Is it Radiomics?

Here you can currently download the RQS calculation sheet in excel. Soon we will release the questionnaire here as online tool to fill out.

The full questionaire in text is descrivbed below:





Image protocol quality

Well-documented image protocols (for example, contrast, slice thickness, energy, etc.) and/or usage of public image protocols allow reproducibility/replicability

+ 1 (if protocols are well-documented)

+ 1 (if public protocol is used)


Multiple segmentations

Possible actions are: segmentation by different physicians/algorithms/software, perturbing segmentations by (random) noise, segmentation at different breathing cycles. Analyse feature robustness to segmentation variabilities

+ 1


Phantom study on all scanners

Detect inter-scanner differences and vendor-dependent features. Analyse feature robustness to these sources of variability

+ 1


Imaging at multiple time points

Collect images of individuals at additional time points. Analyse feature robustness to temporal variabilities (for example, organ movement, organ expansion/ shrinkage)

+ 1


Feature reduction or adjustment for multiple testing

Decreases the risk of overfitting. Overfitting is inevitable if the number of features exceeds the number of samples. Consider feature robustness when selecting features

- 3 (if neither measure is implemented)

+ 3 (if either measure is implemented)


Multivariable analysis with non radiomics features

Permits correlating/inferencing between radiomics and non radiomics features

+ 1


Detect and discuss biological correlates

Demonstration of phenotypic differences (possibly associated with underlying gene–protein expression patterns) deepens understanding of radiomics and biology

+ 1


Cut-off analyses

Determine risk groups by either the median, a previously published cut-off or report a continuous risk variable. Reduces the risk of reporting overly optimistic results

+ 1


Discrimination statistics

Report discrimination statistics (for example, C‑statistic, ROC curve, AUC) and their statistical significance (for example, p‑values, confidence intervals). One can also apply resampling method (for example, bootstrapping, cross-validation)

+ 1 (if a discrimination statistic and its statistical significance are reported)

+ 1 (if a resampling method technique is also applied)


Calibration statistics

Report calibration statistics (for example, Calibration-in‑the-large/slope, calibration plots) and their statistical significance (for example, P‑values, confidence intervals). One can also apply resampling method (for example, bootstrapping, cross-validation)

+ 1 (if a calibration statistic and its statistical significance are reported)

+ 1 (if a resampling method technique is also applied)


Prospective study registered in a trial database

Provides the highest level of evidence supporting the clinical validity and usefulness of the radiomics biomarker

+ 7 (for prospective validation of a radiomics signature in an appropriate trial)



The validation is performed without retraining and without adaptation of the cut-off value, provides crucial information with regard to credible clinical performance

*Datasets should be of comparable size and should have at least 10 events per model feature

- 5 (if validation is missing)

+ 2 (if validation is based on a dataset from the same institute)

+ 3 (if validation is based on a dataset from another institute)

+ 4 (if validation is based on two datasets from two distinct institutes)

+ 4 (if the study validates a previously published signature)

+ 5 (if validation is based on three or more datasets from distinct institutes)


Comparison to ‘gold standard’

Assess the extent to which the model agrees with/is superior to the current ‘gold standard’ method (for example, TNM-staging for survival prediction). This comparison shows the added value of radiomics

+ 2


Potential clinical utility

Report on the current and potential application of the model in a clinical setting (for example, decision curve analysis).

+ 2


Cost-effectiveness analysis

Report on the cost-effectiveness of the clinical application

+ 1


Open science and data

Make code and data publicly available. Open science facilitates knowledge transfer and reproducibility of the study

+ 1 (if scans are open source)

+ 1 (if region of interest segmentations are open source)

+ 1 (if code is open source)

+ 1 (if radiomics features are calculated on a set of representative ROIs and the calculated features and representative ROIs are open source)

Total points (36 = 100%)