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Introduction: Previous literature has shown that clear cell renal cell carcinoma (ccRCC) is becoming a more prevalent diagnosis and that the incidence and mortality differ both regionally and racially. While the molecular profiles for ccRCC are studied regionally through biopsy and sequencing techniques, the genomic landscape and ccRCC diversity data are not well-studied. We conducted a review of the known genomic data on 6 of the most clinically relevant DNA biomarkers in ccRCC: Von Hippel-Landau (vHL), Polybromo-1 (PBRM1), Breast Cancer Gene 1-Associated Protein 1 (BAP1), Histone-Lysine N-Methyltransferase Domain-Containing 2 (SETD2), Mammalian Target of Rapamycin (mTOR), and Lysine-Specific Demethylase 5C (KDM5C). The review compiled genomic diversity data, incidence, and risk factor differences by geographical and racial cohorts., Methods: The review methodology was created using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) principles from articles on PubMed and Embase through July 31, 2023, written and published in English, with diagnoses of primary or metastatic ccRCC via cytology or pathology, recorded the incidence of one or more of the 6 biomarkers, explored gene aberration via sequencing, were epidemiological in nature; and/or discussed basic science research, cohort studies, or retrospective studies., Results: Aberrations in vHL, PBRM1, and SETD2 driving ccRCC are studied frequently, but the data is heterogenous; whereas, there is a paucity in the data regarding KDM5C, PBRM1, and mTOR mutations., Conclusion: Studying the genetic aberrations that frequently occur in different regions gives insight into what current research lacks. When more genomic landscape research arises, precision therapy, risk calculators, and artificial intelligence may help better prognosticate and individualize treatment for those at risk for ccRCC. Provided the scarcity of existing data, and the rising prevalence of ccRCC, more studies must be conducted at the clinical level., (The Author(s). Published by S. Karger AG, Basel.)

Purpose: To compare transperineal (TP) vs transrectal (TR) magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) fusion-guided prostate biopsy (PBx) in a large, ethnically diverse and multiracial cohort., Materials and Methods: Consecutive patients who underwent multiparametric (mp) MRI followed by TP or TR TRUS-fusion guided PBx, were identified from a prospective database (IRB #HS-13-00663). All patients underwent mpMRI followed by 12-14 core systematic PBx. A minimum of two additional target-biopsy cores were taken per PIRADS≥3 lesion. The endpoint was the detection of clinically significant prostate cancer (CSPCa; Grade Group, GG≥2). Statistical significance was defined as p<0.05., Results: A total of 1491 patients met inclusion criteria, with 480 undergoing TP and 1011 TR PBx. Overall, 11% of patients were Asians, 5% African Americans, 14% Hispanic, 14% Others, and 56% White, similar between TP and TR (p=0.4). For PIRADS 3-5, the TP PBx CSPCa detection was significantly higher (61% vs 54%, p=0.03) than TR PBx, but not for PIRADS 1-2 (13% vs 13%, p=1.0). After adjusting for confounders on multivariable analysis, Black race, but not the PBx approach (TP vs TR), was an independent predictor of CSPCa detection. The median maximum cancer core length (11 vs 8mm; p<0.001) and percent (80% vs 60%; p<0.001) were greater for TP PBx even after adjusting for confounders., Conclusions: In a large and diverse cohort, Black race, but not the biopsy approach, was an independent predictor for CSPCa detection. TP and TR PBx yielded similar CSPCa detection rates; however the TP PBx was histologically more informative., Competing Interests: None declared., (Copyright® by the International Brazilian Journal of Urology.)

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as "black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: To evaluate the learning curve of a transperineal (TP) magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) fusion prostate biopsy (PBx)., Materials and Methods: Consecutive patients undergoing MRI followed by TP PBx from May/2017 to January/2023, were prospectively enrolled (IRB# HS-13-00663). All participants underwent MRI followed by 12 to 14 core systematic PBx (SB), with at least 2 additional targeted biopsy (TB) cores per PIRADS ≥3. The biopsies were performed transperineally using an organ tracking image-fusion system. The cohort was divided into chronological quintiles. An inflection point analysis was performed to determine proficiency. Operative time was defined from insertion to removal of the TRUS probe from the patient's rectum. Grade Group ≥2 defined clinically significant prostate cancer (CSPCa). Statistically significant if P < 0.05., Results: A total of 370 patients were included and divided into quintiles of 74 patients. MRI findings and PIRADS distribution were similar between quintiles (P = 0.08). The CSPCa detection with SB+TB was consistent across quintiles: PIRADS 1 and 2 (range, 0%-18%; P = 0.25); PIRADS 3 to 5 (range, 46%-70%; P = 0.12). The CSPCa detection on PIRADS 3 to 5 TB alone, for quintiles 1 to 5, was respectively 44%, 58%, 66%, 41%, and 53% (P = 0.08). The median operative time significantly decreased for PIRADS 1 and 2 (33 min to 13 min; P < 0.01) and PIRADS 3 to 5 (48 min to 19 min; P < 0.01), reaching a plateau after 156 cases. Complications were not significantly different across quintiles (range, 0-5.4%; P = 0.3)., Conclusions: The CSPCa detection remained consistently satisfactory throughout the learning curve of the Transperineal MRI/TRUS fusion prostate biopsy. However, the operative time significantly decreased with proficiency achieved after 156 cases., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Receives consultancy fees from Merck, MSD, and Novartis and has equity in Rocketlane Medical Ventures GmbH - Severin Rodler; Has equity interest in OneLine Health and Karkinos - Inderbir Gill; Is a consultant for Koelis, a speaker for EDAP, and a proctor for Sonablate - Andre Luis Abreu; Other authors do not have any competing interests. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background and Purpose: Considering recent iodinated contrast shortages and a focus on reducing waste, developing protocols with lower contrast dosing while maintaining image quality through artificial intelligence is needed. This study compared reduced iodinated contrast media and standard dose CTP acquisitions, and the impact of deep learning denoising on CTP image quality in preclinical and clinical studies. The effect of reduced X-ray mAs dose was also investigated in preclinical studies., Materials and Methods: Twelve swine underwent 9 CTP examinations each, performed at combinations of 3 different x-ray (37, 67, and 127 mAs) and iodinated contrast media doses (10, 15, and 20 mL). Clinical CTP acquisitions performed before and during the iodinated contrast media shortage and protocol change (from 40 to 30 mL) were retrospectively included. Eleven patients with reduced iodinated contrast media dosages and 11 propensity-score-matched controls with the standard iodinated contrast media dosages were included. A residual encoder-decoder convolutional neural network (RED-CNN) was trained for CTP denoising using k- space-weighted image average filtered CTP images as the target. The standard, RED-CNN-denoised, and k- space-weighted image average noise-filtered images for animal and human studies were compared for quantitative SNR and qualitative image evaluation., Results: The SNR of animal CTP images decreased with reductions in iodinated contrast media and milliampere-second doses. Contrast dose reduction had a greater effect on SNR than milliampere-second reduction. Noise-filtering by k- space-weighted image average and RED-CNN denoising progressively improved the SNR of CTP maps, with RED-CNN resulting in the highest SNR. The SNR of clinical CTP images was generally lower with a reduced iodinated contrast media dose, which was improved by the k- space-weighted image average and RED-CNN denoising ( P  < .05). Qualitative readings consistently rated RED-CNN denoised CTP as the best quality, followed by k- space-weighted image average and then standard CTP images., Conclusions: Deep learning-denoising can improve image quality for low iodinated contrast media CTP protocols, and could approximate standard iodinated contrast media dose CTP, in addition to potentially improving image quality for low milliampere-second acquisitions., (© 2024 by American Journal of Neuroradiology.)

Objective: Patients with relapsed or metastatic head and neck squamous cell carcinoma (HNSCC) after primary local therapy have low response rates with cetuximab, systemic chemotherapy or check point inhibitor therapy. Novel combination therapies with the potential to improve outcomes for patients with HNSCC is an area of high unmet need., Methods: This is a phase II single-arm clinical trial of locally advanced or metastatic HNSCC patients treated with a combination of soluble EphB4-human serum albumin (sEphB4-HSA) fusion protein and pembrolizumab after platinum-based chemotherapy with up to 2 prior lines of treatment. The primary endpoints were safety and tolerability and the primary efficacy endpoint was overall response rate (ORR). Secondary endpoints included progression free survival (PFS) and overall survival (OS). HPV status and EphrinB2 expression were evaluated for outcome., Results: Twenty-five patients were enrolled. Median follow up was 40.4 months (range 9.8 - 40.4). There were 6 responders (ORR 24%). There were 5 responders in the 11 HPV-negative and EphrinB2 positive patients, (ORR 45%) with 2 of these patients achieving a complete response (CR). The median PFS in HPV-negative/EphrinB2 positive patients was 3.2 months (95% CI 1.1, 7.3). Median OS in HPV-negative/EphrinB2 positive patients was 10.9 months (95% CI 2.0, 13.7). Hypertension, transaminitis and fatigue were the most common toxicities., Discussion: The combination of sEphB4-HSA and pembrolizumab has a favorable toxicity profile and favorable activity particularly among HPV-negative EphrinB2 positive patients with HNSCC.

Purpose: We assessed the effect of prophylactic biologic mesh on parastomal hernia (PSH) development in patients undergoing cystectomy and ileal conduit (IC)., Materials and Methods: This phase 3, randomized, controlled trial (NCT02439060) included 146 patients who underwent cystectomy and IC at the University of Southern California between 2015 and 2021. Follow-ups were physical exam and CT every 4 to 6 months up to 2 years. Patients were randomized 1:1 to receive FlexHD prophylactic biological mesh using sublay intraperitoneal technique vs standard IC. The primary end point was time to radiological PSH, and secondary outcomes included clinical PSH with/without surgical intervention and mesh-related complications., Results: The 2 arms were similar in terms of baseline clinical features. All surgeries and mesh placements were performed without any intraoperative complications. Median operative time was 31 minutes longer in patients who received mesh, yet with no statistically significant difference (363 vs 332 minutes, P = .16). With a median follow-up of 24 months, radiological and clinical PSHs were detected in 37 (18 mesh recipients vs 19 controls) and 16 (8 subjects in both arms) patients, with a median time to radiological and clinical PSH of 8.3 and 15.5 months, respectively. No definite mesh-related adverse events were reported. Five patients (3 in the mesh and 2 in the control arm) required surgical PSH repair. Radiological PSH-free survival rates in the mesh and control groups were 74% vs 75% at 1 year and 69% vs 62% at 2 years., Conclusions: Implementation of biologic mesh at the time of IC construction is safe without significant protective effects within 2 years following surgery.

Purpose: To create a nomogram to predict the absence of clinically significant prostate cancer (CSPCa) in males with non-suspicion multiparametric magnetic resonance imaging (mpMRI) undergoing prostate biopsy (PBx)., Materials and Methods: We identified consecutive patients who underwent 3T mpMRI followed by PBx for suspicion of PCa or surveillance follow-up. All patients had Prostate Imaging Reporting and Data System score 1-2 (negative mpMRI). CSPCa was defined as Grade Group ≥2. Multivariate logistic regression analysis was performed via backward elimination. Discrimination was evaluated with area under the receiver operating characteristic (AUROC). Internal validation with 1,000x bootstrapping for estimating the optimism corrected AUROC., Results: Total 327 patients met inclusion criteria. The median (IQR) age and PSA density (PSAD) were 64 years (58-70) and 0.10 ng/mL2 (0.07-0.15), respectively. Biopsy history was as follows: 117 (36%) males were PBx-naive, 130 (40%) had previous negative PBx and 80 (24%) had previous positive PBx. The majority were White (65%); 6% of males self-reported Black. Overall, 44 (13%) patients were diagnosed with CSPCa on PBx. Black race, history of previous negative PBx and PSAD ≥0.15ng/mL2 were independent predictors for CSPCa on PBx and were included in the nomogram. The AUROC of the nomogram was 0.78 and the optimism corrected AUROC was 0.75., Conclusions: Our nomogram facilitates evaluating individual probability of CSPCa on PBx in males with PIRADS 1-2 mpMRI and may be used to identify those in whom PBx may be safely avoided. Black males have increased risk of CSPCa on PBx, even in the setting of PIRADS 1-2 mpMRI., Competing Interests: None declared., (Copyright® by the International Brazilian Journal of Urology.)

Numerous MRI-based artificial intelligence (AI) frameworks have been designed for prostate cancer lesion detection, segmentation, and classification via MRI as a result of intrareader and interreader variability that is inherent to traditional interpretation. Open-source data sets have been released with the intention of providing freely available MRIs for the testing of diverse AI frameworks in automated or semiautomated tasks. Here, an in-depth assessment of the performance of MRI-based AI frameworks for detecting, segmenting, and classifying prostate lesions using open-source databases was performed. Among 17 data sets, 12 were specific to prostate cancer detection/classification, with 52 studies meeting the inclusion criteria., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The application of artificial intelligence (AI) on prostate magnetic resonance imaging (MRI) has shown promising results. Several AI systems have been developed to automatically analyze prostate MRI for segmentation, cancer detection, and region of interest characterization, thereby assisting clinicians in their decision-making process. Deep learning, the current trend in imaging AI, has limitations including the lack of transparency "black box", large data processing, and excessive energy consumption. In this narrative review, the authors provide an overview of the recent advances in AI for prostate cancer diagnosis and introduce their next-generation AI model, Green Learning, as a promising solution., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Image imputation refers to the task of generating a type of medical image given images of another type. This task becomes challenging when the difference between the available images, and the image to be imputed is large. In this manuscript, one such application is considered. It is derived from the dynamic contrast enhanced computed tomography (CECT) imaging of the kidneys: given an incomplete sequence of three CECT images, we are required to impute the missing image. This task is posed as one of probabilistic inference and a generative algorithm to generate samples of the imputed image, conditioned on the available images, is developed, trained, and tested. The output of this algorithm is the "best guess" of the imputed image, and a pixel-wise image of variance in the imputation. It is demonstrated that this best guess is more accurate than those generated by other, deterministic deep-learning based algorithms, including ones which utilize additional information and more complex loss terms. It is also shown that the pixel-wise variance image, which quantifies the confidence in the reconstruction, can be used to determine whether the result of the imputation meets a specified accuracy threshold and is therefore appropriate for a downstream task., (© 2024. The Author(s).)

Introduction: We examine the heterogeneity and distribution of the cohort populations in two publicly used radiological image cohorts, the Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCIA TCGA KIRC) collection and 2019 MICCAI Kidney Tumor Segmentation Challenge (KiTS19), and deviations in real-world population renal cancer data from the National Cancer Database (NCDB) Participant User Data File (PUF) and tertiary center data. PUF data are used as an anchor for prevalence rate bias assessment. Specific gene expression and, therefore, biology of RCC differ by self-reported race, especially between the African American and Caucasian populations. AI algorithms learn from datasets, but if the dataset misrepresents the population, reinforcing bias may occur. Ignoring these demographic features may lead to inaccurate downstream effects, thereby limiting the translation of these analyses to clinical practice. Consciousness of model training biases is vital to patient care decisions when using models in clinical settings., Methods: Data elements evaluated included gender, demographics, reported pathologic grading, and cancer staging. American Urological Association risk levels were used. Poisson regression was performed to estimate the population-based and sample-specific estimation for prevalence rate and corresponding 95% confidence interval. SAS 9.4 was used for data analysis., Results: Compared to PUF, KiTS19 and TCGA KIRC oversampled Caucasian by 9.5% (95% CI, -3.7 to 22.7%) and 15.1% (95% CI, 1.5 to 28.8%), undersampled African American by -6.7% (95% CI, -10% to -3.3%), and -5.5% (95% CI, -9.3% to -1.8%). Tertiary also undersampled African American by -6.6% (95% CI, -8.7% to -4.6%). The tertiary cohort largely undersampled aggressive cancers by -14.7% (95% CI, -20.9% to -8.4%). No statistically significant difference was found among PUF, TCGA, and KiTS19 in aggressive rate; however, heterogeneities in risk are notable., Conclusion: Heterogeneities between cohorts need to be considered in future AI training and cross-validation for renal masses., (© 2023 The Author(s). Published by S. Karger AG, Basel.)

Introduction: Renal cell carcinoma (RCC) is the ninth most common cancer worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a noninvasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME., Methods: TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including random forest, Adaptive Boosting, and ElasticNet, were used to predict TAM population and tumor-TAM clustering., Results: The best models achieved an area under the ROC curve of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively., Conclusion: Our study demonstrates the potential of using CT radiomics-derived imaging markers as a surrogate for assessment of TAM in ccRCC for real-time treatment response monitoring and patient selection for targeted therapies and immunotherapies., (© 2023 The Author(s). Published by S. Karger AG, Basel.)

Objective: To assess the effect of body muscle and fat metrics on the development of radiologic incisional hernia (IH) following robotic nephrectomy., Materials and Methods: We retrospectively reviewed the records of patients who underwent robotic nephrectomy for kidney tumors between 2011 and 2017. All pre- and postoperative CTs were re-reviewed by experienced radiologists for detection of radiologic IH and calculation of the following metrics using Synapse 3D software: cross-sectional psoas muscle mass at the level of L3 and L4 as well as subcutaneous and visceral fat areas. Sarcopenia was defined as psoas muscle index below the lowest quartile. Cox proportional hazard model was constructed to examine the association between muscle and fat metrics and the risk of developing radiologic IH., Results: A total of 236 patients with a median (IQR) age of 64 (54-70) years were included in this study. In a median (IQR) follow-up of 23 (14-38) months, 62 (26%) patients developed radiologic IH. On Cox proportional hazard model, we were unable to detect an association between sarcopenia and risk of IH development. In terms of subcutaneous fat change from pre-op, both lower and higher values were associated with IH development (HR (95% CI) 2.1 (1.2-3.4), p = 0.01 and 2.4 (1.4-4.1), p < 0.01 for < Q1 and ≥ Q3, respectively). Similar trend was found for visceral fat area changes from pre-op with a HR of 2.8 for < Q1 and 1.8 for ≥ Q3., Conclusion: Both excessive body fat gain and loss are associated with development of radiologic IH in patients undergoing robotic nephrectomy., (© 2023. The Author(s).)

Medical imaging is a critical tool in the detection, staging, and treatment planning of upper urinary tract urothelial carcinoma (UTUC). This article reviews the strengths and weaknesses of the different imaging techniques and modalities available clinically. This includes multidetector computed tomography (CT), multiparametric magnetic resonance imaging (MRI), ultrasound (US), and positron emission tomography (PET) for the detection, staging, and management of UTUC. In addition, we review the imaging techniques that are being developed and are on the horizon but have not yet made it to clinical practice. Firstly, we review the imaging findings of primary UTUC and the techniques across multiple modalities. We then discuss imaging findings of metastatic disease. Lastly, we describe the role of imaging in the surveillance after resection of primary UTUC based upon current guidelines.

The objective of this study was to compare transperineal (TP) versus transrectal (TR) magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) fusion prostate biopsy (PBx). Consecutive men who underwent prostate MRI followed by a systematic biopsy. Additional target biopsies were performed from Prostate Imaging Reporting & Data System (PIRADS) 3-5 lesions. Men who underwent TP PBx were matched 1:2 with a synchronous cohort undergoing TR PBx by PSA, Prostate volume (PV) and PIRADS score. Endpoint of the study was the detection of clinically significant prostate cancer (CSPCa; Grade Group ≥ 2). Univariate and multivariable analyses were performed. Results were considered statistically significant if p < 0.05. Overall, 504 patients met the inclusion criteria. A total of 168 TP PBx were pair-matched to 336 TR PBx patients. Baseline demographics and imaging characteristics were similar between the groups. Per patient, the CSPCa detection was 2.1% vs 6.3% (p = 0.4) for PIRADS 1-2, and 59% vs 60% (p = 0.9) for PIRADS 3-5, on TP vs TR PBx, respectively. Per lesion, the CSPCa detection for PIRADS 3 (21% vs 16%; p = 0.4), PIRADS 4 (51% vs 44%; p = 0.8) and PIRADS 5 (76% vs 84%; p = 0.3) was similar for TP vs TR PBx, respectively. However, the TP PBx showed a longer maximum cancer core length (11 vs 9 mm; p = 0.02) and higher cancer core involvement (83% vs 65%; p < 0.001) than TR PBx. Independent predictors for CSPCa detection were age, PSA, PV, abnormal digital rectal examination findings, and PIRADS 3-5. Our study demonstrated transperineal MRI/TRUS fusion PBx provides similar CSPCa detection, with larger prostate cancer core length and percent of core involvement, than transrectal PBx., (© 2023. Springer Nature Limited.)

Objectives: To evaluate the performance of machine learning-augmented MRI-based radiomics models for predicting response to neoadjuvant chemotherapy (NAC) in soft tissue sarcomas., Methods: Forty-four subjects were identified retrospectively from patients who received NAC at our institution for pathologically proven soft tissue sarcomas. Only subjects who had both a baseline MRI prior to initiating chemotherapy and a post-treatment scan at least 2 months after initiating chemotherapy and prior to surgical resection were included. 3D ROIs were used to delineate whole-tumor volumes on pre- and post-treatment scans, from which 1708 radiomics features were extracted. Delta-radiomics features were calculated by subtraction of baseline from post-treatment values and used to distinguish treatment response through univariate analyses as well as machine learning-augmented radiomics analyses., Results: Though only 4.74% of variables overall reached significance at p ≤ 0.05 in univariate analyses, Laws Texture Energy (LTE)-derived metrics represented 46.04% of all such features reaching statistical significance. ROC analyses similarly failed to predict NAC response, with AUCs of 0.40 (95% CI 0.22-0.58) and 0.44 (95% CI 0.26-0.62) for RF and AdaBoost, respectively., Conclusion: Overall, while our result was not able to separate NAC responders from non-responders, our analyses did identify a subset of LTE-derived metrics that show promise for further investigations. Future studies will likely benefit from larger sample size constructions so as to avoid the need for data filtering and feature selection techniques, which have the potential to significantly bias the machine learning procedures., (© 2023. The Author(s).)

Artificial intelligence (AI) is here to stay and will change health care as we know it. The availability of big data and the increasing numbers of AI algorithms approved by the US Food and Drug Administration together will help in improving the quality of care for patients and in overcoming human fatigue barriers. In oncology practice, patients and providers rely on the interpretation of radiologists when making clinical decisions; however, there is considerable variability among readers, and in particular for prostate imaging. AI represents an emerging solution to this problem, for which it can provide a much-needed form of standardization. The diagnostic performance of AI alone in comparison to a combination of an AI framework and radiologist assessment for evaluation of prostate imaging has yet to be explored. Here, we compare the performance of radiologists alone versus a combination of radiologists aided by a modern computer-aided diagnosis (CAD) AI system. We show that the radiologist-CAD combination demonstrates superior sensitivity and specificity in comparison to both radiologists alone and AI alone. Our findings demonstrate that a radiologist + AI combination could perform best for detection of prostate cancer lesions. A hybrid technology-human system could leverage the benefits of AI in improving radiologist performance while also reducing physician workload, minimizing burnout, and enhancing the quality of patient care., Patient Summary: Our report demonstrates the potential of artificial intelligence (AI) for improving the interpretation of prostate scans. A combination of AI and evaluation by a radiologist has the best performance in determining the severity of prostate cancer. A hybrid system that uses both AI and radiologists could maximize the quality of care for patients while reducing physician workload and burnout., (© 2022 The Author(s).)

Objectives: To identify computed tomography (CT)-based radiomic signatures of cluster of differentiation 8 (CD8)-T cell infiltration and programmed cell death ligand 1 (PD-L1) expression levels in patients with clear-cell renal cell carcinoma (ccRCC)., Methods: Seventy-eight patients with pathologically confirmed localized ccRCC, preoperative multiphase CT and tumor resection specimens were enrolled in this retrospective study. Regions of interest (ROI) of the ccRCC volume were manually segmented from the CT images and processed using a radiomics panel comprising of 1708 metrics. The extracted metrics were used as inputs to three machine learning classifiers: Random Forest, AdaBoost, and ElasticNet to create radiomic signatures for CD8-T cell infiltration and PD-L1 expression, respectively., Results: Using a cut-off of 80 lymphocytes per high power field, 59 % were classified to CD8 highly infiltrated tumors and 41 % were CD8 non highly infiltrated tumors, respectively. An ElasticNet classifier discriminated between these two groups of CD8-T cells with an AUC of 0.68 (95 % CI, 0.55-0.80). In addition, based on tumor proportion score with a cut-off of > 1 % tumor cells expressing PD-L1, 76 % were PD-L1 positive and 24 % were PD-L1 negative. An Adaboost classifier discriminated between PD-L1 positive and PD-L1 negative tumors with an AUC of 0.8 95 % CI: (0.66, 0.95). 3D radiomics metrics of graylevel co-occurrence matrix (GLCM) and graylevel run-length matrix (GLRLM) metrics drove the performance for CD8-Tcell and PD-L1 classification, respectively., Conclusions: CT-radiomic signatures can differentiate tumors with high CD8-T cell infiltration with moderate accuracy and positive PD-L1 expression with good accuracy in ccRCC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2022 The Authors.)

Objective: To investigate the incidence, risk factors and natural history of parastomal hernia (PSH)., Materials and Methods: We reviewed the records of patients who underwent radical cystectomy (RC) and ileal conduit (IC) procedure between 2007 and 2020. Patients who had available follow-up computed tomography (CT) imaging were included in this study. All CT scans were re-reviewed for detection of PSH according to Moreno-Matias classification. Patients who developed hernia were followed up and classified into stable or progressive (defined as radiological upgrading and/or need for surgical intervention) groups. Multivariable Cox regression was performed to identify independent predictors of hernia development and progression., Results: A total of 361 patients were included in this study. The incidence of radiological PSH was 30%, graded as I (56.5%), II (12%) and III (31.5%). The median (interquartile range [IQR]) time to radiological hernia was 8 (5-15) months. During the median (IQR) follow-up of 27 (13-47) months in 108 patients with a hernia, 26% patients progressed. The median (IQR) time to progression was 12 (6-21) months. On multivariable analysis, female gender (hazard ratio [HR] 1.86), diabetes (HR 1.81), chronic obstructive pulmonary disease (COPD; HR 1.78) and higher body mass index (BMI; HR 1.07 for each unit) were independent predictors for radiological PSH development. No significant factor was found to be associated with hernia progression., Conclusion: Radiological PSH after RC and IC occurred in 30% of patients, a quarter of whom progressed in a median time of 12 months. Female gender, diabetes, COPD and high BMI were independent predictors for radiological hernia development., (© 2021 The Authors BJU International © 2021 BJU International.)

Background: Cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR (>0.55) is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR chest X-rays (CXRs) aids in the early diagnosis of clinical conditions., Purpose: We propose a deep learning (DL)-based model for automatic CTR calculation to assist radiologists with rapid diagnosis of cardiomegaly and thus optimise the radiology flow., Material and Methods: The study population included 1012 posteroanterior CXRs from a single institution. The Attention U-Net DL architecture was used for the automatic calculation of CTR. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence assistance., Results: U-Net model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], specificity >99%, precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. Furthermore, the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR., Conclusion: Our segmentation-based AI model demonstrated high specificity (>99%) and sensitivity (80%) for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with provision of AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows by reducing radiologists' burden and alerting to an abnormal enlarged heart early on., Competing Interests: Declaration of conflicting interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: A.K. is a Professor at Dr DY Patil Medical College, Pune and is also a co-founder of DeepTek Inc., whose expertise was employed to build this model. V.D. is a Professor of Radiology at Keck school of Medicine, USC, USA and is also on the advisory board of DeepTek Inc; he is a consultant to Radmetrix Inc, Cohere Inc and Westat Inc., (© The Author(s) 2022.)

Objectives: To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV)., Methods: A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC)., Results: The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification., Conclusion: Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC., Key Points: • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively., (© 2021. European Society of Radiology.)

Integrating liquid biopsies of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) with other minimally invasive measures may yield more comprehensive disease profiles. We evaluated the feasibility of concurrent cellular and molecular analysis of CTCs and cfDNA combined with radiomic analysis of CT scans from patients with metastatic castration-resistant PC (mCRPC). CTCs from 22 patients were enumerated, stained for PC-relevant markers, and clustered based on morphometric and immunofluorescent features using machine learning. DNA from single CTCs, matched cfDNA, and buffy coats was sequenced using a targeted amplicon cancer hotspot panel. Radiomic analysis was performed on bone metastases identified on CT scans from the same patients. CTCs were detected in 77% of patients and clustered reproducibly. cfDNA sequencing had high sensitivity (98.8%) for germline variants compared to WBC. Shared and unique somatic variants in PC-related genes were detected in cfDNA in 45% of patients (MAF > 0.1%) and in CTCs in 92% of patients (MAF > 10%). Radiomic analysis identified a signature that strongly correlated with CTC count and plasma cfDNA level. Integration of cellular, molecular, and radiomic data in a multi-parametric approach is feasible, yielding complementary profiles that may enable more comprehensive non-invasive disease modeling and prediction.

Background Coronavirus disease 2019 (COVID-19) has accounted for over 352 million cases and five million deaths globally. Although it affects populations across all nations, developing or transitional, of all genders and ages, the extent of the specific involvement is not very well known. This study aimed to analyze and determine how different were the first and second waves of the COVID-19 pandemic by assessing computed tomography severity scores (CT-SS). Methodology This was a retrospective, cross-sectional, observational study performed at a tertiary care Institution. We included 301 patients who underwent CT of the chest between June and October 2020 and 1,001 patients who underwent CT of the chest between February and April 2021. All included patients were symptomatic and were confirmed to be COVID-19 positive. We compared the CT-SS between the two datasets. In addition, we analyzed the distribution of CT-SS concerning age, comorbidities, and gender, as well as their differences between the two waves of COVID-19. Analysis was performed using the SPSS version 22 (IBM Corp., Armonk, NY, USA). The artificial intelligence platform U-net architecture with Xception encoder was used in the analysis. Results The study data revealed that while the mean CT-SS did not differ statistically between the two waves of COVID-19, the age group most affected in the second wave was almost a decade younger. While overall the disease had a predilection toward affecting males, our findings showed that females were more afflicted in the second wave of COVID-19 compared to the first wave. In particular, the disease had an increased severity in cases with comorbidities such as hypertension, diabetes mellitus, bronchial asthma, and tuberculosis. Conclusions This assessment demonstrated no significant difference in radiological severity score between the two waves of COVID-19. The secondary objective revealed that the two waves showed demographical differences. Hence, we iterate that no demographical subset of the population should be considered low risk as the disease manifestation was heterogeneous., Competing Interests: Dr. Amit Kharat is a Professor of Radiology at the institute and is a co-founder of DeepTek (an AI company whose systems were utilized for COVID-19 scoring)., (Copyright © 2022, Ajmera et al.)

The success of the use of novel therapies in the treatment of advanced urothelial carcinoma has contributed to growing interest in evaluating these therapies at earlier stages of the disease. However, trials evaluating these therapies in the neoadjuvant setting must have clearly defined study elements and appropriately selected end points to ensure the applicability of the trial and enable interpretation of the study results. To advance the development of rational trial design, a public workshop jointly sponsored by the US Food and Drug Administration and the Bladder Cancer Advocacy Network convened in August 2019. Clinicians, clinical trialists, radiologists, biostatisticians, patients, advocates and other stakeholders discussed key elements and end points when designing trials of neoadjuvant therapy for muscle-invasive bladder cancer (MIBC), identifying opportunities to refine eligibility, design and end points for neoadjuvant trials in MIBC. Although pathological complete response (pCR) is already being used as a co-primary end point, both individual-level and trial-level surrogacy for time-to-event end points, such as event-free survival or overall survival, remain incompletely characterized in MIBC. Additionally, use of pCR is limited by heterogeneity in pathological evaluation and the fact that the magnitude of pCR improvement that might translate into a meaningful clinical benefit remains unclear. Given existing knowledge gaps, capture of highly granular patient-related, tumour-related and treatment-related characteristics in the current generation of neoadjuvant MIBC trials will be critical to informing the design of future trials., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Objectives: Our purpose was to differentiate between malignant from benign soft tissue neoplasms using a combination of MRI-based radiomics metrics and machine learning., Methods: Our retrospective study identified 128 histologically diagnosed benign (n = 36) and malignant (n = 92) soft tissue lesions. 3D ROIs were manually drawn on 1 sequence of interest and co-registered to other sequences obtained during the same study. One thousand seven hundred eight radiomics features were extracted from each ROI. Univariate analyses with supportive ROC analyses were conducted to evaluate the discriminative power of predictive models constructed using Real Adaptive Boosting (Adaboost) and Random Forest (RF) machine learning approaches., Results: Univariate analyses demonstrated that 36.89% of individual radiomics varied significantly between benign and malignant lesions at the p ≤ 0.05 level. Adaboost and RF performed similarly well, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81), respectively, after 10-fold cross-validation. Restricting the machine learning models to only sequences extracted from T2FS and STIR sequences maintained comparable performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84), respectively., Conclusion: Machine learning decision classifiers constructed from MRI-based radiomics features show promising ability to preoperatively discriminate between benign and malignant soft tissue masses. Our approach maintains applicability even when the dataset is restricted to T2FS and STIR fluid-sensitive sequences, which may bolster practicality in clinical application scenarios by eliminating the need for complex co-registrations for multisequence analysis., Key Points: • Predictive models constructed from MRI-based radiomics data and machine learning-augmented approaches yielded good discriminative power to correctly classify benign and malignant lesions on preoperative scans, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81) for Real Adaptive Boosting (Adaboost) and Random Forest (RF), respectively. • Restricting the models to only use metrics extracted from T2 fat-saturated (T2FS) and Short-Tau Inversion Recovery (STIR) sequences yielded similar performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84) for Adaboost and RF, respectively. • Radiomics-based machine learning decision classifiers constructed from multicentric data more closely mimic the real-world practice environment and warrant additional validation ahead of prospective implementation into clinical workflows., (© 2021. European Society of Radiology.)