Metrology Standards for Quantitative Imaging Biomarkers

New calibration setup for quantitative DCE-US imaging protocol: Toward standardization

BACKGROUND

The DCE-US (Dynamic Contrast-Enhanced Ultrasonography) imaging protocol predicts the vascular modifications compared with Response Evaluation Criteria in Solid Tumors (RECIST) based mainly on morphological changes. A quantitative biomarker has been validated through the DCE-US multi-centric study for early monitoring of the efficiency of anti-angiogenic cancer treatments. In this context, the question of transposing the use of this biomarker to other types of ultrasound scanners, probes and settings has arisen to maintain the follow-up of patients under anti-angiogenic treatments. As a consequence, radiologists encounter standardization issues between the different generations of ultrasound scanners to perform quantitative imaging protocols.

PURPOSE

The aim of this study was to develop a new calibration setup to transpose the DCE-US imaging protocol to the new generation of ultrasound scanners using both abdominal and linear probes.

METHODS

This calibration method has been designed to be easily reproducible and optimized, reducing the time required and cost incurred. It is based on an original set-up that includes using a concentration splitter to measure the variation of the harmonic signal intensity, obtained from the Area Under the time-intensity Curve (AUC) as a function of various contrast-agent concentrations. The splitter provided four different concentrations simultaneously ranging from 12.5% to 100% of the initial concentration of the SonoVue contrast agent (Bracco Imaging S.p.A., Milan, Italy), therefore, measuring four AUCs in a single injection. The plot of the AUC as a function of the four contrast agent concentrations represents the intensity variation of the harmonic signal: the slope being the calibration parameter. The standardization through this method implied that both generations of ultrasound scanners had to have the same slopes to be considered as calibrated. This method was tested on two ultrasound scanners from the same manufacturer (Aplio500, Aplioi900, Canon Medical Systems, Tokyo, Japan). The Aplio500 used the settings defined by the initial multicenter DCE-US study. The Mechanical Index (MI) and the Color Gain (CG) of the Aplioi900 have been adjusted to match those of the Aplio500. The reliability of the new setup was evaluated in terms of measurement repeatability, and reproducibility with the agreement between the measurements obtained once the two ultrasound scanners were calibrated.

RESULTS

The new setup provided excellent repeatability measurements with a value of 96.8%. Once the two ultrasound scanners have been calibrated for both types of probes, the reproducibility was excellent with the agreement between their respective quantitative measurement was at the lowest 95.4% and at the best 98.8%. The settings of the Aplioi900 (Canon Medical Systems) were adjusted to match those of the Aplio500 (Canon Medical Systems) and these validated settings were for the abdominal probe: MI = 0.13 and CG = 34 dB; and for the linear probe: MI = 0.10 and CG = 38 dB.

CONCLUSION

This new calibration setup provided reliable measurements and enabled the rapid transfer and the use of the DCE-US imaging protocol on new ultrasound scanners, thus permitting a continuation of the therapeutic evaluation of patients through quantitative imaging.

Steps on the Path to Clinical Translation: A workshop by the British and Irish Chapter of the ISMRM

The British and Irish Chapter of the International Society for Magnetic Resonance in Medicine (BIC-ISMRM) held a workshop entitled "Steps on the path to clinical translation" in Cardiff, UK, on 7th September 2022. The aim of the workshop was to promote discussion within the MR community about the problems and potential solutions for translating quantitative MR (qMR) imaging and spectroscopic biomarkers into clinical application and drug studies. Invited speakers presented the perspectives of radiologists, radiographers, clinical physicists, vendors, imaging Contract/Clinical Research Organizations (CROs), open science networks, metrologists, imaging networks, and those developing consensus methods. A round-table discussion was held in which workshop participants discussed a range of questions pertinent to clinical translation of qMR imaging and spectroscopic biomarkers. Each group summarized their findings via three main conclusions and three further questions. These questions were used as the basis of an online survey of the broader UK MR community.

Quantifying the spatial clustering characteristics of radiographic emphysema explains variability in pulmonary function

Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV[Formula: see text], FEV[Formula: see text]-FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded.

Review of the use of radiomics to assess the risk of recurrence in early-stage non-small cell lung cancer

Background and Objective

Radiomics is an emerging field of advanced image analysis that has shown promise as a non-invasive, companion diagnostic in predicting clinical outcomes and response assessments in solid tumors. Radiomics aims to extract high-content information from medical images not visible to the naked eye, especially in early-stage non-small cell lung cancer (NSCLC) patients. Although these patients are being identified by early detection programs, it remains unclear which patients would benefit from adjuvant treatment versus active surveillance. Having a radiomic signature(s) that could predict early recurrence would be beneficial. In this review, an overview of the basic radiomic approaches used to evaluate solid tumors on radiologic scans, including NSCLC is provided followed by a review of relevant literature that supports the use of radiomics to help predict tumor recurrence in early-stage NSCLC patients.

Methods

A review of the radiomic literature from 1985 to present focusing on the prediction of disease recurrence in early-stage NSCLC was conducted. PubMed database was searched using key terms for radiomics and NSCLC. A total of 41 articles were identified and 13 studies were considered suitable for inclusion based upon study population, patient number (n>50), use of well described radiomic methodologies, suitable model building features, and well-defined testing/training and validation where feasible.

Key Content and Findings

Examples of using radiomics in early-stage NSCLC patients will be presented, where disease free survival is a primary consideration. A summary of the findings demonstrates the importance of both the intratumor and peritumoral radiomic signals as a marker of outcomes.

Conclusions

The value of radiomic information for predicting disease recurrence in early-stage NSCLC patients is accumulating. However, overcoming several challenges along with the lack of prospective trials, has inhibited it use as a clinical decision-making support tool in early-stage NSCLC.

Computed Tomography-based Prognostication in Lung Adenocarcinomas through Histopathological Feature Learning: A Retrospective Multicenter Study

Rationale: Modeling imaging surrogates for well-validated histopathological risk factors would enable prognostication in early-stage lung adenocarcinomas. Objectives: We aimed to develop and validate computed tomography (CT)-based deep learning (DL) models for the prognostication of early-stage lung adenocarcinomas through learning histopathological features and to investigate the models' reproducibility using retrospective, multicenter datasets. Methods: Two DL models were trained to predict visceral pleural invasion and lymphovascular invasion, respectively, using preoperative chest CT scans from 1,426 patients with stage I-IV lung adenocarcinomas. The averaged model output was defined as the composite score and evaluated for the prognostic discrimination and its added value to clinicopathological factors in temporal (n = 610) and external test sets (n = 681) of stage I lung adenocarcinomas. The study outcomes were freedom from recurrence (FFR) and overall survival (OS). Interscan and interreader reproducibility were analyzed in 31 patients with lung cancer who underwent same-day repeated CT scans. Results: For the temporal test set, the time-dependent area under the receiver operating characteristic curve was 0.76 (95% confidence interval [CI], 0.71-0.81) for 5-year FFR and 0.67 (95% CI, 0.59-0.75) for 5-year OS. For the external test set, the area under the curve was 0.69 (95% CI, 0.63-0.75) for 5-year OS. The discrimination performance remained stable in 10-year follow-up for both outcomes. The prognostic value of the composite score was independent of and complementary to the clinical factors (adjusted per-percent hazard ratio for FFR [temporal test], 1.04 [95% CI, 1.03-1.05; P < 0.001]; OS [temporal test], 1.03 [95% CI, 1.02-1.04; P < 0.001]; OS [external test], 1.03 [95% CI, 1.02-1.04; P < 0.001]). The likelihood ratio tests indicated added value of the composite score (all P < 0.05). The interscan and interreader reproducibility were excellent (Pearson's correlation coefficient, 0.98 for both). Conclusions: The CT-based composite score obtained from DL of histopathological features predicted survival in early-stage lung adenocarcinomas with high reproducibility.

First-in-human technique translation of oxygen-enhanced MRI to an MR Linac system in patients with head and neck cancer

BACKGROUND AND PURPOSE

Tumour hypoxia is prognostic in head and neck cancer (HNC), associated with poor loco-regional control, poor survival and treatment resistance. The advent of hybrid MRI - radiotherapy linear accelerator or 'MR Linac' systems - could permit imaging for treatment adaptation based on hypoxic status. We sought to develop oxygen-enhanced MRI (OE-MRI) in HNC and translate the technique onto an MR Linac system.

MATERIALS AND METHODS

MRI sequences were developed in phantoms and 15 healthy participants. Next, 14 HNC patients (with 21 primary or local nodal tumours) were evaluated. Baseline tissue longitudinal relaxation time (T1) was measured alongside the change in 1/T1 (termed ΔR1) between air and oxygen gas breathing phases. We compared results from 1.5 T diagnostic MR and MR Linac systems.

RESULTS

Baseline T1 had excellent repeatability in phantoms, healthy participants and patients on both systems. Cohort nasal concha oxygen-induced ΔR1 significantly increased (p < 0.0001) in healthy participants demonstrating OE-MRI feasibility. ΔR1 repeatability coefficients (RC) were 0.023-0.040 s-1 across both MR systems. The tumour ΔR1 RC was 0.013 s-1 and the within-subject coefficient of variation (wCV) was 25% on the diagnostic MR. Tumour ΔR1 RC was 0.020 s-1 and wCV was 33% on the MR Linac. ΔR1 magnitude and time-course trends were similar on both systems.

CONCLUSION

We demonstrate first-in-human translation of volumetric, dynamic OE-MRI onto an MR Linac system, yielding repeatable hypoxia biomarkers. Data were equivalent on the diagnostic MR and MR Linac systems. OE-MRI has potential to guide future clinical trials of biology guided adaptive radiotherapy.

Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability

OBJECTIVES

To evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms.

METHODS

A phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI_70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI_70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated.

RESULTS

92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI_70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI_70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI_70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI_70keV (r² 0.3870-0.6178, all p < 0.001).

CONCLUSIONS

The majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.

Evaluation of surface image guidance and Deep inspiration Breath Hold technique for breast treatments with Halcyon

PURPOSE

To evaluate the accuracy/agreement of a three-camera Catalyst Surface Guided Radiation Therapy (SGRT) system on a closed-gantry Halcyon for Free-Breathing (FB) and Deep Inspiration Breath Hold (DIBH) breast-only treatments.

METHODS

The SGRT positioning agreement with Halcyon couch and cone-beam computed tomography (CBCT) was evaluated on phantom and by evaluation of 2401 FB and 855 DIBH breast-only treatment sessions. The DIBH agreement was evaluated using a programmable moving support. Dose agreement was evaluated for manual SGRT-assisted beam interruption and Halcyon arc beam interruption.

RESULTS

Geometrical phantom agreement was < 0.4 mm. Couch and SGRT agreement for an anthropomorphic phantom resulted in 95% limits of agreement in Right-Left/Feet-Head/Posterior-Anterior (RL/FH/PA) directions of respectively ± 0.4/0.8/0.5 mm and ± 1.1/1.1/0.6 mm in the virtual and real isocenter. FB-SGRT-assisted patient positioning compared to CBCT positioning resulted in RL/FH/PA systematic differences of -0.1/0.1/2.0 mm with standard deviations of 2.7/2.8/2.4 mm. This mean systematic difference had three origins: a) couch sag/isocenter difference of ≤ 0.5 mm. b) Average reconstructed FB-CBCT images do not visually represent the average respiratory position. c) CBCT-based positioning focused on the inner thoracic interface, which can introduce a mean positioning difference between SGRT and CBCT. Manual SGRT-assisted beam interruption and arc interruptions resulted in mean gamma passing rates > 97% (0.5%/0.5 mm) and mean absolute differences < 0.3%.

CONCLUSIONS

Accuracy was comparable with breast-only C-arm SGRT techniques, with different tradeoffs. Depending on the patient's morphology, real-time tracking accuracy in the real isocenter can be reduced. This study demonstrates possible discordances between SGRT and CBCT positioning for breast.

Repeatability and reproducibility of various 4D Flow MRI postprocessing software programs in a multi-software and multi-vendor cross-over comparison study

BACKGROUND

Different software programs are available for the evaluation of 4D Flow cardiovascular magnetic resonance (CMR). A good agreement of the results between programs is a prerequisite for the acceptance of the method. Therefore, the goal was to compare quantitative results from a cross-over comparison in individuals examined on two scanners of different vendors analyzed with four postprocessing software packages.

METHODS

Eight healthy subjects (27 ± 3 years, 3 women) were each examined on two 3T CMR systems (Ingenia, Philips Healthcare; MAGNETOM Skyra, Siemens Healthineers) with a standardized 4D Flow CMR sequence. Six manually placed aortic contours were evaluated with Caas (Pie Medical Imaging, SW-A), cvi42 (Circle Cardiovascular Imaging, SW-B), GTFlow (GyroTools, SW-C), and MevisFlow (Fraunhofer Institute MEVIS, SW-D) to analyze seven clinically used parameters including stroke volume, peak flow, peak velocity, and area as well as typically scientifically used wall shear stress values. Statistical analysis of inter- and intrareader variability, inter-software and inter-scanner comparison included calculation of absolute and relative error (ER), intraclass correlation coefficient (ICC), Bland-Altman analysis, and equivalence testing based on the assumption that inter-software differences needed to be within 80% of the range of intrareader differences.

RESULTS

SW-A and SW-C were the only software programs showing agreement for stroke volume (ICC = 0.96; ER = 3 ± 8%), peak flow (ICC: 0.97; ER = -1 ± 7%), and area (ICC = 0.81; ER = 2 ± 22%). Results from SW-A/D and SW-C/D were equivalent only for area and peak flow. Other software pairs did not yield equivalent results for routinely used clinical parameters. Especially peak maximum velocity yielded poor agreement (ICC ≤ 0.4) between all software packages except SW-A/D that showed good agreement (ICC = 0.80). Inter- and intrareader consistency for clinically used parameters was best for SW-A and SW-D (ICC = 0.56-97) and worst for SW-B (ICC = -0.01-0.71). Of note, inter-scanner differences per individual tended to be smaller than inter-software differences.

CONCLUSIONS

Of all tested software programs, only SW-A and SW-C can be used equivalently for determination of stroke volume, peak flow, and vessel area. Irrespective of the applied software and scanner, high intra- and interreader variability for all parameters have to be taken into account before introducing 4D Flow CMR in clinical routine. Especially in multicenter clinical trials a single image evaluation software should be applied.

Three-dimensional multi-parameter brain mapping using MR fingerprinting

The purpose of this study was to develop and test a 3D multi-parameter MR fingerprinting (MRF) method for brain imaging applications. The subject cohort included 5 healthy volunteers, repeatability tests done on 2 healthy volunteers and tested on two multiple sclerosis (MS) patients. A 3D-MRF imaging technique capable of quantifying T1, T2 and T1ρ was used. The imaging sequence was tested in standardized phantoms and 3D-MRF brain imaging with multiple shots (1, 2 and 4) in healthy human volunteers and MS patients. Quantitative parametric maps for T1, T2, T1ρ, were generated. Mean gray matter (GM) and white matter (WM) ROIs were compared for each mapping technique, Bland-Altman plots and intra-class correlation coefficient (ICC) were used to assess repeatability and Student T-tests were used to compare results in MS patients. Standardized phantom studies demonstrated excellent agreement with reference T1/T2/T1ρ mapping techniques. This study demonstrates that the 3D-MRF technique is able to simultaneously quantify T1, T2 and T1ρ for tissue property characterization in a clinically feasible scan time. This multi-parametric approach offers increased potential to detect and differentiate brain lesions and to better test imaging biomarker hypotheses for several neurological diseases, including MS.

Volumetric Tissue Imaging of Surgical Tissue Specimens Using Micro-Computed Tomography: An Emerging Digital Pathology Modality for Nondestructive, Slide-Free Microscopy-Clinical Applications of Digital Pathology in 3 Dimensions

OBJECTIVES

Micro-computed tomography (micro-CT) is a novel, nondestructive, slide-free digital imaging modality that enables the acquisition of high-resolution, volumetric images of intact surgical tissue specimens. The aim of this systematic mapping review is to provide a comprehensive overview of the available literature on clinical applications of micro-CT tissue imaging and to assess its relevance and readiness for pathology practice.

METHODS

A computerized literature search was performed in the PubMed, Scopus, Web of Science, and CENTRAL databases. To gain insight into regulatory and financial considerations for performing and examining micro-CT imaging procedures in a clinical setting, additional searches were performed in medical device databases.

RESULTS

Our search identified 141 scientific articles published between 2000 and 2021 that described clinical applications of micro-CT tissue imaging. The number of relevant publications is progressively increasing, with the specialties of pulmonology, cardiology, otolaryngology, and oncology being most commonly concerned. The included studies were mostly performed in pathology departments. Current micro-CT devices have already been cleared for clinical use, and a Current Procedural Terminology (CPT) code exists for reimbursement of micro-CT imaging procedures.

CONCLUSIONS

Micro-CT tissue imaging enables accurate volumetric measurements and evaluations of entire surgical specimens at microscopic resolution across a wide range of clinical applications.

Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary Fibrosis

Background Total lung capacity (TLC) has been estimated with use of chest radiographs based on time-consuming methods, such as planimetric techniques and manual measurements. Purpose To develop a deep learning-based, multidimensional model capable of estimating TLC from chest radiographs and demographic variables and validate its technical performance and clinical utility with use of multicenter retrospective data sets. Materials and Methods A deep learning model was pretrained with use of 50 000 consecutive chest CT scans performed between January 2015 and June 2017. The model was fine-tuned on 3523 pairs of posteroanterior chest radiographs and plethysmographic TLC measurements from consecutive patients who underwent pulmonary function testing on the same day. The model was tested with multicenter retrospective data sets from two tertiary care centers and one community hospital, including (a) an external test set 1 (n = 207) and external test set 2 (n = 216) for technical performance and (b) patients with idiopathic pulmonary fibrosis (n = 217) for clinical utility. Technical performance was evaluated with use of various agreement measures, and clinical utility was assessed in terms of the prognostic value for overall survival with use of multivariable Cox regression. Results The mean absolute difference and within-subject SD between observed and estimated TLC were 0.69 L and 0.73 L, respectively, in the external test set 1 (161 men; median age, 70 years [IQR: 61-76 years]) and 0.52 L and 0.53 L in the external test set 2 (113 men; median age, 63 years [IQR: 51-70 years]). In patients with idiopathic pulmonary fibrosis (145 men; median age, 67 years [IQR: 61-73 years]), greater estimated TLC percentage was associated with lower mortality risk (adjusted hazard ratio, 0.97 per percent; 95% CI: 0.95, 0.98; P < .001). Conclusion A fully automatic, deep learning-based model estimated total lung capacity from chest radiographs, and the model predicted survival in idiopathic pulmonary fibrosis. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Sorkness in this issue.

Considerations for Imaging of Malignant Pleural Mesothelioma: A Consensus Statement from the International Mesothelioma Interest Group

Malignant pleural mesothelioma (MPM) is an aggressive primary malignancy of the pleura that presents unique radiologic challenges with regard to accurate and reproducible assessment of disease extent at staging and follow-up imaging. By optimizing and harmonizing technical approaches to imaging MPM, the best quality imaging can be achieved for individual patient care, clinical trials, and imaging research. This consensus statement represents agreement on harmonized, standard practices for routine multimodality imaging of MPM, including radiography, computed tomography, ¹⁸F-2-deoxy-D-glucose positron emission tomography, and magnetic resonance imaging, by an international panel of experts in the field of pleural imaging assembled by the International Mesothelioma Interest Group. In addition, modality-specific technical considerations and future directions are discussed. A bulleted summary of all technical recommendations is provided.

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies

The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor-based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross-validating algorithms. Data standardization and creation of shared databases will be vital. Pre-competitive consortia (public-private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.

Quantitative magnetic resonance imaging (qMRI) in axial spondyloarthritis

Imaging, and particularly MRI, plays a crucial role in the assessment of inflammation in rheumatic disease, and forms a core component of the diagnostic pathway in axial spondyloarthritis. However, conventional imaging techniques are limited by image contrast being non-specific to inflammation and a reliance on subjective, qualitative reader interpretation. Quantitative MRI methods offer scope to address these limitations and improve our ability to accurately and precisely detect and characterise inflammation, potentially facilitating a more personalised approach to management. Here, we review quantitative MRI methods and emerging quantitative imaging biomarkers for imaging inflammation in axial spondyloarthritis. We discuss the potential benefits as well as the practical considerations that must be addressed in the movement toward clinical translation of quantitative imaging biomarkers.

Evaluation of Apparent Diffusion Coefficient Repeatability and Reproducibility for Preclinical MRIs Using Standardized Procedures and a Diffusion-Weighted Imaging Phantom

Relevant to co-clinical trials, the goal of this work was to assess repeatability, reproducibility, and bias of the apparent diffusion coefficient (ADC) for preclinical MRIs using standardized procedures for comparison to performance of clinical MRIs. A temperature-controlled phantom provided an absolute reference standard to measure spatial uniformity of these performance metrics. Seven institutions participated in the study, wherein diffusion-weighted imaging (DWI) data were acquired over multiple days on 10 preclinical scanners, from 3 vendors, at 6 field strengths. Centralized versus site-based analysis was compared to illustrate incremental variance due to processing workflow. At magnet isocenter, short-term (intra-exam) and long-term (multiday) repeatability were excellent at within-system coefficient of variance, wCV [±CI] = 0.73% [0.54%, 1.12%] and 1.26% [0.94%, 1.89%], respectively. The cross-system reproducibility coefficient, RDC [±CI] = 0.188 [0.129, 0.343] µm2/ms, corresponded to 17% [12%, 31%] relative to the reference standard. Absolute bias at isocenter was low (within 4%) for 8 of 10 systems, whereas two high-bias (>10%) scanners were primary contributors to the relatively high RDC. Significant additional variance (>2%) due to site-specific analysis was observed for 2 of 10 systems. Base-level technical bias, repeatability, reproducibility, and spatial uniformity patterns were consistent with human MRIs (scaled for bore size). Well-calibrated preclinical MRI systems are capable of highly repeatable and reproducible ADC measurements.

Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis.

Multiparametric Quantitative Imaging Biomarkers for Phenotype Classification: A Framework for Development and Validation

This manuscript is the third in a five-part series related to statistical assessment methodology for technical performance of multi-parametric quantitative imaging biomarkers (mp-QIBs). We outline approaches and statistical methodologies for developing and evaluating a phenotype classification model from a set of multiparametric QIBs. We then describe validation studies of the classifier for precision, diagnostic accuracy, and interchangeability with a comparator classifier. We follow with an end-to-end real-world example of development and validation of a classifier for atherosclerotic plaque phenotypes. We consider diagnostic accuracy and interchangeability to be clinically meaningful claims for a phenotype classification model informed by mp-QIB inputs, aiming to provide tools to demonstrate agreement between imaging-derived characteristics and clinically established phenotypes. Understanding that we are working in an evolving field, we close our manuscript with an acknowledgement of existing challenges and a discussion of where additional work is needed. In particular, we discuss the challenges involved with technical performance and analytical validation of mp-QIBs. We intend for this manuscript to further advance the robust and promising science of multiparametric biomarker development.

Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies.

Criteria for the translation of radiomics into clinically useful tests

Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially. However, despite the thousands of radiomic studies, the number of settings in which radiomics has been successfully translated into a clinically useful tool or has obtained FDA clearance is comparatively small. This relative dearth might be attributable to factors such as the varying imaging and radiomic feature extraction protocols used from study to study, the numerous potential pitfalls in the analysis of radiomic data, and the lack of studies showing that acting upon a radiomic-based tool leads to a favourable benefit-risk balance for the patient. Several guidelines on specific aspects of radiomic data acquisition and analysis are already available, although a similar roadmap for the overall process of translating radiomics into tools that can be used in clinical care is needed. Herein, we provide 16 criteria for the effective execution of this process in the hopes that they will guide the development of more clinically useful radiomic tests in the future.

Determining the reliable feature change in longitudinal radiomics studies: A methodological approach using the reliable change index

PURPOSE

Determination of reliable change of radiomics feature over time is essential and vital in delta-radiomics, but has not yet been rigorously examined. This study attempts to propose a methodological approach using reliable change index (RCI), a statistical metric to determine the reliability of quantitative biomarker changes by accounting for the baseline measurement standard error, in delta-radiomics. The use of RCI was demonstrated with the MRI data acquired from a group of prostate cancer (PCa) patients treated by 1.5 T MRI-guided radiotherapy (MRgRT).

METHODS

Fifty consecutive PCa patients who underwent five-fractionated MRgRT were retrospectively included, and 1023 radiomics features were extracted from the clinical target volume (CTV) and planning target volume (PTV). The two MRI datasets acquired at the first fraction (MRI11 and MRI21) were used to calculate the baseline feature reliability against image acquisition using intraclass correlation coefficient (ICC). The RCI was constructed based on the baseline feature measurement standard deviation, ICC, and feature value differences at two time points between the fifth (MRI51) and the first fraction MRI (MRI11). The reliable change of features was determined in each patient only if the calculated RCI was over 1.96 or smaller than -1.96. The feature changes between MRI51 and MRI11 were correlated to two patient-reported quality-of-life clinical endpoints of urinary domain summary score (UDSS) and bowel domain summary score (BDSS) in 35 patients using the Spearman correlation test. Only the significant correlations between a feature that was reliably changed in ≥7 patients (20%) by RCI and an endpoint were considered as true significant correlations.

RESULTS

The 352 (34.4%) and 386 (37.7%) features among all 1023 features were determined by RCI to be reliably changed in more than five (10%) patients in the CTV and PTV, respectively. Nineteen features were found reliably changed in the CTV and 31 features in the PTV, respectively, in 10 (20%) or more patients. These features were not necessarily associated with significantly different longitudinal feature values (group p-value < 0.05). Most reliably changed features in more than 10 patients had excellent or good baseline test-retest reliability ICC, while none showed poor reliability. The RCI method ruled out the features to be reliably changed when substantial feature measurement bias was presented. After applying the RCI criterion, only four and five true significant correlations were confirmed with UDSS and BDSS in the CTV, respectively, with low true significance correlation rates of 10.8% (4/37) and 17.9% (5/28). No true significant correlations were found in the PTV.

CONCLUSIONS

The RCI method was proposed for delta-radiomics and demonstrated using PCa MRgRT data. The RCI has advantages over some other statistical metrics commonly used in the previous delta-radiomics studies, and is useful to reliably identify the longitudinal radiomics feature change on an individual basis. This proposed RCI method should be helpful for the development of essential feature selection methodology in delta-radiomics.

Introduction to radiomics for a clinical audience

Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

Pulse-Echo Technique to Compensate for Laminate Membrane Transmission Loss in Phantom-Based Ultrasonic Attenuation Coefficient Measurements

OBJECTIVES

Accurately measuring the attenuation coefficient (AC) of reference phantoms is critical in clinical applications of quantitative ultrasound. Phantom AC measurement requires proper compensation of membrane transmission loss. Conventional methods require separate membrane samples to obtain membrane transmission loss. Unfortunately, separate membrane samples are often unavailable. A pulse-echo approach is proposed herein to compensate for membrane transmission loss without requiring separate membrane samples.

METHODS

The proposed method consists of the following steps. First, the insertion loss, caused by phantom attenuation and membrane transmission loss, is measured. Second, the membrane reflection coefficient is measured. Third, the unknown acoustic parameters of the membrane and phantom material are estimated by fitting theoretical reflection coefficient to the measured one. Finally, the fitted parameters are used to estimate membrane transmission loss and phantom AC. The proposed method was validated through k-Wave simulations and phantom experiments. Experimental AC measurements were repeated on 5 distinct phantoms by 2 operators to assess the repeatability and reproducibility of the proposed method. Five transducers were used to cover a broad bandwidth (0.7-16 MHz).

RESULTS

The acquired AC in the simulations had a maximum error of 0.06 dB/cm-MHz for simulated phantom AC values ranging from 0.5 to 1 dB/cm-MHz. The acquired AC in the experiments had a maximum error of 0.045 dB/cm-MHz for phantom AC values ranging from 0.28 to 1.48 dB/cm-MHz. Good repeatability and cross-operator reproducibility were observed with a mean coefficient of variation below 0.054.

CONCLUSION

The proposed method simplifies phantom AC measurement while providing satisfactory accuracy and precision.

Radiomics for Alzheimer's Disease: Fundamental Principles and Clinical Applications

Alzheimer's disease is a neurodegenerative disease with a huge impact on people's quality of life, life expectancy, and morbidity. The ongoing prevalence of the disease, in conjunction with an increased financial burden to healthcare services, necessitates the development of new technologies to be employed in this field. Hence, advanced computational methods have been developed to facilitate early and accurate diagnosis of the disease and improve all health outcomes. Artificial intelligence is now deeply involved in the fight against this disease, with many clinical applications in the field of medical imaging. Deep learning approaches have been tested for use in this domain, while radiomics, an emerging quantitative method, are already being evaluated to be used in various medical imaging modalities. This chapter aims to provide an insight into the fundamental principles behind radiomics, discuss the most common techniques alongside their strengths and weaknesses, and suggest ways forward for future research standardization and reproducibility.

US Backscatter for Liver Fat Quantification: An AIUM-RSNA QIBA Pulse-Echo Quantitative Ultrasound Initiative

Nonalcoholic fatty liver disease (NAFLD) is believed to affect one-third of American adults. Noninvasive methods that enable detection and monitoring of NAFLD have the potential for great public health benefits. Because of its low cost, portability, and noninvasiveness, US is an attractive alternative to both biopsy and MRI in the assessment of liver steatosis. NAFLD is qualitatively associated with enhanced B-mode US echogenicity, but visual measures of B-mode echogenicity are negatively affected by interobserver variability. Alternatively, quantitative backscatter parameters, including the hepatorenal index and backscatter coefficient, are being investigated with the goal of improving US-based characterization of NAFLD. The American Institute of Ultrasound in Medicine and Radiological Society of North America Quantitative Imaging Biomarkers Alliance are working to standardize US acquisition protocols and data analysis methods to improve the diagnostic performance of the backscatter coefficient in liver fat assessment. This review article explains the science and clinical evidence underlying backscatter for liver fat assessment. Recommendations for data collection are discussed, with the aim of minimizing potential confounding effects associated with technical and biologic variables.

Radiomics in veterinary medicine: Overview, methods, and applications

Radiomics, or quantitative image analysis from radiographic image data, borrows the suffix from other emerging -omics fields of study, such as genomics, proteomics, and metabolomics. This report provides an overview of the general principles of how radiomic features are computed, describes major types of morphological, first order, and texture features, and the applications, challenges, and opportunities of radiomics as applied in veterinary medicine. Some advantages radiomics has over traditional semantic radiological features include standardized methodology in computing semantic features, the ability to compute features in multi-dimensional images, their newfound associations with genomic and pathological abnormalities, and the number of perceptible and imperceptible features available for regression or classification modeling. Some challenges in deploying radiomics in a clinical setting include sensitivity to image acquisition settings and image artifacts, pre- and post-image reconstruction and calculation settings, variability in feature estimates stemming from inter- and intra-observer contouring errors, and challenges with software and data harmonization and generalizability of findings given the challenges of small sample size and patient selection bias in veterinary medicine. Despite this, radiomics has enormous potential in patient-centric diagnostics, prognosis, and theragnostics. Fully leveraging the utility of radiomics in veterinary medicine will require inter-institutional collaborations, data harmonization, and data sharing strategies amongst institutions, transparent and robust model development, and multi-disciplinary efforts within and outside the veterinary medical imaging community.

Pulse-Echo Quantitative US Biomarkers for Liver Steatosis: Toward Technical Standardization

Excessive liver fat (steatosis) is now the most common cause of chronic liver disease worldwide and is an independent risk factor for cirrhosis and associated complications. Accurate and clinically useful diagnosis, risk stratification, prognostication, and therapy monitoring require accurate and reliable biomarker measurement at acceptable cost. This article describes a joint effort by the American Institute of Ultrasound in Medicine (AIUM) and the RSNA Quantitative Imaging Biomarkers Alliance (QIBA) to develop standards for clinical and technical validation of quantitative biomarkers for liver steatosis. The AIUM Liver Fat Quantification Task Force provides clinical guidance, while the RSNA QIBA Pulse-Echo Quantitative Ultrasound Biomarker Committee develops methods to measure biomarkers and reduce biomarker variability. In this article, the authors present the clinical need for quantitative imaging biomarkers of liver steatosis, review the current state of various imaging modalities, and describe the technical state of the art for three key liver steatosis pulse-echo quantitative US biomarkers: attenuation coefficient, backscatter coefficient, and speed of sound. Lastly, a perspective on current challenges and recommendations for clinical translation for each biomarker is offered.

Utility of Ultrasound-Guided Attenuation Parameter for Grading Steatosis With Reference to MRI-PDFF in a Large Cohort

BACKGROUND & AIMS

Ultrasound-guided attenuation parameter (UGAP) is recently developed for noninvasive evaluation of steatosis. However, reports on its usefulness in clinical practice are limited. This prospective multicenter study analyzed the diagnostic accuracy of grading steatosis with reference to magnetic resonance imaging-based proton density fat fraction (MRI-PDFF), a noninvasive method with high accuracy, in a large cohort.

METHODS

Altogether, 1010 patients with chronic liver disease who underwent MRI-PDFF and UGAP were recruited and prospectively enrolled from 6 Japanese liver centers. Linearity was evaluated using intraclass correlation coefficients between MRI-PDFF and UGAP values. Bias, defined as the mean difference between MRI-PDFF and UGAP values, was assessed by Bland-Altman analysis. UGAP cutoffs for pairwise MRI-PDFF-based steatosis grade were determined using area under the receiver-operating characteristic curve (AUROC) analyses.

RESULTS

UGAP values were shown to be normally distributed. However, because PDFF values were not normally distributed, they were log-transformed (MRI-logPDFF). UGAP values significantly correlated with MRI-logPDFF (intraclass correlation coefficient = 0.768). Additionally, Bland-Altman analysis showed good agreement between MRI-logPDFF and UGAP with a mean bias of 0.0002% and a narrow range of agreement (95% confidence interval [CI], -0.015 to 0.015). The AUROCs for distinguishing steatosis grade ≥1 (MRI-PDFF ≥5.2%), ≥2 (MRI-PDFF ≥11.3%), and 3 (MRI-PDFF ≥17.1%) were 0.910 (95% CI, 0.891-0.928), 0.912 (95% CI, 0.894-0.929), and 0.894 (95% CI, 0.873-0.916), respectively.

CONCLUSIONS

UGAP has excellent diagnostic accuracy for grading steatosis with reference to MRI-PDFF. Additionally, UGAP has good linearity and negligible bias, suggesting that UGAP has excellent technical performance characteristics that can be widely used in clinical trials and patient care. (UMIN Clinical Trials Registry, Number: UMIN000041196).

Primary Multiparametric Quantitative Brain MRI: State-of-the-Art Relaxometric and Proton Density Mapping Techniques

This review on brain multiparametric quantitative MRI (MP-qMRI) focuses on the primary subset of quantitative MRI (qMRI) parameters that represent the mobile ("free") and bound ("motion-restricted") proton pools. Such primary parameters are the proton densities, relaxation times, and magnetization transfer parameters. Diffusion qMRI is also included because of its wide implementation in complete clinical MP-qMRI application. MP-qMRI advances were reviewed over the past 2 decades, with substantial progress observed toward accelerating image acquisition and increasing mapping accuracy. Areas that need further investigation and refinement are identified as follows: (a) the biologic underpinnings of qMRI parameter values and their changes with age and/or disease and (b) the theoretical limitations implicitly built into most qMRI mapping algorithms that do not distinguish between the different spatial scales of voxels versus spin packets, the central physical object of the Bloch theory. With rapidly improving image processing techniques and continuous advances in computer hardware, MP-qMRI has the potential for implementation in a wide range of clinical applications. Currently, three emerging MP-qMRI applications are synthetic MRI, macrostructural qMRI, and microstructural tissue modeling.

Ultrasonography for Assessment of Sarcopenia: A Primer

The human skeletal muscle has a pivotal role in preserving health by maintaining mobility, balance, and metabolic homeostasis. Significant muscle loss as a part of aging and accelerated by disease leads to sarcopenia which becomes an important predictor of quality of life in older persons. Therefore, clinical screening for sarcopenia and validation by precise qualitative and quantitative measurement of skeletal muscle mass (MM) and function is at the center-stage of translational research. Many imaging modalities are available, each having their strengths and limitations, either in interpretation, technical processes, time constraints, or expense. B-mode ultrasonography (US) is a relatively novel approach to evaluating muscle. It can measure several parameters such as MM and architecture simultaneously including muscle thickness, cross-sectional area, echogenicity, pennate angle, and fascicle length. It can also evaluate dynamic parameters like muscle contraction force and muscle microcirculation. US has not gained global attention due to a lack of consensus on standardization and diagnostic threshold values to diagnose sarcopenia. However, it is an inexpensive and widely available technique with clinical applicability. The ultrasound-derived parameters correlate well with strength and functional capacity and provide potential prognostic information. Our aim is to present an update on the evidence-based role of this promising technique in sarcopenia, its advantages over the existing modalities, and its limitations in actual practice with the hope that it may emerge as the "stethoscope" for community diagnosis of sarcopenia.

Case report: Optical coherence tomography for monitoring biologic therapy in psoriasis and atopic dermatitis

Biologic therapies are increasingly used to treat chronic inflammatory skin diseases such as psoriasis and atopic dermatitis. In clinical practice, scores based on evaluation of objective and subjective symptoms are used to assess disease severity, leading to evaluation of treatment goals with clinical decisions on treatment initiation, switch to another treatment modality or to discontinue current treatment. However, this visual-based scoring is relatively subjective and inaccurate due to inter- and intraobserver reliability. Optical coherence tomography (OCT) is a fast, high-resolution, in vivo imaging modality that enables the visualization of skin structure and vasculature. We evaluated the use of OCT for quantification and monitoring of skin inflammation to improve objective assessment of disease activity in patients with psoriasis and atopic dermatitis. We assessed the following imaging parameters including epidermal thickness, vascular density, plexus depth, vessel diameter, and vessel count. A total of four patients with psoriasis or atopic dermatitis were treated with biologic agents according to current treatment guidelines. OCT was used to monitor their individual treatment response in a target lesion representing disease activity for 52 weeks. Psoriatic and eczema lesions exhibited higher epidermal thickness, increased vascular density, and higher vessel count compared to uninvolved skin. An upward shift of the superficial vascular plexus accompanied by smaller vessel diameters was seen in psoriasis in contrast to atopic dermatitis, where larger vessels were observed. A response to biologic therapy was characterized by normalization of the imaging parameters in the target lesions in comparison to uninvolved skin during the observation period of 52 weeks. Optical coherence tomography potentially serves as an instrument to monitor biologic therapy in inflammatory skin diseases. Imaging parameters may enable objective quantification of inflammation in psoriasis or atopic dermatitis in selected representative skin areas. OCT may reveal persistent subclinical inflammation in atopic dermatitis beyond clinical remission.

A primer on texture analysis in abdominal radiology

The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.

Repeatability and Reproducibility Assessment of the Apparent Diffusion Coefficient in the Prostate: A Trial of the ECOG-ACRIN Research Group (ACRIN 6701)

BACKGROUND

Uncertainty regarding the reproducibility of the apparent diffusion coefficient (ADC) hampers the use of quantitative diffusion-weighted imaging (DWI) in evaluation of the prostate with magnetic resonance imaging MRI. The quantitative imaging biomarkers alliance (QIBA) profile for quantitative DWI claims a within-subject coefficient of variation (wCV) for prostate lesion ADC of 0.17. Improved understanding of ADC reproducibility would aid the use of quantitative diffusion in prostate MRI evaluation.

PURPOSE

Evaluation of the repeatability (same-day) and reproducibility (multi-day) of whole-prostate and focal-lesion ADC assessment in a multi-site setting.

STUDY TYPE

Prospective multi-institutional.

SUBJECTS

Twenty-nine males, ages 53 to 80 (median 63) years, following diagnosis of prostate cancer, 10 with focal lesions.

FIELD STRENGTH/SEQUENCE

3T, single-shot spin-echo diffusion-weighted echo-planar sequence with four b-values.

ASSESSMENT

Sites qualified for the study using an ice-water phantom with known ADC. Readers performed DWI analyses at visit 1 ("V1") and visit 2 ("V2," 2-14 days after V1), where V2 comprised scans before ("V2pre") and after ("V2post") a "coffee-break" interval with subject removal and repositioning. A single reader segmented the whole prostate. Two readers separately placed region-of-interests for focal lesions.

STATISTICAL TESTS

Reproducibility and repeatability coefficients for whole prostate and focal lesions derived from median pixel ADC. We estimated the wCV and 95% confidence interval using a variance stabilizing transformation and assessed interreader reliability of focal lesion ADC using the intraclass correlation coefficient (ICC).

RESULTS

The ADC biases from b0 -b600 and b0 -b800 phantom scans averaged 1.32% and 1.44%, respectively; mean b-value dependence was 0.188%. Repeatability and reproducibility of whole prostate median pixel ADC both yielded wCVs of 0.033 (N = 29). In 10 subjects with an evaluable focal lesion, the individual reader wCVs were 0.148 and 0.074 (repeatability) and 0.137 and 0.078 (reproducibility). All time points demonstrated good to excellent interreader reliability for focal lesion ADC (ICCV1  = 0.89; ICCV2pre  = 0.76; ICCV2post  = 0.94).

DATA CONCLUSION

This study met the QIBA claim for prostate ADC. Test-retest repeatability and multi-day reproducibility were largely equivalent. Interreader reliability for focal lesion ADC was high across time points.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2 TOC CATEGORY: Pelvis.

Clinical translation of quantitative magnetic resonance imaging biomarkers - An overview and gap analysis of current practice

PURPOSE

This overview of the current landscape of quantitative magnetic resonance imaging biomarkers (qMR IBs) aims to support the standardisation of academic IBs to assist their translation to clinical practice.

METHODS

We used three complementary approaches to investigate qMR IB use and quality management practices within the UK: 1) a literature search of qMR and quality management terms during 2011-2015 and 2016-2020; 2) a database search for clinical research studies using qMR IBs during 2016-2020; and 3) a survey to ascertain the current availability and quality management practices for clinical MRI scanners and associated equipment at research institutions across the UK.

RESULTS

The analysis showed increased use of all qMR methods between the periods 2011-2015 and 2016-2020 and diffusion-tensor MRI and volumetry to be popular methods. However, the "translation ratio" of journal articles to clinical research studies was higher for qMR methods that have evidence of clinical translation via a commercial route, such as fat fraction and T2 mapping. The number of journal articles citing quality management terms doubled between the periods 2011-2015 and 2016-2020; although, its proportion relative to all journal articles only increased by 3.0%. The survey suggested that quality assurance (QA) and quality control (QC) of data acquisition procedures are under-reported in the literature and that QA/QC of acquired data/data analysis are under-developed and lack consistency between institutions.

CONCLUSIONS

We summarise current attempts to standardise and translate qMR IBs, and conclude by outlining the ideal quality management practices and providing a gap analysis between current practice and a metrological standard.

Statistical considerations for repeatability and reproducibility of quantitative imaging biomarkers

Quantitative imaging biomarkers (QIBs) are increasingly used in clinical studies. Because many QIBs are derived through multiple steps in image data acquisition and data analysis, QIB measurements can produce large variabilities, posing a significant challenge in translating QIBs into clinical trials, and ultimately, clinical practice. Both repeatability and reproducibility constitute the reliability of a QIB measurement. In this article, we review the statistical aspects of repeatability and reproducibility of QIB measurements by introducing methods and metrics for assessments of QIB repeatability and reproducibility and illustrating the impact of QIB measurement error on sample size and statistical power calculations, as well as predictive performance with a QIB as a predictive biomarker.

Effect of Gray Value Discretization and Image Filtration on Texture Features of the Pancreas Derived from Magnetic Resonance Imaging at 3T

Radiomics of pancreas magnetic resonance (MR) images is positioned well to play an important role in the management of diseases characterized by diffuse involvement of the pancreas. The effect of image pre-processing configurations on these images has been sparsely investigated. Fifteen individuals with definite chronic pancreatitis (an exemplar diffuse disease of the pancreas) and 15 healthy individuals were included in this age- and sex-matched case-control study. MR images of the pancreas were acquired using a single 3T scanner. A total of 93 first-order and second-order texture features of the pancreas were compared between the study groups, by subjecting MR images of the pancreas to 7 image pre-processing configurations related to gray level discretization and image filtration. The studied parameters of intensity discretization did not vary in terms of their effect on the number of significant first-order texture features. The number of statistically significant first-order texture features varied after filtering (7 with the use of logarithm filter and 3 with the use of Laplacian of Gaussian filter with 5 mm σ). Intensity discretization generally affected the number of significant second-order texture features more markedly than filtering. The use of fixed bin number of 16 yielded 42 significant second-order texture features, fixed bin number of 128–38 features, fixed bin width of 6–24 features, and fixed bin width of 42–26 features. The specific parameters of filtration and intensity discretization had differing effects on radiomics signature of the pancreas. Relative discretization with fixed bin number of 16 and use of logarithm filter hold promise as pre-processing configurations of choice in future radiomics studies in diffuse diseases of the pancreas.

Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies

Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.

Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy

Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.

Short-term repeatability and long-term reproducibility of quantitative MR imaging biomarkers in a single centre longitudinal study

Quantitative imaging biomarkers (QIBs) can be defined as objective measures that are sensitive and specific to changes in tissue physiology. Provided the acquired QIBs are not affected by scanner changes, they could play an important role in disease diagnosis, prognosis, management, and treatment monitoring. The precision of selected QIBs was assessed from data collected on a 3-T scanner in four healthy participants over a 5-year period. Inevitable scanner changes and acquisition protocol revisions occurred during this time. Standard and custom processing pipelines were used to calculate regional brain volume, cortical thickness, T2, T2*, quantitative susceptibility, cerebral blood flow, axial, radial and mean diffusivity, peak width of skeletonized mean diffusivity, and fractional anisotropy from the acquired images. Coefficient of variation (CoV) and intra-class correlation (ICC) indices were determined in the short-term (i.e., repeatable over three acquisitions within 4 weeks) and in the long-term (i.e., reproducible over four acquisition sessions in 5 years). Precision indices varied based on acquisition technique, processing pipeline, and anatomical region. Good repeatability (average CoV=2.40% and ICC=0.78) and reproducibility (average CoV=8.86 % and ICC=0.72) were found over all QIBs. The best performance indices were obtained for diffusion derived biomarkers (CoV∼0.96% and ICCs=0.87); conversely, the poorest indices were found for the cerebral blood flow biomarker (CoV>10% and ICC<0.5). These results demonstrate that changes in protocol, along with hardware and software upgrades, did not affect the estimates of the selected biomarkers and their precision. Further characterization of the QIB is necessary to understand meaningful changes in the biomarkers in longitudinal studies of normal brain aging and translation to clinical research.

Harmonization based on quantitative analysis of standardized uptake value variations across PET/CT scanners: a multicenter phantom study

OBJECTIVES

This study aimed to measure standardized uptake value (SUV) variations across different PET/computed tomography (CT) scanners to harmonize quantification across systems.

METHODS

We acquired images using the National Electrical Manufacturers Association International Electrotechnical Commission phantom from three PET/CT scanners operated using routine imaging protocols at each site. The SUVs of lesions were assessed in the presence of reference values by a digital reference object (DRO) and recommendations by the European Association of Nuclear Medicine (EANM/EARL) to measure inter-site variations. For harmonization, Gaussian filters with tuned full width at half maximum (FWHM) values were applied to images to minimize differences in SUVs between reference and images. Inter-site variation of SUVs was evaluated in both pre- and postharmonization situations. Test-retest analysis was also carried out to evaluate repeatability.

RESULTS

SUVs from different scanners became significantly more consistent, and inter-site differences decreased for SUVmean, SUVmax and SUVpeak from 17.3, 20.7, and 15.5% to 4.8, 4.7, and 2.7%, respectively, by harmonization (P values <0.05 for all). The values for contrast-to-noise ratio in the smallest lesion of the phantom verified preservation of image quality following harmonization (>2.8%).

CONCLUSIONS

Harmonization significantly lowered variations in SUV measurements across different PET/CT scanners, improving reproducibility while preserving image quality.

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.

Reproducibility of apparent diffusion coefficient measurement in normal prostate peripheral zone at 1.5T MRI

PURPOSE

The purpose of this study was to quantify the influence of factors of variability on apparent diffusion coefficient (ADC) estimation in the normal prostate peripheral zone (PZ).

MATERIALS AND METHODS

Fifty healthy volunteers underwent in 2017 (n = 17) or 2020 (n = 33) two-point (0, 800 s/mm²) prostate diffusion-weighted imaging in the morning on 1.5 T scanners A and B from different manufacturers. Additional five-point (50, 150, 300, 500, 800 s/mm²) acquisitions were performed on scanner B in the morning and evening. ADC was measured in PZ at midgland using ADC maps reconstructed with various b-value combinations. ADC distributions from 2017 and 2020 were compared using Wilcoxon rank sum test. ADC obtained in the same volunteers were compared using Bland Altman methodology. The 95% confidence interval upper limit of the repeatability/reproducibility coefficient defined the lowest detectable ADC difference.

RESULTS

Forty-nine participants with a mean age of 24.6 ± 3.8 [SD] years (range: 21-37 years) were finally included. ADC distributions from 2017 and 2020 were not significantly different and were combined. Despite high individual variability, there was no significant bias (10 × 10^-6 mm²/s, P = 0.58) between ADC measurements made on both scanners. On scanner B, differences in lowest b-values chosen within the 0-500 s/mm² range for two-point ADC computation induced significant biases (56-109 × 10^-6 mm²/s, P < 0.0001). ADC was significantly lower in the morning (bias: 33 × 10^-6 mm²/s, P = 0.006). The number of b-values had little influence on ADC values. The lowest detectable ADC difference varied from 85 × 10^-6 to 311 × 10^-6 mm²/s across scanners, b-value combinations and periods of the day.

CONCLUSIONS

The MRI scanner, the lowest b-value used and the period of the day induce substantial variability in ADC computation.

Proton density fat fraction measurements of rotator cuff muscles: Accuracy, repeatability, and reproducibility across readers and scanners

PURPOSE

To determine the accuracy, repeatability, and reproducibility of magnetic resonance imaging-based proton density fat fraction (MRI-PDFF) measurements of rotator cuff muscles between two readers and three different scanners.

METHODS

Thirty-one volunteers underwent serial shoulder MRI examinations of both left and right sides on one 1.5-T MRI scanner and two 3.0-T MRI scanners. Two independent readers measured muscular PDFF of the supraspinatus, infraspinatus/teres minor muscle, and subscapularis. MR spectroscopy-based proton density fat fraction (MRS-PDFF) was regarded as the reference standard for assessing accuracy. A "coffee break" examination method was used to test the repeatability of each scanner. Bland-Altman plots, Pearson correlation, and linear regression analysis were used to assess bias and linearity. The Wilcoxon signed-rank test and Friedman test were applied to evaluate repeatability and reproducibility.

RESULTS

MRI-PDFF measurements indicated strong linearity (R² = 0.749) and small bias (-0.18%) in comparison with the MRS-PDFF measurements. A very strong positive Pearson correlation (r = 0.955-0.986) between the PDFF estimates of the two repeat scans indicated excellent repeatability. The PDFF measurements showed high reproducibility, with a strong positive Pearson correlation (r = 0.668-0.698) and a small mean bias (-0.04 to -0.10%) across different scanners.

CONCLUSION

MRI-PDFF measurements of rotator cuff muscles were highly accurate, repeatable, and reproducible across different readers and scanners, leading us to the conclusion that PDFF can be a reliable and robust quantitative imaging biomarker for longitudinal or multi-center studies.

Radiomics for Predicting Response of Neoadjuvant Chemotherapy in Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis

Purpose

This study examined the methodological quality of radiomics to predict the effectiveness of neoadjuvant chemotherapy in nasopharyngeal carcinoma (NPC). We performed a meta-analysis of radiomics studies evaluating the bias risk and treatment response estimation.

Methods

Our study was conducted through a literature review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We included radiomics-related papers, published prior to January 31, 2022, in our analysis to examine the effectiveness of neoadjuvant chemotherapy in NPC. The methodological quality was assessed using the radiomics quality score. The intra-class correlation coefficient (ICC) was employed to evaluate inter-reader reproducibility. The pooled area under the curve (AUC), pooled sensitivity, and pooled specificity were used to assess the ability of radiomics to predict response to neoadjuvant chemotherapy in NPC. Lastly, the Quality Assessment of Diagnostic Accuracy Studies technique was used to analyze the bias risk.

Results

A total of 12 studies were eligible for our systematic review, and 6 papers were included in our meta-analysis. The radiomics quality score was set from 7 to 21 (maximum score: 36). There was satisfactory ICC (ICC = 0.987, 95% CI: 0.957–0.996). The pooled sensitivity and specificity were 0.88 (95% CI: 0.71–0.95) and 0.82 (95% CI: 0.68–0.91), respectively. The overall AUC was 0.91 (95% CI: 0.88–0.93).

Conclusion

Prediction response of neoadjuvant chemotherapy in NPC using machine learning and radiomics is beneficial in improving standardization and methodological quality before applying it to clinical practice.