Computer assisted characterization of cervical intervertebral disc degeneration in MRI

A texture-based pattern recognition system is proposed for the automatic characterization of cervical intervertebral disc degeneration from saggital magnetic resonance images of the spine. A case sample of 50 manually segmented ROIs, corresponding to 25 normal and 25 degenerated discs, was analyzed and textural features were generated from each disc-ROI. Student's t-test verified the existence of statistically significant differences between textural feature values generated from normal and degenerated discs. This finding is indicative of disc image texture differentiation due to the degeneration of the disc. The generated features were employed in the design of a pattern recognition system based on the Least Squares Minimum Distance classifier. The system achieved a classification accuracy of 94{%} and it may be of value to physicians for the assessment of cervical intervertebral disc degeneration in MRI

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

Bauru School of Dentistry Tele-Health League: an educational strategy applied to research, teaching and extension among applications in tele-health

Tele-health is more than an innovative alternative; it is an excellent tool that enables access to health and education in health, making it possible to minimize distances, optimize time and reduce costs. Based on these advantages, some Brazilian Universities have used these actions in strategies of education, research and extension, aiming at the application of Tele-health in Brazil. In that way, the Bauru School of Dentistry - University of São Paulo (FOB-USP) has applied the use of information and communication technologies in health by means of a "Tele-Health League" (TL), in order to diagnose, prevent and treat diseases, in addition to educate the population and health services. OBJECTIVE: The present study aims to introduce the characteristics of the Tele-Health League of FOB-USP, as well as the development of its projects. MATERIAL AND METHODS: The Tele-Health League consisted as a Diffusion Course approved by the Provost of Culture and Academic Extension of the University of São Paulo. It is composed as a large group enclosing professoriate coordinator, academician principal, contributing professors and league members, those, diversified between undergraduates students, graduated, health employees, technology and information areas. The participant members are evaluated by the presence frequency (minimum of 85%), and by the performance of tests and paperwork about the theoretical content provided. RESULTS: In four years of activities, the TLFOB-USP obtained a high satisfaction index (90%), an increased number of vacancies due to the interest to become a member, more commitment of the professors of the University and the accomplishment of association with other Brazilian leagues. It is emphasized that the approval percentage of the course results in approval from approximately half of its members. Also, it is important to identify and repair the causes related to the quitting of some members. CONCLUSIONS: The results showed that the TLFOB-USP members, adjoining to the professor's participants, develop projects in Tele-helth, in Tele-aid and Tele-education areas, thus resulting in the involvement of the University and the community