A MRI-based radiomics nomogram for evaluation of renal function in ADPKD

Artificial intelligence in fracture detection on radiographs: a literature review

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

Usefulness of Radiomics and Kidney Volume Based on Non-Enhanced Computed Tomography in Chronic Kidney Disease: Initial Report

INTRODUCTION

Chronic kidney disease (CKD) is classified according to the estimated glomerular filtration rate (eGFR), but kidney volume (KV) can also provide meaningful information. Very few radiomics (RDX) studies on CKD have utilized computed tomography (CT). This study aimed to determine whether non-enhanced computed tomography (NECT)-based RDX can be useful in evaluation of patients with CKD and to compare it with KV.

METHODS

The NECT scans of 64 subjects with impaired kidney function (defined as <60 mL/min/1.73 m2) and 60 controls with normal kidney function were retrospectively analyzed. Kidney segmentations, volume measurements, and RDX features extraction were performed. Machine-learning models using RDX were constructed to classify the kidneys as having structural markers of impaired or normal function.

RESULTS

The median KV in the impaired kidney function group was 114.83 mL vs. 159.43 mL (p < 0.001) in the control group. There was a statistically significant strong positive correlation between KV and eGFR (rs = 0.579, p < 0.001) and a strong negative correlation between KV and serum creatinine level (rs = -0.514, p < 0.001). The KV-based models achieved the best area under the curve (AUC) of 0.746, whereas the RDX-based models achieved the best AUC of 0.878.

CONCLUSIONS

RDX can be useful in identifying patients with impaired kidney function on NECT. RDX-based models outperformed KV-based models. RDX has the potential to identify patients with a higher risk of CKD based on imaging, which, as we believe, can indirectly support clinical decision-making.

Magnetic resonance imaging preprocessing and radiomic features for classification of autosomal dominant polycystic kidney disease genotype

Purpose

Our study aims to investigate the impact of preprocessing on magnetic resonance imaging (MRI) radiomic features extracted from the noncystic kidney parenchyma of patients with autosomal dominant polycystic kidney disease (ADPKD) in the task of classifying PKD1 versus PKD2 genotypes, which differ with regard to cyst burden and disease outcome.

Approach

The effect of preprocessing on radiomic features was investigated using a single T2-weighted fat saturated (T2W-FS) MRI scan from PKD1 and PKD2 subjects (29 kidneys in total) from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study. Radiomic feature reproducibility using the intraclass correlation coefficient (ICC) was computed across MRI normalizations (z -score, reference-tissue, and original image), gray-level discretization, and upsampling and downsampling pixel schemes. A second dataset for genotype classification from 136 subjects T2W-FS MRI images previously enrolled in the HALT Progression of Polycystic Kidney Disease study was matched for age, gender, and Mayo imaging classification class. Genotype classification was performed using a logistic regression classifier and radiomic features extracted from (1) the noncystic kidney parenchyma and (2) the entire kidney. The area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance across preprocessing methods.

Results

Radiomic features extracted from the noncystic kidney parenchyma were sensitive to preprocessing parameters, with varying reproducibility depending on the parameter. The percentage of features with good-to-excellent ICC scores ranged from 14% to 58%. AUC values ranged between 0.47 to 0.68 and 0.56 to 0.73 for the noncystic kidney parenchyma and entire kidney, respectively.

Conclusions

Reproducibility of radiomic features extracted from the noncystic kidney parenchyma was dependent on the preprocessing parameters used, and the effect on genotype classification was sensitive to preprocessing parameters. The results suggest that texture features may be indicative of genotype expression in ADPKD.

Development and Validation of a Nomogram for Renal Survival Prediction in Patients with Autosomal Dominant Polycystic Kidney Disease

Introduction

Due to the wide variation in the prognosis of autosomal dominant polycystic kidney disease (ADPKD), prediction of risk of renal survival in ADPKD patients is a tough challenge. We aimed to establish a nomogram for the prediction of renal survival in ADPKD patients.

Methods

We conducted a retrospective observational cohort study in 263 patients with ADPKD. The patients were randomly assigned to a training set (N = 198) and a validation set (N = 65), and demographic and statistical data at baseline were collected. The total kidney volume was measured using stereology. A clinical prediction nomogram was developed based on multivariate Cox regression results. The performance and clinical utility of the nomogram were assessed by calibration curves, the concordance index (C-index), and decision curve analysis (DCA). The nomogram was compared with the height-adjusted total kidney volume (htTKV) model by receiver operating characteristic curve analysis and DCA.

Results

The five independent factors used to construct the nomogram for prognosis prediction were age, htTKV, estimated glomerular filtration rate, hypertension, and hemoglobin. The calibration curve of predicted probabilities against observed renal survival indicated excellent concordance. The model showed very good discrimination with a C-index of 0.91 (0.83-0.99) and an area under the curve of 0.94, which were significantly higher than those of the htTKV model. Similarly, DCA demonstrated that the nomogram had a better net benefit than the htTKV model.

Conclusion

The risk prediction nomogram, incorporating easily assessable clinical parameters, was effective for the prediction of renal survival in ADPKD patients. It can be a useful clinical adjunct for clinicians to evaluate the prognosis of ADPKD patients and provide individualized decision-making.

Diffusion magnetic resonance imaging for kidney cyst volume quantification and non-cystic tissue characterisation in ADPKD

OBJECTIVES

Beyond total kidney and cyst volume (TCV), non-cystic tissue plays an important role in autosomal dominant polycystic kidney disease (ADPKD) progression. This study aims at presenting and preliminarily validating a diffusion MRI (DWI)-based TCV quantification method and providing evidence of DWI potential in characterising non-cystic tissue microstructure.

METHODS

T2-weighted MRI and DWI scans (b = 0, 15, 50, 100, 200, 350, 500, 700, 1000; 3 directions) were acquired from 35 ADPKD patients with CKD stage 1 to 3a and 15 healthy volunteers on a 1.5 T scanner. ADPKD classification was performed using the Mayo model. DWI scans were processed by mono- and segmented bi-exponential models. TCV was quantified on T2-weighted MRI by the reference semi-automatic method and automatically computed by thresholding the pure diffusivity (D) histogram. The agreement between reference and DWI-based TCV values and the differences in DWI-based parameters between healthy and ADPKD tissue components were assessed.

RESULTS

There was strong correlation between DWI-based and reference TCV (rho = 0.994, p < 0.001). Non-cystic ADPKD tissue had significantly higher D, and lower pseudo-diffusion and flowing fraction than healthy tissue (p < 0.001). Moreover, apparent diffusion coefficient and D values significantly differed by Mayo imaging class, both in the whole kidney (Wilcoxon p = 0.007 and p = 0.004) and non-cystic tissue (p = 0.024 and p = 0.007).

CONCLUSIONS

DWI shows potential in ADPKD to quantify TCV and characterise non-cystic kidney tissue microstructure, indicating the presence of microcysts and peritubular interstitial fibrosis. DWI could complement existing biomarkers for non-invasively staging, monitoring, and predicting ADPKD progression and evaluating the impact of novel therapies, possibly targeting damaged non-cystic tissue besides cyst expansion.

CLINICAL RELEVANCE STATEMENT

This study shows diffusion-weighted MRI (DWI) potential to quantify total cyst volume and characterise non-cystic kidney tissue microstructure in ADPKD. DWI could complement existing biomarkers for non-invasively staging, monitoring, and predicting ADPKD progression and evaluating the impact of novel therapies, possibly targeting damaged non-cystic tissue besides cyst expansion.

KEY POINTS

• Diffusion magnetic resonance imaging shows potential to quantify total cyst volume in ADPKD. • Diffusion magnetic resonance imaging might allow to non-invasively characterise non-cystic kidney tissue microstructure. • Diffusion magnetic resonance imaging-based biomarkers significantly differ by Mayo imaging class, suggesting their possible prognostic value.

Abdominal Imaging in ADPKD: Beyond Total Kidney Volume

In the context of autosomal dominant polycystic kidney disease (ADPKD), measurement of the total kidney volume (TKV) is crucial. It acts as a marker for tracking disease progression, and evaluating the effectiveness of treatment strategies. The TKV has also been recognized as an enrichment biomarker and a possible surrogate endpoint in clinical trials. Several imaging modalities and methods are available to calculate the TKV, and the choice depends on the purpose of use. Technological advancements have made it possible to accurately assess the cyst burden, which can be crucial to assessing the disease state and helping to identify rapid progressors. Moreover, the development of automated algorithms has increased the efficiency of total kidney and cyst volume measurements. Beyond these measurements, the quantification and characterization of non-cystic kidney tissue shows potential for stratifying ADPKD patients early on, monitoring disease progression, and possibly predicting renal function loss. A broad spectrum of radiological imaging techniques are available to characterize the kidney tissue, showing promise when it comes to non-invasively picking up the early signs of ADPKD progression. Radiomics have been used to extract textural features from ADPKD images, providing valuable information about the heterogeneity of the cystic and non-cystic components. This review provides an overview of ADPKD imaging biomarkers, focusing on the quantification methods, potential, and necessary steps toward a successful translation to clinical practice.