The TRUTH confirmed: validation of an intraindividual comparison of gadobutrol and gadoteridol for imaging of glioblastoma using quantitative enhancement analysis.

BACKGROUND: Previous intraindividual comparative studies evaluating gadobutrol and gadoteridol for contrast-enhanced magnetic resonance imaging (MRI) of brain tumours have relied on subjective image assessment, potentially leading to misleading conclusions. We used artificial intelligence algorithms to objectively compare the enhancement achieved with these contrast agents in glioblastoma patients. METHODS: Twenty-seven patients from a prior study who received identical doses of 0.1 mmol/kg gadobutrol and gadoteridol (with appropriate washout in between) were evaluated. Quantitative enhancement (QE) maps of the normalised enhancement of voxels, derived from computations based on the comparison of contrast-enhanced T1-weighted images relative to the harmonised intensity on unenhanced T1-weighted images, were compared. Bland-Altman analysis, linear regression analysis and Pearson correlation coefficient (r) determination were performed to compare net QE and per-region of interest (per-ROI) average QE (net QE divided by the number of voxels). RESULTS: No significant differences were observed for comparisons performed on net QE (mean difference -24.37 ± 620.8, p = 0.840, r = 0.989) or per-ROI average QE (0.0043 ± 0.0218, p = 0.313, r = 0.958). Bland-Altman analysis revealed better per-ROI average QE for gadoteridol-enhanced MRI in 19/27 (70.4%) patients although the mean difference (0.0043) was close to zero indicating high concordance and the absence of fixed bias. CONCLUSIONS: The enhancement of glioblastoma achieved with gadoteridol and gadobutrol at 0.1 mmol/kg bodyweight is similar indicating that these agents have similar contrast efficacy and can be used interchangeably, confirming the results of a prior double-blind, randomised, intraindividual, crossover study.

Effect of automated image registration on radiologist interpretation.

In this study, we present preliminary data on the effect of automated 3D image alignment on the time to arrive at a decision about an imaging finding, the agreement of multiple of multiple observers, the prevalence of comparison examinations, and technical success rates for the image alignment algorithm. We found that automated image alignment reduced the average time to make a decision by 25% for cases where the structures are rigid, and when the scanning protocol is similar. For cases where these are not true, there is little or no benefit. In our practice, 54% of cases had prior examinations that could be automatically aligned. The overall benefit seen in our department for highly similar exams might be 20% for neuro and 10% for body; the benefit seen in other practices is likely to vary based on scanning practices and prevalence of prior examinations.

Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients.

The goal of this study was to create an algorithm which would quantitatively compare serial magnetic resonance imaging studies of brain-tumor patients. A novel algorithm and a standard classify-subtract algorithm were constructed. The ability of both algorithms to detect and characterize changes was compared using a series of digital phantoms. The novel algorithm achieved a mean sensitivity of 0.87 (compared with 0.59 for classify-subtract) and a mean specificity of 0.98 (compared with 0.92 for classify-subtract) with regard to identification of voxels as changing or unchanging and classification of voxels into types of change. The novel algorithm achieved perfect specificity in seven of the nine experiments. The novel algorithm was additionally applied to a short series of clinical cases, where it was shown to identify visually subtle changes. Automated change detection and characterization could facilitate objective review and understanding of serial magnetic resonance imaging studies in brain-tumor patients.

Part 2. Automated change detection and characterization applied to serial MR of brain tumors may detect progression earlier than human experts.

An algorithm was developed which compares serial MRI brain examinations of brain tumor patients and judges them as either "stable" or "progressing". A set of 88 serial MR cases were obtained, consisting of cases which were stable and remained stable for at least 8 months, cases which were stable but progressed in less than 8 months, and cases which were progressing. The algorithm was run and its output was compared to the original clinical interpretation. Of the exam pairs which were judged stable and which remained stable at least 8 months after the later examination, the algorithm diagnosed 45/46 as stable. For exam pairs judged to be progressing, the algorithm judged 15/17 to be progressing. Of the exam pairs which were judged stable, but which went on to progress less than 8 months after the later of the pair, 16/25 were judged by the algorithm to be progressing.

Change detection & characterization: a new tool for imaging informatics and cancer research.

Modern imaging systems are able to produce a rich and diverse array of information, regarding various facets of anatomy and function. The quantity of information produced by these systems is so bountiful, however, as to have the potential to become a hindrance to clinical assessment. In the context of serial image evaluation, computer-based change detection and characterization is one important mechanism to process the information produced by imaging systems, so as to reduce the quantity of data, direct the attention of the physician to regions of the data which are the most informative for their purposes, and present the data in the form in which it will be the most useful. Change detection and characterization algorithms may serve as a basis for the creation of an objective definition of progression, which will reduce inter and intra-observer variability, and facilitate earlier detection of disease and recurrence, which in turn may lead to improved outcomes. Decreased observer variability combined with increased acuity should make it easier to discover promising therapies. Quantitative measures of the response to these therapies should provide a means to compare the effectiveness of treatments under investigation. Change detection may be applicable to a broad range of cancers, in essentially all anatomical regions. The source of information upon which change detection comparisons may be based is likewise broad. Validation of algorithms for the longitudinal assessment of cancer patients is expected to be challenging, though not insurmountable, as the many facets of the problem mean that validation will likely need to be approached from a variety of vantage points. Change detection and characterization is quickly becoming a very active field of investigation, and it is expected that this burgeoning field will help to facilitate cancer care both in the clinic and research.

A review of the automated detection of change in serial imaging studies of the brain.

Serial imaging is frequently performed on patients with diseases of the brain, to track and observe changes. Magnetic resonance imaging provides very detailed and rich information, and is therefore used frequently for this application. The data provided by MR can be so plentiful; however, that it obfuscates the information the radiologist seeks. A system which could reduce the large quantity of primitive data to a smaller and more informative subset of data, emphasizing change, would be useful. This article discusses motivating factors for the production of an automated process to this effect, and reviews the approaches of previous authors. The discussion is focused on brain tumors and multiple sclerosis, but many of the ideas are applicable to other disease processes, as well.