Influence of feature calculating parameters on the reproducibility of CT radiomic features: a thoracic phantom study

Liu, Ying; Tan, Guangming; Vangel, Mark; Hall, Jonathan; Cai, Wenli

doi:10.21037/qims-19-921

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…Radiomics (1)(2)(3)(4)(5) holds great promises in clinical oncology to aid better diagnosis, prognosis, and personalized clinical decision making, as witnessed by a huge number of research studies published in recent years (6)(7)(8)(9)(10)(11)(12)(13). However, the broad validity and generality of radiomics are much hindered by the concerns on its reliability (14)(15)(16)(17)(18)(19)(20). Besides disease, tissue, and physiological characteristics in the patients, variability and uncertainty of radiomics can be introduced in many procedures of the complicated workflow.…”

Section: Introductionmentioning

confidence: 99%

Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy

Yuan¹,

Xue²,

Lo³

et al. 2021

Quant Imaging Med Surg

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Background: MRI pulse sequences and imaging parameters substantially influence the variation of MRI radiomics features, thus impose a critical challenge on MRI radiomics reproducibility and reliability. This study aims to prospectively investigate the impact of various imaging parameters on MRI radiomics features in a 3D T2-weighted (T2W) turbo-spin-echo (TSE) pulse sequence for MR-guided-radiotherapy (MRgRT).Methods: An anthropomorphic phantom was scanned using a 3D-T2W-TSE MRgRT sequence at 1.5T under a variety of acquisition imaging parameter changes. T1 and T2 relaxation times of the phantom were also measured. 93 first-order and texture radiomics features in the original and 14 transformed images, yielding 1,395 features in total, were extracted from 10 volumes-of-interest (VOIs). The percentage deviation (d%) of radiomics feature values from the baseline values and intra-class correlation coefficient (ICC) with the baseline were calculated. Robust radiomics features were identified based on the excellent agreement of radiomics feature values with the baseline, i.e., the averaged d% <5% and ICC >0.90 in all VOIs for all imaging parameter variations. Results:The radiomics feature values changed considerably but to different degrees with different imaging parameter adjustments, in the ten VOIs. The deviation d% ranged from 0.02% to 321.3%, with a mean of 12.5% averaged for all original features in all ten VOIs. First-order and GLCM features were generally more robust to imaging parameters than other features in the original images. There were also significantly different radiomics feature values (ANOVA, P<0.001) between the original and the transformed images, exhibiting quite different robustness to imaging parameters. 330 out of 1395 features (23.7%) robust to imaging parameters were identified. GLCM and GLSZM features had the most (42.5%, 153/360) and least (3.8%, 9/240) robust features in the original and transformed images, respectively.Conclusions: This study helps better understand the quantitative dependence of radiomics feature values on imaging parameters in a 3D-T2W-TSE sequence for MRgRT. Imaging parameter heterogeneity should be considered as a significant source of radiomics variability and uncertainty, which must be well harmonized for reliable clinical use. The identified robust features to imaging parameters are helpful for the pre-selection of radiomics features for reliable radiomics modeling.

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Section: Introductionmentioning

confidence: 99%

Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy

Yuan¹,

Xue²,

Lo³

et al. 2021

Quant Imaging Med Surg

View full text Add to dashboard Cite

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“…Significant ICC values between 0.75 and 0.9 and greater than 0.9 were considered as indicating good and excellent reliability, respectively. To complement the evaluation of ICC, Coefficient of Variation was considered in order to evaluate within-lesion variations: imaging features with good to excellent ICC but with COV > 20% were considered not-reproducible [30].…”

Section: Discussionmentioning

confidence: 99%

A tri-modal tissue-equivalent anthropomorphic phantom for PET, CT and multi-parametric MRI radiomics

et al. 2022

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“…Considering that the ICC calculation relies on the natural variance of the underlying data, testing repeatability alone may be insufficient. Kendall’s W ( 58 ) can also evaluate IOV consistency when three or more operators are present, i.e., test–retest analysis is a necessity to maximize the robustness of imaging attributes.…”

Section: Radiomicsmentioning

confidence: 99%

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

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Influence of feature calculating parameters on the reproducibility of CT radiomic features: a thoracic phantom study

Cited by 11 publications

References 23 publications

Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy

Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy

A tri-modal tissue-equivalent anthropomorphic phantom for PET, CT and multi-parametric MRI radiomics

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

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