OBJECTIVES:To develop and test a convolutional neural network model for automated segmentation of complicated cystic renal masses (cCRMs) on MRI. METHODS:This multicenter retrospective study analysed 210 cCRMs between October 2019 and May 2021, divided into training/internal validation (n = 150, Institution 1) and test sets (n = 60, Institutions 2-4). Comparative 3D V-Net and U-Net models were developed across 7 MRI sequences (T2-weighted, diffusion-weighted, apparent diffusion coefficient maps, unenhanced T1-weighted, and enhanced corticomedullary, nephrographic, and excretory phases images). A total of 14 models were developed, and 7 pairwise comparisons were performed between the 3D V-Net and U-Net models. Segmentation performance was evaluated using Dice similarity coefficient (DSC) and Hausdorff distance (HD), with subgroup analysis of small cCRMs (≤40 mm). RESULTS:In the test set, the excretory-phase V-Net (EPV-Net model) showed the highest DSC, and perform better than the corresponding U-Net (EPU-Net model) across all cCRMs (DSC: 0.74 ± 0.05 vs 0.70 ± 0.06, P < .001; HD: 27.41 ± 7.44 mm vs 39.18 ± 11.07 mm, P < .001) and the 35 small cCRMs subgroup (DSC: 0.74 ± 0.05 vs 0.70 ± 0.06, P < .001; HD: 27.48 mm ± 6.32 vs 38.72 ± 10.69 mm, P < .001). CONCLUSIONS:The 3D EPV-Net model demonstrated good segmentation accuracy, even for small lesions, supporting its clinical utility for cCRMs evaluation. ADVANCES IN KNOWLEDGE:This automated approach may streamline workflow compared to manual segmentation in cCRMs assessment.

Objectives To establish a 3D V-Net-based segmentation model for adrenal glands on abdominal CT images and validate its performance in multicentre datasets, including chest CT images. Methods CT images of adrenal glands were retrospectively collected for the training of the adrenal segmentation model. Abdominal CT scans with normal and abnormal adrenal glands (N = 5660) were recruited as the model development cohort and were split into training, internal validation, and internal test sets for the development of the segmentation model. Two groups of health screening subjects were included for model validation: 1 from the same institution (N = 6126, validation cohort 1) and 1 from an outside institution (N = 931, validation cohort 2). Their chest CT images were used for model validation. The Dice similarity coefficient (DSC) was used to evaluate the efficacy of the model. Results The DSC of the test set for left and right adrenal segmentation were 0.920 (0.890-0.930) and 0.910 (0.890-0.930), respectively. In the validation cohorts, the DSC were 0.816 (0.744-0.866) for the left adrenal gland and 0.819 (0.743-0.865) for the right adrenal gland in validation cohort 1, and 0.752 (0.666-0.820) for the left adrenal gland and 0.747 (0.673-0.812) for the right adrenal gland in validation cohort 2. Conclusions The 3D V-Net-based adrenal segmentation model achieves considerable segmentation efficacy and demonstrates generalizability from abdominal CT to chest CT, making it suitable for use in CT images with various scanning protocols. Advances in knowledge The study developed a deep learning model using 3D V-Net for the segmentation of adrenal glands on CT images, achieving good performance of normal and abnormal glands in validation cohorts with different scanning protocols and from multiple institutions, demonstrating its potential as a “flagging” system aiding diagnosis.

Chronic non-bacterial prostatitis (CNP), a prevalent and debilitating urological disorder affecting 8.4% of men aged 15-60 years, presents significant clinical challenges due to the paucity of targeted therapies and poor patient adherence. To address this unmet medical need, we developed an innovative multifunctional nanoplatform (QM (Zn) NPs) by integrating Ti3C2 MXene with a quercetin-zinc coordination complex (Que-Zn) for precision CNP therapy. This system leverages chondroitin sulfate (Chs)-mediated CD44 targeting to achieve selective accumulation in inflamed prostate tissue, thereby enhancing Zn2+ bioavailability while enabling co-delivery of MXene and Que-Zn therapeutic payloads. Upon localization, QM (Zn) NPs orchestrate a coordinated therapeutic cascade: MXene scavenges reactive oxygen species (ROS) via electron-deficient sites, while Que-Zn drives M1-to-M2 macrophage repolarization and facilitates Zn2+ cellular uptake. The accumulated intracellular Zn2+ critically upregulates metallothionein 1 (Mt1), activating the IKK/NF-κB/IκB axis to resolve inflammation and oxidative damage. Transcriptomic analysis unequivocally identified Mt1 as the pivotal mediator of Zn2+-driven microenvironment reprogramming. Notably, QM (Zn) NPs not only significantly alleviated pelvic pain by mitigating neuronal oxidative stress but also exhibited excellent biocompatibility. This work pioneers a targeted nano-theranostic strategy that synergistically restores zinc homeostasis, quenches ROS, and reprograms immune responses, thereby establishing a transformative paradigm for CNP management.

To investigate the feasibility of employing deep learning models for automated segmentation and classification of adrenal incidental abnormalities on low-dose CT images. Four distinct CT cohorts were retrospectively collected for deep learning models development (cohort A, n = 2574; cohort B, n = 1205), internal evaluation (cohort C, n = 3681), and external evaluation (cohort D, n = 779). Two experienced uroradiologists independently reviewed the CT images and labeled the adrenal glands as normal or abnormal based on predefined criteria encompassing both density and morphological abnormalities, with any discrepancies resolved through consultation. The model development cohorts were divided into a training set, a validation set, and a test set. Deep learning models for segmentation and classification were trained and evaluated on internal and external sets, with the dice similarity coefficient (DSC), area under precision–recall curves (AUPRC), and area under receiver operating characteristic curves (AUROC) as evaluation metrics. Adrenal descriptions from radiology reports were extracted to compare with the model’s performance. For adrenal gland segmentation, the DSC values for the test set, internal validation cohort, and external validation cohort were 0.839 (IQR: 0.783–0.871), 0.870 (IQR: 0.819–0.902), and 0.799 (IQR: 0.729–0.849), respectively. For adrenal gland classification, the AI model achieved AUPRC values of 0.913, 0.753, and 0.927 in the test set, internal validation cohort, and external validation cohort, respectively, outperforming routine radiology reporting (AUPRC: 0.809, 0.708, 0.591; all P < 0.05). Corresponding AUROC values were 0.956, 0.942, and 0.977 for the AI model, which also outperformed routine radiology reporting (AUROC: 0.889, 0.705, 0.551; all P < 0.05). The deep learning models showed promise in automated adrenal segmentation and classification, highlighting AI’s potential to improve detection of adrenal abnormalities in LDCT scans. This study has been registered on ClinicalTrials.gov on August 25, 2025, with the unique identifier NCT07198152.

BACKGROUND:Although the clear cell likelihood score (ccLS) v2.0 demonstrates high specificity for clear cell renal cell carcinoma (ccRCC), its performance to characterize general malignancy in small renal masses (SRMs) remains limited. PURPOSE:To develop and validate a modified clear cell likelihood score (m-ccLS) incorporating the pseudocapsule to improve malignancy detection in SRMs while preserving specificity for diagnosing ccRCC. STUDY TYPE:This study was retrospective in type. SUBJECTS:352 patients with pathologically proven SRMs were included: development (n = 235), internal validation (n = 60), and external validation (n = 57). FIELD STRENGTH/SEQUENCE:Imaging was performed at 3.0 and 1.5 T using fast spin-echo T2-weighted imaging, single-shot echo planar diffusion-weighted imaging, 3D spoiled gradient echo (GRE) T1-weighted dynamic contrast-enhanced imaging, and in- and opposed-phase using T1-weighted GRE. ASSESSMENT:14 radiologists blinded to histopathology independently evaluated each SRM using ccLS v2.0 and m-ccLS scores in separate reading sessions; four, five, and five readers interpreted the development, internal, and external cohorts, respectively. STATISTICAL TESTS:Random-effects logistic regression, receiver operating characteristic curve, DeLong test, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and Fleiss Kappa test were used. The statistical significance level was p < 0.05. RESULTS:For malignancy detection, m-ccLS showed a significantly higher area under the curve (AUC) than ccLS v2.0 across the development (0.850 vs. 0.772), internal validation (0.856 vs. 0.779), and external validation (0.803 vs. 0.720) cohorts with improved classification (NRI = 0.270, 0.045, and 0.028) and discrimination (IDI = 0.132, 0.206, and 0.120). For diagnosing ccRCC, m-ccLS and ccLS v2.0 showed similar results (0.908 vs. 0.894, p = 0.250; 0.912 vs. 0.898, p = 0.134; 0.865 vs. 0.838, p = 0.065) in development, internal, and external validation cohorts, respectively. m-ccLS category 3 contained fewer ccRCCs (33.3% vs. 72.5%; 15.8% vs. 47.2%; 7.6% vs. 26.9%) and malignancies (79.2% vs. 88.7%; 71.6% vs. 73.0%; 55.4% vs. 63.9%) than ccLS v2.0 category 3. DATA CONCLUSION:m-ccLS improves malignancy detection in SRMs compared with ccLS v2.0 without impairing diagnostic performance for ccRCC. EVIDENCE LEVEL:4. TECHNICAL EFFICACY:Stage 2.

A major focus of contemporary medical research is the primary pathological cardiovascular diseases, whereas stress-induced vascular senescence is a process with degenerative changes in vascular morphology, structure, and function caused by oxidative stress (OS), inflammation, autophagy disorders, and other factors. Silent information regulator of transcription 1 (SIRT1), which may be a key gene linking oxidative stress and aging, has extensive biological functions in anti-oxidation, anti-inflammation, anti-apoptosis, and other aspects. This paper focuses on the protective effects and main mechanisms of SIRT1 on stress-induced vascular senescence and provides clues for preventing and treating diseases related to stress-induced vascular senescence.

OBJECTIVE:This study aims to develop a cascaded deep learning (DL) system based on multiparametric MRI to establish an automated pipeline for the segmentation and classification of small renal masses (SRMs). MATERIALS AND METHODS:A retrospective collection of SRM patients with pathologically confirmed from three institutions was conducted. MRI data from Institution 1 were randomly divided into a training set and an internal test set. Data from other institutions served as the external test set. A cascaded DL system was developed, incorporating automated segmentation and benign-malignant classification. Diagnostic performance was evaluated using receiver operating characteristic analysis and compared against three radiologists of varying experience. RESULTS:A total of 965 patients with SRM were included. Institution 1 contributed 888 cases, with 712 used for training and 176 as an internal test set; Institutions 2 and 3 provided 77 cases as an external test set. The optimal classification model using automated segmentation labels achieved AUCs of 0.936 and 0.788 on internal and external test sets, respectively. Performance was comparable to models using manual segmentation (internal: 0.936 vs. 0.944, P = 0.671; external: 0.788 vs. 0.832, P = 0.629). On the external test set, the model performed comparably to the senior radiologist, while it significantly outperformed the senior radiologist on the internal test set. The model significantly outperformed the junior radiologist on both test sets. This finding remained consistent in the subgroup of tumors smaller than 3 cm. CONCLUSION:The cascaded DL system demonstrated robust performance across multiple centers, enabling non-invasive and efficient discrimination of SRM malignancy, showing promise as a clinical support tool.

PURPOSE:The aim of this study was to develop and evaluate a natural language processing (NLP) system that automatically detects and classifies discrepancies between preliminary and final radiology reports, with the goal of enhancing resident education through structured feedback. METHODS:A total of 889 deidentified lumbar spine MRI reports (768 with revisions) from December 2023 to March 2024 were retrospectively analyzed. Preliminary full diagnostic reports were generated by trainee residents during daytime rotations; final reports were subsequently verified by attending radiologists remotely. Discrepancies in the diagnostic impression section were extracted using a multistep NLP pipeline: sentence segmentation, Bidirectional Encoder Representations From Transformers-based sentence matching, GPT-4-based named entity recognition, and rule-based classification into 11 correction types (missed diagnosis, misdiagnosis, missed image feature, misidentified image feature, localization error, diagnostic reasoning error, clinical query omission, severity error, confidence difference, typographic error, and terminology refinement). Ground truth was established by three radiologists. System performance was evaluated for each correction type individually using accuracy, sensitivity, specificity, and intraclass coefficient. Trends in resident and attending radiologist performance were analyzed at the report level. RESULTS:The NLP system achieved high accuracy (0.983-0.999), sensitivity (0.977-1.000), and specificity (0.900-1.000) for each of the 11 correction types, with strong interrater reliability (intraclass correlation coefficient > 0.75). Most common corrections were misdiagnosis (504 of 768 [65.6%]) and missed diagnosis (356 of 768 [46.4%]). Residents showed significant variability in error rates, especially in missed diagnosis (range, 11.1%-59.1% across 16 residents) and misdiagnosis (range, 24.0%-71.1% across 16 residents). Attending radiologists exhibited marked heterogeneity in correction patterns (n = 6; individual workload range, 95-187 reports; median, 159 reports), with significant variability across all major error types (P < .001 for missed diagnosis [20.6%-82.0%], misdiagnosis [31.4%-66.7%], localization error [15.8%-54.7%], and terminology refinement [3.2%-36.7%]). CONCLUSIONS:The NLP-based discrepancy tracking system accurately identifies and classifies report modifications, enabling scalable, targeted feedback for radiology residents. Variability among residents and attending radiologists highlights the need for individualized training and standardized review practices.

To develop and validate a machine learning (ML)-based pipeline for automated segmentation and classification of complicated cystic renal masses (cCRMs) on MRI. This multicenter retrospective study enrolled 275 patients (median age, 48 years; 85 females) with pathologically confirmed 275 cCRMs (203 malignant) who underwent renal MRI from January 2013 to December 2023. cCRMs from one institution were used as a training set (n = 215), while those from the other three institutions served as a test set (n = 60). 3D V-Net and random forest algorithms were employed for segmentation and classification, respectively. Segmentation and classification performance was evaluated using the Dice similarity coefficient (DSC) and the area under the curve (AUC), respectively. Two junior and two senior radiologists independently classified cCRMs in the test set into Bosniak categories II–IV based on the Bosniak classification, version 2019. In the test set, the ML pipeline achieved DSC of 0.718 for cCRMs (n = 60) on excretory phase images. Additionally, classification performance of the ML pipeline (AUC = 0.835, 95

ObjectiveThis study aims to explore the clinical efficacy and safety of a urethroplasty technique for fossa navicularis (FN) strictures using a transurethral annular inlay oral mucosa graft.MethodsA retrospective analysis was conducted on clinical data from patients with urethral meatus and navicular fossa stricture who underwent transurethral reconstruction using annular inlay oral mucosa graft urethroplasty in Tangdu Hospital from July 2021 to October 2024. Operation success was defined as the ability to pass the F24 urethral probe, and the secondary outcome is the urinary flow rate and patient satisfaction at 1, 3, 6, and 12 months.ResultsAll 12 patients successfully completed the surgery. The average age was 56.8 ± 6.8 years, and the average length of urethral stricture was 1.6 ± 0.2 cm. Three patients had a history of transurethral endoscopic surgery, eight had penile lichen sclerosus (LS), and one had no obvious causes. Over a median follow-up of 19 months, the average maximum urinary flow rate was 21.2 ± 3.2 ml/s at 3 months, and the average maximum urinary flow rate was 19.5 ± 4.2 ml/s at 12 months. One patient experienced urine pain with a thin stream and spraying urination after 3 months. Physical examination of the urethral meatus opening scar was found, and the symptoms disappeared after urethral incision. The follow-up survey of sexual life after 1 year showed that seven patients (58.3%) had successful life within 1 year compared with two patients (16.7%) before surgery. There were no cases of erectile dysfunction and poor wound healing. The patients were either very satisfied (75%) or satisfied (25%) with the operation. All patients would recommend urethroplasty to others.ConclusionTransurethral annular inlay oral mucosa urethroplasty for urethral meatus and navicular fossa stricture reconstruction is a safe, feasible, and effective surgical method, cosmetic effect on the penile head. This technique has advantages in improving sexual function and better penile head cosmesis.

Bacterial prostatitis represents a specific form of prostatitis, primarily resulting from bacterial infection and significantly impairing the life quality of patients. In this paper, we respond to the inability of conventional drugs to simultaneously address both bacterial infection and oxidative stress in the treatment of prostatitis by designing a multifunctional nanoparticle, called QM (Cu) NPs, with dual functionality. QM (Cu) NPs have the capacity to generate reactive oxygen radicals to eradicate bacteria under the influence of laser irradiation. Additionally, they are capable of rapidly scavenging the surplus free radicals, thereby restoring the intracellular redox homeostasis in the absence of laser illumination. A comprehensive characterization of QM (Cu) NPs was conducted, followed by an in-depth analysis of their effects on cells. The therapeutic efficacy of QM (Cu) NPs in multimodal treating bacterial prostatitis was then demonstrated. Furthermore, the outcomes of transcriptomic and molecular biology experiments indicated that QM (Cu) NPs markedly regulate the NF-κB p65 and Nrf2-Keap1 signaling pathways, thereby influencing inflammatory and oxidative stress processes. In conclusion, QM (Cu) NPs simultaneously addressed the dual challenges of antibacterial and antioxidant properties, thereby underscoring their potential clinical applications in the treatment of bacterial prostatitis.

To evaluate the efficacy of a simple method based on qualitative MRI features for characterizing clear cell renal cell carcinoma (ccRCC) in small renal masses (SRMs). This retrospective multicenter study included pathologically confirmed SRM patients who underwent multiparametric MRI between March 2017 and November 2023 at three institutions. Univariable logistic regression and Fleiss κ coefficient were employed to determine features with significant diagnostic value and high consistency for ccRCC. A simple method was developed based on the selected features using multivariable logistic regression. The performance of the method was compared with the clear cell likelihood score (ccLS) using DeLong test and McNemar test. A total of 200 SRMs from 194 patients (116 men; median age: 54 years) were included. Intense corticomedullary enhancement, microscopic fat, and pseudocapsule were selected to construct the simple method, which considered a mass to be ccRCC if any two of the aforementioned three signs were present. Compared with ccLS, our method demonstrated similar sensitivity (0.824 versus 0.725, P = 0.227) and specificity (0.840 versus 0.860, P > 0.999). The AUC for the simple method and ccLS was 0.832 (95

Rationale and Objectives To improve the diagnostic recognition of papillary renal neoplasm with reverse polarity (PRNRP) through comprehensive analysis of computed tomography (CT) and magnetic resonance imaging (MRI) findings. Materials and Methods A retrospective multi-center study was conducted on patients with pathologically confirmed PRNRPs from 2019 to 2024, encompassing six institutions. Clinical and pathological data were meticulously documented. Preoperative CT (n=23) and MRI (n=9) features were independently evaluated in consensus by two genitourinary radiologists, focusing on tumor location, morphologic features, attenuation, signal intensity, and enhancement patterns. Postoperative outcomes were assessed through medical record review or telephone follow-up. Results The study cohort comprised 26 patients (mean age 62±12 years, 13 men) with 26 well-defined PRNRPs (mean diameter 2.2±1.0 cm) were included. 15 cases (58%) were situated in the right kidney, 18(69%) were exophytic, and 23(88%) were quasi-spherical. A pseudocapsule was identified in eight cases (89%) on MRI. All cases demonstrated iso- or slight hyperattenuation (43.1±13.6 HU) on non-contrast CT, hypointensity on T2-weighted imaging (T2WI), and mild diffusion restriction on diffusion-weighted imaging (DWI). All cases exhibited mild or moderate enhancement in the corticomedullary phase, followed by progressive enhancement in the nephrographic and excretory phases. Concomitant renal cysts were found in 17 cases (65%). All cases showed no evidence of recurrence or metastasis. Conclusion PRNRP typically presents as a small hypovascular renal mass, and characterized by a pseudocapsule, iso- or slight hyperattenuation on non-contrast CT, heterogeneous T2WI hypointensity, and mild diffusion restriction on DWI.

Rationale and Objective: Accurate differentiation between benign and malignant cystic renal masses (CRMs) is challenging in clinical practice. This study aimed to develop MRI-based machine learning models for differentiating between benign and malignant CRMs and compare the best-performing model with the Bosniak classification, version 2019 (BC, version 2019). Methods: Between 2009 and 2021, consecutive surgery-proven CRM patients with renal MRI were enrolled in this multicenter study. Models were constructed to differentiate between benign and malignant CRMs using logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms, respectively. Meanwhile, two radiologists classified CRMs into I-IV categories according to the BC, version 2019 in consensus in the test set. A subgroup analysis was conducted to investigate the performance of the best- performing model in complicated CRMs (II-IV lesions in the test set). The performances of models and BC, version 2019 were evaluated using the area under the receiver operating characteristic curve (AUC). Performance was statistically compared between the best- performing model and the BC, version 2019. Results: 278 and 48 patients were assigned to the training and test sets, respectively. In the test set, the AUC and accuracy of the LR model, the RF model, the SVM model, and the BC, version 2019 were 0.884 and 75.0%, 0.907 and 83.3%, 0.814 and 72.9%, and 0.893 and 81.2%, respectively. Neither the AUC nor the accuracy of the RF model that performed best were significantly different from the BC, version 2019 (P = 0.780, P = 0.065). The RF model achieved an AUC and accuracy of 0.880 and 81.0% in complicated CRMs. Conclusions: The MRI-based RF model can accurately differentiate between benign and malignant CRMs with comparable perfor- mance to the BC, version 2019, and has good performance in complicated CRMs, which may facilitate treatment decision-making and is less affected by interobserver disagreements.

BACKGROUND:Accurate preoperative assessment of ureteral length is crucial for effective ureteral stenting. PURPOSE:Utilize a deep learning approach to measure ureter length on CT urography (CTU) images and compare the obtained results with those derived from other estimation methods. METHODS:In a retrospective cohort (cohort A, n = 411), CTU images were collected and used to develop a 3D deep learning model for the segmentation of bilateral ureters. The centerline of the ureters was determined based on the segmentation, and the length of the ureters was automatically obtained (CTU_ai). Another cohort (cohort B, n = 220) was collected as the hold-out test for the model. All patients in cohort B had KUB, non-contrast enhanced CT (CT NoC), and CTU images. Cohort B utilized eight measurement methods, with one annotated by two radiologists serving as the reference standard (CTU_ref) and the remaining seven as the studied methods, including three measurement methods applied to CTU (CTU_ai, CTU_oblique, CTU_slice), two applied to CT NoC (CT_oblique, CT_slice), and two applied to KUB (KUB_short, KUB_long). The results of the seven studied methods were compared to those of the reference in cohort B. RESULTS:Among the 220 patients (96 females, 124 males), 437 ureters were measured for length (218 left, 219 right), with a median length of 24.7 (IQR 23.2-26.2) cm. No significant differences were observed between genders or laterality (both P > 0.05). Moreover, there was no correlation between ureteral length and age (r = -0.027, P = 0.573). The ureteral length measured by CTU_ai was not significantly different from that measured by CTU_ref (P = 0.514), whereas the length measured by the other studied methods was significantly different from that measured by CTU_ref (all P < 0.001). The ICC values with their 95 % confidence intervals (CIs) for the comparison between the reference standard (CTU_ref) and the other measurement methods: CTU_ai (ICC = 0.852, 95 % CI 0.825-0.876), CTU_oblique (ICC = 0.351, 95 % CI -0.083-0.689), CTU_slice (ICC = 0.269, 95 % CI -0.095-0.573), CTU_oblique_slice (ICC = 0.059, 95 % CI -0.032-0.218), CTU_slice (ICC = 0.049, 95 % CI -0.028-0.188), KUB_short (ICC = 0.151, 95 % CI 0.051-0.247), and KUB_long (ICC = 0.147, 95 % CI 0.034-0.253). For CTU_ai, in 89.0 % of the ureters, the ureteral length deviation was within 20 mm of the reference standard, which was the highest among all the studied methods (all P < 0.001). CONCLUSION:The deep learning model offers a reliable and accurate tool for ureteral length measurement on CTU images, which could enhance the effectiveness of ureteral stenting procedures. Its performance surpasses traditional measurement methods, making it a promising technology for integration into clinical practice.

BackgroundClear cell likelihood score (ccLS) is reliable for diagnosing small renal masses (SRMs). However, the diagnostic value of Clear cell likelihood score version 1.0 (ccLS v1.0) and v2.0 for common subtypes of SRMs might be a potential score extension.PurposeTo compare the diagnostic performance and interobserver agreement of ccLS v1.0 and v2.0 for characterizing five common subtypes of SRMs.Study TypeRetrospective.Population797 patients (563 males, 234 females; mean age, 53 ± 12 years) with 867 histologically proven renal masses.Field Strength/Sequences3.0 and 1.5 T/T2 weighted imaging, T1 weighted imaging, diffusion‐weighted imaging, a dual‐echo chemical shift (in‐ and opposed‐phase) T1 weighted imaging, multiphase dynamic contrast‐enhanced imaging.AssessmentSix abdominal radiologists were trained in the ccLS algorithm and independently scored each SRM using ccLS v1.0 and v2.0, respectively. All SRMs had definite pathological results. The pooled area under curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the diagnostic performance of ccLS v1.0 and v2.0 for characterizing common subtypes of SRMs. The average κ values were calculated to evaluate the interobserver agreement of the two scoring versions.Statistical TestsRandom‐effects logistic regression; Receiver operating characteristic analysis; DeLong test; Weighted Kappa test; Z test. The statistical significance level was P < 0.05.ResultsThe pooled AUCs of clear cell likelihood score version 2.0 (ccLS v2.0) were statistically superior to those of ccLS v1.0 for diagnosing clear cell renal cell carcinoma (ccRCC) (0.907 vs. 0.851), papillary renal cell carcinoma (pRCC) (0.926 vs. 0.888), renal oncocytoma (RO) (0.745 vs. 0.679), and angiomyolipoma without visible fat (AMLwvf) (0.826 vs. 0.766). Interobserver agreement for SRMs between ccLS v1.0 and v2.0 is comparable and was not statistically significant (P = 0.993).ConclusionThe diagnostic performance of ccLS v2.0 surpasses that of ccLS v1.0 for characterizing ccRCC, pRCC, RO, and AMLwvf. Especially, the standardized algorithm has optimal performance for ccRCC and pRCC. ccLS has potential as a supportive clinical tool.Evidence Level4.Technical EfficacyStage 2.

Background The question of whether segmentectomy and lobectomy have similar survival outcomes for patients with early-stage non-small cell lung cancer (NSCLC) is a matter of debate.Methods A cohort study and randomized controlled trial were included, comparing segmentectomy and lobectomy, by utilizing computerized access to the Pubmed, Web of Science, and Cochrane Library databases up until July 2022. The Cochrane Collaboration tool was used to evaluate the randomized controlled trials, while the Newcastle-Ottawa Scale (NOS) was used to evaluate the cohort studies. Sensitivity analyses were also carried out.Results The analysis incorporated 17 literature studies, including one randomized controlled trial and 16 cohort studies, and was divided into a segmentectomy group (n = 2081) and a lobectomy group (n = 2395) based on the type of surgery the patient underwent. Each study was followed up from 27 months to 130.8 months after surgery. Over survival (OS): HR = 1.14, 95%CI(0.97,1.32), P = 0.10; disease-free survival (DFS): HR = 1.13, 95%CI(0.91,1.41), P = 0.27; recurrence-free survival (RFS): HR = 0.95, 95%CI(0.81,1.12), P = 0.54.Conclusion The results of the study suggest that the survival outcomes of the segmentectomy group were not inferior to that of the lobectomy group. Segmentectomy should therefore be considered as a treatment option for early stage NSCLC.

Objective: Real-word data on long-acting luteinizing hormone-releasing hormone (LHRH) agonists in Chinese patients with prostate cancer are limited. This study aimed to determine the real-world effectiveness and safety of the LHRH agonist, goserelin, particularly the long-acting 10.8-mg depot formulation, and the follow-up patterns among Chinese prostate cancer patients. Methods: This was a multicenter, prospective, observational study in hormone treatment-naïve patients with localized or locally advanced prostate cancer who were prescribed goserelin 10.8-mg depot every 12 weeks or 3.6-mg depot every 4 weeks with or without an anti-androgen. The patients had follow-up evaluations for 26 weeks. The primary outcome was the effectiveness of goserelin in reducing serum testosterone and prostate-specific antigen (PSA) levels. The secondary outcomes included testosterone and PSA levels, attainment of chemical castration (serum testosterone <50 ng/dL), and goserelin safety. The exploratory outcome was the monitoring pattern for serum testosterone and PSA. All analyses were descriptive. Results: Between September 2017 and December 2019, a total of 294 eligible patients received ≥ 1 dose of goserelin; 287 patients (97.6%) were treated with goserelin 10.8-mg depot. At week 24 ± 2, the changes from baseline [standard deviation (95% confidence interval)] in serum testosterone (n = 99) and PSA (n = 131) were −401.0 ng/dL [308.4 ng/dL (−462.5, −339.5 ng/dL)] and −35.4 ng/mL [104.4 ng/mL (−53.5, −17.4 ng/mL)], respectively. Of 112 evaluable patients, 100 (90.2%) achieved a serum testosterone level < 50 ng/dL. Treatment-emergent adverse events (TEAEs) and severe TEAEs occurred in 37.1% and 10.2% of patients, respectively. The mean testing frequency (standard deviation) was 1.6 (1.5) for testosterone and 2.2 (1.6) for PSA. Conclusions: Goserelin 10.8-mg depot effectively achieved and maintained castration and was well-tolerated in Chinese patients with localized and locally advanced prostate cancer.

Bacterial prostatitis is a bacterial infection of the prostate gland presenting with lower quadrant abdominal pain, urination disorders and poor fertility. In recent years, reports have emerged on the significantly reduced efficacy of fluoroquinolone drugs attributed to multiple drug-resistant bacteria, emphasizing the need for new drugs. In this study, we designed a targeting drug delivery system via curcumin copper complex grafted with hyaluronic acid. Subsequently, the prepared system was characterized using FT-IR, XRD, SEM, XPS and 1H NMR methods. In addition to the substantial improvement in the solubility of the carrier, its antibacterial performance and targeting ability were improved. Interestingly, the grafting of hyaluronic acid endowed the carrier with excellent CD44 receptor targeting function and good water solubility, and the complexation of copper ions greatly enhanced its antibacterial capability, especially the inhibitory effect on E. coli. The anti-prostatitis effect of the drug was evaluated comprehensively by establishing a bacterial prostatitis model infected by E. coli. Assessment of the anti-prostatitis effects in vivo indicated that the Cur-Cu@HA delivery system could effectively promote recovery from bacterial prostatitis by downregulating inflammation. In conclusion, our Cur-Cu@HA delivery system has great potential for treating bacterial prostatitis.

OBJECTIVE:To compare the performance of Clear Cell Likelihood Score (ccLS) v1.0 and v2.0 in diagnosing clear cell renal cell carcinoma (ccRCC) from small renal masses (SRM). METHODS:We retrospectively analyzed the clinical data and MR images of patients with pathologically confirmed solid SRM from the First Medical Center of the Chinese PLA General Hospital between January 1, 2018, and December 31, 2021, and from Beijing Friendship Hospital of Capital Medical University and Peking University First Hospital between January 1, 2019 and May 17, 2021. Six abdominal radiologists were trained for use of the ccLS algorithm and scored independently using ccLS v1.0 and ccLS v2.0. Random- effects logistic regression modeling was used to generate plot receiver operating characteristic curves (ROC) to evaluate the diagnostic performance of ccLS v1.0 and ccLS v2.0 for ccRCC, and the area under curve (AUC) of these two scoring systems were compared using the DeLong's test. Weighted Kappa test was used to evaluate the interobserver agreement of the ccLS score, and differences in the weighted Kappa coefficients was compared using the Gwet consistency coefficient. RESULTS:In total, 691 patients (491 males, 200 females; mean age, 54 ± 12 years) with 700 renal masses were included in this study. The pooled accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ccLS v1.0 for diagnosing ccRCC were 77.1%, 76.8%, 77.7%, 90.2%, and 55.7%, as compared with 80.9%, 79.3%, 85.1%, 93.4%, 60.6% with ccLS v2.0, respectively. The AUC of ccLS v2.0 was significantly higher than that of ccLS v1.0 for diagnosis of ccRCC (0.897 vs 0.859; P < 0.01). The interobserver agreement did not differ significantly between ccLS v1.0 and ccLS v2.0 (0.56 vs 0.60; P > 0.05). CONCLUSION:ccLS v2.0 has better performance for diagnosing ccRCC than ccLS v1.0 and can be considered for use to assist radiologists with their routine diagnostic tasks.