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.

Image processing

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 entropy_{IH, CT, FBS:25HU} and entropy_{RLM, 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 maximum_{CM, 2D:avg}: GLCM-based joint maximum feature, calculated by averaging the feature for every in-slice GLCM.
• short runs emphasis_{RLM, 3D:mrg}: RLM-based short runs emphasis feature, calculated from an RLM that was aggregated by merging the RLM of each 3D direction.
• mean_IS: intensity statistical mean feature, calculated over the 3D ROI volume.
• grey level variance_{SZM, 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:

• mean_{IS, LIN:2D:2mm}: intensity statistical mean feature, calculated after bilinear interpolation with the slice planes to uniform voxel sizes of 2mm.
• mean_{IH, NNB:3D:1mm}: intensity histogram mean feature, calculated after trilinear interpolation to uniform voxel sizes of 1mm.
• joint maximum_{CM, 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:

• mean_{IS, 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.
• mean_{IS, 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.
• mean_{IS, 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:

• mean_{IH,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 variance_{SZM,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
	Linkage 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

	GLDZM
	Linkage 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
	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.