Impact of different scanners and acquisition parameters on robustness of MR radiomics features based on women’s cervix

Mi, Honglan; Yuan, Mingyuan; Suo, Shiteng; Cheng, Jian; Li, Suqin; Duan, Shaofeng; Lü, Qing

doi:10.1038/s41598-020-76989-0

Cited by 17 publications

(22 citation statements)

References 39 publications

(34 reference statements)

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“…The main novelty of the current study lies in its multicenter aspect. Although previous studies have already identified acquisition parameters [13,15,16], segmentation [19], and post-processing methods [15,18] as factors affecting feature reproducibility, these studies have so far mainly been performed on non-MRI data or in small patient cohorts or phantoms. The extent to which these effects influence feature reproducibility and may obscure correlations with common clinical outcomes in a representative "real life" clinical cohort of MRI data acquired at various institutions has not been previously reported.…”

Section: Discussionmentioning

confidence: 99%

“…Even in controlled prospective study designs with harmonized acquisition protocols, variation in ADC can still be a limiting factor as coefficients of variation range as high as 4–37% depending on the measured organ [ 23 , 24 ]. Differences in acquisition settings have previously also been shown to substantially reduce inter- and intra-scanner reproducibility of T2W-MRI radiomics features, with particularly poor results for higher-order features [ 13 , 16 ].…”

Section: Discussionmentioning

confidence: 99%

“…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%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…To extract stable features, image preprocessing is essential to exclude the influence of different machines, scanning parameters, reconstruction algorithms, and noise. 54 , 55 The procedures of image preprocessing usually include resampling, standardization, normalization, smoothing, and filtering ( Figure 1 ).…”

Section: Workflowmentioning

confidence: 99%

Radiomics in Nasopharyngeal Carcinoma

Duan

Xiong

Tian

et al. 2022

Clin Med Insights Oncol

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Nasopharyngeal carcinoma (NPC) is one of the most common head and neck malignancies, and the primary treatment methods are radiotherapy and chemotherapy. Radiotherapy alone, concurrent chemoradiotherapy, and induction chemotherapy combined with concurrent chemoradiotherapy can be used according to different grades. Treatment options and prognoses vary greatly depending on the grade of disease in the patients. Accurate grading and risk assessment are required. Recently, radiomics has combined a large amount of invisible high-dimensional information extracted from computed tomography, magnetic resonance imaging, or positron emission tomography with powerful computing capabilities of machine-learning algorithms, providing the possibility to achieve an accurate diagnosis and individualized treatment for cancer patients. As an effective tumor biomarker of NPC, the radiomic signature has been widely used in grading, differential diagnosis, prediction of prognosis, evaluation of treatment response, and early identification of therapeutic complications. The process of radiomic research includes image segmentation, feature extraction, feature selection, model establishment, and evaluation. Many open-source or commercial tools can be used to achieve these procedures. The development of machine-learning algorithms provides more possibilities for radiomics research. This review aimed to summarize the application of radiomics in NPC and introduce the basic process of radiomics research.

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“…The problem is not unique to CT, as magnetic resonance (MR) imaging intensity is also highly dependent on manufacturer, sequence, and acquisition parameters 5 . A recent study of cervix MR showed that few MR features were robust across scanners and acquisition parameters, while another study of brain MR demonstrated that MR-derived radiomic features vary widely with pulse sequence 6 , 7 .…”

Section: Introductionmentioning

confidence: 99%

Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects

Horng

Singh

Yousefi

et al. 2022

Sci Rep

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Radiomic features have a wide range of clinical applications, but variability due to image acquisition factors can affect their performance. The harmonization tool ComBat is a promising solution but is limited by inability to harmonize multimodal distributions, unknown imaging parameters, and multiple imaging parameters. In this study, we propose two methods for addressing these limitations. We propose a sequential method that allows for harmonization of radiomic features by multiple imaging parameters (Nested ComBat). We also employ a Gaussian Mixture Model (GMM)-based method (GMM ComBat) where scans are split into groupings based on the shape of the distribution used for harmonization as a batch effect and subsequent harmonization by a known imaging parameter. These two methods were evaluated on features extracted with CapTK and PyRadiomics from two public lung computed tomography datasets. We found that Nested ComBat exhibited similar performance to standard ComBat in reducing the percentage of features with statistically significant differences in distribution attributable to imaging parameters. GMM ComBat improved harmonization performance over standard ComBat (− 11%, − 10% for Lung3/CAPTK, Lung3/PyRadiomics harmonizing by kernel resolution). Features harmonized with a variant of the Nested method and the GMM split method demonstrated similar c-statistics and Kaplan–Meier curves when used in survival analyses.

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Impact of different scanners and acquisition parameters on robustness of MR radiomics features based on women’s cervix

Cited by 17 publications

References 39 publications

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

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

Radiomics in Nasopharyngeal Carcinoma

Generalized ComBat harmonization methods for radiomic features with multi-modal distributions and multiple batch effects

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