Final Results From a Phase I Trial and Expansion Cohorts of Cabozantinib and Nivolumab Alone or With Ipilimumab for Advanced/Metastatic Genitourinary Tumors.

PURPOSE: Cabozantinib and nivolumab (CaboNivo) alone or with ipilimumab (CaboNivoIpi) have shown promising efficacy and safety in patients with metastatic urothelial carcinoma (mUC), metastatic renal cell carcinoma (mRCC), and rare genitourinary (GU) tumors in a dose-escalation phase I study. We report the final data analysis of the safety, overall response rate (ORR), progression-free survival (PFS), and overall survival (OS) of the phase I patients and seven expansion cohorts. METHODS: This is an investigator-initiated, multicenter, phase I trial. CaboNivo doublet expansion cohorts included (1) mUC, (2) mRCC, and (3) adenocarcinoma of the bladder/urachal; CaboNivoIpi triplet expansion cohorts included (1) mUC, (2) mRCC, (3) penile cancer, and (4) squamous cell carcinoma of the bladder and other rare GU tumors (ClinicalTrials.gov identifier: NCT02496208). RESULTS: The study enrolled 120 patients treated with CaboNivo (n = 64) or CaboNivoIpi (n = 56), with a median follow-up of 49.2 months. In 108 evaluable patients (CaboNivo n = 59; CaboNivoIpi n = 49), the ORR was 38% (complete response rate 11%) and the median duration of response was 20 months. The ORR was 42.4% for mUC, 62.5% for mRCC (n = 16), 85.7% for squamous cell carcinoma of the bladder (n = 7), 44.4% for penile cancer (n = 9), and 50.0% for renal medullary carcinoma (n = 2). Grade ≥ 3 treatment-related adverse events occurred in 84% of CaboNivo patients and 80% of CaboNivoIpi patients. CONCLUSION: CaboNivo and CaboNivoIpi demonstrated clinical activity and safety in patients with multiple GU malignancies, especially clear cell RCC, urothelial carcinoma, and rare GU tumors such as squamous cell carcinoma of the bladder, small cell carcinoma of the bladder, adenocarcinoma of the bladder, renal medullary carcinoma, and penile cancer.

A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer.

The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prostatectomy (RP) and to assess the correlation of AI-estimated proportions of different Gleason patterns with biochemical recurrence-free survival (RFS), metastasis-free survival (MFS), and overall survival (OS). Training and validation of algorithms for cancer detection and grading were completed with three large datasets containing a total of 580 whole-mount prostate slides from 191 RP patients at two centers and 6218 annotated needle biopsy slides from the publicly available Prostate Cancer Grading Assessment dataset. A cancer detection model was trained using MobileNetV3 on 0.5 mm × 0.5 mm cancer areas (tiles) captured at 10× magnification. For cancer grading, a Gleason pattern detector was trained on tiles using a ResNet50 convolutional neural network and a selective CutMix training strategy involving a mixture of real and artificial examples. This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. In an additional test cohort of RP patients who were clinically followed over 30 years, quantitative Gleason pattern AI estimates achieved concordance indexes of 0.69, 0.72, and 0.64 for predicting RFS, MFS, and OS times, outperforming the control experiments and International Society of Urological Pathology system (ISUP) grading by pathologists. Finally, unsupervised clustering of test RP patient specimens into low-, medium-, and high-risk groups based on AI-estimated proportions of each Gleason pattern resulted in significantly improved RFS and MFS stratification compared with ISUP grading. In summary, deep learning-based quantitative Gleason scoring using a selective CutMix training strategy may improve prognostication after prostate cancer surgery.

Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI.

OBJECTIVE: To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm. MATERIALS AND METHODS: This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE. RESULTS: A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24). CONCLUSION: Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

Explainable drug repurposing via path based knowledge graph completion.

Drug repurposing aims to find new therapeutic applications for existing drugs in the pharmaceutical market, leading to significant savings in time and cost. The use of artificial intelligence and knowledge graphs to propose repurposing candidates facilitates the process, as large amounts of data can be processed. However, it is important to pay attention to the explainability needed to validate the predictions. We propose a general architecture to understand several explainable methods for graph completion based on knowledge graphs and design our own architecture for drug repurposing. We present XG4Repo (eXplainable Graphs for Repurposing), a framework that takes advantage of the connectivity of any biomedical knowledge graph to link compounds to the diseases they can treat. Our method allows methapaths of different types and lengths, which are automatically generated and optimised based on data. XG4Repo focuses on providing meaningful explanations to the predictions, which are based on paths from compounds to diseases. These paths include nodes such as genes, pathways, side effects, or anatomies, so they provide information about the targets and other characteristics of the biomedical mechanism that link compounds and diseases. Paths make predictions interpretable for experts who can validate them and use them in further research on drug repurposing. We also describe three use cases where we analyse new uses for Epirubicin, Paclitaxel, and Predinisone and present the paths that support the predictions.

Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging.

Introduction: This study explores the use of the latest You Only Look Once (YOLO V7) object detection method to enhance kidney detection in medical imaging by training and testing a modified YOLO V7 on medical image formats. Methods: Study includes 878 patients with various subtypes of renal cell carcinoma (RCC) and 206 patients with normal kidneys. A total of 5657 MRI scans for 1084 patients were retrieved. 326 patients with 1034 tumors recruited from a retrospective maintained database, and bounding boxes were drawn around their tumors. A primary model was trained on 80% of annotated cases, with 20% saved for testing (primary test set). The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model. Ten benchmark training sets were created with generated coordinates on not-segmented patients. The final model used to predict the kidney in the primary test set. We reported the positive predictive value (PPV), sensitivity, and mean average precision (mAP). Results: The primary training set showed an average PPV of 0.94 ± 0.01, sensitivity of 0.87 ± 0.04, and mAP of 0.91 ± 0.02. The best primary model yielded a PPV of 0.97, sensitivity of 0.92, and mAP of 0.95. The final model demonstrated an average PPV of 0.95 ± 0.03, sensitivity of 0.98 ± 0.004, and mAP of 0.95 ± 0.01. Conclusion: Using a semi-supervised approach with a medical image library, we developed a high-performing model for kidney detection. Further external validation is required to assess the model's generalizability.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.

Burden of Disease Study of Patients with Neovascular Age-Related Macular Degeneration in Spain.

INTRODUCTION: Neovascular age-related macular degeneration (nAMD) is a progressive retinal disease that causes severe and irreversible vision loss. The disease can therefore have a significant impact on the life of patients' and their families. The aim of this study was to evaluate the socio-economic burden of nAMD in Spain. METHODS: The annual cost per patient with nAMD was estimated for the first, second, and third year (or beyond) of treatment since diagnosis. Several cost categories were considered including direct healthcare costs (DHC), direct non-healthcare costs (DNHC), labor productivity losses (LPL), and intangible costs (IC) related to loss of quality of life. The average annual cost per patient was estimated by assigning a unit price or financial proxy to the resources consumed per patient. Reference year of costs was 2021. RESULTS: The mean annual cost of nAMD was estimated at €17,265, €15,403, and €14,465 per patient in the first, second, and third year of treatment after diagnosis. There was an additional one-off cost of €744 associated with the diagnosis of nAMD. DHC accounted for most of the total annual cost per patient independent of the year of treatment since diagnosis (48% in year 1; 42% in year 2; 39% in year 3). Similarly, DNHC had an important contribution to the total costs (32% in year 1; 35% in year 2; 37% in year 3), followed by IC (20% in year 1; 23% in year 2; 24% in year 3), while the contribution of patients' LPL was minimal. CONCLUSION: This study estimated a high economic burden associated with nAMD for patients and their families, the healthcare system, and society at large. There is a need to improve the management of these patients to reduce the impact of nAMD disease progression.

Burden of Disease Study of Patients with Diabetic Macular Oedema in Spain.

INTRODUCTION: Diabetic macular oedema (DMO) is a complication of diabetic retinopathy that can result in vision loss. The disease can impact different spheres of a patient's life, including physical and psychological health, work, and activities of daily living, entailing an important use of healthcare and non-healthcare resources. This study aimed to estimate the socio-economic burden of DMO in Spain. METHODS: The burden of DMO was estimated from a societal perspective, per patient, year of treatment since diagnosis, and type of treatment. Four categories were considered: direct healthcare costs (DHC), direct non-healthcare costs (DNHC), labour productivity losses (LPL), and intangible costs (IC) associated with loss of quality of life. Average annual costs were calculated by multiplying the resources used per patient by their corresponding unit price (or financial proxy). For a more accurate estimation, differences in resource use between treatments (intravitreal anti-vascular endothelial growth factor injections of ranibizumab or aflibercept, and intravitreal dexamethasone implants) and year since diagnosis (first, second, and third year or beyond) were considered and presented separately. The reference year for costs was 2021. RESULTS: The average annual costs of DMO in the first year of treatment after diagnosis was estimated at €18,774, €17,512, and €16,188 per patient treated with ranibizumab, aflibercept, and dexamethasone, respectively. This burden would be reduced to €15,783, €15,701, and €12,233 in the second year, and to €15,119, €15,043, and €12,790 in the third year, respectively. Diagnosis of DMO entails an additional one-off cost of €485. DHC accounted for the greatest proportion of total annual costs per patient, independent of the year, with LPL also making an important contribution to total costs. CONCLUSIONS: The socio-economic impact of DMO on patients, the healthcare system, and society at large is substantial. The constant increase in its prevalence accentuates the need for planning and implementation of healthcare strategies to prevent vision loss and reduce the socio-economic burden of the disease.

Feasibility and reliability of a quality indicator system for an ambulatory dispensing service.

Background: For a medication dispensing service to function with quality, continuous evaluation is required, which is why it is necessary to have reliable measurement tools that make it possible. Quality indicators can serve as tools for managing quality, as they are variables that directly or indirectly measure changes in a situation and help evaluate the progress made in addressing it. This article aims to determine the feasibility and reliability of a quality indicator system for a drug dispensing service for paediatric outpatients in two Mexican hospitals. Methods: A study of the development type of health systems and services at a microlevel was conducted from October 2020 to October 2021 in the pharmaceutical service of two Mexican hospitals. To determine the feasibility of the quality indicators, a retrospective evaluation was performed, which considered the indicators that could be calculated with the available information to be feasible. To determine reliability, an inter-observer agreement study (Kappa (κ)) was performed. Results: The feasibility analysis revealed that all five reference indicators related to the structure were feasible in both hospitals. In the Infantil of the Californias hospital, all six process indicators evaluated were feasible, whilst only one was found feasible in H+ Querétaro. As for outcome indicators, only one was feasible in the Infantil of the Californias hospital. The causes of non-feasibility in both hospitals were the non-documentation of the primary data related to the stages of the process and the lack of instruments to measure patient satisfaction. The reliability of the indicators showed little variability. Conclusion: Although not all indicators were feasible, solutions were proposed so that the 15 reference indicators could be used if an organization decided to do so. The reliability of the indicators was demonstrated, evidencing the importance of the data sheet as a tool to generate valid reliable measures.This article is part of the Hospital pharmacy, rational use of medicines and patient safety in Latin America Special Issue: https://www.drugsincontext.com/special_issues/hospital-pharmacy-rational-use-of-medicines-and-patient-safety-in-latin-america/.

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification.

RATIONALE AND OBJECTIVES: Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI. MATERIAL AND METHODS: An expert genitourinary radiologist conducted prospective clinical assessments of MRI scans for 634 patients and assigned risk for EPE using a grading technique. The training set and held-out independent test set consisted of 507 patients and 127 patients, respectively. Existing deep-learning AI models for prostate organ and lesion segmentation were leveraged to extract area and distance features for random forest classification models. Model performance was evaluated using balanced accuracy, ROC AUCs for each EPE grade, as well as sensitivity, specificity, and accuracy compared to EPE on histopathology. RESULTS: A balanced accuracy score of .390 ± 0.078 was achieved using a lesion detection probability threshold of 0.45 and distance features. Using the test set, ROC AUCs for AI-assigned EPE grades 0-3 were 0.70, 0.65, 0.68, and 0.55 respectively. When using EPE≥ 1 as the threshold for positive EPE, the model achieved a sensitivity of 0.67, specificity of 0.73, and accuracy of 0.72 compared to radiologist sensitivity of 0.81, specificity of 0.62, and accuracy of 0.66 using histopathology as the ground truth. CONCLUSION: Our AI workflow for assigning imaging-based EPE grades achieves an accuracy for predicting histologic EPE approaching that of physicians. This automated workflow has the potential to enhance physician decision-making for assessing the risk of EPE in patients undergoing treatment for prostate cancer due to its consistency and automation.

Impact of age on the transmission of SARS-CoV-2 in healthcare workers : Influence of nonoccupational risk factors.

BACKGROUND: The incidence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV­2) infection was highest among older adults early in the COVID-19 pandemic; however, this pattern was later reversed with young adults showing the highest incidence. The aim of this study was to identify risk factors in healthcare workers (HCWs) associated with this evolution. METHODS: We conducted a survey nested within a prospective cohort study of 680 HCWs from a tertiary referral public hospital who received 2 doses of SARS-CoV­2 vaccine in January and February 2021 (VACCICO-VAO cohort). In October 2022 all participants were invited to participate in a survey. Risk factors were tested for association with COVID-19 ever, the number of COVID-19 episodes, and the time to the first episode. RESULTS: Among 350 respondents (51% response rate, 90% female, mean age 48.1 years), 323 COVID-19 episodes were diagnosed during the study period. Multivariable analysis revealed that age < 35 years vs. > 50 years (odds ratio, OR 2.12, 95% confidence interval, CI 1.27-3.51; P = 0.004) and not maintaining social distance at social events (OR: 1.82, 95% CI: 1.16-3.19; P = 0.011) were associated with a higher risk of COVID-19. Age < 35 years (hazard ratio, HR 1.70, 95% CI 1.14-2.54; P = 0.010), and not maintaining social distance (HR 1.34, 95% CI 1.05-1.72; P = 0.020) were also associated with the time to the first episode. CONCLUSIONS: The youngest HCWs had the highest incidence rate of COVID-19, which was not explained by occupational risk factors or health conditions. The increase in nonoccupational exposure since the end of the lockdowns in summer 2020 could by a key factor.

[Main Types of Cysts in Dermatopathology: Part 2]. / Principales tipos de quistes en dermatopatología: Parte 2.

This is the second article in a two-part series published in this journal, in which we examine the histopathological characteristics, as well as the differential diagnosis, of the main entities that present as cystic and pseudocystic structures in cutaneous biopsy. In this second article, we address ciliated cutaneous cysts, branchial cysts, Bartholin's cysts, omphalomesenteric cysts, thymic cysts, thyroglossal duct cysts, synovial cysts, and median raphe cysts, as well as mucocele, ganglion, and auricular and digital myxoid pseudocysts.

The socioeconomic burden of adult attention-deficit/hyperactivity disorder in Spain.

Introduction: The symptoms of attention-deficit/hyperactivity disorder (ADHD) in adults highly interfere with function in multiple dimensions, increasing the economic burden associated with ADHD. The aim of this study was to explore the impact of ADHD in Spanish adults and estimate the associated economic burden within the healthcare, social, economic, and legal domains. Methods: An economic model was developed from a social perspective using a bottom-up approach, based on the scientific literature and a multidisciplinary expert group. Results: The cost incurred per diagnosed adult patient with ADHD included an annual cost of €15,652 and a one-time cost of €7,893 (3,035 M€ and 1,531 M€ for Spain, respectively). Regarding the annual cost, 50% was attributed to costs within the economic domain, of which 53% were work-absenteeism-related. Moreover, 28% was attributed to costs within the social domain, of which 74% were substance-abuse-related. Regarding the one-time cost, 52% was attributed to costs within the healthcare domain, of which approximately 50% were hospitalization-related costs. Moreover, 42% was attributed to costs within the legal domain, of which 62% were imprisonment-related costs. Conclusions: This is the first report on the socioeconomic burden of ADHD in Spanish adults, shedding light on the large burden that adult ADHD poses on the healthcare system and society at large, as symptoms have been shown to impact almost every aspect of life. This is particularly important for undiagnosed/untreated patients with ADHD in Spain, as appropriate treatments have shown positive results in these areas and may reduce its associated socioeconomic burden.

Evaluating Diagnostic Accuracy and Inter-reader Agreement of the Prostate Imaging After Focal Ablation Scoring System.

Background and objective: Focal therapy (FT) is increasingly recognized as a promising approach for managing localized prostate cancer (PCa), notably reducing treatment-related morbidities. However, post-treatment anatomical changes present significant challenges for surveillance using current imaging techniques. This study aimed to evaluate the inter-reader agreement and efficacy of the Prostate Imaging after Focal Ablation (PI-FAB) scoring system in detecting clinically significant prostate cancer (csPCa) on post-FT multiparametric magnetic resonance imaging (mpMRI). Methods: A retrospective cohort study was conducted involving patients who underwent primary FT for localized csPCa between 2013 and 2023, followed by post-FT mpMRI and a prostate biopsy. Two expert genitourinary radiologists retrospectively evaluated post-FT mpMRI using PI-FAB. The key measures included inter-reader agreement of PI-FAB scores, assessed by quadratic weighted Cohen's kappa (κ), and the system's efficacy in predicting in-field recurrence of csPCa, with a PI-FAB score cutoff of 3. Additional diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were also evaluated. Key findings and limitations: Scans from 38 patients were analyzed, revealing a moderate level of agreement in PI-FAB scoring (κ = 0.56). Both radiologists achieved sensitivity of 93% in detecting csPCa, although specificity, PPVs, NPVs, and accuracy varied. Conclusions and clinical implications: The PI-FAB scoring system exhibited high sensitivity with moderate inter-reader agreement in detecting in-field recurrence of csPCa. Despite promising results, its low specificity and PPV necessitate further refinement. These findings underscore the need for larger studies to validate the clinical utility of PI-FAB, potentially aiding in standardizing post-treatment surveillance. Patient summary: Focal therapy has emerged as a promising approach for managing localized prostate cancer, but limitations in current imaging techniques present significant challenges for post-treatment surveillance. The Prostate Imaging after Focal Ablation (PI-FAB) scoring system showed high sensitivity for detecting in-field recurrence of clinically significant prostate cancer. However, its low specificity and positive predictive value necessitate further refinement. Larger, more comprehensive studies are needed to fully validate its clinical utility.

Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.

BACKGROUND: Pathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC). PURPOSE: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment. STUDY TYPE: Retrospective analysis of a prospectively maintained cohort. POPULATION: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023. FIELD STRENGTH AND SEQUENCES: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences. ASSESSMENT: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures. STATISTICAL TESTS: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported. RESULTS: The best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported. DATA CONCLUSION: Radiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results.

INTRODUCTION: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI. MATERIAL AND METHODS: We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP). RESULTS: A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72. CONCLUSION: Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers.