Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR’s usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics−Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item’s explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/. As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at https://radiomic.github.io/CLEAR-E3/. Graphical Abstract

The contrast agent (CA) dose for abdominal computed tomography (CT) is typically based on patient total body weight (TBW), ignoring adipose tissue distribution. We report on our experience of dosing according to the lean body weight (LBW). After Ethics Committee approval, we retrospectively screened 219 consecutive patients, 18 being excluded for not matching the inclusion criteria. Thus, 201 were analysed (106 males), all undergoing a contrast-enhanced abdominal CT with iopamidol (370 mgI/mL) or iomeprol (400 mgI/mL). LBW was estimated using validated formulas. Liver contrast-enhancement (CEL) was measured. Data were reported as mean ± standard deviation. Pearson correlation coefficient, ANOVA, and the Levene test were used. Mean age was 66 ± 13 years, TBW 72 ± 15 kg, LBW 53 ± 11 kg, and LBW/TBW ratio 74 ± 8%; body mass index was 26 ± 5 kg/m2, with 9 underweight patients (4%), 82 normal weight (41%), 76 overweight (38%), and 34 obese (17%). The administered CA dose was 0.46 ± 0.06 gI/kg of TBW, corresponding to 0.63 ± 0.09 gI/kg of LBW. A negative correlation was found between TBW and CA dose (r = -0.683, p < 0.001). CEL (Hounsfield units) was 51 ± 18 in underweight patients, 44 ± 8 in normal weight, 42 ± 9 in overweight, and 40 ± 6 in obese, with a significant difference for both mean (p = 0.004) and variance (p < 0.001). A low but significant positive correlation was found between CEL and CA dose in gI per TBW (r = 0.371, p < 0.001) or per LBW (r = 0.333, p < 0.001). The injected CA dose was highly variable, with obese patients receiving a lower dose than underweight patients, as a radiologist-driven ‘compensation effect’. Diagnostic abdomen CT examinations may be obtained using 0.63 gI/kg of LBW.

The value of arterial spin labelling in adults glioma grading: systematic review and meta-analysis The value of arterial spin labelling in adults glioma grading: systematic review and meta-analysis

Objectives: To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas. Methods: PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity. Results: We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8–44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73–0.83, I² = 94.1%) with a high inter-study heterogeneity. Studies with external validation and including only WHO-grade IV gliomas had significantly lower AUC values (0.65; 95% CI, 0.57–0.73, p < 0.01). Conclusions: Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice. Clinical relevance statement: MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery. Key Points: • Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others.