ESR Statement on the Validation of Imaging Biomarkers

Medical imaging capable of generating imaging biomarkers, specifically radiology and nuclear medicine image acquisition and analysis processes, differs from frequently used comparators like blood or urine biomarkers. This difference arises from the sample acquisition methodology. While different analysis methodologies and equipment provide slightly different results in any analytical domain, unlike blood or urine analysis where the samples are obtained by simple extraction or excretion, in radiology the acquisition of the sample is heterogeneous by design, since complex equipment from different vendors is used. Therefore, with this additional degree of freedom in medical imaging, there is still risk of persistent heterogeneity of image quality through time, due to different technological implementations across vendors and protocols used in different centres. Quantitative imaging biomarkers have yet to demonstrate an impact on clinical practice due to this lack of comprehensive standardisation in terms of technical aspects of image acquisition, analysis algorithms, processes and clinical validation. The aim is establishing a standard methodology based on metrology for the validation of image acquisition and analysis methods used in the extraction of biomarkers and radiomics data. The appropriate implementation of the guidelines herein proposed by radiology departments, research institutes and industry will allow for a significant reduction in inter-vendor & inter-centre variability in imaging biomarkers and determine the measurement error obtained, enabling them to be used in imaging-based criteria for diagnosis, prognosis or treatment response, ultimately improving clinical workflows and patient care. The validation of developed analytical methods must be based on a technical performance validation and clinical validation

Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival

[EN] Purpose: To determine if preoperative vascular heterogeneity of glioblastoma is predictive of overall survival of patients undergoing standard-of-care treatment by using an unsupervised multiparametric perfusion-based habitat-discovery algorithm. Materials and Methods: Preoperative magnetic resonance (MR) imaging including dynamic susceptibility-weighted contrast material-enhanced perfusion studies in 50 consecutive patients with glioblastoma were retrieved. Perfusion parameters of glioblastoma were analyzed and used to automatically draw four reproducible habitats that describe the tumor vascular heterogeneity: high-angiogenic and low-angiogenic regions of the enhancing tumor, potentially tumor-infiltrated peripheral edema, and vasogenic edema. Kaplan-Meier and Cox proportional hazard analyses were conducted to assess the prognostic potential of the hemodynamic tissue signature to predict patient survival. Results: Cox regression analysis yielded a significant correlation between patients' survival and maximum relative cerebral blood volume (rCBV(max)) and maximum relative cerebral blood flow (rCBF(max)) in high-angiogenic and low-angiogenic habitats (P < .01, false discovery rate-corrected P < .05). Moreover, rCBF(max) in the potentially tumor-infiltrated peripheral edema habitat was also significantly correlated (P < .05, false discovery rate-corrected P < .05). Kaplan-Meier analysis demonstrated significant differences between the observed survival of populations divided according to the median of the rCBV(max) or rCBF(max) at the high-angiogenic and low-angiogenic habitats (log-rank test P < .05, false discovery rate-corrected P < .05), with an average survival increase of 230 days. Conclusion: Preoperative perfusion heterogeneity contains relevant information about overall survival in patients who undergo standard-of-care treatment. The hemodynamic tissue signature method automatically describes this heterogeneity, providing a set of vascular habitats with high prognostic capabilities.Study supported by H2020 European Institute of Innovation and Technology (POC-2016.SPAIN-07) and Universitat Politecnica de Valencia (PAID-10-14). J.J.A., E.F.G., and J.M.G.G. supported by Secretaria de Estado de Investigacion, Desarrollo e Innovacion (DPI2016-80054-R, TIN2013-43457-R). E.F.G. supported by CaixaImpulse program from Fundacio Bancaria "la Caixa" (LCF/TR/CI16/10010016). E.F.G and A.A.B. supported by the Universitat Politecnica de Valencia Instituto Investigacion Sanitaria de La Fe (C05).Juan -Albarracín, J.; Fuster García, E.; Pérez-Girbés, A.; Aparici-Robles, F.; Alberich Bayarri, A.; Revert Ventura, AJ.; Martí Bonmatí, L.... (2018). Glioblastoma: Vascular Habitats Detected at Preoperative Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging Predict Survival. Radiology. 287(3):944-954. https://doi.org/10.1148/radiol.2017170845S944954287

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research

Prostate Diffusion Weighted-Magnetic Resonance Image analysis using Multivariate Curve Resolution methods

[EN] Multivariate Curve Resolution (MCR) has been applied on prostate Diffusion Weighted-Magnetic Resonance Images (DW-MRI). Different physiological-based modeling approaches of the diffusion process have been submitted to validation by sequentially incorporating prior knowledge on the MCR constraints. Results validate the biexponential diffusion modeling approach and show the capability of the MCR models to find, characterize and locate the behaviors related to the presence of an early prostate tumor.The authors want to thank prof. Anna de Juan for her comments and help in using the software for this study. This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI 2011-28112-004-02.Aguado Sarrió, E.; Prats-Montalbán, JM.; Sanz Requena, R.; Marti Bonmati, L.; Alberich Bayarri, Á.; Ferrer Riquelme, AJ. (2015). Prostate Diffusion Weighted-Magnetic Resonance Image analysis using Multivariate Curve Resolution methods. Chemometrics and Intelligent Laboratory Systems. 140:43-48. https://doi.org/10.1016/j.chemolab.2014.11.002S434814

PRIMAGE project : predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consortium, including the European Society for Paediatric Oncology, two imaging biobanks, and three prominent European paediatric oncology units. The project is constructed as an observational in silico study involving high-quality anonymised datasets (imaging, clinical, molecular, and genetics) for the training and validation of machine learning and multiscale algorithms. The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers, tumour growth simulation, advanced visualisation of confidence scores, and machine-learning approaches. The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma. External validation will be performed on data recruited from independent collaborative centres. Final results will be available for the scientific community at the end of the project, and ready for translation to other malignant solid tumours

Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC.

BACKGROUND: Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. METHODS: A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. RESULTS/CONCLUSIONS: Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified

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

Use of 3.0-T MR Imaging for Evaluation of the Abdomen

The most important advantage of 3.0-T magnetic resonance (MR) imaging systems is their increased signal-to-noise ratio (SNR) compared with 1.5-T systems. The higher SNR can be used to shorten acquisition time, achieve higher spatial resolution, or a combination of the two, thereby improving image quality and clinical diagnosis. In fact, 3.0-T MR imaging systems have already proved superior to 1.5-T systems in neuroradiologic and musculoskeletal applications. In the abdomen, 3.0-T MR imaging is uniquely beneficial for techniques such as enhanced and nonenhanced hepatic imaging, diffusion-weighted imaging, angiography, MR pancreatography, and colonography. Admittedly, 3.0-T abdominal imaging has important technical limitations, such as standing wave artifact, chemical shift artifact, susceptibility artifact, and safety issues such as increased energy deposition within the patient's body. Furthermore, 3.0-T abdominal MR imaging is still in the early stages of development and requires substantial modifications of the pulse sequences and hardware components used for 1.5-T imaging. Nevertheless, the ability to obtain physiologic and functional information within reasonably short acquisition times with 3.0-T abdominal MR imaging bodes well for the future of this imaging technique. (C) RSNA, 2009 . radiographics.rsna.or