Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study

Stanzione, Arnaldo; Galatola, Roberta; Cuocolo, Renato; Romeo, Valeria; Verde, Francesco; Mainenti, Pier Paolo; Brunetti, Arturo; Maurea, Simone

doi:10.3390/diagnostics12030578

Cited by 16 publications

(7 citation statements)

References 61 publications

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“…Another key point to underline is the absence of feature robustness testing for multiple segmentations in more than half of the articles (57%). However, comparing our result to that of other reviews [ 43 , 44 ], where the authors stated that only very few articles included multiple segmentations, it can be considered a slight improvement.…”

Section: Discussionsupporting

confidence: 43%

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

et al. 2022

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Background: Radiomics of salivary gland imaging can support clinical decisions in different clinical scenarios, such as tumors, radiation-induced xerostomia and sialadenitis. This review aims to evaluate the methodological quality of radiomics studies on salivary gland imaging. Material and Methods: A systematic search was performed, and the methodological quality was evaluated using the radiomics quality score (RQS). Subgroup analyses according to the first author’s professional role (medical or not medical), journal type (radiological journal or other) and the year of publication (2021 or before) were performed. The correlation of RQS with the number of patients was calculated. Results: Twenty-three articles were included (mean RQS 11.34 ± 3.68). Most studies well-documented the imaging protocol (87%), while neither prospective validations nor cost-effectiveness analyses were performed. None of the included studies provided open-source data. A statistically significant difference in RQS according to the year of publication was found (p = 0.009), with papers published in 2021 having slightly higher RQSs than older ones. No differences according to journal type or the first author’s professional role were demonstrated. A moderate relationship between the overall RQS and the number of patients was found. Conclusions: Radiomics application in salivary gland imaging is increasing. Although its current clinical applicability can be affected by the somewhat inadequate quality of the papers, a significant improvement in radiomics methodologies has been demonstrated in the last year.

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Section: Discussionsupporting

confidence: 43%

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

et al. 2022

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“…Furthermore, a latest review of the current literature proposed a Radiomics Quality Score (RQS) to assess the quality of published results (25). Very recently, another review analyzed several studies on the application of radiomics in the field of adrenal disease, using RQS to assess their methodological quality (26). In this perspective, we applied this concept to our study and obtained an RQS of 7 (higher so far than those calculated for evaluation of the adrenal masses).…”

Section: Discussionmentioning

confidence: 91%

Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan

et al. 2022

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New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed via multivariate logistic regression with backward and stepwise selection. A 11-variable prediction model was constructed, and a ROC curve was used to differentiate patients with high probability of functioning AI. Using a threshold value of >−275.147, we obtained a sensitivity of 93.75% and a specificity of 100% in diagnosing functioning AI. On the basis of these results, computed tomography (CT) texture analysis appears a promising tool in the diagnostic definition of AIs.

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“…One study systematically reviewed the diagnostic accuracy of CT texture analysis in adrenal tumors (64). In another review, Stanzione et al summarized the application of radiomics in adrenal cross-sectional imaging and assessed the methodological quality by RQS (65). Generally, more comprehensive and in-depth analyses of diagnostic performance of radiomics in adrenal masses were done in our study.…”

Section: Rqs Scoring Item Icc (95% Ci)mentioning

confidence: 98%

Diagnostic performance of radiomics in adrenal masses: A systematic review and meta-analysis

2022

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Objectives(1) To assess the methodological quality and risk of bias of radiomics studies investigating the diagnostic performance in adrenal masses and (2) to determine the potential diagnostic value of radiomics in adrenal tumors by quantitative analysis.MethodsPubMed, Embase, Web of Science, and Cochrane Library databases were searched for eligible literature. Methodological quality and risk of bias in the included studies were assessed by the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS). The diagnostic performance was evaluated by pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Spearman’s correlation coefficient and subgroup analysis were used to investigate the cause of heterogeneity. Publication bias was examined using the Deeks’ funnel plot.ResultsTwenty-eight studies investigating the diagnostic performance of radiomics in adrenal tumors were identified, with a total of 3579 samples. The average RQS was 5.11 (14.2% of total) with an acceptable inter-rater agreement (ICC 0.94, 95% CI 0.93–0.95). The risk of bias was moderate according to the result of QUADAS-2. Nine studies investigating the use of CT-based radiomics in differentiating malignant from benign adrenal tumors were included in the quantitative analysis. The pooled sensitivity, specificity, DOR and AUC with 95% confidence intervals were 0.80 (0.68-0.88), 0.83 (0.73-0.90), 19.06 (7.87-46.19) and 0.88 (0.85–0.91), respectively. There was significant heterogeneity among the included studies but no threshold effect in the meta-analysis. The result of subgroup analysis demonstrated that radiomics based on unenhanced and contrast-enhanced CT possessed higher diagnostic performance, and second-order or higher-order features could enhance the diagnostic sensitivity but also increase the false positive rate. No significant difference in diagnostic ability was observed between studies with machine learning and those without.ConclusionsThe methodological quality and risk of bias of studies investigating the diagnostic performance of radiomics in adrenal tumors should be further improved in the future. CT-based radiomics has the potential benefits in differentiating malignant from benign adrenal tumors. The heterogeneity between the included studies was a major limitation to obtaining more accurate conclusions.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/ CRD 42022331999 .

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Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study

Cited by 16 publications

References 61 publications

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan

Diagnostic performance of radiomics in adrenal masses: A systematic review and meta-analysis

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