The accuracy of ADC measurements in liver is improved by a tailored and computationally efficient local-rigid registration algorithm

This study describes post-processing methodologies to reduce the effects of physiological motion in measurements of apparent diffusion coefficient (ADC) in the liver. The aims of the study are to improve the accuracy of ADC measurements in liver disease to support quantitative clinical characterisation and reduce the number of patients required for sequential studies of disease progression and therapeutic effects. Two motion correction methods are compared, one based on non-rigid registration (NRA) using freely available open source algorithms and the other a local-rigid registration (LRA) specifically designed for use with diffusion weighted magnetic resonance (DW-MR) data. Performance of these methods is evaluated using metrics computed from regional ADC histograms on abdominal image slices from healthy volunteers. While the non-rigid registration method has the advantages of being applicable on the whole volume and in a fully automatic fashion, the local-rigid registration method is faster while maintaining the integrity of the biological structures essential for analysis of tissue heterogeneity. Our findings also indicate that the averaging commonly applied to DW-MR images as part of the acquisition protocol should be avoided if possible

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers (Jan , 10.1007/s00330-020-07598-8, 2021)

A Correction to this paper has been published:Radiolog

Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically driven quantitative biomarkers

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials.Radiolog

The clinical applications of internal receiver coils in magnetic resonance imaging

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN005738 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: a multi-site study of liver metastases

Contains fulltext : 181592.pdf (publisher's version ) (Open Access)Apparent Diffusion Coefficient (ADC) is a potential quantitative imaging biomarker for tumour cell density and is widely used to detect early treatment changes in cancer therapy. We propose a strategy to improve confidence in the interpretation of measured changes in ADC using a data-driven model that describes sources of measurement error. Observed ADC is then standardised against this estimation of uncertainty for any given measurement. 20 patients were recruited prospectively and equitably across 4 sites, and scanned twice (test-retest) within 7 days. Repeatability measurements of defined regions (ROIs) of tumour and normal tissue were quantified as percentage change in mean ADC (test vs. re-test) and then standardised against an estimation of uncertainty. Multi-site reproducibility, (quantified as width of the 95% confidence bound between the lower confidence interval and higher confidence interval for all repeatability measurements), was compared before and after standardisation to the model. The 95% confidence interval width used to determine a statistically significant change reduced from 21.1 to 2.7% after standardisation. Small tumour volumes and respiratory motion were found to be important contributors to poor reproducibility. A look up chart has been provided for investigators who would like to estimate uncertainty from statistical error on individual ADC measurements

Assessing myeloma bone disease with whole-body diffusion-weighted imaging: comparison with x-ray skeletal survey by region and relationship with laboratory estimates of disease burden

AimTo estimate and compare the extent of myeloma bone disease by skeletal region using whole-body diffusion-weighted imaging (WB-DWI) and skeletal survey (SS) and record interobserver agreement, and to investigate differences in imaging assessments of disease extent and apparent diffusion coefficient (ADC) between patients with pathological high versus low disease burden.Materials and methodsTwenty patients with relapsed myeloma underwent WB-DWI and SS. Lesions were scored by number and size for each skeletal region by two independent observers using WB-DWI and SS. Observer scores, ADC, and ADC-defined volume of tumour-infiltrated marrow were compared between patients with high and low disease burden (assessed by serum paraproteins and marrow biopsy).ResultsObserver scores were higher on WB-DWI than SS in every region (p<0.05) except the skull, with greater interobserver reliability in rating the whole skeleton (WB-DWI: ICC = 0.74, 95% CI: 0.443–0.886; SS: ICC = 0.44, 95% CI: 0.002–0.730) and individual body regions. WB-DWI scores were not significantly higher in patients with high versus low disease burden (observer 1: mean ± SD: 48.8 ± 7, 38.6 ± 14.5, observer 2: mean ± SD: 37.3 ± 13.5, 30.4 ± 15.5; p = 0.06, p = 0.35).ConclusionWB-DWI demonstrated more lesions than SS in all regions except the skull with greater interobserver agreement. Sensitivity is not a limiting factor when considering WB-DWI in the management pathway of patients with myeloma

Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial

Contains fulltext : 202142.pdf (publisher's version ) (Open Access

A 3D Voxel Neighborhood Classification Approach within a Multiparametric MRI Classifier for Prostate Cancer Detection

Prostate Magnetic Resonance Imaging (MRI) is one of the most promising approaches to facilitate prostate cancer diagnosis. The effort of research community is focused on classification techniques of MR images in order to predict the cancer position and its aggressiveness. The reduction of False Negatives (FNs) is a key aspect to reduce mispredictions and to increase sensitivity. In order to deal with this issue, the most common approaches add extra filtering algorithms after the classification step; unfortunately, this solution increases the prediction time and it may introduce errors. The aim of this study is to present a methodology implementing a 3D voxel-wise neighborhood features evaluation within a Support Vector Machine (SVM) classification model. When compared with a common single-voxel-wise classification, the presented technique increases both specificity and sensitivity of the classifier, without impacting on its performances. Different neighborhood sizes have been tested to prove the overall good performance of the classificatio