Reproducibility of renal volume measurement in adults using 3-dimensional sonography

Type 2 diabetes albuminuric and non-albuminuric phenotypes have different morphological and functional ultrasound features of diabetic kidney disease

AIMS

Whether different diabetic kidney disease (DKD) phenotypes recognise differences in morphological and vascular properties of the kidney is still unexplored. We evaluated the potential role of kidney ultrasonography in differentiating DKD phenotypes in subjects with type 2 diabetes.

MATERIALS AND METHODS

This is a cross-sectional, single-centre study. Total (TRV) and parenchymal renal volumes (PRV) were calculated by applying the ellipsoid formula for conventional (2D) ultrasonography and with manual segmentation for 3D ultrasonography, and then adjusted for body surface area (aTRV, aPRV). Renal resistive index (RI) was contextually determined. DKD phenotypes have been defined based on increased urinary albumin-to-creatinine ratio (ACR >30 mg/g) and/or reduced eGFR (<60 ml/min/1.73 m² ). Recruitment was planned to have groups of the same size.

RESULTS

Among 256 subjects, 26.2% had No-DKD, 24.6% increased albuminuria only (Alb⁺ ), 24.2% non-albuminuric DKD (Alb^- DKD), and 25.0% albuminuric DKD (Alb⁺ DKD). Compared to No-DKD, RI was significantly higher in all DKD phenotypes, being the highest in Alb⁺ DKD, and with a significant trend of RI > 0.70 to increase across phenotypes. In comparison with No-DKD, both 2D and 3D volumes were increased in Alb⁺ and significantly reduced in Alb^- DKD as well as in Alb⁺ DKD, with significantly lower volumes in Alb^- DKD as compared to Alb⁺ DKD at the same reduced levels of eGFR. In adjusted regressions, compared to No-DKD, RI was associated with Alb⁺ ; both RI and aPRV3D were associated with Alb⁺ DKD; only aPRV3D with Alb^- DKD. Compared to No-DKD, Receiver Operating Characteristic curve analyses, designed taking into account conventional risk factors, showed that US parameters did not ameliorate the characterisation of Alb⁺ and Alb⁺ DKD, while aPRV3D significantly improved the phenotyping of Alb^- DKD.

CONCLUSIONS

As a novel information, we reported that, in type 2 diabetes, the emerging normoalbuminuric DKD phenotype showed reduced TRVs and PRVs even when compared, at similarly reduced eGFR levels, with Alb⁺ DKD opening. In perspective, these findings suggest a possible role of imaging for better discrimination of DKD phenotypes in clinical practice.

Total Kidney Volume Measurements in ADPKD by 3D and Ellipsoid Ultrasound in Comparison to Magnetic Resonance Imaging

Background and Objectives: Total kidney volume (TKV) is a validated prognostic biomarker for autosomal dominant polycystic kidney disease (ADPKD). TKV by magnetic resonance imaging (MRI) and manual segmentation is considered the "reference standard", but is time-consuming and not readily accessible. By contrast, 3-dimensional ultrasound (3D ultrasound) provides a promising technology for TKV measurements with unknown potential. Here, we report a comparative study of TKV measurements by 3D ultrasound vs. the conventional methods by ultrasound ellipsoid and MRI ellipsoid. Design, setting, participants, and measurements: Single-center prospective study of 142 patients who completed a standardized 3D ultrasound and MRI. TKV by 3D ultrasound and ultrasound ellipsoid were compared to those by MRI. We assessed the agreement of TKV measurements by Bland-Altman plots and misclassification of the Mayo Clinic Imaging Classes (MCIC) between the different imaging methods, and prediction of MCIC 1C-1E by average ultrasound kidney length >16.5 cm. Results: Compared to MRI manual segmentation, MRI ellipsoid, 3D ultrasound, and ultrasound ellipsoid underestimated TKV (mean difference: -3.2%, -9.1%, and -11.0%) with MCIC misclassified in 11%, 21% and 22% of patients, respectively; most misclassified cases by MRI ellipsoid (11/16), 3D ultrasound (23/30), and ultrasound ellipsoid (26/31) were placed into a lower MCIC. Prediction of the high-risk MCIC (1C-1E) by MRI ellipsoid, 3D ultrasound, and ultrasound ellipsoid all yielded high positive predictive value (96%, 95%, 98%), and specificity (96%, 96%, 99%). However, both negative predictive value (90%, 88%, 95%) and sensitivity (88%, 85%, 94%) were lower for 3D ultrasound and ultrasound ellipsoid compared to MRI ellipsoid. An average ultrasound kidney length >16.5 cm was highly predictive of MCIC 1C-1E only in patients aged <45 years. Conclusions: TKV measurements in ADPKD by 3D ultrasound and ultrasound ellipsoid displayed similar bias, variability, and are less accurate than MRI ellipsoid. Prediction of high-risk MCIC (1C-1E) by all three methods provides high positive predictive value, but ultrasound ellipsoid is simpler to use and more readily available.

Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

Ultrasonographic renal volume in Chinese children: Results of 1683 cases

BACKGROUND

At present, little information has been made available in the evaluation of renal volume in pediatric groups of different ages.

PURPOSE

The purposes of the study are to evaluate the relationship between anthropometric measurements and renal volume measured with three-dimensional ultrasonography in Chinese children who have normal kidneys, and to attempt to develop reliable reference values of renal volume to estimate the renal sizes.

METHODS

A total of 1572 Chinese Han children suffering from stomachache, cryptorchidism and neurogenic enuresis with no history of renal disease or pathological abnormalities that might affect measurements, aged 1month to 12years (mean, 5.64years) were examined bilateral kidneys by ultrasonography. The measurements of renal volume were determined using QLAB software in IU22 units (Philips Medical Systems, Holland). Anthropometric indices including sex, age, height and weight were collected for reviewed analysis.

RESULTS

A total of 1683 children were included, and renal volume of 1572 cases (93.4%) was accepted. There was no significant difference between renal volumes of male and female separately in left and right kidneys (P=0.844 and P=0.621, respectively), whereas there was a significant difference between mean left and right renal volumes (P=0.000). Age, height and weight were all significant correlations with renal volume (R(2), 0.885 and 0.913 for the left and right kidneys, respectively, both P=0.000), and age was the strongest correlation with renal volume (r, 0.472 and 0.399 for the left and right kidneys, respectively) among the anthropometric indices. We drew regression equations to estimate renal volume as follows: left renal volume (cm(3))=0.441×age+0.156×height+0.398×weight+6.677 and right renal volume (cm(3))=0.256×age+0.195×height+0.632×weight+1.788, and developed reference values of renal volume separately for the left and right kidneys in different age groups.

CONCLUSIONS

Regression equations have been developed, which define the renal volume from three-dimensional ultrasonography and may assist pediatricians in monitoring renal growth and detecting of unsuspected bilateral increases or decreases in renal size.