# Radiomics reporting guidelines and nomenclature¶

Reliable and complete reporting is necessary to ensure reproducibility and validation of results. To help provide a complete report on image processing and image biomarker extraction, we present the guidelines below, as well as a nomenclature system to uniquely features.

## Reporting guidelines¶

These guidelines are partially based on the work of [Sollini2017N][Lambin2017][Sanduleanu2018iu][Traverso2018yr]. Additionally, guidelines are derived from the image processing and feature calculation steps described within this document. An earlier version was reported elsewhere [vallieres2017responsible].

### Patient¶

 Topic Modality Item Description Region of interest [1] 1 Describe the region of interest that is being imaged. Patient preparation 2a Describe specific instructions given to patients prior to image acquisition, e.g. fasting prior to imaging. 2b Describe administration of drugs to the patient prior to image acquisition, e.g. muscle relaxants. 2c Describe the use of specific equipment for patient comfort during scanning, e.g. ear plugs. Radioactive tracer PET, SPECT 3a Describe which radioactive tracer was administered to the patient, e.g. 18F-FDG. PET, SPECT 3b Describe the administration method. PET, SPECT 3c Describe the injected activity of the radioactive tracer at administration. PET, SPECT 3d Describe the uptake time prior to image acquisition. PET, SPECT 3e Describe how competing substance levels were controlled. [2] Contrast agent 4a Describe which contrast agent was administered to the patient. 4b Describe the administration method. 4c Describe the injected quantity of contrast agent. 4d Describe the uptake time prior to image acquisition. 4e Describe how competing substance levels were controlled. Comorbidities 5 Describe if the patients have comorbidities that affect imaging. [3]

### Acquisition [4]¶

 Topic Modality Item Description Acquisition protocol 6 Describe whether a standard imaging protocol was used, and where its description may be found. Scanner type 7 Describe the scanner type(s) and vendor(s) used in the study. Imaging modality 8 Clearly state the imaging modality that was used in the study, e.g. CT, MRI. Static/dynamic scans 9a State if the scans were static or dynamic. Dynamic scans 9b Describe the acquisition time per time frame. Dynamic scans 9c Describe any temporal modelling technique that was used. Scanner calibration 10 Describe how and when the scanner was calibrated. Patient instructions 11 Describe specific instructions given to the patient during acquisition, e.g. breath holding. Anatomical motion correction 12 Describe the method used to minimise the effect of anatomical motion. Scan duration 13 Describe the duration of the complete scan or the time per bed position. Tube voltage CT 14 Describe the peak kilo voltage output of the X-ray source. Tube current CT 15 Describe the tube current in mA. Time-of-flight PET 16 State if scanner time-of-flight capabilities are used during acquisition. RF coil MRI 17 Describe what kind RF coil used for acquisition, incl. vendor. Scanning sequence MRI 18a Describe which scanning sequence was acquired. MRI 18b Describe which sequence variant was acquired. MRI 18c Describe which scan options apply to the current sequence, e.g. flow compensation, cardiac gating. Repetition time MRI 19 Describe the time in ms between subsequent pulse sequences. Echo time MRI 20 Describe the echo time in ms. Echo train length MRI 21 Describe the number of lines in k-space that are acquired per excitation pulse. Inversion time MRI 22 Describe the time in ms between the middle of the inverting RF pulse to the middle of the excitation pulse. Flip angle MRI 23 Describe the flip angle produced by the RF pulses. Acquisition type MRI 24 Describe the acquisition type of the MRI scan, e.g. 3D. k-space traversal MRI 25 Describe the acquisition trajectory of the k-space. Number of averages/ excitations MRI 26 Describe the number of times each point in k-space is sampled. Magnetic field strength MRI 27 Describe the nominal strength of the MR magnetic field.

### Reconstruction [5]¶

 Topic Modality Item Description In-plane resolution 28 Describe the distance between pixels, or alternatively the field of view and matrix size. Image slice thickness 29 Describe the slice thickness. Image slice spacing 30 Describe the distance between image slices. [6] Convolution kernel CT 31a Describe the convolution kernel used to reconstruct the image. CT 31b Describe settings pertaining to iterative reconstruction algorithms. Exposure CT 31c Describe the exposure (in mAs) in slices containing the region of interest. Reconstruction method PET 32a Describe which reconstruction method was used, e.g. 3D OSEM. PET 32b Describe the number of iterations for iterative reconstruction. PET 32c Describe the number of subsets for iterative reconstruction. Point spread function modelling PET 33 Describe if and how point-spread function modelling was performed. Image corrections PET 34a Describe if and how attenuation correction was performed. PET 34b Describe if and how other forms of correction were performed, e.g. scatter correction, randoms correction, dead time correction etc. Reconstruction method MRI 35a Describe the reconstruction method used to reconstruct the image from the k-space information. MRI 35b Describe any artifact suppression methods used during reconstruction to suppress artifacts due to undersampling of k-space. Diffusion-weigh ted imaging DWI-MRI 36 Describe the b-values used for diffusion-weigh ting.

### Image registration¶

 Topic Modality Item Description Registration method 37 Describe the method used to register multi-modality imaging.

### Data conversion¶

 Topic Modality Item Description SUV normalisation PET 38 Describe which standardised uptake value (SUV) normalisation method is used. ADC computation DWI-MRI 39 Describe how apparent diffusion coefficient (ADC) values were calculated. Other data conversions 40 Describe any other conversions that are performed to generate e.g. perfusion maps.

### Post-acquisition processing¶

 Topic Modality Item Description Anti-aliasing 41 Describe the method used to deal with anti-aliasing when down-sampling during interpolation. Noise suppression 42 Describe methods used to suppress image noise. Post-reconstruc tion smoothing filter PET 43 Describe the width of the Gaussian filter (FWHM) to spatially smooth intensities. Skull stripping MRI (brain) 44 Describe method used to perform skull stripping. Non-uniformity correction [7] MRI 45 Describe the method and settings used to perform non-uniformity correction. Intensity normalisation 46 Describe the method and settings used to normalise intensity distributions within a patient or patient cohort. Other post-acquisitio n processing methods 47 Describe any other methods that were used to process the image and are not mentioned separately in this list.

### Segmentation¶

 Topic Modality Item Description Segmentation method 48a Describe how regions of interest were segmented, e.g. manually. 48b Describe the number of experts, their expertise and consensus strategies for manual delineation. 48c Describe methods and settings used for semi-automatic and fully automatic segmentation. 48d Describe which image was used to define segmentation in case of multi-modality imaging. Conversion to mask 49 Describe the method used to convert polygonal or mesh-based segmentations to a voxel-based mask.

### Image interpolation¶

 Topic Modality Item Description Interpolation method 50a Describe which interpolation algorithm was used to interpolate the image. 50b Describe how the position of the interpolation grid was defined, e.g. align by center. 50c Describe how the dimensions of the interpolation grid were defined, e.g. rounded to nearest integer. 50d Describe how extrapolation beyond the original image was handled. Voxel dimensions 51 Describe the size of the interpolated voxels. Intensity rounding CT 52 Describe how fractional Hounsfield Units are rounded to integer values after interpolation.

### ROI interpolation¶

 Topic Modality Item Description Interpolation method 53 Describe which interpolation algorithm was used to interpolate the region of interest mask. Partially masked voxels 54 Describe how partially masked voxels after interpolation are handled.

### Re-segmentation¶

 Topic Modality Item Description Re-segmentation methods 55 Describe which methods and settings are used to re-segment the ROI intensity mask.

### Discretisation¶

 Topic Modality Item Description Discretisation method [8] 56a Describe the method used to discretise image intensities. 56b Describe the number of bins (FBN) or the bin size (FBS) used for discretisation. 56c Describe the lowest intensity in the first bin for FBS discretisation. [9]

### Image transformation¶

 Topic Modality Item Description Image filter [10] 57 Describe the methods and settings used to filter images, e.g. Laplacian-of-Ga ussian.

### Image biomarker computation¶

 Topic Modality Item Description Biomarker set 58 Describe which set of image biomarkers is computed and refer to their definitions or provide these. IBSI compliance 59 State if the software used to extract the set of image biomarkers is compliant with the IBSI benchmarks. [11] Robustness 60 Describe how robustness of the image biomarkers was assessed, e.g. test-retest analysis. Software availability 61 Describe which software and version was used to compute image biomarkers.

### Image biomarker computation - texture parameters¶

 Topic Modality Item Description Texture matrix aggregation 62 Define how texture-matrix based biomarkers were computed from underlying texture matrices. Distance weighting 63 Define how CM, RLM, NGTDM and NGLDM weight distances, e.g. no weighting. CM symmetry 64 Define whether symmetric or asymmetric co-occurrence matrices were computed. CM distance 65 Define the (Chebyshev) distance at which co-occurrence of intensities is determined, e.g. 1. SZM linkage distance 66 Define the distance and distance norm for which voxels with the same intensity are considered to belong to the same zone for the purpose of constructing an SZM, e.g. Chebyshev distance of 1. DZM linkage distance 67 Define the distance and distance norm for which voxels with the same intensity are considered to belong to the same zone for the purpose of constructing a DZM, e.g. Chebyshev distance of 1. DZM zone distance norm 68 Define the distance norm for determining the distance of zones to the border of the ROI, e.g. Manhattan distance. NGTDM distance 69 Define the neighbourhood distance and distance norm for the NGTDM, e.g. Chebyshev distance of 1. NGLDM distance 70 Define the neighbourhood distance and distance norm for the NGLDM, e.g. Chebyshev distance of 1. NGLDM coarseness 71 Define the coarseness parameter for the NGLDM, e.g. 0.

### Machine learning and radiomics analysis¶

 Topic Modality Item Description Diagnostic and prognostic modelling 72 See the TRIPOD guidelines for reporting on diagnostic and prognostic modelling. Comparison with known factors 73 Describe where performance of radiomics models is compared with known (clinical) factors. Multicollineari ty 74 Describe where the multicollineari ty between image biomarkers in the signature is assessed. Model availability 75 Describe where radiomics models with the necessary pre-processing information may be found. Data availability 76 Describe where imaging data and relevant meta-data used in the study may be found.

## Feature nomenclature¶

Image features may be extracted using a variety of different settings, and may even share the same name. A feature nomenclature is thus required. Let us take the example of differentiating the following features: i) intensity histogram-based entropy, discretised using a fixed bin size algorithm with 25 HU bins, extracted from a CT image; and ii) grey level run length matrix entropy, discretised using a fixed bin number algorithm with 32 bins, extracted from a PET image. To refer to both as entropy would be ambiguous, whereas to add a full textual description would be cumbersome. In the nomenclature proposed below, the features would be called entropyIH, CT, FBS:25HU and entropyRLM, PET, FBN:32, respectively.

Features are thus indicated by a feature name and a subscript. As the nomenclature is designed to both concise and complete, only details for which ambiguity may exist are to be explicitly incorporated in the subscript. The subscript of a feature name may contain the following items to address ambiguous naming:

1. An abbreviation of the feature family (required).
2. The aggregation method of a feature (optional).
3. A descriptor describing the modality the feature is based on, the specific channel (for microscopy images), the specific imaging data (in the case of repeat imaging or delta-features) sets, conversions (such as SUV and SUL), and/or the specific ROI. For example, one could write PET:SUV to separate it from CT and PET:SUL features (optional).
4. Spatial filters and settings (optional).
5. The interpolation algorithm and uniform interpolation grid spacing (optional).
6. The re-segmentation range and outlier filtering (optional).
7. The discretisation method and relevant discretisation parameters, i.e. number of bins or bin size (optional).
8. Feature specific parameters, such as distance for some texture features (optional).

Optional descriptors are only added to the subscript if there are multiple possibilities. For example, if only CT data is used, adding the modality to the subscript is not required. Nonetheless, such details must be reported as well (see section 4.1).

The sections below have tables with permanent IBSI identifiers for concepts that were defined within this document.

### Abbreviating feature families¶

The following is a list of the feature families in this document and their suggested abbreviations:

feature family abbreviation
morphology MORPH HCUG
local intensity LI 9ST6
intensity-based statistics IS, STAT UHIW
intensity histogram IH ZVCW
intensity-volume histogram IVH P88C
grey level co-occurrence matrix GLCM, CM LFYI
grey level run length matrix GLRLM, RLM TP0I
grey level size zone matrix GLSZM, SZM 9SAK
grey level distance zone matrix GLDZM, DZM VMDZ
neighbourhood grey tone difference matrix NGTDM IPET
neighbouring grey level dependence matrix NGLDM REK0

### Abbreviating feature aggregation¶

The following is a list of feature families and the possible aggregation methods:

morphology, LI
-, features are 3D by definition DHQ4
IS, IH, IVH
2D averaged over slices (rare) 3IDG
-,3D calculated over the volume (default) DHQ4
GLCM, GLRLM
2D:avg averaged over slices and directions BTW3
2D:mrg, 2Dsmrg merged directions per slice and averaged SUJT
2.5D:avg, 2.5D:dmrg merged per direction and averaged JJUI
2.5D:mrg, 2.5D:vmrg merged over all slices ZW7Z
3D:avg averaged over 3D directions ITBB
3D:mrg merged 3D directions IAZD
GLSZM, GLDZM, NGTDM, NGLDM
2D averaged over slices 8QNN
2.5D merged over all slices 62GR
3D calculated from single 3D matrix KOBO

In the list above, ’–’ signifies an empty entry which does not need to be added to the subscript. The following examples highlight the nomenclature used above:

• joint maximumCM, 2D:avg: GLCM-based joint maximum feature, calculated by averaging the feature for every in-slice GLCM.
• short runs emphasisRLM, 3D:mrg: RLM-based short runs emphasis feature, calculated from an RLM that was aggregated by merging the RLM of each 3D direction.
• meanIS: intensity statistical mean feature, calculated over the 3D ROI volume.
• grey level varianceSZM, 2D: SZM-based grey level variance feature, calculated by averaging the feature value from the SZM in each slice over all the slices.

### Abbreviating interpolation¶

The following is a list of interpolation methods and the suggested notation. Note that # is the interpolation spacing, including units, and dim is 2D for interpolation with the slice plane and 3D for volumetric interpolation.

interpolation method notation
none INT:–
nearest neighbour interpolation NNB:dim:#
linear interpolation LIN:dim:#
cubic convolution interpolation CCI:dim:#
cubic spline interpolation CSI:dim:#, SI3:dim:#

The dimension attribute and interpolation spacing may be omitted if this is clear from the context. The following examples highlight the nomenclature introduced above:

• meanIS, LIN:2D:2mm: intensity statistical mean feature, calculated after bilinear interpolation with the slice planes to uniform voxel sizes of 2mm.
• meanIH, NNB:3D:1mm: intensity histogram mean feature, calculated after trilinear interpolation to uniform voxel sizes of 1mm.
• joint maximumCM, 2D:mrg, CSI:2D:2mm: GLCM-based joint maximum feature, calculated by first merging all GLCM within a slice to single GLCM, calculating the feature and then averaging the feature values over the slices. GLCMs were determined in the image interpolated within the slice plane to 2 $$\times$$ 2mm voxels using cubic spline interpolation.

### Describing re-segmentation¶

Re-segmentation can be noted as follows:

re-segmentation method notation
none RS:–
range RS:[#,#] USB3
outlier filtering RS:#$$\sigma$$ 7ACA

In the table above # signify numbers. A re-segmentation range can be half-open, i.e. RS:[#,$$\infty$$). Re-segmentation methods may be combined, i.e. both range and outlier filtering methods may be used. This is noted as RS:[#,#]+#$$\sigma$$ or RS:#$$\sigma$$+[#,#]. The following are examples of the application of the above notation:

• meanIS, CT, RS:[-200,150]: intensity statistical mean feature, based on an ROI in a CT image that was re-segmented within a [-200,150] HU range.
• meanIS, PET:SUV, RS:[3,∞): intensity statistical mean feature, based on an ROI in a PET image with SUV values, that was re-segmented to contain only SUV of 3 and above.
• meanIS, MRI:T1, RS:3σ: intensity statistical mean feature, based on an ROI in a T1-weighted MR image where the ROI was re-segmented by removing voxels with an intensity outside a $$\mu \pm 3\sigma$$ range.

### Abbreviating discretisation¶

The following is a list of discretisation methods and the suggested notation. Note that # is the value of the relevant discretisation parameter, e.g. number of bins or bin size, including units.

discretisation method notation
none DIS:–
fixed bin size FBS:# Q3RU
fixed bin number FBN:# K15C
histogram equalisation EQ:#
Lloyd-Max, minimum mean squared LM:#, MMS:#

In the table above, # signify numbers such as the number of bins or their width. Histogram equalisation of the ROI intensities can be performed before the “none”, “fixed bin size”, “fixed bin number” or “Lloyd-Max, minimum mean squared” algorithms defined above, with # specifying the number of bins in the histogram to be equalised. The following are examples of the application of the above notation:

• meanIH,PET:SUV,RS[0,∞],FBS:0.2: intensity histogram mean feature, based on an ROI in a SUV-PET image, with bin-width of 0.2 SUV, and binning from 0.0 SUV.
• grey level varianceSZM,MR:T1,RS:3σ,FBN:64: size zone matrix-based grey level variance feature, based on an ROI in a T1-weighted MR image, with $$3\sigma$$ re-segmentation and subsequent binning into 64 bins.

### Abbreviating feature-specific parameters¶

Some features and feature families require additional parameters, which may be varied. These are the following:

GLCM
Co-occurrence matrix symmetry
-, SYM symmetrical co-occurrence matrices
ASYM asymmetrical co-occurrence matrices (not recommended)
Distance
δ:#, δ-∞:# Chebyshev ($$\ell_{∞}$$) norm with distance # (default) PVMT
δ-2:# Euclidean ($$\ell_{2}$$) norm with distance # G9EV
δ-1:# Manhattan ($$\ell_{1}$$) norm with distance # LIFZ
Distance Weighting
-,w:1 no weighting (default)
w:f weighting with function $$f$$
GLRLM
distance weighting
-,w:1 no weighting (default)
w:f weighting with function $$f$$
GLSZM
δ:#, δ-∞:# Chebyshev ($$\ell_{∞}$$) norm with distance # (default) PVMT
δ-2:# Euclidean ($$\ell_{2}$$) norm with distance # G9EV
δ-1:# Manhattan ($$\ell_{1}$$) norm with distance # LIFZ
GLDZM
δ:#, δ-∞:# Chebyshev ($$\ell_{∞}$$) norm with distance # (default) PVMT
δ-2:# Euclidean ($$\ell_{2}$$) norm with distance # G9EV
δ-1:# Manhattan ($$\ell_{1}$$) norm with distance # LIFZ
zone distance norm
l-∞:# Chebyshev ($$\ell_{∞}$$) norm PVMT
l-2:# Euclidean ($$\ell_{2}$$) norm G9EV
-,l-1:# Manhattan ($$\ell_{1}$$) norm (default) LIFZ
NGTDM
distance
δ:#, δ-∞:# Chebyshev ($$\ell_{∞}$$) norm with distance # (default) PVMT
δ-2:# Euclidean ($$\ell_{2}$$) norm with distance # G9EV
δ-1:# Manhattan ($$\ell_{1}$$) norm with distance # LIFZ
weighting
-,w:1 no weighting (default)
w:f weighting with function f
NGLDM
dependence coarseness
α:# dependence coarseness parameter with value #
distance
δ:#, δ-∞:# Chebyshev ($$\ell_{∞}$$) norm with distance # (default) PVMT
δ-2:# Euclidean ($$\ell_{2}$$) norm with distance # G9EV
δ-1:# Manhattan ($$\ell_{1}$$) norm with distance # LIFZ
weighting
-,w:1 no weighting (default)
w:f weighting with function $$f$$

In the above tables, # represents numbers.

 [1] Also referred to as volume of interest.
 [2] An example is glucose present in the blood which competes with the uptake of 18F-FDG tracer in tumour tissue. To reduce competition with the tracer, patients are usually asked to fast for several hours and a blood glucose measurement may be conducted prior to tracer administration.
 [3] An example of a comorbidity that may affect image quality in 18F-FDG PET scans are type I and type II diabetes melitus, as well as kidney failure.
 [4] Many acquisition parameters may be extracted from DICOM header meta-data, or calculated from them.
 [5] Many reconstruction parameters may be extracted from DICOM header meta-data.
 [6] Spacing between image slicing is commonly, but not necessarily, the same as the slice thickness.
 [7] Also known as bias-field correction.
 [8] Discretisation may be performed separately to create intensity-volume histograms. If this is indeed the case, this should be described as well.
 [9] This is typically set by range re-segmentation.
 [10] The IBSI has not introduced image transformation into the standardised image processing scheme, and is in the process of benchmarking various common filters. This section may therefore be expanded in the future.
 [11] A software is compliant if and only if it is able to reproduce the image biomarker benchmarks for the digital phantom and for one or more image processing configurations using the radiomics CT phantom. Reviewers may demand that you provide the IBSI compliance spreadsheet for your software.