Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing

Traverso, Alberto; Kazmierski, Michal; Shi, Zhenwei; Kalendralis, Petros; Welch, Mattea; Nissen, Henrik Dahl; Jaffray, David A.; Dekker, André; Wee, Leonard

doi:10.1016/j.ejmp.2019.04.009

Cited by 47 publications

(35 citation statements)

References 33 publications

(43 reference statements)

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“…We found large variation of radiomic feature in repeatability study even within a short time gap of 30 min "coffee-break". Overall, less than 50% of features had a good repeatability (ICC > 0.9) using phantom scans, in agreement with previously published literature [19][20][21] . When considering time-series analysis of radiomic features (e.g.…”

Section: Discussionsupporting

confidence: 90%

Repeatability and reproducibility study of radiomic features on a phantom and human cohort

Jha

Mithun

Jaiswar

et al. 2021

Sci Rep

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The repeatability and reproducibility of radiomic features extracted from CT scans need to be investigated to evaluate the temporal stability of imaging features with respect to a controlled scenario (test–retest), as well as their dependence on acquisition parameters such as slice thickness, or tube current. Only robust and stable features should be used in prognostication/prediction models to improve generalizability across multiple institutions. In this study, we investigated the repeatability and reproducibility of radiomic features with respect to three different scanners, variable slice thickness, tube current, and use of intravenous (IV) contrast medium, combining phantom studies and human subjects with non-small cell lung cancer. In all, half of the radiomic features showed good repeatability (ICC > 0.9) independent of scanner model. Within acquisition protocols, changes in slice thickness was associated with poorer reproducibility compared to the use of IV contrast. Broad feature classes exhibit different behaviors, with only few features appearing to be the most stable. 108 features presented both good repeatability and reproducibility in all the experiments, most of them being wavelet and Laplacian of Gaussian features.

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Section: Discussionsupporting

confidence: 90%

Repeatability and reproducibility study of radiomic features on a phantom and human cohort

Jha

Mithun

Jaiswar

et al. 2021

Sci Rep

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“…Studies investigating sources of variation in imaging data have so far mainly focused on CT and PET and only one of 35 studies in a systematic review on radiomics feature reproducibility focused on MRI [9]. Some recent studies have explored variations in quantitative MRI analysis, though mainly in phantoms [13][14][15][16] or small (< 48 patients) single-center [13,17,18] or bi-institutional [19] patient cohorts. The current study aimed to add to these previous data by analyzing a large representative sample of rectal MRIs acquired at multiple institutions in the Netherlands to gain insight into how variations in "real life" clinical MRI data can affect radiomics studies.…”

Section: Introductionmentioning

confidence: 99%

Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility

et al. 2021

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Objectives To investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software. Methods T2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient. Results Image features differed significantly (p < 0.001) between centers with more substantial variations in ADC compared to T2W-MRI. In total, 64.3% of the variation in mean ADC was explained by differences in hardware and acquisition, compared to 0.4% by patient-intrinsic factors. Feature reproducibility between expert and non-expert segmentations was good to excellent (median ICC 0.89–0.90). Reproducibility for single-slice versus whole-volume segmentations was substantially poorer (median ICC 0.40–0.58). Between software packages, reproducibility was good to excellent (median ICC 0.99) for most features (first-order/shape/GLCM/GLRLM) but poor for higher-order (GLSZM/NGTDM) features (median ICC 0.00–0.41). Conclusions Significant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models. Key Points • Features derived from T2W-MRI and in particular ADC differ significantly between centers when performing multicenter data analysis. • Variations in ADC are mainly (> 60%) caused by hardware and image acquisition differences and less so (< 1%) by patient- or tumor-intrinsic variations. • Features derived using different image segmentations (expert/non-expert) were reproducible, provided that whole-volume segmentations were used. When using different feature extraction software packages with similar settings, higher-order features were less reproducible.

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“…Although these results are promising, issues regarding features robustness as well as the comparability of results, including inter-observer segmentation variability, need to be addressed 19 – 24 . In order to extract clinically useful information from medical images and to use features as clinical biomarkers, it is important that extracted features are reproducible, standardized and robust 25 , 26 .…”

Section: Introductionmentioning

confidence: 99%

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

Granzier

Verbakel

Ibrahim

et al. 2020

Sci Rep

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Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness. Therefore, our study aim was to determine the robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI. A total of 129 histologically confirmed breast tumors were segmented manually in three dimensions on the first post-contrast T1-weighted MR exam by four observers: a dedicated breast radiologist, a resident, a Ph.D. candidate, and a medical student. Robust features were assessed using the intraclass correlation coefficient (ICC > 0.9). The inter-observer variability was evaluated by the volumetric Dice Similarity Coefficient (DSC). The mean DSC for all tumors was 0.81 (range 0.19-0.96), indicating a good spatial overlap of the segmentations based on observers of varying expertise. In total, 41.6% (552/1328) and 32.8% (273/833) of all RadiomiX and Pyradiomics features, respectively, were identified as robust and were independent of inter-observer manual segmentation variability. Radiomics is a technique that is used to extract large amounts of quantitative information from routine medical images that decode information about a region of interest (ROI). The majority of radiomics articles published concerns its application in the oncological field 1-4. Here, radiomics bears the advantage of non-invasively quantifying the underlying phenotype of the entire tumor for multiple lesions simultaneously, in contrast to tissue biopsy, which samples only a small part of a single (often heterogeneous) tumor 2,5. The ability to characterize the tumor and to establish links to the underlying biology 6 and ultimately clinical outcomes, allows a more patienttailored treatment 7 , enabling 'precision medicine' 8,9. Recently, several articles have outlined the potential clinical applicability of radiomics in the field of breast cancer for different purposes, e.g. diagnosis 10,11 , tumor response prediction 12-14 , prediction of molecular tumor subtype 15,16 , and prediction of axillary lymph node metastases 17,18. Although these results are promising, issues regarding features robustness as well as the comparability of results, including inter-observer segmentation variability, need to be addressed 19-24. In order to extract clinically useful information from medical images and to use features as clinical biomarkers, it is important that extracted

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Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing

Cited by 47 publications

References 33 publications

Repeatability and reproducibility study of radiomic features on a phantom and human cohort

Repeatability and reproducibility study of radiomic features on a phantom and human cohort

Sources of variation in multicenter rectal MRI data and their effect on radiomics feature reproducibility

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

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