Clinical outcomes for many childhood brain tumours remain poor, despite our increasing understanding of the underlying disease biology. Advances in molecular diagnostics have refined our ability to classify tumour types and subtypes, and efforts are underway across multiple international paediatric neuro-oncology consortia to take novel biological insights in the worst prognosis entities into innovative clinical trials. Whilst for the first time we are designing such studies on the basis of disease-specific biological data, the levels of preclincial evidence in appropriate model systems on which these trials are initiated is still widely variable. We have considered these issues between CONNECT, PNOC and ITCC-Brain, and developed a framework in which we can assess novel concepts being brought forward for possible clinical translation. Whilst not intended to be proscriptive for every possible circumstance, these criteria provide a basis for self-assessment of evidence by laboratory scientists, and a platform for discussion and rational decision-making prior to moving forward clinically.

BACKGROUND: Joint space assessment for rheumatoid arthritis (RA) by ordinal conventional radiographic scales is susceptible to floor and ceiling effects. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides superior resolution, and may detect earlier changes. The goal of this work was to compare existing 3D methods to calculate joint space width (JSW) metrics in human metacarpophalangeal (MCP) joints with HR-pQCT and reach consensus for future studies. Using the consensus method, we established reproducibility with repositioning as well as feasibility for use in second-generation HR-pQCT scanners.
METHODS: Three published JSW methods were compared using datasets from individuals with RA from three research centers. A SPECTRA consensus method was developed to take advantage of strengths of the individual methods. Using the SPECTRA method, reproducibility after repositioning was tested and agreement between scanner generations was also established.
RESULTS: When comparing existing JSW methods, excellent agreement was shown for JSW minimum and mean (ICC 0.987-0.996) but not maximum and volume (ICC 0.000-0.897). Differences were identified as variations in volume definitions and algorithmic differences that generated high sensitivity to boundary conditions. The SPECTRA consensus method reduced this sensitivity, demonstrating good scan-rescan reliability (ICC >0.911) except for minimum JSW (ICC 0.656). There was strong agreement between results from first- and second-generation HR-pQCT (ICC >0.833).
CONCLUSIONS: The SPECTRA consensus method combines unique strengths of three independently-developed algorithms and leverages underlying software updates to provide a mature analysis to measure 3D JSW. This method is robust with respect to repositioning and scanner generations, suggesting its suitability for detecting change.