Meniscus treatment and age associated with narrower radiographic joint space width 2–3 years after ACL reconstruction: data from the MOON onsite cohort

SummaryObjectiveTo identify risk factors for radiographic signs of post-traumatic osteoarthritis (OA) 2–3 years after anterior cruciate ligament (ACL) reconstruction through multivariable analysis of minimum joint space width (mJSW) differences in a specially designed nested cohort.MethodsA nested cohort within the Multicenter Orthopaedic Outcomes Network (MOON) cohort included 262 patients (148 females, average age 20) injured in sport who underwent ACL reconstruction in a previously uninjured knee, were 35 or younger, and did not have ACL revision or contralateral knee surgery. mJSW on semi-flexed radiographs was measured in the medial compartment using a validated computerized method. A multivariable generalized linear model was constructed to assess mJSW difference between the ACL reconstructed and contralateral control knees while adjusting for potential confounding factors.ResultsUnexpectedly, we found the mean mJSW was 0.35 mm wider in ACL reconstructed than in control knees (5.06 mm (95% CI 4.96–5.15 mm) vs 4.71 mm (95% CI 4.62–4.80 mm), P < 0.001). However, ACL reconstructed knees with meniscectomy had narrower mJSW compared to contralateral normal knees by 0.64 mm (95% C.I. 0.38–0.90 mm) (P < 0.001). Age (P < 0.001) and meniscus repair (P = 0.001) were also significantly associated with mJSW difference.ConclusionSemi-flexed radiographs can detect differences in mJSW between ACL reconstructed and contralateral normal knees 2–3 years following ACL reconstruction, and the unexpected wider mJSW in ACL reconstructed knees may represent the earliest manifestation of post-traumatic osteoarthritis and warrants further study

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

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials

Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability