Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment

Ponsiglione, Andrea; Stanzione, Arnaldo; Cuocolo, Renato; Ascione, Raffaele; Gambardella, Michele; Giorgi, M. De; Nappi, Carmela; Cuocolo, A.; Imbriaco, Massimo

doi:10.1007/s00330-021-08375-x

Cited by 35 publications

(25 citation statements)

References 35 publications

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“…In spite of these common issues across radiomics studies, there are some non-oncology specific issues. Contrary to the oncological field, the concept of biological correlate did not clearly fit the current topic [ 17 ], since the inflammatory diseases do not always relate to genomics. In prognostic studies, comparison to “gold standard” is not suitable for non-oncological diseases without a widely accepted “gold standard,” while the tumor staging is usually employed as the “gold standard” of survival prediction.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, a significant number of previous studies did not perform feature reduction and validation. As a result, the other non-oncological radiomics reviews showed lower RQS ratings [ 15 , 17 ]. Our review is in line with these non-oncological radiomics reviews with a comparable ideal percentage of RQS of 20.3%.…”

Section: Discussionmentioning

confidence: 99%

“…Most of the radiomics studies were oncological, but radiomics has potential clinical application in the non-oncological field [ 30 ]. Several reviews have summarized the role of radiomics in non-oncological diseases, including mild cognitive impairment and Alzheimer’s disease [ 15 ], COVID-19 and viral pneumonia [ 16 ], and cardiac diseases [ 17 ]. The study quality evaluated by RQS was the main concern of these reviews.…”

Section: Discussionmentioning

confidence: 99%

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A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

Zhong

Xing

et al. 2022

Insights Imaging

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Background Multiple tools have been applied to radiomics evaluation, while evidence rating tools for this field are still lacking. This study aims to assess the quality of pancreatitis radiomics research and test the feasibility of the evidence level rating tool. Results Thirty studies were included after a systematic search of pancreatitis radiomics studies until February 28, 2022, via five databases. Twenty-four studies employed radiomics for diagnostic purposes. The mean ± standard deviation of the adherence rate was 38.3 ± 13.3%, 61.3 ± 11.9%, and 37.1 ± 27.2% for the Radiomics Quality Score (RQS), the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guideline for preprocessing steps, respectively. The median (range) of RQS was 7.0 (− 3.0 to 18.0). The risk of bias and application concerns were mainly related to the index test according to the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The meta-analysis on differential diagnosis of autoimmune pancreatitis versus pancreatic cancer by CT and mass-forming pancreatitis versus pancreatic cancer by MRI showed diagnostic odds ratios (95% confidence intervals) of, respectively, 189.63 (79.65–451.48) and 135.70 (36.17–509.13), both rated as weak evidence mainly due to the insufficient sample size. Conclusions More research on prognosis of acute pancreatitis is encouraged. The current pancreatitis radiomics studies have insufficient quality and share common scientific disadvantages. The evidence level rating is feasible and necessary for bringing the field of radiomics from preclinical research area to clinical stage.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

Zhong

Xing

et al. 2022

Insights Imaging

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“…This has been demonstrated by multiple systematic reviews performed in other fields of medical imaging [12,[87][88][89]. Unfortunately, this finding has also been recently confirmed in the setting of cardiovascular imaging, in particular regarding CT and CMR research [90]. Out of 53 papers reviewed, the median quality score was only 19.4% (interquartile range = 11.1-33.3%), which is not satisfactory.…”

Section: Discussionmentioning

confidence: 96%

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Spadarella

Perillo

Ugga

et al. 2022

Curr Cardiovasc Imaging Rep

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Purpose of Review This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging. Recent Findings Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases. Summary The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.

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“…This value is significantly higher than the RQS for the majority of retrospective studies dealing with traditional volumetric imaging (i.e. computed tomography and magnetic resonance imaging), these being reported as, on average, 11 (lungs), 7 (heart) and 10 (liver) [3][4][5] .…”

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confidence: 77%

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Moro

Boldrini

Lenkowicz

et al. 2022

Ultrasound in Obstet & Gyne

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We thank Drs Zhang and Zhang for their Correspondence discussing our manuscript and are grateful for their comments regarding the limitations of our first application of ultrasound radiomics-based prediction of high-risk endometrial cancer.They open their commentary referring to the radiomics quality score (RQS), a practical tool proposed in 2017 by one of the leading radiomics research groups 1 , and calculated a score of 15 (maximum score = 36) points for our paper 2 . Questionably, they did not consider the potential clinical utility of our model (RQS + 2 points) and the availability of the open-source code (RQS + 1 points) (as stated in the cited Moddicom 2015 publication, https://github.com/kbolab/moddicom), which would have increased our RQS to 18. This value is significantly higher than the RQS for the majority of retrospective studies dealing with traditional volumetric imaging (i.e. computed tomography and magnetic resonance imaging), these being reported as, on average, 11 (lungs), 7 (heart) and 10 (liver) [3][4][5] .We are fully aware that image preprocessing is a crucial step prior to feature extraction in the framework of traditional radiomics, and our group was among the first to describe the variability between different ultrasound equipment used for radiomics purposes in gynecology 6 . Nevertheless, in this first large, multicenter study, we decided to focus on unfiltered real-world data images, excluding only very low-quality images that did not permit clear and reliable identification of the endometrial lesion, as we chose to rely on examiner expertise for

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Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment

Cited by 35 publications

References 35 publications

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

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