Reproducibility of CT Radiomic Features within the Same Patient:                    Influence of Radiation Dose and CT Reconstruction Settings

Meyer, Mathias; Ronald, James; Vernuccio, Federica; Nelson, Rendon C.; Ramírez-Giraldo, Juan Carlos; Solomon, Justin; Patel, Bhavik N.; Samei, Ehsan; Marin, Daniele

doi:10.1148/radiol.2019190928

Cited by 180 publications

(161 citation statements)

References 22 publications

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“…Most papers ( n = 28) described retrospective analyses, while four reported planned secondary analyses of prospectively acquired data [ 32 , 33 , 34 , 35 ]. Nineteen authors analyzed computed tomography (CT) [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], eight magnetic resonance imaging (MRI) [ 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ], three positron-emission tomography (PET)/CT [ 59 , 60 , 61 ], and two multiple imaging modalities (CT and MRI; PET and MRI, respectively) [ 62 , 63 ]. Various software applications were used for texture analysis, with these being custom-made in a large proportion of cases ( n = 10).…”

Section: Resultsmentioning

confidence: 99%

“…The average radiomic quality score (RQS) [ 64 ] was in the 10 ± 6.5 (range 1–22), roughly 25% of the maximum score ( n = 36). Only four studies (16%) [ 33 , 34 , 39 , 40 ] had a score higher than 18 (>50% of the maximum score). The main limitations in quality were the following: no cost-effectiveness analysis (32 studies, 100%); lack of open-data repositories ( n = 31, 97%); no phantom calibration ( n = 31, 97%); failure to include a calibration statistic ( n = 30, 94%); lack of prospective design ( n = 28, 87%); and missing validation cohort ( n = 18, 56%).…”

Section: Resultsmentioning

confidence: 99%

“…Similar results were reported in the MRI setting: Peerlings et al, used the concordance correlation coefficient to test the reproducibility of an array of first- and second-level radiomics parameters over time (multiple MRI) and different MRI systems on apparent diffusion coefficient maps, finding good stability with most parameters [ 58 ]. Conversely, two studies reported that radiomic parameters derived from CT scans are affected by slice thickness setting and ROI size [ 34 , 50 ]. Dercle et al, demonstrated that ROI area size, metastatic site, and the individual characteristics of image acquisitions should be considered as confounding factors in the evaluation of tumor entropy [ 35 ].…”

Section: Resultsmentioning

confidence: 99%

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Radiomics of Liver Metastases: A Systematic Review

Fiz

Viganò

Gennaro

et al. 2020

Cancers

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Multidisciplinary management of patients with liver metastases (LM) requires a precision medicine approach, based on adequate profiling of tumor biology and robust biomarkers. Radiomics, defined as the high-throughput identification, analysis, and translational applications of radiological textural features, could fulfill this need. The present review aims to elucidate the contribution of radiomic analyses to the management of patients with LM. We performed a systematic review of the literature through the most relevant databases and web sources. English language original articles published before June 2020 and concerning radiomics of LM extracted from CT, MRI, or PET-CT were considered. Thirty-two papers were identified. Baseline higher entropy and lower homogeneity of LM were associated with better survival and higher chemotherapy response rates. A decrease in entropy and an increase in homogeneity after chemotherapy correlated with radiological tumor response. Entropy and homogeneity were also highly predictive of tumor regression grade. In comparison with RECIST criteria, radiomic features provided an earlier prediction of response to chemotherapy. Lastly, texture analyses could differentiate LM from other liver tumors. The commonest limitations of studies were small sample size, retrospective design, lack of validation datasets, and unavailability of univocal cut-off values of radiomic features. In conclusion, radiomics can potentially contribute to the precision medicine approach to patients with LM, but interdisciplinarity, standardization, and adequate software tools are needed to translate the anticipated potentialities into clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Radiomics of Liver Metastases: A Systematic Review

Fiz

Viganò

Gennaro

et al. 2020

Cancers

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show abstract

“…Another barrier in quantitative image analyses is the variation in reconstructed images caused by variations in scanners, and scanning and reconstruction protocols (101)(102)(103). For radiomics, those variations have been indicated as the major source of variability in radiomic features, limiting the reproducibility of results and generalizability of radiomics (101,(104)(105)(106). DL can help overcome this barrier by neutralizing images with various image styles.…”

Section: Image Neutralizationmentioning

confidence: 99%

Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges

Hwang

Park

2020

Korean J Radiol

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Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.

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“…Several studies have shown that various conditions affecting image quality significantly influence the reliability of RF measurements. For example, inconsistencies across CT scanners such as, spatial resolution, tube current, noise, and reconstruction algorithm may decrease the reliability of CT-derived RFs [9][10][11][12][13][14] . The reliability of RFs also depends on the techniques used for lesion segmentation, grey-level discretization and quantization range 8,[15][16][17][18] .…”

mentioning

confidence: 99%

Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters

Park

Kim

Heo

et al. 2020

Sci Rep

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The reliability of radiomics features (RFs) is crucial for quantifying tumour heterogeneity. We assessed the influence of imaging, segmentation, and processing conditions (quantization range, bin number, signal-to-noise ratio [SNR], and unintended outliers) on RF measurement. Low SNR and unintended outliers increased the standard deviation and mean values of histograms to calculate the first-order RFs. Variations in imaging processing conditions significantly altered the shape of the probability distribution (centre of distribution, extent of dispersion, and segmentation of probability clusters) in second-order RF matrices (i.e. grey-level co-occurrence and grey-level run length), thereby eventually causing fluctuations in RF estimation. Inconsistent imaging and processing conditions decreased the number of reliably measured RFs in terms of individual RF values (intraclass correlation coefficient ≥0.75) and inter-lesion RF ratios (coefficient of variation <15%). No RF could be reliably estimated under inconsistent SNR and inclusion of outlier conditions. By contrast, with high SNR and no outliers, all first-order RFs, 11 (42%) grey-level co-occurrence RFs and five (42%) grey-level run length RFs showed acceptable reliability. Our study suggests that optimization of SNR, exclusion of outliers, and application of relevant quantization range and bin number should be performed to ensure the robustness of radiomics studies assessing tumor heterogeneity.

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Reproducibility of CT Radiomic Features within the Same Patient: Influence of Radiation Dose and CT Reconstruction Settings

Cited by 180 publications

References 22 publications

Radiomics of Liver Metastases: A Systematic Review

Radiomics of Liver Metastases: A Systematic Review

Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges

Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters

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