Imaging Pearls ❯ Kidney ❯ Artificial Intelligence
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- IgG4-related disease (IgG4-RD) is an immune-mediated disorder marked by fibro-inflammatory masses that can infiltrate multiple organ systems. Due to its relatively recent discovery and limited understanding of its pathophysiology, IgG4-related disease may be difficult to recognize and is consequently potentially underdiagnosed. Renal involvement is becoming regarded as one of the key features of this disease. To date, the most well-recognized renal complication of IgG4-related disease is tubulointerstitial nephritis, but membranous glomerulonephritis, renal masses, and retroperitoneal fibrosis have also been reported.
Renal Manifestations of IgG4-Related Disease: A Concise Review.
Towheed ST, Zanjir W, Ren KYM, et al..
Int J Nephrol. 2024 Jun 24;2024:4421589. - Renal involvement is now regarded as one of the key features of this disease. In 2004, the first reports of an association between Type 1 autoimmune pancreatitis and renal dysfunction were identified. Since that time, renal dysfunction has also been associated with Mikulicz's syndrome , IgG4-related hepatic involvement , and other extra-renal IgG4-RD syndromes. In these initial reports, renal involvement is manifested as tubulointerstitial nephritis, which to date is still the most well-recognized renal manifestation of IgG4-RD. However, glomerular involvement has also been described—membranous glomerulonephritis is the primary glomerular injury pattern noted in the literature. Additional described manifestations include renal masses and retroperitoneal fibrosis that can secondarily affect the renal system The diversity of potential manifestations in the kidney has led to more encompassing terminology entitled IgG4-related kidney disease (IgG4-RKD).
Renal Manifestations of IgG4-Related Disease: A Concise Review.
Towheed ST, Zanjir W, Ren KYM, et al..
Int J Nephrol. 2024 Jun 24;2024:4421589. - IgG4 Renal Disease
Abnormal renal radiologic findings:
Multiple low-density lesions on enhanced computed tomography
Diffuse kidney enlargement
Hypovascular solitary mass in the kidney
Hypertrophic lesion of the renal pelvic wall without irregularity of the renal pelvic surface - Radiologically, one of the most reliable findings is the presence of multiple low-attenuation renal lesions on contrast-enhanced CT, as depicted. Additional findings may include diffuse kidney enlargement and solitary renal masses mimicking neoplasms, among others . Clinically, the onset and course of renal involvement can be acute but are generally slowly progressive.
Renal Manifestations of IgG4-Related Disease: A Concise Review.
Towheed ST, Zanjir W, Ren KYM, et al..
Int J Nephrol. 2024 Jun 24;2024:4421589. - Immunoglobulin G4–related disease (IgG4-RD) is a systemic fibroinflammatory disease characterized by focal or diffuse organ infiltration of IgG4-bearing plasma cells. The diagnosis of IgG4-RD is based on a combination of clinical, serologic, radiologic, and histopathologic findings. IgG4-RD has been reported to affect almost all organ systems. The kidney is the most frequently involved of the genitourinary organs. The most common renal manifestation of IgG4-RD is IgG4-RD tubulointerstitial nephritis, followed by membranous glomerulonephropathy and, less frequently, obstructive nephropathy involving the renal pelvis, ureter, or retroperitoneum.
Immunoglobulin G4–related Disease of the Genitourinary System: Spectrum of Imaging Findings and Clinical-Pathologic Features
Ji Woon Oh, Sung Eun Rha, Moon Hyung Choi, Soon Nam Oh, Seo Yeon Youn, and Joon-Il Choi
RadioGraphics 2020 40:5, 1265-1283 - - The kidneys are the genitourinary organs most commonly involved with IgG4-RD. Kidney involvement is found in approximately one-fourth to one-third of patients with IgG4-RDautoimmune pancreatitis, but can also occur without the involvement of other organs.
- IgG4-RD of the kidney predominantly involves the renal cortex, but the renal pelvis, renal sinus, and perirenal space can be involved. The most frequent renal manifestation of IgG4-RD is IgG4-RD tubulointerstitial nephritis, followed by IgG4-RD membranous glomerulonephropathy. Less frequently, obstructive nephropathy can be caused by postrenal obstruction secondary to renal pelvis, ureter, or retroperitoneal involvement.
Immunoglobulin G4–Related Disease of the Genitourinary System: Spectrum of Imaging Findings and Clinical-Pathologic Features
Ji Woon Oh, Sung Eun Rha, Moon Hyung Choi, et al.
RadioGraphics 2020 40:5, 1265-1283 - - Renal parenchymal IgG4-RD may show several imaging pat-terns, including multiple nodular lesions, diffuse patchy infiltrative lesions, and a single nodular lesion. Of these, the multiple nodular type is the most common imaging finding for IgG4-RD of the kidney.
- Ureteral IgG4-RD can be classified into three types on the basis of gross morphologic features: polypoid mass-forming lesions, segmental ureteral wall thickening, and periureteral fibrosis.
- IgG4-RD involving the kidneys, ureter, bladder, urethra, prostate, testes, and female reproductive organs can show a broad spectrum of imaging findings, such as a localized mass in or surrounding the involved organ or diffuse enlargement of the involved organ, which may mimic a variety of benign and malignant diseases.
Immunoglobulin G4–Related Disease of the Genitourinary System: Spectrum of Imaging Findings and Clinical-Pathologic Features
Ji Woon Oh, Sung Eun Rha, Moon Hyung Choi, et al.
RadioGraphics 2020 40:5, 1265-1283 
Immunoglobulin G4–Related Disease of the Genitourinary System: Spectrum of Imaging Findings and Clinical-Pathologic Features
Ji Woon Oh, Sung Eun Rha, Moon Hyung Choi, et al.
RadioGraphics 2020 40:5, 1265-1283- The characteristic imaging findings of autoimmune pancreatitis, which include sausage-like enlargement of the pancreas and a peripancreatic halo, can be strongly suggestive of IgG4-RD if they are detected in the proper clinical context that includes (a) mild abdominal symptoms, usually without acute attacks of pancreatitis;(b) occasional occurrence of obstructive jaundice; (c) increased serum gamma globulin, IgG, and/or IgG4 concentrations; and (d) occasional association with other organ involvement .The presence of a peripancreatic halo corresponding to a fibroinflammatory process extending into the peripancreatic adipose tissue is a useful imaging finding for the diagnosis of IgG4-RD and for differentiating it from pancreatic cancer or other pancreatitis.
Immunoglobulin G4–Related Disease of the Genitourinary System: Spectrum of Imaging Findings and Clinical-Pathologic Features
Ji Woon Oh, Sung Eun Rha, Moon Hyung Choi, et al.
RadioGraphics 2020 40:5, 1265-1283 - Patients with IgG4-RD of the kidney present at an average age of 65 years (range, 14–85 years) with a male-to-female ratio of 4:1 to 3:1, which is similar to that in patients with IgG4-RD involving other organs. Patients with IgG4-RD of the kidney usually present with mild symptoms or are asymptomatic. The usual symptoms include hematuria and an elevated serum creatinine level caused by acute renal injury, obstructive uropathy, and flank pain similar to that with renal malignancy. However, two well-recognized major clinical manifestations are incidental abnormal imaging findings during systemic screening workup for IgG4-RD and unexplained renal dysfunction. Approximately 80% of patients with IgG4-RD of the kidney show elevation of serum IgG4 levels.
Immunoglobulin G4–Related Disease of the Genitourinary System: Spectrum of Imaging Findings and Clinical-Pathologic Features
Ji Woon Oh, Sung Eun Rha, Moon Hyung Choi, et al.
RadioGraphics 2020 40:5, 1265-1283 - IgG4-RD of the kidney may show a broad spectrum of imaging manifestations according to the involved anatomic location and stage of disease. Because the imaging findings of IgG4-RD of the kidney are diverse and nonspecific, radiologists should be familiar with them when considering IgG4-RD in the differential diagnosis. According to its location, IgG4-RD of the kidney can be divided into renal parenchymal lesions, renal pelvic and/or sinus lesions, and perinephric lesions. Of these, renal parenchymal lesions are the most common manifestations of IgG4-RD of the kidney.
Immunoglobulin G4–Related Disease of the Genitourinary System: Spectrum of Imaging Findings and Clinical-Pathologic Features
Ji Woon Oh, Sung Eun Rha, Moon Hyung Choi, et al.
RadioGraphics 2020 40:5, 1265-1283 - “Retroperitoneal fibrosis is a fibroinflammatory disease that develops around the abdominal aorta and the iliac arteries and spreads into the adjacent retroperitoneum. It can be either idiopathic(>75% of cases) or secondary to infections, malig-nancies, drugs, or other conditions. IgG4-RD is the cause of up to two-thirds of cases of idiopathic retroperitoneal fibrosis. Ureteral involvement is the most common complication of retroperitoneal fibrosis, which usually causes medial deviation of the ureter and/or obstruction by extrinsic compression. Ureteral encasement can be unilateral or bilateral. At imaging, it is difficult to differentiate IgG4-RD retroperitoneal fibrosis from other causes. Typical imaging findings of idiopathic retroperitoneal fibrosis are a well-demarcated but irregular soft-tissue mass or plaque surrounding the anterolateral sides of the abdominal aorta and its major branches .”
Immunoglobulin G4–Related Disease of the Genitourinary System: Spectrum of Imaging Findings and Clinical-Pathologic Features
Ji Woon Oh, Sung Eun Rha, Moon Hyung Choi, et al.
RadioGraphics 2020 40:5, 1265-1283 - “IgG4-RD can involve almost every organ in the genitourinary system. Of these, the kidneys are the most frequently involved organs. IgG4-RDinvolving the kidneys, ureter, bladder, urethra, prostate, testes, and female reproductive organs, can show a broad spectrum of imaging findings, such as a localized mass in or surrounding the involved organ or diffuse enlargement of the involved organ, which may mimic a variety of both benign and malignant diseases. The diagnosis of IgG4-RD is based on a combination of clinical history, imaging findings, serologic markers, and characteristic histopathologic features. Although imaging findings are nonspecific for genitourinary system involvement of IgG4-RD, imaging has a key role in the detection of disease and monitoring of treatment response.”
Immunoglobulin G4–Related Disease of the Genitourinary System: Spectrum of Imaging Findings and Clinical-Pathologic Features
Ji Woon Oh, Sung Eun Rha, Moon Hyung Choi, et al.
RadioGraphics 2020 40:5, 1265-1283 - The classic triad of flank pain, a palpable abdominal mass, and hematuria occurs in less than 10% of patients with newly diagnosed RCC.23 Because the retroperitoneal space can accommodate substantial tumor growth prior to symptom onset, only large RCCs are detected by palpation. Currently, the widespread use of abdominal imaging leads to incidental RCC detection in 37% to 61% of cases. With increased incidental detection, gross hematuria is currently reported in less than 25% of patients and occurs more often in advanced disease. Approximately 1.3% of patients with gross hematuria are diagnosed with RCC.
Renal Cell Carcinoma: A Review
Tracy L. Rose, William Y. Kim
JAMA. 2024;332(12):1001-1010
- As artificial intelligence continues to shape the medical imaging landscape, its integration into the field of urologic oncology has led to impressive results. For prostate cancer diagnostics, machine learning has shown promise in refining clinically-significant lesion detection, with some success in deciphering ambiguous lesions on multiparametric MRI. For kidney cancer, radiomics has emerged as a valuable tool for better distinguishing between benign and malignant renal masses and predicting tumor behavior from CT or MRI scans. Meanwhile, in the arena of bladder cancer, there is a burgeoning emphasis on prediction of muscle invasive cancer and forecasting disease trajectory. However, many studies showing promise in these areas face challenges due to limited sample sizes and the need for broader external validation.
AI-powered radiomics: revolutionizing detection of urologic malignancies.
Gelikman, David G.a; Rais-Bahrami, et al.
Current Opinion in Urology 34(1):p 1-7, January 2024. - Radiomics integrated with artificial intelligence offers a pioneering approach to urologic oncology, ushering in an era of enhanced diagnostic precision and reduced invasiveness, guiding patient-tailored treatment plans. Researchers must embrace broader, multicentered endeavors to harness the full potential of this field.
AI-powered radiomics: revolutionizing detection of urologic malignancies.
Gelikman, David G.a; Rais-Bahrami, et al.
Current Opinion in Urology 34(1):p 1-7, January 2024.
- “Renal cancer is responsible for over 100,000 yearly deaths and is principally discovered in computed tomography (CT) scans of the abdomen. CT screening would likely increase the rate of early renal cancer detection, and improve general survival rates, but it is expected to have a prohibitively high financial cost. Given recent advances in artificial intelligence (AI), it may be possible to reduce the cost of CT analysis and enable CT screening by automating the radiological tasks that constitute the early renal cancer detection pipeline. This review seeks to facilitate further interdisciplinary research in early renal cancer detection by summarizing our current knowledge across AI, radiology, and oncology and suggesting useful directions for future novel work.”
Artificial intelligence for early detection of renal cancer in computed tomography: A review
William C. McGough et al.
Cambridge Prisms: Precision Medicine,1, e4, 1–9 - “Deep learning-based classifiers can achieve high accuracy in CT images with very little manual intervention. Tanaka et al. (2020) sought to quantify small (≤4 cm) renal mass detection accuracy in CT using axial CT slices and a fine-tuned InceptionV3 CNN; they differentiated malignant and benign masses with a maximum AUC of 0.846 in CECT and 0.562 in NCCT. Pedersen et al. (2020) trained a similar 2D slice-classifying CNN, but used it to classify each slice within each known mass’ 3D volumes to enable a slice-based voting system to differentiate patient-level RC from oncocytoma, returning a perfect validation accuracy of 100%. Han et al. (2019) sought to differentiate between clear cell RCC (ccRCC) and non-ccRCC from known RCC masses, using radiologist-selected axial CT slices from NCCT and two CECT phases, and achieved sub-type classification AUCs between 0.88 and 0.94 in an internal testing dataset.”
Artificial intelligence for early detection of renal cancer in computed tomography: A review
William C. McGough et al.
Cambridge Prisms: Precision Medicine,1, e4, 1–9 - “Given the potential for RC early detection in LDCT, there is a need for more research quantifying RC segmentation performance in LDCT. Investigations into general NCCT segmentation have shown that using synthetic contrast enhancement as an auxiliary training task in MTL can improve segmentation accuracy. Therefore, an investigation in renal LDCT segmentation may be improved by introducing synthetic enhancement to CECT as an auxiliary learning task in MTL. Such an investigation would likely be complicated by Standley et al. (2020) findings – that MTL task relationships can be unique to each configuration of network architecture, hyperparameters, and dataset domain.”
Artificial intelligence for early detection of renal cancer in computed tomography: A review
William C. McGough et al.
Cambridge Prisms: Precision Medicine,1, e4, 1–9 - “This manuscript highlights and summarizes existing AI method in RC diagnosis and suggests how these can be repurposed to enable RC early detection. After summarizing existing segmentation, classification, and other AI methods in RC diagnosis, a review of analogous cancer detection and diagnosis methods across broader cancer literature and computer vision was conducted. Contrasting the RC-specific workflows to their equivalents across computer vision and other cancer domains allowed the generation of novel RC-specific research proposals that may enable AI-based RC early detection.”
Artificial intelligence for early detection of renal cancer in computed tomography: A review
William C. McGough et al.
Cambridge Prisms: Precision Medicine,1, e4, 1–9
- “Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions.”
Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review
Matteo Ferro et al.
Ther Adv Urol 2023, Vol. 15: 1–26 - “AI evidence so far indicates a strong association with improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions, and its algorithms that can adjust scanner settings to improve image acquisition (especially the gray zone levels) and standardization of scanner protocols between institutions will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers. Radiomics holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions, but integration in clinical practice will have to be preceded by standardized radiomics models and methodology, and future prospective external validation of obtained data and their comparison with existing traditional, well-validated tools, will have to be performed prior to further integration in current practice.”
Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review
Matteo Ferro et al.
Ther Adv Urol 2023, Vol. 15: 1–26
- “After training and fine-tuning, the test set, which should be ideally made of external and unseen data, is used to assess the generalizability of the AI model. So an important point before an AI model can be deployed in the real world, it that its performances be validated using a large validation test with a variety of diagnoses from different databases that also include rare conditions and probably anatomical variations. Large data for AI models is the key because they help increase the confidence in predictions and allows robust internal and external validations and testing. However, large datasets raise several issues such as reliability of original data but also inclusion of rare conditions. One option to increase the prevalence of rare conditions or obtain a distribution that mirrors those of the real world to better train the model is to enrich the dataset using synthetic images obtained with data augmentation techniques, but this requires further investigation.”
Applications of Artificial Intelligence in Urological Oncology Imaging: More Data Are Needed
Philippe Soyer, Anthony Dohan, and Maxime Barat
Canadian Association of Radiologists’ Journal 2023, Vol. 0(0) 1–2
- OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
MATERIALS AND METHODS. A systematic literature search was conducted using PubMed to identify original research studies about the application of AI to renal mass characterization. Besides baseline study characteristics, a total of 15 methodologic quality items were extracted and evaluated on the basis of the following four main categories: modeling, performance evaluation, clinical utility, and transparency items. The qualitative synthesis was presented using descriptive statistics with an accompanying narrative.
CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
Kocak B et al.
AJR 2020; 215:1113–1122 - OBJECTIVE. The objective of our study was to systematically review the literature about the application of artificial intelligence (AI) to renal mass characterization with a focus on the methodologic quality items.
CONCLUSION. To bring AI-based renal mass characterization from research to practice, future studies need to improve modeling and performance evaluation strategies and pay attention to clinical utility and transparency issues.
Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
Kocak B et al.
AJR 2020; 215:1113–1122 - “As a broad concept, artificial intelligence (AI) covers a wide variety of machine learning (ML) methods or algorithms that create models without strict rule-based programming beforehand. These algorithms can improve and correct themselves through experience. The goal of AI tools is to predict certain outcomes using multiple variables. In the field of medical imaging, there has been extensive interest in AI tools.”
Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
Kocak B et al.
AJR 2020; 215:1113–1122 - "In this study, we systematically reviewed 30 studies about the application of AI to re- nal mass characterization. Our focus was on the methodologic quality items related to modeling, performance evaluation, clinical utility, and transparency. The quality items were favorable for modeling and perfor- mance evaluation categories for most stud- ies. On the other hand, they were poor in terms of clinical utility evaluation and transparency for most studies.”
Artificial Intelligence in Renal Mass Characterization: A Systematic Review of Methodologic Items Related to Modeling, Performance Evaluation, Clinical Utility, and Transparency
Kocak B et al.
AJR 2020; 215:1113–1122