A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease.

OBJECTIVES: To develop a high-performance, rapid semi-automated method (Sheffield TKV Tool) for measuring total kidney volume (TKV) from magnetic resonance images (MRI) in patients with autosomal dominant polycystic kidney disease (ADPKD). METHODS: TKV was initially measured in 61 patients with ADPKD using the Sheffield TKV Tool and its performance compared to manual segmentation and other published methods (ellipsoidal, mid-slice, MIROS). It was then validated using an external dataset of MRI scans from 65 patients with ADPKD. RESULTS: Sixty-one patients (mean age 45 ± 14 years, baseline eGFR 76 ± 32 ml/min/1.73 m2) with ADPKD had a wide range of TKV (258-3680 ml) measured manually. The Sheffield TKV Tool was highly accurate (mean volume error 0.5 ± 5.3% for right kidney, - 0.7 ± 5.5% for left kidney), reproducible (intra-operator variability - 0.2 ± 1.3%; inter-operator variability 1.1 ± 2.9%) and outperformed published methods. It took less than 6 min to execute and performed consistently with high accuracy in an external MRI dataset of T2-weighted sequences with TKV acquired using three different scanners and measured using a different segmentation methodology (mean volume error was 3.45 ± 3.96%, n = 65). CONCLUSIONS: The Sheffield TKV Tool is operator friendly, requiring minimal user interaction to rapidly, accurately and reproducibly measure TKV in this, the largest reported unselected European patient cohort with ADPKD. It is more accurate than estimating equations and its accuracy is maintained at larger kidney volumes than previously reported with other semi-automated methods. It is free to use, can run as an independent executable and will accelerate the application of TKV as a prognostic biomarker for ADPKD into clinical practice. KEY POINTS: • This new semi-automated method (Sheffield TKV Tool) to measure total kidney volume (TKV) will facilitate the routine clinical assessment of patients with ADPKD. • Measuring TKV manually is time consuming and laborious. • TKV is a prognostic indicator in ADPKD and the only imaging biomarker approved by the FDA and EMA

A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis

Aims: Pulmonary arterial hypertension (PAH) is a progressive condition with high mortality. Quantitative cardiovascular magnetic resonance (CMR) imaging metrics in PAH target individual cardiac structures and have diagnostic and prognostic utility but are challenging to acquire. The primary aim of this study was to develop and test a tensor-based machine learning approach to holistically identify diagnostic features in PAH using CMR, and secondarily, visualize and interpret key discriminative features associated with PAH. Methods and results: Consecutive treatment naive patients with PAH or no evidence of pulmonary hypertension (PH), undergoing CMR and right heart catheterization within 48 h, were identified from the ASPIRE registry. A tensor-based machine learning approach, multilinear subspace learning, was developed and the diagnostic accuracy of this approach was compared with standard CMR measurements. Two hundred and twenty patients were identified: 150 with PAH and 70 with no PH. The diagnostic accuracy of the approach was high as assessed by area under the curve at receiver operating characteristic analysis (P < 0.001): 0.92 for PAH, slightly higher than standard CMR metrics. Moreover, establishing the diagnosis using the approach was less time-consuming, being achieved within 10 s. Learnt features were visualized in feature maps with correspondence to cardiac phases, confirming known and also identifying potentially new diagnostic features in PAH. Conclusion: A tensor-based machine learning approach has been developed and applied to CMR. High diagnostic accuracy has been shown for PAH diagnosis and new learnt features were visualized with diagnostic potential

An artificial intelligence generated automated algorithm to measure total kidney volume in ADPKD

Introduction Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI) generated method for routinely measuring total kidney volume (TKV). Methods An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T MRI data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium which was first manually segmented by a single human operator. As an independent validation cohort, we utilised 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single centre. The tool was then implemented for clinical use and its performance analysed. Results The training / internal validation cohort was younger (mean age 44.0 vs 51.5 years) and the female-male ratio higher (1.2 v 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had PKD1 mutations (79%) and typical disease (Mayo Imaging Class 1, 86%). The median DICE score on the clinical validation dataset between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic dataset was 56 (±28) min whereas manual corrections of the algorithm output took 8.5 (±9.2) min per scan. Conclusions Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application

A comparative case study of the automation of six Victorian school libraries

This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field

MO012: Development of an Accurate Automated Segmentation Algorithm to Measure Total Kidney Volume in ADPKD Suitable for Clinical Application (The Cystvas Study)

Abstract BACKGROUND AND AIMS A major barrier to the routine adoption of total kidney volume (TKV) as a clinical biomarker of disease for autosomal dominant polycystic disease (ADPKD) is the significant human operator time required even by experienced analysts (typically, 45–90 min per patient). Several groups have investigated automated and semi-automated kidney segmentation methods to either reduce or eliminate the human interaction required. However, such tools have mostly been developed using data from single centers, which may not translate well to other centers. To date, there has been little attempt to develop or validate algorithms using multi-center and multi-scanner data. Here, we report an automated segmentation tool capable of high performance across different patient populations and scanner sequences using 1.5 T MRI data from four centers (the CYSTic consortium). The algorithm was subsequently tested in a separate clinical cohort to assess its likely performance during routine clinical use. METHOD All 1.5 T studies from the CYSTic trial were downloaded (acquired from Siemens Avanto, GE Optima and Siemens Aera, using different sequences). Cases with poor image quality or with sections of kidney missing from the field of view were excluded. A single, experienced operator selected the most appropriate image series for segmentation and manually segmented each patient's kidneys using a commercial software program (MIM Encore). There were 454 kidneys segmented from 227 scans. These data were used for algorithm training and validation. In addition, 48 routine clinical scans from the Sheffield 3D Lab were extracted from the archives along with their original segmentations (performed by six different analysts), to use as a test set. None of the patients in the clinical test set were included in the training set. An ensemble U-net algorithm was created using the nnUNet approach Isensee et al. (nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Meth 2021; 18(2): 203–211), whereby the CYSTic data were used in a 5-fold cross-validation, with stratification across the four centers (i.e. each center contributed 80% of the available data to algorithm training in each fold). Algorithm training proceeded according to the standard heuristic nnUnet functions, using a 3D architecture, for 100 epochs. Segmented kidneys were split into left and right sides during post-processing, through analysis of the position of the center of gravity of segmented regions. Once trained, the five algorithms from cross-validation were applied in an ensemble to the clinical test cohort. RESULTS In both cross-validation and clinical testing phases, the median DICE score was 0.96 for each kidney (IQR of 0.95–0.97 in cross-validation on both sides, And 0.95–0.97 on the left side for clinical testing and 0.96 for the right). The median total kidney volume error was −0.46% (−2.02 to 1.27) for the left side in cross-validation and −0.82% (−2.55 to 0.86) for the right. In the clinical testing phase, the median volume errors were −1.8% (−3.69 to 1.29), left and −1.79% (−3.95 to 0.65), right. The mean time taken to manually segment kidneys in the CYSTIc dataset was 54 min per scan (SD of 31 min). Use of the algorithm as a first pass segmentation, with subsequent checking and editing by an operator, would significantly reduce human input time to a few minutes per case CONCLUSION Our new algorithm demonstrates high accuracy compared to the gold standard of manual TKV segmentation and performs well in a wide range of patients with ADPKD imaged using different scanners at several European centers. Its high performance in a real-world clinical dataset demonstrates that such tools can provide a reliable means of measuring TKV in routine practice and reduces the previous barrier of analyst time and experience. </jats:sec

Individualized everolimus treatment for tuberous sclerosis-related angiomyolipoma promotes treatment adherence and response

ABSTRACT Background Everolimus is a potential alternative to embolization and nephrectomy for managing tuberous sclerosis complex (TSC)-associated renal angiomyolipoma (AML). In 2016, National Health Service England approved its use through regional centres for renal AML ≥30 mm showing interval growth. Evidence of lesion stabilization or reduction after 6 months is mandated for continuation of long-term treatment. Methods From November 2016 to June 2021, all potentially eligible adult TSC patients with AML across Yorkshire and Humber were referred for assessment and monitoring. Eligible patients underwent baseline renal magnetic resonance imaging (MRI) assessment and a follow-up MRI scan after 6 months on everolimus. Dose titration was guided by trough levels and lesion responsiveness using a new 3D MRI volumetric protocol. Results Of 28 patients commencing treatment, 19 tolerated everolimus for &amp;gt;3 months. Overall, 11 patients (40%) discontinued treatment, mostly due to recurrent infections (42%) and allergic reactions (25%). Sixty-eight percent required dose adjustments from the initiating dose (10 mg) due to sub-optimal trough levels (38%), minimal AML response (15%) or adverse events (47%). 3D volumetric assessment confirmed a reduction in AML volume of a pre-selected index lesion in all treatment-naïve cases (n = 14), showing superiority over 2D measurements of lesion diameter. Conclusion In this cohort, everolimus promoted AML regression in all patients who tolerated the drug for &amp;gt;6 months with stabilization observed over 3 years. Trough levels enabled individual dose titration to maximize responsiveness and minimize side effects. The use of 3D MRI assessment of lesion volume was superior to 2D measurements of lesion diameter in monitoring treatment response. </jats:sec