Note: Use the checklist in conjunction with the main text for clarification of all items.
Yes, details provided; No, details not provided; n/e, not essential; n/a, not applicable; Page, page number
| Section | No. | Item | Yes | No | n/a | Page |
|---|---|---|---|---|---|---|
| Title | ||||||
| 1 |
?
Indicate the use of radiomics in the title.
The following details can also be considered to be specified in the title: radiomic technique
(e.g., hand-crafted, engineered, deep, delta, etc.), modality (e.g., computed tomography [CT],
magnetic resonance imaging [MRI], ultrasound), important aspects of the scans (e.g., unenhanced,
dynamic), use of machine learning (e.g., machine learning-based), external validation, and
multi-center design.
Relevant title, specifying the radiomic methodology
|
|||||
| Abstract | ||||||
| 2 |
?
Provide a structured summary of the purpose, methods, results,
and conclusions, presenting only the most important aspects directly related to the purpose of the study.
The abstract should be understandable on its own, without reading the main text. Considering the submission
guidelines of the journals, it is recommended to specify the following items: the baseline characteristics
(e.g., number of patients, scans, images, classes), data source (e.g., public, institutional), study nature
(e.g., prospective, retrospective), segmentation technique (e.g., automated, semi-automated, or manual),
feature extraction technique (e.g., hand-crafted, engineered, deep), dimensionality reduction techniques
(e.g., feature selection, reproducibility analysis, multi-collinearity), modeling details (e.g., algorithms/models),
validation technique (e.g., cross-validation), unseen testing (internal hold-out, external testing), model performance metrics
(e.g., area under the curve) with uncertainty measures (e.g., confidence intervals), number of the final set of features,
traditional statistical methods with p-values, and open science status (e.g., public availability of data, code, and/or model).
Structured summary with relevant information |
|||||
| Keywords | ||||||
| 3 |
?
List the primary keywords that indicate
(e.g., radiomics, texture analysis) and characterize a radiomic study
(e.g., machine learning, deep learning, computed tomography, magnetic resonance imaging,
reproducibility), unless the journal requires exclusive use of certain terms (e.g.,
MeSH terms, which do not yet include radiomics-specific terms).
Relevant keywords for radiomics |
|||||
| Introduction | ||||||
| 4 |
?
Define the scientific or clinical problem with
a summary of the related literature and knowledge gaps, including a short review
of the current state of knowledge. Describe why the scientific question is technically
or clinically important.
Scientific or clinical background |
|||||
| 5 |
?
Describe why a radiomic approach is considered.
Performance and problematic aspects of currently used methods need to be described.
Mention what the radiomics approach would offer to solve these problems. Clearly state
how radiomics could affect clinical practice considering the study question.
Rationale for using a radiomic approach |
|||||
| 6 |
?
Describe the purpose of the study
while focusing on the scientific problem. Mention the expected
contributions to the current literature.
Study objective(s) |
|||||
| Method | ||||||
| Study Design | 7 |
?
Indicate that the CLEAR checklist was used for
reporting and submit the checklist as supplemental data. Do the same with other checklists
or guidelines if used in addition to the CLEAR checklist.
Adherence to guidelines or checklists (e.g., CLEAR checklist) |
||||
| 8 |
?
Describe the ethical questions to
ensure that the study was conducted appropriately. Give information about
ethical approval, informed consent, and data protection (e.g., de-identification)
if the data is from private sources.
Ethical details (e.g., approval, consent, data protection) |
|||||
| 9 |
?
Describe how the sample size or power
was determined before or after the study (
e.g., sample size/power calculation, based on availability).
Sample size calculation |
|||||
| 10 |
?
Indicate whether the study is prospective
or retrospective and case/control or cohort, etc. In the case of prospective studies,
provide registration details if available.
Study nature (e.g., retrospective, prospective) |
|||||
| 11 |
?
Define the inclusion criteria first.
Then, specify the exclusion criteria. Avoid redundancies by using the
opposite of the inclusion criteria as exclusion criteria.
Specify the selection process (e.g., random, consecutive). Keep the
numeric details of eligibility for the results.
Eligibility criteria |
|||||
| 12 |
?
Provide a flowchart for summarizing
the key methodological steps in the study. Due to the complex nature of
the radiomic approaches, such flowcharts help readers better understand
the methodology.
Flowchart for technical pipeline |
|||||
| Data | 13 |
?
State the data source (e.g., private, public, mixed
[both private and public]). State clearly which data source is used in different
data partitions. Provide web links and references if the source is public. Give
the image or patient identifiers as a supplement if public data is used.
Data source (e.g., private, public) |
||||
| 14 |
?
State if any part of the dataset was used
in a previous publication. Describe the differences between the
current study and previous studies in terms of study purpose and methodology.
Data overlap |
|||||
| 15 |
?
Describe the data split into training, validation,
and test sets. Mention that multiple splits are created (e.g., k-fold cross-validation
or bootstrapping). Specify how the assignment was done (e.g., random, semi-random, manual,
center-wise, chronological order). Indicate the ratio of each partition, with class proportions.
Describe at which level the data is split (e.g., patient-wise, image-wise, study-wise, scanner-wise,
institution-wise). Clearly state the measures undertaken to avoid information leakage across datasets (
e.g., creating the hold-out test set before feature normalization, feature selection, hyperparameter
optimization, and model training) [23]. Note that any test data should only be used once for evaluation
of the final model to prevent optimistic biases. Declare the systematic differences among the data partitions.
Data split methodology |
|||||
| 16 |
?
Provide the imaging protocol and acquisition parameters with
post-processing details. Define physical pixel and voxel dimensions. Clearly state whether single or
multiple or various scanners are used, with the number of instances for each protocol. Define the timing of the phase if a contrast medium was used.
State the patient preparation (drug administration, blood sugar control before the scans, etc.) if performed.
Imaging protocol (i.e., image acquisition and processing) |
|||||
| 17 |
?
Describe the data elements appearing as non-radiomic predictors.
Non-radiomic variables might be demographic characteristics (e.g., age, gender, ethnicity),
widely used traditional laboratory biomarkers (e.g., carcinoembryonic antigen), or traditional
approaches used in daily clinical practice (e.g., radiologist’s qualitative reading, Hounsfield Unit
evaluation, Response Evaluation Criteria in Solid Tumours [RECIST], Response Assessment in Neuro-Oncology
[RANO] criteria). It would be helpful to know how these predictors were identified (e.g., based on a literature review).
If applicable,
describe any transformation of predictors (e.g., binarization of continuous predictors, the grouping of levels of categorical variables).
Definition of non-radiomic predictor variables |
|||||
| 18 |
?
Describe the reference standard or outcome
measure that the radiomic approach will predict (e.g., pathological grade,
histopathological subtypes, genomic markers, local-regional control, survival, etc.).
Provide the rationale for the choice of the reference standard
(e.g., higher reproducibility rates). Clearly state the reproducibility concerns, potential
biases, and limitations of the reference standard.
Definition of the reference standard (i.e., outcome variable) |
|||||
| Segmentation | 19 |
?
Indicate which software programs or tools are used
for segmentation or annotation. Specify the version of the software and the exact
configuration parameters. Provide reference and web link to the software. Describe the
segmentation method (e.g., automatic, semi-automatic, manual). Provide the rules of the
segmentation (e.g., margin shrinkage or expansion from the visible contour, included/excluded regions).
Provide figures to show the segmentation style. Provide image registration details (e.g., software, version,
link, parameters) if segmentation is propagated for multi-modal (e.g., CT and MR), multi-phase (e.g., unenhanced,
arterial, venous phase CT), or multi-sequence (e.g., T2-weighted, post-contrast T1-weighted, diffusion-weighted
imaging) analyses. If radiomic features are extracted from 2D images on a single slice, please explain with which
criteria the slice is chosen. In the case of several lesions, explain if all the lesions are segmented and describe
how the feature values are aggregated. If only one lesion is chosen, describe the criteria (e.g., the primitive
or the most voluminous).
Segmentation strategy |
||||
| 20 |
?
State how many readers performed the segmentation,
as well as their experience. In the case of multiple readers, describe how the
final form of segmentation is achieved (e.g., the consensus of readers, intersection
of segmentations, independent segmentation for further reproducibility analysis, sequential
refinements from numerous expert raters until convergence), which is particularly important for
the training data because the
segmentation process on the test data should be as close to the clinical practice as possible, that is, the segmentation of a single reader.
Details of operators performing segmentation |
|||||
| Pre-processing | 21 |
?
Indicate which software programs or tools are used for
pre-processing. Specify the version of the software and the exact configuration parameters.
Provide reference and web link to the software, if available. Describe all pre-processing techniques
and associated parameters applied to the image including the normalization (e.g., minimum-maximum normalization,
standardization, logarithmic transformation, bias field correction), de-noising, skull stripping (also known as
brain extraction), interpolation to create uniform images (e.g., in terms of slice thickness),
standardized uptake value conversion, and registration. Also, state if an image or feature-based harmonization technique was used.
Image pre-processing details |
||||
| 22 |
?
Specify the resampling technique
(e.g., linear, cubic b-spline) applied to the pixels or voxels.
Provide the physical pixel and voxel dimensions after resampling.
Resampling method and its parameters |
|||||
| 23 |
?
Specify the discretization method (e.g., fixed bin width,
fixed bin count method, or histogram equalization) used for hand-crafted radiomic feature extraction.
Report the rationale for using a particular discretization technique. Indicate the number of grey levels
for the fixed bin count method or the bin width as well as the value of the first level (or minimum and maximum bounds)
for the fixed bin width method. Any experimental detail with different discretization methods and values is important to declare.
Discretization method and its parameters |
|||||
| 24 |
?
Provide the image types from which the radiomic features
are extracted, e.g., original or images with convolutional filters (e.g., Laplacian
of Gaussian edge enhancement, wavelet decomposition)
[24]. Also, give nuances about the parameters of transformed image types (e.g., sigma
values of Laplacian of Gaussian filtering).
Image types (e.g., original, filtered, transformed) |
|||||
| Feature extraction | 25 |
?
Indicate which software programs or tools are
used for radiomic feature extraction. Specify the version of the software
and the exact configuration parameters (also see Item#55). Provide reference
and web link to the software. Indicate if the software adheres to the benchmarks/certification
of IBSI [25]. Specify the general feature types, such as deep features, hand-crafted features,
engineered features, or a combination. Refer to the mathematical formulas of the hand-crafted and
engineered features. Provide formulas and code if new hand-crafted features are introduced. Present
the architectural details for deep feature extraction. Provide details of any feature engineering performed.
Specify whether radiomic features are extracted in a two-dimensional (2D) plane, 2D tri-planar, or three-dimensional (3D) space. If 2D
features are extracted from 3D segmentation, provide reasons (e.g., large slice thickness) as to why such an approach is followed.
Feature extraction method |
||||
| 26 |
?
Provide the radiomic feature classes (e.g., shape, first-order, grey-level co-occurrence matrix).
Use IBSI terminology for feature classes [25]. Specify the number of features per feature class. Mention if any feature class is excluded with reason.
Feature classes |
|||||
| 27 |
?
Indicate the total number of features per instance.
If applicable, provide the number of features per imaging modality and its components (e.g., phase for CT, sequence for MRI, etc.).
Number of features |
|||||
| 28 |
?
After providing all modified parameters of
pre-processing and radiomic feature extraction, state clearly that all
other parameters remained as a default configuration.
Default configuration statement for remaining parameters |
|||||
| Data preparation | 29 |
?
State if, and how much,
missing data are present in the study. If so, provide details as to
how it was addressed (e.g., deletion, substitution, or imputation).
Handling of missing data |
||||
| 30 |
?
Indicate the balance status of the classes according to the reference
standard. Provide details about how class imbalance is handled. Specify the techniques (e.g.,
synthetic minority over-sampling, simple over-sampling through replication, under-sampling)
used to achieve the class balance. Clearly state these data augmentation
and under-sampling strategies are applied only in the training set.
Details of class imbalance |
|||||
| 31 |
?
Describe the reliability analysis done to
assess the influence of segmentation differences. An intra- and inter-rater
reproducibility analysis must be considered in manual and semi-automatic methods.
Provide details about the statistical tests used for the reliability analysis (e.g.,
intraclass
correlation coefficient along with types) [26]. Mention the independence of assessment.
Clearly state the reliabilty analysis is performed using the training set only.
Details of segmentation reliability analysis |
|||||
| 32 |
?
If applicable, describe the normalization
technique applied to the radiomic feature data (e.g., minimum-maximum normalization,
standardization, logarithmic transformation, ComBat normalization [choice of the batch,
parametric or not, with or without empirical Bayes]). Specify the normalization scale.
It is important to emphasize that
this procedure is applied to the numeric radiomic feature data, not the images, in the
training set and independently applied to the validation and test sets.
Feature scaling details (e.g., normalization, standardization) |
|||||
| 33 |
?
Specify the dimension reduction methods used,
if applicable (e.g., collinearity analysis, reproducibility analysis,
algorithm-based feature selection). Provide details about the statistical
methods used. For example, provide the relevant statistical cut-off values for
each step (e.g., features with intraclass correlation coefficient ≤0.9 are excluded).
Clearly state the dimension reduction that is
performed using the training set. Specify how the final number of features is achieved,
for instance, the “rule of thumb” of ten features maximum for each instance.
Dimension reduction details |
|||||
| Modeling | 34 |
?
Provide the name and version of software programs
or packages used for modeling. Refer to the related publication of the software if
available. Specify the algorithms used to create models with architectural details including
inputs, outputs, and all intermediate components. The description of the architecture should be complete
to allow for exact replication by other investigators (also see Item#55 and Item#56). When a previously
described architecture is used, refer to the previous work
and specify any modification. If the final model involved an ensemble of algorithms, specify the type of
ensemble (e.g., stacking, majority voting, averaging, etc.).
Algorithm details |
||||
| 35 |
?
Describe the training process with adequate
detail. Specify the augmentation technique, stopping criteria for training,
hyperparameter tuning strategy (e.g., random, grid-search, Bayesian), range of hyperparameter
values used in tuning, optimization techniques, regularization parameters, and initialization
of model parameters (e.g., random, transfer learning). If transfer learning is applied, clearly state
which layers or parameters are frozen or affected.
Training and tuning details |
36 |
?
Describe the method (e.g., directed acyclic graphs)
for the detection of potential confounders (e.g., differences in tumour
size between cohorts, different image acquisition parameters such as slice thickness,
and differences in patient populations between primary and secondary hospitals) [27, 28].
Please also describe how confounding was addressed (e.g., covariate adjustment).
Handling of confounders |
|||
| 37 |
?
Describe how the final model was selected.
Two broad categories for these are probabilistic (e.g., Akaike information
criterion, Bayesian information criterion) and resampling methods (e.g., random
train-test split, cross-validation, bootstrap validation) [12, 29]. Clearly state that
only the training and validation sets are used for model selection. State if the model
complexity
is considered in selection, for instance, the “one standard error rule” [30]. Specify
which performance metrics were used to select the final model.
Model selection strategy |
|||||
| Evaluation | 38 |
?
Clearly state whether
the model was internally or externally tested. The term
“external testing” should only be used for the process that
involves data usage from different institutions. In the case
of external testing, specify the number of sites providing
data and further details about whether they are used for multiple
testing or in a single test. Describe the data characteristics and
state if there are any differences among training, validation, internal
testing, and external testing datasets (e.g., different scanners, different
readers for segmentation, different ethnicity). Again, note that any test data
should only be used once for evaluation to prevent biased performance metric estimates.
Testing technique (e.g., internal, external) |
||||
| 39 |
?
Specify the performance metrics to
evaluate the predictive ability of the models. Justify the selected
metrics according to the
characteristics of the data (e.g., class imbalance).
Beware of the potential pitfalls and follow recommendations when selecting
the appropriate performance metrics
Performance metrics and rationale for choosing |
|||||
| 40 |
?
Describe the uncertainty evaluation (e.g., robustness,
sensitivity analysis, calibration analysis if applicable) and measures of uncertainty
quantification (e.g., confidence intervals, standard deviation).
Uncertainty evaluation and measures (e.g., confidence intervals) |
|||||
| 41 |
?
Specify the statistical software and version used.
Indicate which method was used for the comparison of the model performance such as the DeLong's test
[32, 33], McNemar's test [34], or Bayesian approaches [35]. Provide a statistical threshold for the comparison
(e.g., p<0.05) along with confidence intervals if applicable to the method or metric.
Also, state if multiplicity is considered and corrected when comparing multiple models
(e.g., p-value adjustment, Bonferroni correction, false-discovery rate). Report
threshold values to stratify data into groups for statistical testing (e.g.,
the operating point on the receiver operating characteristic [ROC] curve to
define the confusion matrix, cut-off values for defining strata in survival analysis).
Statistical performance comparison (e.g., DeLong's test) |
|||||
| 42 |
?
Indicate whether comparisons with non-radiomic
approaches (e.g., clinical parameters, laboratory parameters, traditional radiological evaluations)
are performed. Non-radiomic approaches can be combined with radiomic data as well (e.g., clinical-radiomic evaluation).
Explain how the clinical utility is assessed, such as with decision curve analysis [36].
Comparison with non-radiomic and combined methods |
|||||
| 43 |
?
Describe the techniques used to increase the interpretability
and explainability of the models created, if applicable [37]. Figures (e.g., class activation
maps, feature maps, SHapley Additive exPlanations,
accumulated local effects, partial dependence plots, etc.) related to the interpretability and
explainability of the proposed radiomic model can be provided.
Interpretability and explainability methods |
|||||
| Results | ||||||
| 44 |
?
Provide the baseline demographic,
clinical, and imaging characteristics in text and/or tables. Report
the information separately for training, validation (i.e., cross-validation),
and test datasets, along with grouping based on the reference standard or non-radiomic variables.
Associated statistical tests should also be provided to identify if the sets are identical
or not. Provide whether any confounder is detected and handled appropriately.
Baseline demographic and clinical characteristics |
|||||
| 45 |
?
Provide a flowchart for summarizing eligibility
criteria with the number of included and excluded instances.
If more than one data source is involved, please give details for each source separately.
Flowchart for eligibility criteria |
|||||
| 46 |
?
Give statistical information (e.g.,
distribution of features based on outcome variables) of the selected
features for inclusion into the model. Provide the name and number of reproducible
features (e.g., for segmentation reproducibility, for reproducibility against image perturbations).
Create a table for the selected features with details of feature name, class, and image type. Also,
provide results of reproducibility statistics. Reproducibility metrics of selected features can be
presented in
tables or supplementary files. Figures (e.g., boxplots, correlation matrix, feature importance plots)
and tables of descriptive summaries of features can be provided.
Feature statistics (e.g., reproducibility, feature selection) |
|||||
| 47 |
?
Provide the performance metrics for training, validation
(e.g., multiple splits like cross-validation, bootstrapping, etc.), and unseen test data,
separately. A summary of the most important findings should be given in the text. Provide
the ‘no information rate’ as well. Details can be provided in figures (e.g., ROC curves, precision-recall curves)
and tables. It is a good practice to provide figures for calibration statistics to show the robustness of model
performance. Present
additional figures to showcase examples of true and false predictions to help readers better
understand the strengths and limitations of the proposed strategy.
Model performance evaluation |
|||||
| 48 |
?
Give the results about the comparison of radiomic approaches with
non-radiomic (e.g., visual analysis, clinical only parameters) or combined approaches in the text and
preferably on a table. Present the results for training, validation, and test data, separately. Provide
uncertainty measures (e.g., confidence intervals, standard deviation, etc.) and statistical comparison
results with p-values for each. Confusion matrices must also be provided. Aside from the predictive performance,
specify which model is superior to others in terms of clinical utility. The clinical utility can be presented with
a decision curve analysis. For the decision curve analysis, quantify the net benefit according to optimal probability
thresholds, with multiple cut-points associated with different clinical views.
Also, provide the rationale for why a specific threshold could be appropriate and clearly
state what is meant by all and none strategies.
Comparison with non-radiomic and combined approaches |
|||||
| Discussion | ||||||
| 49 |
?
Provide a summary of the work and an overview of
the most important findings. No statistical information is needed. Try to
position the study into one of the following categories: proof of
concept evaluation, technical task-specific evaluation, clinical evaluation,
and post-deployment evaluation [38]. Summarize the contribution to the literature.
Overview of important findings |
|||||
| 50 |
?
Provide the most important and relevant previous works.
Mention the most prominent differences between the current study and the previous works.
Previous works with differences from the current study |
|||||
| 51 |
?
Summarize the practical implications of the results.
Describe the key impact of the work on the field. Highlight the potential clinical
value and role of the study. Discuss any issues that may hamper the successful translation
of the study into real-world clinical practice. Also, provide future expectations and possible
next steps that others might build upon the current work.
Practical implications |
|||||
| 52 |
?
Clearly state the strengths and the limitations of the current work.
Any issue that may lead to potential bias, uncertainty, reproducibility,
robustness, and generalizability problems should be declared.
Strengths and limitations (e.g., bias and generalizability issues) |
|||||
| Open Science | ||||||
| Data availability | 53 |
?
[Please note this item is “not essential” but “recommended”] Provide relevant raw or processed image data
considering the regulatory constraints of the institutions involved. Segmentation
data can also be shared unless the segmentation is done as part of the workflow. In
situations where sharing of the entire dataset is not possible, an end-to-end analysis workflow
applied to a representative sample, or a public dataset with similar characteristics can facilitate
the ability of the readers in reproducing key components of the analysis [39]. Also, specify the reason if
the data is not available.
Sharing images along with segmentation data [n/e] |
||||
| 54 |
?
Share selected radiomic feature
data along with clinical variables or labels with the public, if possible (i.e.,
in accordance with the regulatory constraints of the institute). Specify the reason
if the radiomic feature data is not available.
Sharing radiomic feature data |
|||||
| Code availability | 55 |
?
Share the pre-processing and feature
extraction parameter scripts or settings (e.g., YAML file in PyRadiomics or complete
textual description). If it is not available in a script format, then the
parameter configuration as appeared in the software program can be shared as a screenshot.
Sharing pre-processing scripts or settings |
||||
| 56 |
?
Share the modeling scripts [40]. Code scripts should include
sufficient information to replicate the presented analysis (e.g., to train and test pipeline),
with all dependencies and relevant comments to easily understand and build upon the method. Even
if the actual input dataset used cannot be shared, in situations where a similar dataset is available publicly,
it should be used to share an example workflow with all pre- and post-processing steps included.
Specify the reason in case the source code is not available.
Sharing source code for modeling |
|||||
| Model availability | 57 |
?
Share the final model files for internal or external
testing [40]. Describe how inputs should be prepared to use the model.
Also, include the source code that was used for pre-processing the input data.
Specify the reason in case the final model data is not available.
Sharing final model files |
||||
| 58 |
?
[Please note this item is “not essential” but “recommended”] An easy-to-use tool (e.g., standalone executable applications,
notebooks, websites, virtual machines, etc.) can be created and shared with or without source
code that is based on the model created [40]. The main aim is to be able to test or validate the
model by other research groups. With this approach, users even without experience in machine learning
or coding can also test the proposed models.
Sharing a ready-to-use system [n/e] |
|||||