Ureteral contrast findings as a potential predictor for invasive intervention in high-grade pediatric renal trauma: A retrospective analysis

OBJECTIVES

To determine if patient variables were associated with intervention in pediatric patients presenting with high-grade renal injuries.

METHODS

A retrospective review of pediatric patients presenting with grade IV/V renal injury between 2003 and 2021 at a Level 1 trauma center was performed. Renal injury grade was verified and updated based upon the 2018 American Association for the Surgery of Trauma injury scale. Multivariable logistic regression analyses were performed.

RESULTS

Seventy-five patients (median age 13 years old, IQR 9-16) with Grade IV (n = 53) or Grade V (n = 22) injury were identified. 33% (25/75) had immediate renal intervention within 24 h of admission. Of the remaining 50 children who were observed, 47 had blunt trauma, and outcomes were analyzed. The median age of observed patients was 12 years (IQR 8-14) and 30% (14/47) had intervention. Delayed images on CT showed ureteral contrast was present in 87% (41/47) of observed patients. Multivariable analysis demonstrated that the presence of contrast in the ureter is associated with significantly lower odds of intervention, OR 0.06 [0-0.73, 95% CI], p = 0.03.

CONCLUSION

After grades IV and V blunt renal injury, for those children who are considered safe to observe, AAST grade of injury did not associate with procedural intervention. The presence of contrast in the ureter on delayed CT imaging was associated with a significantly lower odds of procedural intervention.

Usefulness of multiparametric MRI for local staging of bladder cancer

INTRODUCTION

Under staging and over staging are not uncommon with traditional MRI while staging bladder cancer. Current improvements in MRI technology due to addition of functional MR sequences that is, dynamic contrast-enhanced (DCE) imaging and diffusion-weighted imaging (DWI) have enhanced its clinical utility. The current study was designed to look for staging accuracy of multiparametric MRI (mp-MRI) that is, T2W + DCE + DWI, over conventional MRI.

MATERIAL AND METHODS

Forty patients with bladder cancer were included were subjected to mp-MRI on a 3T scanner with a phased array body coil. Four MR image sets that is, T2W, T2W + DCE, T2W + DWI, and T2W + DCE + DWI were interpreted. Accuracy of each image set was determined separately and was compared with the gold standard histopathological staging.

RESULT

Staging accuracy of different image set increased from T2W (55%) to DCE (72.5%) to DWI (80%). Maximum accuracy was seen in mp-MRI (T2W + DWI + DCE) (87.5%). While differentiating non muscle invasive from muscle invasive disease (⩽T1 vs ⩾T2 stage) staging accuracy increased from T2W (65%) to DCE (80%) to DWI (85%) with maximum in mp-MRI (90%).

CONCLUSION

mp-MRI offers high staging accuracy for bladder cancer.

Optimized Transfer Learning With Hybrid Feature Extraction for Uterine Tissue Classification Using Histopathological Images

Endometrial cancer, termed uterine cancer, seriously affects female reproductive organs, and the analysis of histopathological images formed a golden standard for diagnosing this cancer. Sometimes, early detection of this disease is difficult because of the limited capability of modeling complicated relationships among histopathological images and their interpretations. Moreover, many previous methods do not effectively handle the cell appearance variations. Hence, this study develops a novel classification technique called transfer learning convolution neural network with artificial bald eagle optimization (TL-CNN with ABEO) for the classification of uterine tissue. Here, preprocessing is done by the median filter, followed by image enhancement by the multiple identities representation network (MIRNet). Moreover, pelican crow search optimization (PCSO) is used for adapting weights in MIRNet, where PCSO is generated by combining the crow search algorithm (CSA) and pelican optimization algorithm (POA). Then, segmentation quality assessment (SQA) helps in tissue segmentation, and deep convolutional neural network (DCNN) helps in parameter selection that is trained by fractional PCSO (FPCSO). Furthermore, feature extraction is done and, finally, cell classification is done by TL with CNN, which is trained by the proposed ABEO algorithm. Here, ABEO is newly developed by the integration of the bald eagle search (BES) algorithm and artificial hummingbird algorithm (AHA). Furthermore, ABEO + TL-CNN achieved a high accuracy of 89.59%, a sensitivity of 90.25%, and a specificity of 89.89% by utilizing the cancer image archive dataset.

Retrograde urethrography (RUG) combined with voiding cystourethrography (VCUG) versus surgical findings in assessment of urethral strictures length

BACKGROUND AND OBJECTIVE

Retrograde urethrography (RUG) combined with voiding cystourethrography (VCUG) is the most common and preferred imaging modality for evaluating urethral strictures, despite its well-known limitations and disadvantages. In this study, we assessed the clinical relevance of RUG + VCUG, along with intraoperative assessment in measuring male urethral strictures.

METHOD

This study was a single-center retrospective study involving 134 male patients diagnosed with urethral stricture disease. All participants underwent RUG + VCUG before the intervention, and the results were interpreted by a single radiologist. The location and length of urethral strictures were assessed. The accuracy of urethral stricture measurements obtained from combined VCUG and RUG imaging was compared to intraoperative measurements, which served as the reference standard. Urethral strictures were classified into three types: membranous and bulbomembranous, bulbar, and penile.

RESULTS

A total of 130 patients were included (38.14 ± 12.05 years) in the study. For patients with membranous and bulbar strictures, there were statistically significant differences in stricture length measurements between VCUG + RUG and surgical evaluation (p < 0.05). However, for patients with penile strictures, the differences in stricture length measurements between VCUG + RUG and surgical evaluation were not statistically significant (p = 0.448).

CONCLUSION

This study suggests that RUG + VCUG may underestimate urethral stricture, particularly in the membranous and bulbar regions.

Segmentation-assisted vessel centerline extraction from cerebral CT Angiography

BACKGROUND

The accurate automated extraction of brain vessel centerlines from Computed tomographic angiography (CTA) images plays an important role in diagnosing and treating cerebrovascular diseases such as stroke. Despite its significance, this task is complicated by the complex cerebrovascular structure and heterogeneous imaging quality.

PURPOSE

This study aims to develop and validate a segmentation-assisted framework designed to improve the accuracy and efficiency of brain vessel centerline extraction from CTA images. We streamline the process of lumen segmentation generation without additional annotation effort from physicians, enhancing the effectiveness of centerline extraction.

METHODS

The framework integrates four modules: (1) pre-processing techniques that register CTA images with a CT atlas and divide these images into input patches, (2) lumen segmentation generation from annotated vessel centerlines using graph cuts and robust kernel regression, (3) a dual-branch topology-aware UNet (DTUNet) that optimizes the use of the annotated vessel centerlines and the generated lumen segmentation via a topology-aware loss (TAL) and its dual-branch structure, and (4) post-processing methods that skeletonize and refine the lumen segmentation predicted by the DTUNet.

RESULTS

An in-house dataset derived from a subset of the MR CLEAN Registry is used to evaluate the proposed framework. The dataset comprises 10 intracranial CTA images, and 40 cube CTA sub-images with a resolution of128 × 128 × 128 $128 \times 128 \times 128$ voxels. Via five-fold cross-validation on this dataset, we demonstrate that the proposed framework consistently outperforms state-of-the-art methods in terms of average symmetric centerline distance (ASCD) and overlap (OV). Specifically, it achieves an ASCD of 0.84, anOV 1.0 $\textrm {OV}_{1.0}$ of 0.839, and anOV 1.5 $\textrm {OV}_{1.5}$ of 0.885 for intracranial CTA images, and obtains an ASCD of 1.26, anOV 1.0 $\textrm {OV}_{1.0}$ of 0.779, and anOV 1.5 $\textrm {OV}_{1.5}$ of 0.824 for cube CTA sub-images. Subgroup analyses further suggest that the proposed framework holds promise in clinical applications for stroke diagnosis and treatment.

CONCLUSIONS

By automating the process of lumen segmentation generation and optimizing the network design of vessel centerline extraction, DTUnet achieves high performance without introducing additional annotation demands. This solution promises to be beneficial in various clinical applications in cerebrovascular disease management.

State of the art review of AI in renal imaging

Renal cell carcinoma (RCC) as a significant health concern, with incidence rates rising annually due to increased use of cross-sectional imaging, leading to a higher detection of incidental renal lesions. Differentiation between benign and malignant renal lesions is essential for effective treatment planning and prognosis. Renal tumors present numerous histological subtypes with different prognoses, making precise subtype differentiation crucial. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), shows promise in radiological analysis, providing advanced tools for renal lesion detection, segmentation, and classification to improve diagnosis and personalize treatment. Recent advancements in AI have demonstrated effectiveness in identifying renal lesions and predicting surveillance outcomes, yet limitations remain, including data variability, interpretability, and publication bias. In this review we explored the current role of AI in assessing kidney lesions, highlighting its potential in preoperative diagnosis and addressing existing challenges for clinical implementation.

A deep learning approach for quantifying CT perfusion parameters in stroke

Objective. Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. However, conventional methods for quantifying CTP images, such as singular value decomposition (SVD), often lead to oscillations in the estimated residue function and underestimation of tissue perfusion. In addition, the use of global arterial input function (AIF) potentially leads to erroneous parameter estimates. We aim to develop a method for accurately estimating physiological parameters from CTP images.Approach. We introduced a Transformer-based network to learn voxel-wise temporal features of CTP images. With global AIF and concentration time curve (CTC) of brain tissue as inputs, the network estimated local AIF and flow-scaled residue function. The derived parameters, including cerebral blood flow (CBF) and bolus arrival delay (BAD), were validated on both simulated and patient data (ISLES18 dataset), and were compared with multiple SVD-based methods, including standard SVD (sSVD), block-circulant SVD (cSVD) and oscillation-index SVD (oSVD).Main results.On data simulating multiple scenarios, local AIF estimated by the proposed method correlated with true AIF with a coefficient of 0.97 ± 0.04 (P < 0.001), estimated CBF with a mean error of 4.95 ml/100 g min-1, and estimated BAD with a mean error of 0.51 s; the latter two errors were significantly lower than those of the SVD-based methods (P < 0.001). The CBF estimated by the SVD-based methods were underestimated by 10% ∼ 15%. For patient data, the CBF estimates of the proposed method were significantly higher than those of the sSVD method in both normally perfused and ischemic tissues, by 13.83 ml/100 g min-1or 39.33% and 8.55 ml/100 g min-1or 57.73% (P < 0.001), respectively, which was in agreement with the simulation results.Significance. The proposed method is capable of estimating local AIF and perfusion parameters from CTP images with high accuracy, potentially improving CTP's performance and efficiency in diagnosing and treating ischemic stroke.

Multiparametric MR Urography: State of the Art

MR urography (MRU) is an imaging technique that provides comprehensive evaluation of the kidneys, pelvicalyceal system, ureters, and urinary bladder. Although CT urography (CTU) remains the first-line imaging modality for the urinary tract, incremental improvements in MRU have allowed simultaneous imaging of the kidneys, collecting system, and urinary bladder with superior contrast resolution and tissue characterization, equivalent visualization of the upper tracts, and similar specificity for detection of noncalculous diseases of the collecting system compared with that of CTU. MRU has evolved into an alternative to CTU in the broader patient population and a first-line examination in specific patient populations for which CTU is less preferred. This subgroup includes pediatric patients, pregnant patients, patients needing recurring studies, and patients with poor renal function or severe allergies to iodinated contrast material. The most common techniques encompassing a conventional MRU examination include static-fluid T2-weighted imaging and gadolinium-enhanced urothelial and excretory phase imaging. The addition of dynamic contrast-enhanced MRI and diffusion-weighted imaging results in multiparametric MRU that increases diagnostic accuracy. Newer techniques, such as parallel imaging, compressed sensing, radial k-space sampling, and deep learning-based image reconstruction, can shorten examination times and improve image quality and patient compliance. Successful MRU interpretation relies on technique optimization, knowledge of various urinary tract pathologic conditions, and familiarity with different sequences, potential interpretive pitfalls, and artifacts. ©RSNA, 2025 Supplemental material is available for this article.

Quantitative Parameters of Intravoxel Incoherent Movement Imaging and Dynamic Contrast Enhancement MRI for the Prediction of HER2-Zero, -Low, and -Positive Breast Cancers

RATIONALE AND OBJECTIVES

To explore the predictive value of quantitative parameters from intravoxel incoherent movement (IVIM) imging and dynamic contrast enhancement MRI (DCE-MRI) for HER2 expression in breast cancer.

MATERIALS AND METHODS

This retrospective study included 167 women with breast cancer who underwent MRI from December 2019 to December 2023, categorized into 48 HER2-positive, 78 HER2-low and 41 HER2-zero cancers. All patients underwent IVIM imaging and DCE-MRI. Statistical analyses, including one-way ANOVA, Kruskal-Wallis test and χ2 test, were employed to compare clinical data, MRI features, and MRI quantitative parameters including standard ADC(ADC), pure diffusion coefficient(D), perfusion-related diffusion coefficient(D*), perfusion fraction(f), volume transfer constant(Ktrans), extravascular extracellular interstitial volume ratio(Ve) and rate constant(Kep) between the three groups. Multivariable logistic regression was used to identify independent predictors for distinguishing HER2 expressions. The diagnostic efficacy of significant IVIM and DCE parameters for different HER2 expressions was analyzed using receiver operator characteristic (ROC) curves.

RESULTS

Peritumoral edema, histological grade and Kep achieved an AUC of 0.86(95%CI:0.78,0.91) in distinguishing HER2-positive tumors from HER2-low expressing tumors and were independent predictors for differentiating these two groups. Among HER2-positive and -zero breast cancers, the combined model of D*, Ktrans and Kep had an AUC of 0.74(95%CI:0.63,0.82) for the prediction of HER2-positive versus HER2-zero cancers, and its prediction efficiency was not improved compared with that of a single parameter(P > .05).

CONCLUSION

Quantitative parameters from intravoxel incoherent movement imaging and dynamic contrast enhancement MRI can predict different HER2 expressions in breast cancer from different perspectives, with implications for therapy.

Revolutionizing prostate cancer therapy: Artificial intelligence - Based nanocarriers for precision diagnosis and treatment

Prostate cancer is one of the major health challenges in the world and needs novel therapeutic approaches to overcome the limitations of conventional treatment. This review delineates the transformative potential of artificial intelligence (AL) in enhancing nanocarrier-based drug delivery systems for prostate cancer therapy. With its ability to optimize nanocarrier design and predict drug delivery kinetics, AI has revolutionized personalized treatment planning in oncology. We discuss how AI can be integrated with nanotechnology to address challenges related to tumor heterogeneity, drug resistance, and systemic toxicity. Emphasis is placed on strong AI-driven advancements in the design of nanocarriers, structural optimization, targeting of ligands, and pharmacokinetics. We also give an overview of how AI can better predict toxicity, reduce costs, and enable personalized medicine. While challenges persist in the way of data accessibility, regulatory hurdles, and interactions with the immune system, future directions based on explainable AI (XAI) models, integration of multimodal data, and green nanocarrier designs promise to move the field forward. Convergence between AI and nanotechnology has been one key step toward safer, more effective, and patient-tailored cancer therapy.

An overview of utilizing artificial intelligence in localized prostate cancer imaging

INTRODUCTION

Prostate cancer (PCa) is a leading cause of cancer-related deaths among men, and accurate diagnosis is critical for effective management. Multiparametric MRI (mpMRI) has become an essential tool in PCa diagnosis due to its superior spatial resolution which enables detailed anatomical, functional information and its resultant ability to detect clinically significant PCa. However, challenges such as subjective interpretation methods and high inter-reader variability remain. In recent years, artificial intelligence (AI) has emerged as a promising solution to enhance the diagnostic performance of mpMRI by automating key tasks such as prostate segmentation, lesion detection, classification.

AREAS COVERED

This review provides a comprehensive overview of the current AI applications in prostate mpMRI, discussing advancements in automated image analysis and how AI-driven models are developed to improve detection and risk stratification. A literature search was conducted to examine both machine learning and deep learning techniques applied in this field, highlighting key studies and future directions.

EXPERT OPINION

While AI models have shown significant promise, their clinical integration remains limited due to the need for larger, multi-institutional validation studies. As AI continues to evolve, multimodal approaches combining imaging with clinical data are likely to play pivotal role in personalized PCa diagnosis, treatment planning.

Feasibility study of AI-assisted multi-parameter MRI diagnosis of prostate cancer

Distinguishing between benign and malignant prostate lesions in magnetic resonance imaging (MRI) poses challenges that affect prostate cancer screening accuracy. We propose a novel computer-aided diagnosis (CAD) system to extract cancerous lesions from the prostate in multi-parametric MRI (mp-MRI), assessing the feasibility of using artificial intelligence for detecting clinically significant prostate cancer (PCa). A retrospective study was conducted on 106 patients who underwent mp-MRI from 2021 to 2024 at a single center. We analyzed three sequences (T2W, DCE, and DWI) and collected 137 mp-MRI images corresponding to pathological sections. From these, we obtained 274 sets of ROI data, using 206 for training and validation, and 68 for testing. A feature extractor was developed using a pre-trained ResNet50 model combined with a multi-head attention mechanism to fuse modality-specific features and perform classification. The experimental results indicate that our model demonstrates high classification performance, achieving an AUC of 0.89. The PR curve shows high precision across most recall values, with an AUC of 0.91. We developed a novel CAD system based on deep learning ResNet50 models to assess the risk of prostate malignancy in mpMRI images. High classification ability is achieved, and combining the attention mechanism or optimization strategy can improve the performance of the model in medical imaging.

Predictive Potential of Contrast-Enhanced MRI-Based Delta-Radiomics for Chemoradiation Responsiveness in Muscle-Invasive Bladder Cancer

Background/Objectives: Delta-radiomics involves analyzing feature variations at different acquisition time-points. This study aimed to assess the utility of delta-radiomics feature analysis applied to contrast-enhanced (CE) and non-contrast-enhanced (NE) T1-weighted images (WI) in predicting the therapeutic response to chemoradiotherapy (CRT) in patients diagnosed with muscle-invasive bladder cancer (MIBC). Methods: Forty-three patients with non-metastatic MIBC (cT2-4N0M0) who underwent partial or radical cystectomy after induction CRT were, retrospectively, reviewed. Pathological complete response (pCR) to CRT was defined as the absence of residual viable tumor cells in the cystectomy specimen. Identical volumes of interest corresponding to the index bladder cancer lesions on CE- and NE-T1WI on pre-therapeutic 1.5-T MRI were collaboratively delineated by one radiologist and one urologist. Texture analysis was performed using "LIFEx" software. The subtraction of radiological features between CE- and NE-T1WI yielded 112 delta-radiomics features, which were utilized in multiple machine-learning algorithms to construct optimal predictive models for CRT responsiveness. Additionally, the predictive performance of the radiomics model constructed using CE-T1WI alone was assessed. Results: Twenty-one patients (49%) achieved pCR. The best-performing delta-radiomics model, employing the "Extreme Gradient Boosting" algorithm, yielded an area under the receiver operating characteristic curve (AUC) of 0.85 (95% confidence interval [CI]: 0.75-0.95), utilizing four signal intensity-based delta-radiomics features. This outperformed the best model derived from CE-T1WI alone (AUC: 0.63, 95% CI: 0.50-0.75), which incorporated two morphological features and one signal intensity-based radiomics feature. Conclusions: Delta-radiomics analysis applied to pre-therapeutic CE- and NE-MRI demonstrated promising predictive ability for CRT responsiveness prior to treatment initiation.

Preoperative Assessment of Ki-67 Labeling Index in Pituitary Adenomas Using Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI

BACKGROUND

Ki-67 labeling index (Ki-67 LI) is a proliferation marker that is correlated with aggressive behavior and prognosis of pituitary adenomas (PAs). Dynamic contrast-enhanced MRI (DCE-MRI) may potentially contribute to the preoperative assessment of Ki-67 LI.

PURPOSE

To investigate the feasibility of assessing Ki-67 LI of PAs preoperatively using delta-radiomics based on DCE-MRI.

STUDY TYPE

Retrospective.

POPULATION

605 PA patients (female = 47.1%, average age = 52.2) from two centers (high Ki-67 LI (≥ 3%) = 229; low Ki-67 LI (< 3%) = 376), divided into a training set (n = 313), an internal validation set (n = 196), and an external validation set (n = 96).

FIELD STRENGTH/SEQUENCE

1.5-T and 3-T, DCE-MRI.

ASSESSMENT

This study developed a non-delta-radiomics model based on the non-delta-radiomic features directly extracted from four phases, a delta-radiomics model based on the delta-radiomic features, and a combined model integrating clinical parameters (Knosp grade and tumor diameter) with delta-radiomic features. U test, recursive feature elimination (RFE), and least absolute shrinkage and selection operator (LASSO) regression were utilized to select important radiomic features. Support vector machine (SVM), XGBoost (XGB), logistic regression (LR), and Gaussian naive Bayes (GNB) were utilized to develop the models.

STATISTICAL TESTS

Receiver operating characteristic (ROC) curve. Calibration curve. Decision curve analysis (DCA). Intraclass correlation coefficients (ICC). DeLong test for ROC curves. U test or t test for numerical variables. Fisher's test or Chi-squared test for categorical variables. A p-value < 0.05 was considered statistically significant.

RESULTS

The combined model demonstrated the best performance in preoperatively assessing the Ki-67 LI of PAs, achieving AUCs of 0.937 and 0.897 in the internal and external validation sets, respectively. The models based on delta-radiomic features outperformed the non-delta-radiomic model.

DATA CONCLUSION

A delta-radiomics-based model using DCE-MRI may show high diagnostic performance for preoperatively assessing the Ki-67 LI status of PAs.

EVIDENCE LEVEL

3 Technical Efficacy: Stage 2.

Zinner syndrome: report of a case and whole exome sequencing

BACKGROUND

Zinner syndrome is a rare congenital malformation of the male genitourinary system, characterized by a triad: seminal vesicle cyst, unilateral renal agenesis, and ipsilateral ejaculatory duct obstruction. The etiology of this uncommon disease remains largely elusive; however, genetic mutations may contribute to its development. In this report, we present a case of symptomatic Zinner syndrome that was surgically treated, alongside an investigation into the potential genetic basis of the syndrome via whole exome sequencing.

CASE PRESENTATION

We report the case of an 18-year-old male presenting with urinary pain and was diagnosed with right renal agenesis and a left seminal vesicle cyst following comprehensive imaging. The patient also experienced perineal pain and urgency, without symptoms of frequent urination, dysuria, or hematuria, and no familial history of genitourinary anomalies was documented. He successfully underwent laparoscopic resection of a pelvic mass, with pathological examination confirming a seminal vesicle cyst. Postoperative recovery was uneventful. Whole exome sequencing of blood and tissue samples highlighted myeloma overexpressed gene (MYEOV), B melanoma antigen family member (BAGE), and N-acetylated-alpha-linked acidic dipeptidase 2 (NAALAD2) as potential mutated genes related to Zinner syndrome. Additionally, two predisposing genetic variants were identified.

CONCLUSIONS

Zinner syndrome is a rare condition commonly diagnosed via various imaging modalities. Surgical resection remains the most effective treatment for symptomatic cases. Gene sequencing provides valuable insights into the genetic etiology of Zinner syndrome, enhancing our understanding and potentially guiding future diagnostic approaches.

Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo

Cancer is a major global health challenge, accounting for nearly one in six deaths worldwide. Early diagnosis significantly improves survival rates and patient outcomes, yet in resource-limited settings, the scarcity of medical resources often leads to late-stage diagnosis. Integrating artificial intelligence (AI) with smartphone-based imaging systems offers a promising solution by providing portable, cost-effective, and widely accessible tools for early cancer detection. This paper introduces advanced smartphone-based imaging systems that utilize various imaging modalities for in vivo detection of different cancer types and highlights the advancements of AI for in vivo cancer detection in smartphone-based imaging. However, these compact smartphone systems face challenges like low imaging quality and restricted computing power. The use of advanced AI algorithms to address the optical and computational limitations of smartphone-based imaging systems provides promising solutions. AI-based cancer detection also faces challenges. Transparency and reliability are critical factors in gaining the trust and acceptance of AI algorithms for clinical application, explainable and uncertainty-aware AI breaks the black box and will shape the future AI development in early cancer detection. The challenges and solutions for improving AI accuracy, transparency, and reliability are general issues in AI applications, the AI technologies, limitations, and potentials discussed in this paper are applicable to a wide range of biomedical imaging diagnostics beyond smartphones or cancer-specific applications. Smartphone-based multimodal imaging systems and deep learning algorithms for multimodal data analysis are also growing trends, as this approach can provide comprehensive information about the tissue being examined. Future opportunities and perspectives of AI-integrated smartphone imaging systems will be to make cutting-edge diagnostic tools more affordable and accessible, ultimately enabling early cancer detection for a broader population.

Uncommon onset of lower urinary tract symptoms in a young adult: The impact of a large utricle cyst in a young adult

Lower urinary tract symptoms (LUTS) in young adults are uncommon, making diagnosis difficult. This report describes the case of a young adult with no medical history who developed LUTS, which included urgency, nocturia, dysuria, and pelvic pain that lasted six months. The absence of fever or infectious symptoms raised concerns about atypical etiologies. Comprehensive testing, including bacterial urine and sperm screening, ruled out an infectious cause. Imaging indicated that the underlying cause was a prostatic utricle cyst, which is an embryological remnant that goes unnoticed in many cases but may cause symptoms when it is large. The successful endoscopic excision of the cyst resulted in total discomfort relief.

Multiparametric MRI for Bladder Cancer: A Practical Approach to the Clinical Application of VI-RADS

Multiparametric MRI of the bladder is highly accurate in the detection and local staging of bladder cancer. The Vesical Imaging Reporting and Data System (VI-RADS) scoring system has improved the diagnostic accuracy, reproducibility, and interpretability of bladder MRI in the assessment of the invasion of the muscularis propria. There are several technical details concerning bladder MRI that need to be strictly applied to obtain the highest possible diagnostic potential from the MRI. In addition, image evaluation, accurate interpretation, and reporting need to be standardized to optimize diagnostic accuracy and interreader agreement. This review describes the patient population for bladder MRI and discusses, with a practical approach, the correct acquisition protocol for optimal image quality using VI-RADS with reporting tips, pitfalls, and challenges for its clinical application. This review also discusses the latest evidence, clinical implications, current controversies, and future challenges, including gaps in knowledge, of the VI-RADS scoring system.

Cystitis glandularis: MR imaging characteristics in 27 patients

PURPOSE

To explore the diagnostic characteristics of cystitis glandularis (CG) using magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A retrospective study was conducted on pathologically confirmed patients who underwent bladder MRI examination between January 2019 and November 2023. Image analysis was jointly conducted, with emphasis on lesion location, morphology, size, signal intensity, and pattern of enhancement, by two genitourinary radiologists with 22 and 15 years of experience, respectively.

RESULTS

A total of 27 patients with 27 lesions were included (median age 47 years, 24 males). The lesions were mostly located in the bladder trigone area (18/27). The lesions could be categorized as focal thickening (17/27), nodular (8/27), and diffuse thickening of the entire bladder (2/27) in morphological terms. On T2-weighted imaging (T2WI), 15 of 17 focal thickening lesions appeared as a slightly hyperintense thickened inner layer, with a higher signal in the center of the thickened inner layer, resembling a sandwich sign, and 6 of 8 nodular lesions were slightly hyperintense. On T1-weighted imaging (T1WI), 19 patients showed slight hypointensity. The lesions on DWI showed mainly high (5/27) and slightly high signal (21/27), with an average mean apparent diffusion coefficient (mADC) value of 2.171 ± 0.052 × 10-3mm2/s. Among the 23 patients who underwent dynamic contrast-enhanced (DCE) scanning, 18 lesions showed mild enhancement in the arterial phase (average 1.7 times comparing to unenhanced phase), and the degree of enhancement gradually increased in the venous and delayed phases (average 2.2 and 2.3 times compared to the unenhanced phase, respectively), showing a progressive enhancement pattern.

CONCLUSION

On MRI, the majority of CG manifest as focal thickening or nodules in the bladder trigone area, showing slight hyperintensity on T2WI, slight hypointensity on T1WI, and a progressive enhancement pattern, without significant restriction on DWI. Focal thickening lesions may exhibit a special sandwich sign.

The immune duality of osteopontin and its therapeutic implications for kidney transplantation

Osteopontin (OPN) is a multifunctional glycoprotein with various structural domains that enable it to perform diverse functions in both physiological and pathological states. This review comprehensively examines OPN from multiple perspectives, including its protein structure, interactions with receptors, interactions with immune cells, and roles in kidney diseases and transplantation. This review explores the immunological duality of OPN and its significance and value as a biomarker and therapeutic target in kidney transplantation. In cancer, OPN typically promotes tumor evasion by suppressing the immune system. Conversely, in immune-related kidney diseases, particularly kidney transplantation, OPN activates the immune system by enhancing the migration and activation of immune cells, thereby exacerbating kidney damage. This immunological duality may stem from different OPN splice variants and the exposure, after cleavage, of different structural domains, which play distinct biological roles in cellular interactions. Additionally, OPN has a significant biological impact posttransplantation and on chronic kidney disease and, highlighting its importance as a biomarker and potential therapeutic target. Future research should further explore the specific mechanisms of OPN in kidney transplantation to improve treatment strategies and enhance patient quality of life.

Advancements in noninvasive techniques for transplant rejection: from biomarker detection to molecular imaging

Transplant rejection remains a significant barrier to the long-term success of organ transplantation. Biopsy, although considered the gold standard, is invasive, costly, and unsuitable for routine monitoring. Traditional biomarkers, such as creatinine and troponin, offer limited predictive value owing to their low specificity, and conventional imaging techniques often fail to detect early organ damage, increasing the risk of undiagnosed rejection episodes. Considering these limitations, emerging noninvasive biomarkers and molecular imaging techniques hold promise for the early and accurate detection of transplant rejection, enabling personalized management strategies. This review highlights noninvasive biomarkers that predict, diagnose, and assess transplant prognosis by reflecting graft injury, inflammation, and immune responses. For example, donor-derived cell-free DNA (dd-cfDNA) is highly sensitive in detecting early graft injury, whereas gene expression profiling effectively excludes moderate-to-severe acute rejection (AR). Additionally, microRNA (miRNA) profiling enhances the diagnostic specificity for precise AR detection. Advanced molecular imaging techniques further augment the monitoring of rejection. Fluorescence imaging provides a high spatiotemporal resolution for AR grading, ultrasound offers real-time and portable monitoring, and magnetic resonance delivers high tissue contrast for anatomical assessments. Nuclear imaging modalities such as single photon emission computed tomography and positron emission tomography, enable dynamic visualization of immune responses within transplanted organs. Notably, dd-cfDNA and nuclear medicine imaging have already been integrated into clinical practice, thereby demonstrating the translational potential of these techniques. Unlike previous reviews, this work uniquely synthesizes advancements in both noninvasive biomarkers and molecular imaging, emphasizing their complementary strengths. Biomarkers deliver molecular-level insights, whereas imaging provides spatial and temporal resolution. Together, they create a synergistic framework for comprehensive and precise transplant monitoring. By bridging these domains, this review underscores their individual contributions and collective potential to enhance diagnostic accuracy, improve patient outcomes, and guide future research and clinical applications in transplant medicine.

Urologic Imaging: Infections and Inflammation

Urinary infections are common. Typically, infections of the upper and lower urinary tract do not require imaging. However, in the presence of confirmed or suspected complicated urinary tract infections, imaging to assess for underlying causes and complications is required. Computed tomography imaging is useful in identifying predisposing structural abnormalities of the urinary tracts and complications of urologic infection. Ultrasonography can be used to identify hydronephrosis and may be used to guide percutaneous intervention. Recurrent chronic infections can lead to end organ damage or chronic granulomatous processes. Rarely, systemic inflammatory disorders can involve the upper and lower urinary tracts.

VI steps to achieve VI-RADS assessment

Bladder cancer is categorized into nonmuscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), distinguished by the presence of detrusor muscle invasion. Urothelial cell carcinoma is the most common subtype of bladder cancer. Transurethral resection of bladder tumor (TURBT) is the standard approach for staging and managing NMIBC, while radical cystectomy remains the cornerstone treatment for MIBC. Multiparametric magnetic resonance imaging (mpMRI), comprising morphological imaging sequences (high-resolution T2-weighted images) and functional imaging sequences (dynamic contrast-enhanced images and diffusion-weighted images), serves as an ideal modality. It provides high-contrast resolution for visualizing bladder wall layers, thereby enabling proper and timely staging of bladder cancer. MRI can guide sampling resection and identify patients understaged after primary TURBT, facilitating appropriate surgical restaging. In 2018, the Vesical Imaging Reporting and Data System (VI-RADS), implementing a 5-point scale, was developed to standardize MRI protocols and reporting criteria-including tumor location, size, morphology, and invasiveness. The aim of this article is to navigate through all the steps to achieve VI-RADS assessment and to discuss practical pearls and pitfalls in the use of mpMRI. This approach can aid in pre-TURBT prediction of muscle invasion, representing an important asset in bladder cancer staging.

An empirical study of using radiology reports and images to improve intensive care unit mortality prediction

Objectives

The predictive intensive care unit (ICU) scoring system is crucial for predicting patient outcomes, particularly mortality. Traditional scoring systems rely mainly on structured clinical data from electronic health records, which can overlook important clinical information in narratives and images.

Materials and Methods

In this work, we build a deep learning-based survival prediction model that utilizes multimodality data for ICU mortality prediction. Four sets of features are investigated: (1) physiological measurements of Simplified Acute Physiology Score (SAPS) II, (2) common thorax diseases predefined by radiologists, (3) bidirectional encoder representations from transformers-based text representations, and (4) chest X-ray image features. The model was evaluated using the Medical Information Mart for Intensive Care IV dataset.

Results

Our model achieves an average C-index of 0.7829 (95% CI, 0.7620-0.8038), surpassing the baseline using only SAPS-II features, which had a C-index of 0.7470 (95% CI: 0.7263-0.7676). Ablation studies further demonstrate the contributions of incorporating predefined labels (2.00% improvement), text features (2.44% improvement), and image features (2.82% improvement).

Discussion and Conclusion

The deep learning model demonstrated superior performance to traditional machine learning methods under the same feature fusion setting for ICU mortality prediction. This study highlights the potential of integrating multimodal data into deep learning models to enhance the accuracy of ICU mortality prediction.

The role of the radiologist in the evaluation of male infertility: recommendations of the European Society of Urogenital Radiology-Scrotal and Penile Imaging Working Group (ESUR-SPIWG) for scrotal imaging

OBJECTIVES

The Scrotal and Penile Imaging Working Group (SPIWG) of the European Society of Urogenital Radiology (ESUR) aimed to produce recommendations on the role of the radiologist in the evaluation of male infertility focused on scrotal imaging.

METHODS

The authors independently performed an extensive literature Medline search and a review of the clinical practice and consensus opinion of experts in the field.

RESULTS

Scrotal ultrasound (US) is useful in investigating male infertility. US abnormalities related to abnormal sperm parameters (sperm concentration, total count, motility, and morphology) are low testicular volume (TV), testicular inhomogeneity (TI), cryptorchidism, testicular microlithiasis (TML), high-grade varicocele, bilateral absence of vas deferens, bilateral dilation and echotexture abnormalities of the epididymis. The proposed ESUR-SPIWG recommendations for imaging in the evaluation of male infertility are therefore: to measure TV; investigate TI; perform annual (US) follow-ups up to age 55 in men with a history of cryptorchidism/orchidopexy and/or in men with TML plus "additional risk factors" or with "starry sky" TML; perform scrotal/inguinal US in men with nonpalpable testis; perform scrotal US in men with abnormal sperm parameters to investigate lesions suggestive of tumors; evaluate varicocele in a standardized way; evaluate the presence or absence of vas deferens; investigate the epididymis to detect indirect signs suggesting obstruction and/or inflammation.

CONCLUSIONS

The ESUR-SPIWG recommends investigating infertile men with scrotal US focusing on TV, inhomogeneity, localization, varicocele, vas deferens, and epididymal abnormalities. Cryptorchidism, TML, and lesions should be detected in relation to the risk of testicular tumors.

CLINICAL RELEVANCE STATEMENT

The ESUR-SPIWG recommendations on scrotal imaging in the assessment of male infertility are useful to standardize the US examination, focus on US abnormalities most associated with abnormal semen parameters in an evidence-based manner, and provide a standardized report to patients.

KEY POINTS

So far, ESUR-SPIWG recommendations on scrotal imaging in the assessment of male infertility were not available. The ESUR-SPIWG recommends investigating infertile men with scrotal US focusing on testicular volume, inhomogeneity, localization, varicocele, vas deferens and epididymal abnormalities, and assessing cryptorchidism, testicular microlithiasis and lesions in relation to the risk of testicular tumors. The ESUR-SPIWG recommendations on scrotal imaging in the assessment of male infertility are useful to standardize the US examination, focus on US abnormalities most associated with abnormal sperm parameters in an evidence-based manner, and provide a standardized report to patients.

Imaging of Urologic Trauma

Urologic trauma encompassed a wide range of injuries affecting the urologic tract, resulting from various traumatic events or iatrogenic procedures. This review explores the clinical presentation, diagnostic strategies, and management approaches of urologic trauma, emphasizing the critical role of imaging, particularly computed tomography, in accurately assessing and guiding treatment decisions. Renal, ureteral, bladder, and urethral trauma are comprehensively discussed, including mechanisms of injury, classification systems, and therapeutic interventions. In addition, we discuss potential complications such as post-traumatic urinoma, delayed bleeding, urinary fistula, perinephric abscess, pyelonephritis, and hydronephrosis.

Utility of MRI in NMIBC and feasibility of avoiding Re-TURB in carefully selected patients: a systematic review

OBJECTIVE

This systematic review was conducted to synthesize current research on the role of repeated transurethral resection of the bladder (re-TURB) and the emerging use of magnetic resonance imaging (MRI) in discerning patient suitability for safely foregoing this procedure.

EVIDENCE ACQUISITION

Employing a methodical literature search, we consulted several bibliographic databases including PubMed, Science Direct, Scopus, and Embase. The review process adhered strictly to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines.

EVIDENCE SYNTHESIS

We evaluated data from 667 patients (mean age 65.8 years; age range 59-75 years) who underwent MRI prior to potential re-TURB. The gap between initial TURB and MRI was reported as 42 days in one study, while the interval between MRI and subsequent cystoscopy, with or without biopsy, varied from 21 days to 3 months. Initial TURB pathology for non-muscle invasive bladder cancer (NMIBC) patients identified stage Ta in 177 (42.5%) and T1 in 246 (57.5%) patients across three studies. High-grade and low-grade pathologic classifications were reported in 377 (64.5%) and 207 (35.5%) patients respectively in two studies. The VI-RADS scoring system's sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the detection of bladder cancer recurrence were 89%, 85.5%, 82.7%, and 96%, respectively. A total of 365 patients (54.7%) underwent re-TUR. Among NMIBC patients, re-TUR pathology revealed Ta in 22 cases (5.4%) and pT1 in 179 cases (44%) with VI-RADS 1-2, while no cases of Ta (0%) and 37 cases of T1 (9%) were reported with VI-RADS 4-5, as documented in two studies. Notably, only 69 patients (10.7%) were identified as having MIBC across all studies.

CONCLUSION

MRI is demonstrating reliability as a diagnostic tool for non-muscle invasive bladder cancers. The VI-RADS scoring system appears to be a promising approach in selecting patients for re-TURB. DW-MRI may serve as a primary diagnostic examination for patient follow-up post-TURB.

Diagnostic performance of artificial intelligence in detection of renal cell carcinoma: a systematic review and meta-analysis

OBJECTIVES

The detection of renal cell carcinoma (RCC) tumors in the earlier stages is of great importance for more effective treatment. Encouraged by the key role of imaging in the management of RCC, we conducted a systematic review and meta-analysis of the studies that made use of artificial intelligence (AI) for the detection of RCC to quantitatively determine the performance of AI for distinguishing related renal lesions.

MATERIALS AND METHODS

PubMed, Scopus, CENTRAL, and Embase electronic databases were systematically searched in November 2024 to identify studies that applied AI for the detection or classification of RCC. We conducted a meta-analysis to evaluate the diagnostic performance of utilized algorithms. Moreover, meta-regression was conducted over suspected covariates to evaluate potential sources of inter-study heterogeneity. Publication bias and quality assessment were also done for the included studies.

RESULTS

Sixty-four studies were included in this systematic review, of which 31 studies were selected for meta-analysis. The studies assessing algorithms' performance on internal validation showed pooled sensitivity and specificity of 85% (95% confidence interval [CI], 82 to 87) and 76% (95% CI, 70 to 80), respectively. Moreover, externally validated Al algorithms had a pooled sensitivity and specificity of 80% (95% CI, 73 to 84) and 90% (95% CI, 84 to 93), respectively. Studies that performed internal validation for clinician performance had a pooled sensitivity of 79% (95% CI, 72 to 85) and specificity of 60% (95% CI, 49 to 70).

CONCLUSION

The findings of the present study validate the acceptable performance of AI algorithms when contrasted with medical professionals in the identification and categorization of RCC. Nevertheless, the presence of heterogeneity between studies and the absence of coherence in the results underscore the necessity for the cautious interpretation of these results and additional prospective studies.

Screening and prognostic roles of renal volumetry and scintigraphy in the assessment of living kidney transplant donors, considering the early recovery of the residual renal function

BACKGROUND

The existing criteria for living kidney donors (LKDs)in Japan are controversial. We evaluated the roles of computed tomography volumetry (CTV) and 99 m Tc-diethylenetriamine penta-acetic acid (DTPA) scintigraphy in assessing preoperative and postoperative renal function and predicting early recovery of residual renal function.

METHODS

We retrospectively reviewed the medical charts of 175 consecutive LKDs who underwent donor nephrectomy (DN) at our institution between 2006 and 2022. Preoperative renal volume was assessed using enhanced CTV, and screening of renal functions was performed using 99 m Tc-DTPA scintigraphy. We evaluated the estimated glomerular filtration rate (eGFR), single-kidney eGFR (skeGFR), and recovery rate three months after DN.

RESULTS

We included 55 men and 81 women (median age, 59 years; median follow-up period, 73 months). Age > 60 years, hypertension, and total kidney volume/body surface area (TKV/BSA) < 170 mL/m2 independently predicted preoperative eGFR < 80 mL/min/1.73 m2, whereas total measured GFR < 80 mL/min/1.73 m2 independently predicted preoperative eGFR < 70 mL/min/1.73 m2. Regarding postoperative renal function, residual KV/BSA < 85 mL/m2 and ΔskeGFR ≤ 9 mL/min/1.73 m2 independently predicted postoperative eGFR < 60% of preoperative eGFR, and TKV/BSA < 170 mL/m2 independently predicted early recovery of skeGFR.

CONCLUSIONS

CTV may be used as a reliable prognostic screening tool to select LKDs and assess their split renal functions before DN, and renal scintigraphy may help select the optimal LKD.

Diagnostic value of dual-layer spectral detector CT parameters for differentiating high- from low-grade bladder cancer

OBJECTIVES

This study aimed to investigate the diagnostic value of spectral parameters of dual-layer spectral detector computed tomography (DLCT) in distinguishing between low- and high-grade bladder cancer (BCa).

METHODS

This single-center retrospective study included pathologically confirmed BCa patients who underwent preoperative contrast-enhanced DLCT. Patients were divided into low- and high-grade groups based on pathology. We measured and calculated the following spectral CT parameters: iodine density (ID), normalized ID (NID), arterial enhancement fraction (AEF), extracellular volume (ECV) fraction, virtual non-contrast (VNC), slope of the attenuation curve, and Z effective (Zeff). Univariate and multivariable logistic regression analyses were used to determine the best predictive factors in differentiating between low- and high-grade BCa. We used receiver operating characteristic curve analysis to assess diagnostic performance and decision curve analysis to determine the net benefit.

RESULTS

The study included 64 patients (mean age, 64 ± 11.0 years; 46 men), of whom 42 had high-grade BCa and 22 had low-grade BCa. Univariate analysis revealed that differences in ID and NID in the corticomedullary phase, AEF, ECV, VNC, and Zeff images were statistically significant (p = 0.001-0.048). Multivariable analysis found that AEF was the best predictor of high-grade tumors (p = 0.006). With AEF higher in high-grade BCa, AEF results were as follows: area under the curve (AUC), 0.924 (95% confidence interval, 0.861-0.988); sensitivity, 95.5%; specificity, 81.0%; and accuracy, 85.9%. The cutoff valve of AEF for predicting high-grade BCa was 67.7%.

CONCLUSION

Using DLCT AEF could help distinguish high-grade from low-grade BCa.

CRITICAL RELEVANCE STATEMENT

This research demonstrates that the arterial enhancement fraction (AEF), a parameter derived from dual-layer spectral detector CT (DLCT), effectively distinguishes between high- and low-grade bladder cancer, thereby aiding in the selection of appropriate clinical treatment strategies.

KEY POINTS

This study investigated the value of dual-layer spectral detector CT in the assessment of bladder cancer (BCa) histological grade. The spectral parameter arterial enhancement fraction could help determine BCa grade. Our results can help clinicians formulate initial treatment strategies and improve prognostications.

Computer-assisted diagnosis to improve diagnostic pathology: A review

ABSTRACT

With an increasing demand for accuracy and efficiency in diagnostic pathology, computer-assisted diagnosis (CAD) emerges as a prominent and transformative solution. This review aims to explore the practical applications, implications, strengths, and weaknesses of CAD applied to diagnostic pathology. A comprehensive literature search was conducted to include English-language studies focusing on CAD tools, digital pathology, and Artificial intelligence (AI) applications in pathology. The review underscores the transformative potential of CAD tools in pathology, particularly in streamlining diagnostic processes, reducing turnaround times, and augmenting diagnostic accuracy. It emphasizes the strides made in digital pathology, the integration of AI, and the promising prospects for prognostic biomarker discovery using computational methods. Additionally, ethical considerations regarding data privacy, equity, and trust in AI deployment are examined. CAD has the potential to revolutionize diagnostic pathology. The insights gleaned from this review offer a panoramic view of recent advancements. Ultimately, this review aims to guide future research, influence clinical practice, and inform policy-making by elucidating the promising horizons and potential pitfalls of integrating CAD tools in pathology.

Radiologic evaluation of the kidney transplant donor and recipient

The kidney is the most common solid organ transplant globally and rates continue to climb, driven by the increasing prevalence of end stage renal disease (ESRD). Compounded by advancements in surgical techniques and immunosuppression leading to longer graft survival, radiologists evermore commonly evaluate kidney transplant patients and candidates, underscoring their role along the transplant process. Multiphase computed tomography (CT) with multiplanar and 3D reformatting is the primary method for evaluating renal donor candidates, detailing renal size, vascular/collecting system anatomy, and identifying significant pathologies such as renal vascular diseases and nephrolithiasis. Ultrasound is the preferred initial postoperative imaging modality for graft evaluation due to its low cost, accessibility, noninvasiveness, and lack of radiation. CT and magnetic resonance imaging (MRI) may be useful adjunctive imaging techniques in diagnosing transplant pathology when ultrasound alone is not diagnostic. Kidney transplant complications are categorized by an approximate timeline framework, aiding in differential diagnosis based on onset, duration, and severity and include perinephric fluid collections, graft compression, iatrogenic injuries, vascular compromise, graft rejection, and neoplastic processes. This review discusses imaging strategies and important findings along the transplant timeline, from donor assessment to long-term recipient complications.

Validation of Clinical Dynamic Contrast-Enhanced Magnetic Resonance Imaging Perfusion Modeling and Neoadjuvant Chemotherapy Response Prediction in Breast Cancer Using 18FDG and 64Cu-DOTA-Trastuzumab Positron Emission Tomography Studies

PURPOSE

Perfusion modeling presents significant opportunities for imaging biomarker development in breast cancer but has historically been held back by the need for data beyond the clinical standard of care (SoC) and uncertainty in the interpretability of results. We aimed to design a perfusion model applicable to breast cancer SoC dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) series with results stable to low temporal resolution imaging, comparable with published results using full-resolution DCE-MRI, and correlative with orthogonal imaging modalities indicative of biophysical markers.

METHODS

Subsampled high-temporal-resolution DCE-MRI series were run through our perfusion model and resulting fits were compared for consistency. The fits were also compared against previously published results from institutions using the full resolution series. The model was then evaluated on a separate cohort for validity of biomarker indications. Finally, the model was used as a fundamental part of predicting response to neoadjuvant chemotherapy (NACT).

RESULTS

Temporally subsampled DCE-MRI series yield perfusion fit variations on the scale of 1% of the tumor median value when input frames are varied. Fits generated from pseudoclinical series are within the variation range seen between imaging sites (ρ = 0.55), voxel-wise. The model also demonstrates significant correlations with orthogonal positron emission tomography imaging, indicating potential for use as a biomarker proxy. Specifically, using the perfusion fits as the grounding for a biophysical simulation of response, we correctly predict the pathologic complete response status after NACT in 15 of 18 patients, for an accuracy of 0.83, with a specificity and sensitivity of 0.83 as well.

CONCLUSION

Clinical DCE-MRI data may be leveraged to provide stable perfusion fit results and indirectly interrogate the tumor microenvironment. These fits can then be used downstream for prediction of response to NACT with high accuracy.

A radiologist's guide to the galaxy of complications post total pelvic exenteration for rectal cancers

Total pelvic exenteration (TPE) is a complicated morbid surgery with a patient having to cope with two permanent stomas lifelong. TPE is often the only option for potential cure that can be offered to patients with low/very low rectal cancers with multicompartment involvement. While the Clavien Dindo classification is used for clinically assessing the severity of complications, it does not guide making an imaging diagnosis (1). Radiologists are often unaware of the complications post-TPE surgery, what imaging modality to use, and how to diagnose these. The complications can be fatal if undiagnosed or misinterpreted and can be certainly managed with a good prognosis if promptly detected and treated (2). This article will focus on normal expected postoperative anatomy in the pelvis and perineum; with emphasis on recognition of signs that may aid in the diagnosis of complications in a bed of surgically altered anatomy. Systematic identification and evaluation of the various conduits and stomas; imaging appearances of normal and abnormal pelvic and perineal reconstruction techniques; and a patterned approach to the diagnosis of early and delayed complications post-TPE will be illustrated using a collection of cases.

Artificial intelligence can help individualize Wilms tumor treatment by predicting tumor response to preoperative chemotherapy

PURPOSE

To create a computer-aided prediction (CAP) system to predict Wilms tumor (WT) responsiveness to preoperative chemotherapy (PC) using pre-therapy contrast-enhanced computed tomography (CECT).

MATERIALS AND METHODS

A single-center database was reviewed for children <18 years diagnosed with WT and received PC between 2001 and 2021. Patients were excluded if pre- and post-PC CECT were not retrievable. According to the Response Evaluation Criteria in Solid Tumors criteria, volumetric response was considered favorable if PC resulted in ≥30% tumor volume reduction. Histological response was considered favorable if post-nephrectomy specimens had ≥66% necrosis. Four steps were used to create the prediction model: tumor delineation; extraction of shape, texture and functionality-based features; integration of the extracted features and selection of the prediction model with the highest diagnostic performance. K-fold cross-validation allowed the presentation of all data in the training and testing phases.

RESULTS

A total of 63 tumors in 54 patients were used to train and test the prediction model. Patients were treated with 4-8 weeks of vincristine/actinomycin-D combination. Favorable volumetric and histologic responses were achieved in 46 tumors (73.0%) and 38 tumors (60.3%), respectively. Among machine learning classifiers, support vector machine had the best diagnostic performance with an accuracy, sensitivity, and specificity of 95.24%, 95.65%, and 94.12% for volumetric and 84.13%, 89.47%, 88% for histologic response prediction.

CONCLUSIONS

Based on pre-therapy CECT, CAP systems can help identify WT that are less likely to respond to PC with excellent accuracy. These tumors can be offered upfront surgery, avoiding the cons of PC.

Diagnostic accuracy and inter-reader agreement of the nacVI-RADS for bladder cancer treated with neoadjuvant chemotherapy: a prospective validation study

OBJECTIVE

The primary aim was to determine the performance of neoadjuvant chemotherapy VI-RADS (nacVI-RADS) in predicting response to systemic therapy in patients with MIBC and to evaluate its inter-reader agreement.

MATERIALS AND METHODS

Prospective study, including patients with non-metastatic muscle-invasive bladder cancer (MIBC) who underwent neoadjuvant chemotherapy before radical cystectomy (RC). Patients underwent pre- and post-treatment MRI. Radiological response was evaluated by two experienced radiologists using nacVI-RADS scoring system. Reference standard was defined using histopathological findings. Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy were calculated to assess nacVI-RADS performance for each reader. Inter-reader agreement was determined with Cohen's k statistics.

RESULTS

Fifty-five patients with non-metastatic MIBC, 46 males (84%) and 9 females (16%) with a median age of 69 (interquartile range (IQR) 66-72 years) were enrolled. Diagnostic performance of nacVI-RADS in detecting complete response to neoadjuvant chemotherapy showed a sensitivity of 76.5-85.3% and specificity of 76.2-81%. The area under the curve was 0.93 (95% CI: 0.86-0.99) for detecting any residual tissue, for the more experienced reader. Inter-reader agreement was optimal with a K of 0.85. In the multivariable logistic regression model, the variables showing independent correlation with response prediction to neoadjuvant therapy were nacVI-RADS score (p = 0.01 for the more experienced reader) and tumor regression grade (TRG; p < 0.001).

CONCLUSION

NacVI-RADS scoring system offers a reliable and reproducible approach, employing a well-structured and easily interpretable method, to assess the response to systemic therapy in patients with MIBC.

KEY POINTS

Question There is a lack of a standardized approach to distinguish between responders and non-responders to neoadjuvant chemotherapy for muscle-invasive bladder cancer. Findings The neoadjuvant chemotherapy VI-RADS (nacVI-RADS) score diagnostic performance for detecting complete response to neoadjuvant chemotherapy showed 85.3% sensitivity, 81% specificity, and an AUC of 0.93. Clinical relevance NacVI-RADS score represents a valid predictor of response to neoadjuvant systemic therapy, impacting therapeutic decision-making and improving overall patients' management.

Precise vesical wall staging of bladder cancer in the era of precision medicine: has it been fulfilled?

Urinary bladder cancer is a global disease that poses medical and socioeconomic challenges to patients and healthcare systems. Predicting detrusor invasiveness and pathological grade of bladder cancer by the radiologist is imperative for informed decision-making and effective patient-tailored therapy. Cystoscopy and TURBT are the current gold standard for preoperative histologic diagnosis and local pathological staging but are compromised by their intrusiveness, under-sampling, and staging inaccuracies. Over the last few decades, incredible imaging technology advancements have enabled radiologists to progress in these grading and staging tasks. MRI has become widely accepted as a noninvasive alternative. It supplements morphologic data with functional insights into the tumor microenvironment, enhancing tumor characterization and predicting the detrusor's histologic grade and invasiveness status. Radiomics is a promising field that helps radiologists achieve higher accuracies in bladder cancer staging, re-staging, and direct treating teams to potential management readjustments. Such knowledge leaps hold promise for personalized management of bladder cancer in a precision medicine era.

Revolutionizing treatment for disorders of consciousness: a multidisciplinary review of advancements in deep brain stimulation

Among the existing research on the treatment of disorders of consciousness (DOC), deep brain stimulation (DBS) offers a highly promising therapeutic approach. This comprehensive review documents the historical development of DBS and its role in the treatment of DOC, tracing its progression from an experimental therapy to a detailed modulation approach based on the mesocircuit model hypothesis. The mesocircuit model hypothesis suggests that DOC arises from disruptions in a critical network of brain regions, providing a framework for refining DBS targets. We also discuss the multimodal approaches for assessing patients with DOC, encompassing clinical behavioral scales, electrophysiological assessment, and neuroimaging techniques methods. During the evolution of DOC therapy, the segmentation of central nuclei, the recording of single-neurons, and the analysis of local field potentials have emerged as favorable technical factors that enhance the efficacy of DBS treatment. Advances in computational models have also facilitated a deeper exploration of the neural dynamics associated with DOC, linking neuron-level dynamics with macroscopic behavioral changes. Despite showing promising outcomes, challenges remain in patient selection, precise target localization, and the determination of optimal stimulation parameters. Future research should focus on conducting large-scale controlled studies to delve into the pathophysiological mechanisms of DOC. It is imperative to further elucidate the precise modulatory effects of DBS on thalamo-cortical and cortico-cortical functional connectivity networks. Ultimately, by optimizing neuromodulation strategies, we aim to substantially enhance therapeutic outcomes and greatly expedite the process of consciousness recovery in patients.

Prostate cancer prognosis using machine learning: A critical review of survival analysis methods

Prostate Cancer is a disease that affects the male reproductive system. The irregularity of the symptoms makes it hard for the clinicians to pinpoint the disease in the earlier stages. Techniques such as Machine Learning, Data Science, Deep Learning, etc. have been employed on the biomedical data to identify the symptoms of the patients and predict their stage and the chances of their survival. The survival analysis of prostate cancer is essential as it guides the clinicians to recommend the optimal treatment for the patient. Building an accurate model from electronic data using machine learning is quite difficult. This review article presents a systematic literature review focused on the area of prostate cancer survival analysis utilizing machine learning and other soft computing techniques. Through an extensive evaluation of the available research, we have identified and summarized key insights from the selected studies. A comprehensive comparison of various approaches for survival and treatment predictions in the literature has been conducted. Additionally, the gaps in previous research have been discussed, highlighting areas for further investigation and providing future recommendations. By synthesizing the current knowledge in prostate cancer survival analysis, this review contributes to the understanding of the field and lays the foundation for future advancements.

Arterial input function estimation compensating for inflow and partial voluming in dynamic contrast-enhanced MRI

Both inflow and the partial volume effect (PVE) are sources of error when measuring the arterial input function (AIF) in dynamic contrast-enhanced (DCE) MRI. This is relevant, as errors in the AIF can propagate into pharmacokinetic parameter estimations from the DCE data. A method was introduced for flow correction by estimating and compensating the number of the perceived pulse of spins during inflow. We hypothesized that the PVE has an impact on concentration-time curves similar to inflow. Therefore, we aimed to study the efficiency of this method to compensate for both effects simultaneously. We first simulated an AIF with different levels of inflow and PVE contamination. The peak, full width at half-maximum (FWHM), and area under curve (AUC) of the reconstructed AIFs were compared with the true (simulated) AIF. In clinical data, the PVE was included in AIFs artificially by averaging the signal in voxels surrounding a manually selected point in an artery. Subsequently, the artificial partial volume AIFs were corrected and compared with the AIF from the selected point. Additionally, corrected AIFs from the internal carotid artery (ICA), the middle cerebral artery (MCA), and the venous output function (VOF) estimated from the superior sagittal sinus (SSS) were compared. As such, we aimed to investigate the effectiveness of the correction method with different levels of inflow and PVE in clinical data. The simulation data demonstrated that the corrected AIFs had only marginal bias in peak value, FWHM, and AUC. Also, the algorithm yielded highly correlated reconstructed curves over increasingly larger neighbourhoods surrounding selected arterial points in clinical data. Furthermore, AIFs measured from the ICA and MCA produced similar peak height and FWHM, whereas a significantly larger peak and lower FWHM was found compared with the VOF. Our findings indicate that the proposed method has high potential to compensate for PVE and inflow simultaneously. The corrected AIFs could thereby provide a stable input source for DCE analysis.

MRI-based vector radiomics for predicting breast cancer HER2 status and its changes after neoadjuvant therapy

PURPOSE

To develop a novel MRI-based vector radiomic approach to predict breast cancer (BC) human epidermal growth factor receptor 2 (HER2) status (zero, low, and positive; task 1) and its changes after neoadjuvant therapy (NAT) (positive-to-positive, positive-to-negative, and positive-to-pathologic complete response; task 2).

MATERIALS AND METHODS

Both dynamic contrast-enhanced (DCE) MRI data and multi-b-value (MBV) diffusion-weighted imaging (DWI) data were acquired in BC patients at two centers. Vector-radiomic and conventional-radiomic features were extracted from both DCE-MRI and MBV-DWI. After feature selection, the following models were built using the retained features and logistic regression: vector model, conventional model, and combined model that integrates the vector-radiomic and conventional-radiomic features. The models' performances were quantified by the area under the receiver-operating characteristic curve (AUC).

RESULTS

The training/external test set (center 1/2) included 483/361 women. For task 1, the vector model (AUCs=0.73∼0.86) was superior to (p<.05) the conventional model (AUCs=0.68∼0.81), and the addition of vector-radiomic features to conventional-radiomic features yielded an incremental predictive value (AUCs=0.80∼0.90, p<.05). For task 2, the combined MBV-DWI model (AUCs=0.85∼0.89) performed better than (p<.05) the conventional MBV-DWI model (AUCs=0.73∼0.82). In addition, for the combined DCE-MRI model and the combined MBV-DWI model, the former (AUCs=0.85∼0.90) outperformed (p<.05) the latter (AUCs=0.80∼0.85) in task 1, whereas the latter (AUCs=0.85∼0.89) outperformed (p<.05) the former (AUCs=0.76∼0.81) in task 2. The above results are true for the training and external test sets.

CONCLUSIONS

MRI-based vector radiomics may predict BC HER2 status and its changes after NAT and provide significant incremental prediction over and above conventional radiomics.

[Use of clinical ultrasound in primary care: Hematuria]

Hematuria is a frequent entity in primary care. The differential diagnosis covers multiple causes: physiological, pharmacological, false hematuria and urological pathologies, being fundamental in its study to assess the possible malignant neoplastic causes. Urologic ultrasound is a non-invasive technique, using a 3.5-5MHz concave probe, with the patient lying supine and the bladder full. After anamnesis, physical examination, study of urinarium sediment and laboratory analysis to determine renal function, ultrasound allows the family doctor to confirm or rule out a large number of processes related to the etiology of hematuria: cysts and kidney masses, renal lithiasis, nephrocalcinosis, benign prostatic hyperplasia, polyps or vesical masses... However, this alone is not sufficient to establish a firm diagnosis in all cases. Currently, there is no general consensus about the most appropriate diagnostic sequence in the study of hematuria, and several clinical guidelines were chosen for the application of different strategies depending on the risk factors. However, ultrasound together with cystoscopy has been positioned as the most cost-effective diagnostic strategy in most cases. The use of ultrasound in the evaluation of the patient with hematuria in primary care allows a valuable diagnostic approach to be made, detecting warning signs and properly orienting the patient's referral to other levels, if necessary, early.

Transperineal anastomotic urethroplasty with distal transection versus proximal transection: How to predict?

Objectives

To evaluate the pubourethral stump angle (PUA) to determine the site of urethral transection during transperineal anastomotic urethroplasty (TAU).

Patients and methods

Patients diagnosed with pelvic fracture urethral distraction defect who underwent preoperative magnetic resonance (MR) urethrography and were treated with TAU between June 2019 and December 2021 were retrospectively reviewed. According to the site of urethral transection during TAU, patients were classified into proximal and distal groups receiving TAU with proximal and distal transection, respectively. The demographic and clinical data were recorded. The PUA was measured on sagittal T2-weighted MR urethrography. The relationship between the site of urethral transection and PUA was analyzed.

Results

Sixty-seven patients were included. Forty-one and 26 patients were included in the proximal and distal groups, respectively. Finally, the success rates in the proximal and distal groups were 95.1% and 92.3%, respectively. The PUAs were 123.7° ± 14.6° and 86.5° ± 9.8° (p = 0.005), respectively. The curves for the 2 groups intersected between 90° and 110°. The scribing effects at 90°, 100°, and 110° in the 2 groups were compared in detail. Compared with 90° and 110°, 100° had the highest sensitivity as the demarcation line.

Conclusions

In the treatment of pelvic fracture urethral distraction defect, the PUA on MR urethrography is an objective and valid parameter for evaluating the site of urethral transection during TAU. A PUA >100° indicates that proximal transection should be preferentially attempted.

Zinner syndrome: a radiological journey through a little known condition

Zinner syndrome is a rare congenital urological entity, secondary to an alteration in embryogenesis between 4th and 13th weeks of gestation, specifically because of abnormalities in the development of the distal mesonephric duct. It is characterized by the triad of unilateral renal agenesis, cystic dilatation of the ipsilateral seminal vesicle and ipsilateral ejaculatory duct obstruction. The aim of this article is to provide the reader with all the necessary information to be able to suspect the presence of this syndrome, reviewing its physiopathology, clinical manifestations and the imaging techniques that enable its diagnosis, emphasizing those radiological findings by MRI that should lead us to think about it. This work is illustrated with representative radiological images of cases belonging to our institution, including patients with different variants of Zinner syndrome. We also include an overview of the embryology of the male urogenital system, to remember the role of the mesonephric duct and the ureteral bud in the formation of the different urogenital structures, as well as a differential diagnosis that allows us to differentiate seminal vesicle cysts from other pelvic cystic lesions.

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

Histopathological tissue classification is a fundamental task in computational pathology. Deep learning (DL)-based models have achieved superior performance but centralized training suffers from the privacy leakage problem. Federated learning (FL) can safeguard privacy by keeping training samples locally, while existing FL-based frameworks require a large number of well-annotated training samples and numerous rounds of communication which hinder their viability in real-world clinical scenarios. In this article, we propose a lightweight and universal FL framework, named federated deep-broad learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication. By simply integrating a pretrained DL feature extractor, a fast and lightweight broad learning inference system with a classical federated aggregation approach, FedDBL can dramatically reduce data dependency and improve communication efficiency. Five-fold cross-validation demonstrates that FedDBL greatly outperforms the competitors with only one-round communication and limited training samples, while it even achieves comparable performance with the ones under multiple-round communications. Furthermore, due to the lightweight design and one-round communication, FedDBL reduces the communication burden from 4.6 GB to only 138.4 KB per client using the ResNet-50 backbone at 50-round training. Extensive experiments also show the scalability of FedDBL on model generalization to the unseen dataset, various client numbers, model personalization and other image modalities. Since no data or deep model sharing across different clients, the privacy issue is well-solved and the model security is guaranteed with no model inversion attack risk. Code is available at https://github.com/tianpeng-deng/FedDBL.

Initial experience in implementing quantitative DCE-MRI to predict breast cancer therapy response in a multi-center and multi-vendor platform setting

Quantitative dynamic contrast-enhanced (DCE) MRI as a promising method for the prediction of breast cancer response to neoadjuvant chemotherapy (NAC) has been demonstrated mostly in single-center and single-vendor platform studies. This preliminary study reports the initial experience in implementing quantitative breast DCE-MRI in multi-center (MC) and multi-vendor platform (MP) settings to predict NAC response. MRI data, including B1 mapping, variable flip angle (VFA) measurements of native tissue R1 (R1,0), and DCE-MRI, were acquired during NAC at three sites using 3T systems with Siemens, Philips, and GE platforms, respectively. High spatiotemporal resolution DCE-MRI was performed using similar vendor product sequences with k-space undersampling during acquisition and view sharing during reconstruction. A breast phantom was used for quality assurance/quality control (QA/QC) across sites. The Tofts model (TM) and shutter-speed model (SSM) were used for pharmacokinetic (PK) analysis of the DCE data. Additionally, tumor region of interest (ROI)- vs. voxel-based analyses in combination with the use of VFA-measured R1,0 vs. fixed, literature-reported R1,0 were investigated to determine the optimal analysis approach. Results from 15 patients who completed the study are reported. Voxel-based PK analysis using fixed R1,0 was deemed the optimal approach, which allowed the inclusion of data from one vendor platform where VFA measurements produced ≥100% overestimation of R1,0. The semi-quantitative signal enhancement ratio (SER) and quantitative PK parameters outperformed the tumor longest diameter (LD) in the prediction of pathologic complete response (pCR) vs. non-pCR after the first NAC cycle, whereas Ktrans consistently provided more accurate predictions than both SER and LD after the first NAC cycle and at the NAC midpoint. Both TM and SSM Ktrans and kep were excellent predictors of response at the NAC midpoint with ROC AUC >0.90, while the SSM parameters (AUC ≥0.80) performed better than their TM counterparts (AUC <0.80) after the first NAC cycle. The initial experience of this ongoing study indicates the importance of QA/QC using a phantom and suggests that deploying voxel-based PK analysis using a fixed R1,0 may mitigate random errors from R1,0 measurements across platforms and potentially eliminate the need for B1 and VFA acquisitions in MC and MP trials.