MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study

A CT-based intratumoral and peritumoral radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma

OBJECTIVES

This study aimed to develop and validate a computed tomography (CT)-based intratumoral and peritumoral radiomics nomogram to improve the stratification of postoperative recurrence risk in patients with localized clear cell renal cell carcinoma (ccRCC).

METHODS

This two-center study included 447 patients with localized ccRCC. Patients from Center A were randomly split into a training set (n = 281) and an internal validation set (IVS) (n = 114) in a 7:3 ratio, while 52 patients from Center B formed the external validation set (EVS). Radiomics features from preoperative CT were obtained from the internal area of tumor (IAT), the internal and peritumoral areas of the tumor at 3 mm (IPAT 3 mm), and 5 mm (IPAT 5 mm). The least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct a radiomics score to develop radiomics model (RM). A clinical model (CM) was also established using significant clinical factors. Furthermore, a fusion model (FM) was developed by integrating independent predictors from both clinical factors and the radiomics score (Radscore) through multivariate Cox proportional hazards regression. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell's concordance index (C-index), and decision curve analysis (DCA).

RESULTS

Compared to both the IAT model and the IPAT 3 mm model, the IPAT 5 mm radiomics model demonstrated superior predictive performance for tumor recurrence (C-index: 0.924 vs. 0.915-0.923 in the IVS; 0.952 vs. 0.920-0.944 in the EVS). Therefore, the IPAT 5 mm radiomics score was incorporated into the development of the fusion model. The FM exhibited outstanding predictive accuracy, achieving a C-index of 0.938 in the IVS, significantly outperforming the CM (0.889, P = 0.03). Notably, in the EVS, the RM surpassed both the CM and FM (C-index: 0.952 vs. 0.904-0.940, P > 0.05). Furthermore, decision curve analysis indicated that the FM provided the highest net clinical benefit in the IVS, while both the FM and RM demonstrated substantially greater net benefit than the CM in the EVS.

CONCLUSIONS

The radiomics model and the fusion model, which integrate both intratumoral and peritumoral features, offer accurate prediction of recurrence risk in patients with localized ccRCC. These models have the potential to aid in personalized treatment planning, optimized surveillance strategies, and treatment strategies for patients with clear cell renal cell carcinoma.

Differential diagnosis of clear cell renal cell carcinoma with low signal intensity on T2WI from angiomyolipoma without visible fat on MR imaging

PURPOSE

This study aimed to determine the potential of magnetic resonance imaging (MRI) parameters in differentiating between angiomyolipoma without visible fat (AML.wovf) and clear cell renal cell carcinoma (ccRCC) with low signal intensity on T2-weighted imaging (T2WI).

MATERIALS AND METHODS

This is a retrospective study involving 36 cases of ccRCC and 17 cases of AML.wovf from September 2016 to July 2023. All patients underwent histological examination on resected specimens and contrast-enhanced magnetic resonance imaging (CE-MRI). Clinical characteristics such as age, gender, and symptoms of hematuria and lumbago were recorded. A panel of MRI parameters were analyzed, including the tumor growth patterns, the wedge-shaped sign, pseudocapsule formation, the arterial-to-delayed enhancement ratio (ADER), and the apparent diffusion coefficient (ADC). The potential of these MRI parameters in distinguishing ccRCC from AML.wovf was finally determined and visualized in a nomogram.

RESULTS

There were no significant differences in age, gender, and clinical symptoms between the ccRCC and AML.wovf groups. The wedge-shaped sign was more prevalent in patients with AML.wovf (p = 0.027), while pseudocapsule formation was mainly observed in cases of ccRCC (p < 0.001). Quantitative MRI revealed a significantly lower ADC in patients with AML.wovf (p = 0.007). Pseudocapsule formation (OR = 140.29, p = 0.004), the wedge-shaped sign (OR = 0.05, p = 0.047), and ADC (OR = 36.22, p = 0.037) were independent predictors for differentiating between AML.wovf and ccRCC, and their combination demonstrated the highest diagnostic accuracy, with an area under the curve (AUC) of 0.913 in the receiver operating characteristic (ROC) analysis.

CONCLUSION

A combination of MRI parameters, including the wedge-shaped sign, pseudocapsule formation, and ADC, can accurately differentiate between AML.wovf and ccRCC.

MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal tumors

Purpose

We aim to develop an MRI-based radiomics model to improve the accuracy of differentiating non-ccRCC from benign renal tumors preoperatively.

Methods

The retrospective study included 195 patients with pathologically confirmed renal tumors (134 non-ccRCCs and 61 benign renal tumors) who underwent preoperative renal mass protocol MRI examinations. The patients were divided into a training set (n = 136) and test set (n = 59). Simple t-test and the Least Absolute Shrink and Selection Operator (LASSO) were used to select the most valuable features and the rad-scores of them were calculated. The clinicoradiologic models, single-sequence radiomics models, multi-sequence radiomics models and combined models for differentiation were constructed with 2 classifiers (support vector machine (SVM), logistic regression (LR)) in the training set and used for differentiation in the test set. Ten-fold cross validation was applied to obtain the optimal hyperparameters of the models. The performances of the models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). Delong's test was performed to compare the performances of models.

Results

After univariate and multivariate logistic regression analysis, the independent risk factors to differentiate non-ccRCC from benign renal tumors were selected as follows: age, tumor region, hemorrhage, pseudocapsule and enhancement degree. Among the 14 machine learning classification models constructed, the combined model with LR has the highest efficiency in differentiating non-ccRCC from benign renal tumors. The AUC in the training set is 0.964, and the accuracy is 0.919. The AUC in the test set is 0.936, and the accuracy is 0.864.

Conclusion

The MRI-based radiomics machine learning is feasible to differentiate non-ccRCC from benign renal tumors, which could improve the accuracy of clinical diagnosis.

High peripheral neutrophil and monocyte count distinguishes renal cell carcinoma from renal angiomyolipoma and predicts poor prognosis of renal cell carcinoma

Background

The presence of peripheral inflammatory cells has been linked to the prognosis of cancer. This study aims to investigate the distinct roles of absolute neutrophil count (ANC) and absolute monocyte count (AMC) in differentiating renal cell carcinoma (RCC) from renal angiomyolipoma (RAML), as well as their prognostic significance in RCC.

Methods

We conducted a comprehensive analysis of peripheral immune cell data, clinicopathological data, and tumor characteristics in patients diagnosed with RCC or RAML from January 2015 to December 2021. Receiver operating characteristic (ROC) curves, as well as univariate and multivariate analyses, were employed to assess the diagnostic utility of AMC and ANC in differentiating between RCC and RAML. Kaplan-Meier curve analysis was used to study the survival of RCC patients with different AMC and ANC. The prognostic value of AMC and ANC in RCC was investigated using COX univariate and multivariate analysis. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used for bioinformatic correlation analysis.

Results

A total of 1120 eligible patients were included in the study. The mean preoperative AMC and ANC in patients with RCC were found to be significantly higher compared to those in patients with RAML (P = 0.001 and P < 0.001, respectively). High preoperative AMC and ANC significantly correlated with smoking history, tumor length, gross hematuria, and high T Stage, N stage, and pathological grade. In multivariate analyses, an ANC> 3.205 *10^9/L was identified to be independently associated with the presence of RCC (HR = 1.618, P = 0.008). High AMC and ANC were significantly associated with reduced OS and PFS (P < 0.05), and ANC may be an independent prognostic factor. Public database analysis showed that signature genes of tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs) were highly expressed in ccRCC.

Conclusions

Elevated preoperative ANC and AMC can distinguish RCC from RAML and predict poor prognosis in patients with RCC. Furthermore, the signature genes of TAMs and TANs exhibit high expression levels in clear cell RCC.

The radiomics-clinical nomogram for predicting the response to initial superselective arterial embolization in renal angiomyolipoma, a preliminary study

Purpose

The aim of this study was to explore a radiomics-clinical model for predicting the response to initial superselective arterial embolization (SAE) in renal angiomyolipoma (RAML).

Materials and methods

A total of 78 patients with RAML were retrospectively enrolled. Clinical data were recorded and evaluated. Radiomic features were extracted from preoperative contrast-enhanced CT (CECT). Least absolute shrinkage and selection operator (LASSO) and intra- and inter-class correlation coefficients (ICCs) were used in feature selection. Logistic regression analysis was performed to develop the radiomics, clinical, and combined models where the fivefold cross-validation method was used. The predictive performance and calibration were evaluated by the receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) was used to measure clinical usefulness.

Results

The tumor shrinkage rate was 29.7% in total, and both fat and angiomyogenic components were significantly reduced. In the radiomics model, 12 significant features were selected. In the clinical model, maximum diameter (p = 0.001), angiomyogenic tissue ratio (p = 0.032), aneurysms (p = 0.048), and post-SAE time (p = 0.002) were significantly associated with greater volume reduction after SAE. Because of the severe linear dependence between radiomics signature and some clinical parameters, the combined model eventually included Rad-score, aneurysm, and post-SAE time. The radiomics-clinical model showed better discrimination (mean AUC = 0.83) than the radiomics model (mean AUC = 0.60) and the clinical model (mean AUC = 0.82). Calibration curve and DCA showed the goodness of fit and clinical usefulness of the radiomics-clinical model.

Conclusions

The radiomics-clinical model incorporating radiomics features and clinical parameters can potentially predict the positive response to initial SAE in RAML and provide support for clinical treatment decisions.

MR Perfusion Imaging for Kidney Disease

Renal perfusion reflects overall function of a kidney. As an important indicator of kidney diseases, renal perfusion can be noninvasively measured by multiple methods of MR imaging, such as dynamic contrast-enhanced MR imaging, intravoxel incoherent motion analysis, and arterial spin labeling method. In this article we introduce the principle of the methods, review their recent technical improvements, and then focus on summarizing recent applications of the methods in assessing various renal diseases. By this review, we demonstrate the capability and clinical potential of the imaging methods, with the hope of accelerating their adoption to clinical practice.

Patterns of cortical oxygenation may predict the response to stenting in subjects with renal artery stenosis: A radiomics-based model

BACKGROUND

Percutaneous-transluminal renal angioplasty (PTRA) and stenting aim to halt the progression of kidney disease in patients with renal artery stenosis (RAS), but its outcome is often suboptimal. We hypothesized that a model incorporating markers of renal function and oxygenation extracted using radiomics analysis of blood oxygenation-level dependent (BOLD)-MRI images may predict renal response to PTRA in swine RAS.

MATERIALS AND METHODS

Twenty domestic pigs with RAS were scanned with CT and BOLD MRI before and 4 weeks after PTRA. Stenotic (STK) and contralateral (CLK) kidney volume, blood flow (RBF), and glomerular filtration rate (GFR) were determined, and BOLD-MRI R2 * maps were generated before and after administration of furosemide, a tubular reabsorption inhibitor. Radiomics features were extracted from pre-PTRA BOLD maps and Robust features were determined by Intraclass correlation coefficients (ICC). Prognostic models were developed to predict post-PTRA renal function based on the baseline functional and BOLD-radiomics features, using Lasso-regression for training, and testing with resampling.

RESULTS

Twenty-six radiomics features passed the robustness test. STK oxygenation distribution pattern did not respond to furosemide, whereas in the CLK radiomics features sensitive to oxygenation heterogeneity declined. Radiomics-based model predictions of post-PTRA GFR (r = 0.58, p = 0.007) and RBF (r = 0.68; p = 0.001) correlated with actual measurements with sensitivity and specificity of 92% and 67%, respectively. Models were unsuccessful in predicting post-PTRA systemic measures of renal function.

CONCLUSIONS

Several radiomics features are sensitive to cortical oxygenation patterns and permit estimation of post-PTRA renal function, thereby distinguishing subjects likely to respond to PTRA and stenting.

Research on texture images and radiomics in urology: a review of urological MR imaging applications

PURPOSE OF REVIEW

Tumor volume and heterogenicity are associated with diagnosis and prognosis of urological cancers, and assessed by conventional imaging. Quantitative imaging, Radiomics, using advanced mathematical analysis may contain information imperceptible to the human eye, and may identify imaging-based biomarkers, a new field of research for individualized medicine. This review summarizes the recent literature on radiomics in kidney and prostate cancers and the future perspectives.

RECENT FINDINGS

Radiomics studies have been developed and showed promising results in diagnosis, in characterization, prognosis, treatment planning and recurrence prediction in kidney tumors and prostate cancer, but its use in guiding clinical decision-making remains limited at present due to several limitations including lack of external validations in most studies, lack of prospective studies and technical standardization.

SUMMARY

Future challenges, besides developing prospective and validated studies, include automated segmentation using artificial intelligence deep learning networks and hybrid radiomics integrating clinical data, combining imaging modalities and genomic features. It is anticipated that these improvements may allow identify these noninvasive, imaging-based biomarkers, to enhance precise diagnosis, improve decision-making and guide tailored treatment.

Insights into Renal Cell Carcinoma with Novel Imaging Approaches

This article presents a comprehensive overview of new imaging approaches and techniques for improving the assessment of renal masses and renal cell carcinoma. The Bosniak classification, version 2019, as well as the clear cell likelihood score, version 2.0, will be discussed as new imaging algorithms using established techniques. Additionally, newer modalities, such as contrast-enhanced ultrasound, dual energy computed tomography, and molecular imaging, will be discussed in conjunction with emerging radiomics and artificial intelligence techniques. Current diagnostic algorithms combined with newer approaches may be an effective way to overcome existing limitations in renal mass and RCC characterization.

Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects

Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

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. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.