Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

A DEEP LEARNING-BASED CAD SYSTEM FOR RENAL ALLOGRAFT ASSESSMENT: DIFFUSION, BOLD, AND CLINICAL BIOMARKERS.

Recently, studies for non-invasive renal transplant evaluation have been explored to control allograft rejection. In this paper, a computer-aided diagnostic system has been developed to accommodate with an early-stage renal transplant status assessment, called RT-CAD. Our model of this system integrated multiple sources for a more accurate diagnosis: two image-based sources and two clinical-based sources. The image-based sources included apparent diffusion coefficients (ADCs) and the amount of deoxygenated hemoglobin (R2*). More specifically, these ADCs were extracted from 47 diffusion weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, …, b1000 s/mm2), while the R2* values were extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (2ms, 7ms, 12ms, 17ms, and 22ms). The clinical sources included serum creatinine (SCr) and creatinine clearance (CrCl). First, the kidney was segmented through the RT-CAD system using a geometric deformable model called a level-set method. Second, both ADCs and R2* were estimated for common patients (N = 30) and then were integrated with the corresponding SCr and CrCl. Last, these integrated biomarkers were considered the discriminatory features to be used as trainers and testers for future deep learning-based classifiers such as stacked auto-encoders (SAEs). We used a k-fold cross-validation criteria to evaluate the RT-CAD system diagnostic performance, which achieved the following scores: 93.3%, 90.0%, and 95.0% in terms of accuracy, sensitivity, and specificity in differentiating between acute renal rejection (AR) and non-rejection (NR). The reliability and completeness of the RT-CAD system was further accepted by the area under the curve score of 0.92. The conclusions ensured that the presented RT-CAD system has a high reliability to diagnose the status of the renal transplant in a non-invasive way.

A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction.

This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.

Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI.

OBJECTIVE: Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. METHODS: This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + b-value) diffusion-weighted (DW) magnetic resonance imaging (MRI) data. The CAD process starts from kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The evolution is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and on-going kidney-background visual appearances. A B-spline-based three-dimensional data alignment is employed to handle local deviations due to breathing and heart beating. Then, empirical cumulative distribution functions of apparent diffusion coefficients of the segmented DW-MRI at different b-values are collected as discriminatory transplant status features. Finally, a deep-learning-based classifier with stacked nonnegative constrained autoencoders is employed to distinguish between rejected and nonrejected renal transplants. RESULTS: In our initial "leave-one-subject-out" experiment on 100 subjects, [Formula: see text] of the subjects were correctly classified. The subsequent four-fold and ten-fold cross-validations gave the average accuracy of [Formula: see text] and [Formula: see text], respectively. CONCLUSION: These results demonstrate the promise of this new CAD system to reliably diagnose renal transplant rejection. SIGNIFICANCE: The technology presented here can significantly impact the quality of care of renal transplant patients since it has the potential to replace the gold standard in kidney diagnosis, biopsy.

Models and methods for analyzing DCE-MRI: a review.

PURPOSE: To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches. METHODS: DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases. RESULTS: Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors. CONCLUSIONS: Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Association of maturation period blood pressure with dialysis access patency.

Problematic dialysis vascular access is a major health issue. The purpose of this study was to evaluate for potentially modifiable factors associated with access patency, particularly, the association of early postoperative, or maturation period, blood pressure with patency. A retrospective review was performed of patients who had undergone placement of an arteriovenous fistula or graft. Demographic, operative, and postoperative factors were evaluated for possible association with access primary patency using univariate and multivariate Cox regression analyses. Seventy-three patients over a 3-year review period were examined. Overall analysis showed a significant association of absence of peripheral vascular disease, aspirin use, and absence of previous permanent dialysis access with higher primary patency rates. Fistula subgroup analysis showed that higher blood pressure during the maturation period relative to preoperative blood pressure was associated with lower patency rates. For grafts, race was significantly associated with patency, with blacks having higher patency rates than whites. Multiple clinical factors were found to have a significant association with dialysis access primary patency. The finding of an association of maturation period blood pressure with fistula patency suggests that the maturation period environment, specifically hemodynamics during this time, may play an important role in dialysis access patency.

Critical and honest conversations: the evidence behind the "Choosing Wisely" campaign recommendations by the American Society of Nephrology.

Estimates suggest that one third of United States health care spending results from overuse or misuse of tests, procedures, and therapies. The American Board of Internal Medicine Foundation, in partnership with Consumer Reports, initiated the "Choosing Wisely" campaign to identify areas in patient care and resource use most open to improvement. Nine subspecialty organizations joined the campaign; each organization identified five tests, procedures, or therapies that are overused, are misused, or could potentially lead to harm or unnecessary health care spending. Each of the American Society of Nephrology's (ASN's) 10 advisory groups submitted recommendations for inclusion. The ASN Quality and Patient Safety Task Force selected five recommendations based on relevance and importance to individuals with kidney disease.Recommendations selected were: (1) Do not perform routine cancer screening for dialysis patients with limited life expectancies without signs or symptoms; (2) do not administer erythropoiesis-stimulating agents to CKD patients with hemoglobin levels ≥10 g/dl without symptoms of anemia; (3) avoid nonsteroidal anti-inflammatory drugs in individuals with hypertension, heart failure, or CKD of all causes, including diabetes; (4) do not place peripherally inserted central catheters in stage 3-5 CKD patients without consulting nephrology; (5) do not initiate chronic dialysis without ensuring a shared decision-making process between patients, their families, and their physicians.These five recommendations and supporting evidence give providers information to facilitate prudent care decisions and empower patients to actively participate in critical, honest conversations about their care, potentially reducing unnecessary health care spending and preventing harm.

Dialysis vascular access management by interventional nephrology programs at University Medical Centers in the United States.

The development of interventional nephrology has undoubtedly led to an improvement in patient care at many facilities across the United States. However, these services have traditionally been offered by interventional nephrologists in the private practice arena. While interventional nephrology was born in the private practice setting, several academic medical centers across the United States have now developed interventional nephrology programs. University Medical Centers (UMCs) that offer interventional nephrology face challenges, such as smaller dialysis populations, limited financial resources, and real or perceived political "turf" issues." Despite these hurdles, several UMCs have successfully established interventional nephrology as an intricate part of a larger nephrology program. This has largely been accomplished by consolidating available resources and collaborating with other specialties irrespective of the size of the dialysis population. The collaboration with other specialties also offers an opportunity to perform advanced procedures, such as application of excimer laser and endovascular ultrasound. As more UMCs establish interventional nephrology programs, opportunities for developing standardized training centers will improve, resulting in better quality and availability of nephrology-related procedures, and providing an impetus for research activities.