬ç§».

OBJECTIVES: Pelvic lymph node metastasis (PLNM) is an important factor that affects the stage and prognosis of prostate cancer. Invasive extended pelvic lymph node dissection (ePLND) is the most effective method for clinically diagnosing PLNM. Accurate preoperative prediction of PLNM can reduce unnecessary ePLND. This study aims to investigate the clinical value of radiomics nomogram in predicting PLNM of prostate cancer based on T2-weighted imaging (T2WI). METHODS: Magnetic resonance (MR) data of 71 patients with prostate cancer who underwent ePLND from January 2017 to June 2021 in Peking University First Hospital were collected retrospectively. All patients were assigned into a training set (January 2017 to December 2020, n=56, containing 186 lymph nodes) and a test set (January 2021 to June 2021, n=15, containing 45 lymph nodes) according to the examination time of multiparametric magnetic resonance imaging (mpMRI). Two radiologists matched the dissected lymph nodes on MRI images, and manually annotated the region of interest (ROI). Based on the outlined ROI, 3 metastatic lymph node prediction models were established: Model 1 (only image features of T2WI), Model 2 (radiomics features based on random forest), and Model 3 (combination of the image and radiomics features). A nomogram was also established. The clinicopathologic characteristics of the patients were obtained from the medical records, including age, the Gleason score, the level of prostate-specific antigen (PSA), and clinical and pathological T stage. The preoperative radiological features of the pelvic lymph nodes (LNs) include size of LNs (the short and long diameters) and volume of LNs. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the 3 models and decision curve analysis (DCA) was used to evaluate the clinical benefits of the models. RESULTS: No significant differences were found between the training set and test set regarding age, Gleason scores, PSA level, and clinical and pathological T stage (all P>0.05). The differences in volume, short diameter and long diameter between metastatic and non-metastatic LNs were statistically significant in both training set and test set (all P<0.05). In multivariate regression analysis, the short diameter and marginal status of LNs were included in Model 1. Eighteen omics features were selected to construct Model 2. The signal distribution of LNs and Rad score were the significant risk factors for predicting metastasis of pelvic LNs in Model 3. The C-index of nomogram based on Model 3 reached 0.964, and the calibration curve showed that the model had high calibration degree. In the test set, the area under the curves of Model 1, 2, and 3 were 0.78, 0.93, and 0.96 respectively, Model 2 and Model 3 showed significantly higher diagnostic efficiency than Model 1 (Model 1 vs Model 2, P=0.019; Model 1 vs Model 3, P=0.020). There was no significant difference in the area under the curve between Model 2 and Model 3 (P=0.649). The DCA results of the 3 models showed that all models obtained higher net benefits than the PLNM-all or PLNM-none protocol in different ranges of threshold probabilities and Model 3 had the highest clinical benefit. CONCLUSIONS: The radiomics nomogram based on T2WI shows a good predictive efficacy for preoperative PLNM in patients with prostate cancer, which could be served as an imaging biomarker to optimize decision-making and adjust adjuvant treatments.