A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features

Park, Ji Eun; Kim, Ho Sung; Kim, Dong-Hyun; Kim, Jung Youn; Cho, Se Jin

doi:10.1186/s12885-019-6504-5

Cited by 93 publications

(76 citation statements)

References 66 publications

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“…There were no studies that reported on cost-effectiveness, imaging used on phantom models, a prospectively validated radiomic signature in an appropriate clinical trial or performed clinical utility statistics (beyond just discussion of uses). 21 Further details are provided in Online Table 3.…”

Section: Resultsmentioning

confidence: 99%

“…This is consistent with other neuro-oncologic radiomic studies in the literature. 21 The RQS has some limitations, however. For example, greater emphasis is placed on the image-acquisition parameters 12 than on the image-normalization process (making the voxel, section thickness, and matrix size similar among MR imaging scans), despite the latter being important for optimal translation into multiinstitutional contexts.…”

Section: Discussionmentioning

confidence: 99%

“…and J.K. A k statistic 19 was considered for the RQS, similar to that used in previous studies; 20 however, for quantitativelydefined criteria, it was determined that resolution by discussion would be superior. 21…”

Section: Quality Assessmentmentioning

confidence: 99%

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Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

Bhandari¹,

Liong

Koppen³

et al. 2020

AJNR Am J Neuroradiol

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BACKGROUND: Determination of isocitrate dehydrogenase (IDH) status and, if IDH-mutant, assessing 1p19q codeletion are an important component of diagnosis of World Health Organization grades II/III or lower-grade gliomas. This has led to research into noninvasively correlating imaging features ("radiomics") with genetic status. PURPOSE: Our aim was to perform a diagnostic test accuracy systematic review for classifying IDH and 1p19q status using MR imaging radiomics, to provide future directions for integration into clinical radiology. DATA SOURCES: Ovid (MEDLINE), Scopus, and the Web of Science were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy guidelines. STUDY SELECTION: Fourteen journal articles were selected that included 1655 lower-grade gliomas classified by their IDH and/or 1p19q status from MR imaging radiomic features. DATA ANALYSIS: For each article, the classification of IDH and/or 1p19q status using MR imaging radiomics was evaluated using the area under curve or descriptive statistics. Quality assessment was performed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the radiomics quality score. DATA SYNTHESIS: The best classifier of IDH status was with conventional radiomics in combination with convolutional neural network-derived features (area under the curve ¼ 0.95, 94.4% sensitivity, 86.7% specificity). Optimal classification of 1p19q status occurred with texture-based radiomics (area under the curve ¼ 0.96, 90% sensitivity, 89% specificity). LIMITATIONS: A meta-analysis showed high heterogeneity due to the uniqueness of radiomic pipelines. CONCLUSIONS: Radiogenomics is a potential alternative to standard invasive biopsy techniques for determination of IDH and 1p19q status in lower-grade gliomas but requires translational research for clinical uptake. ABBREVIATIONS: AI ¼ artificial intelligence; AUC ¼ area under the curve; CNN ¼ convolutional neural network; IDH ¼ isocitrate dehydrogenase; IDH-mut ¼ IDH-mutant; LGG ¼ lower-grade gliomas; ML ¼ machine learning; PRISMA-DTA ¼ Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy; QUADAS-2 ¼ Quality Assessment of Diagnostic Accuracy Studies 2; RQS ¼ radiomics quality score; SVM ¼ support vector machine; VASARI ¼ Visually Accessible Rembrandt Images; WHO ¼ World Health Organization

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

Bhandari¹,

Liong

Koppen³

et al. 2020

AJNR Am J Neuroradiol

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“…The Radiomics Quality Score of our study was 13, which was 36% of the ideal score of 36. This was slightly higher than the average score of 11 assessed in the systemic review 34 .…”

Section: Methodsmentioning

confidence: 54%

Prospective evaluation of metabolic intratumoral heterogeneity in patients with advanced gastric cancer receiving palliative chemotherapy

Yoo

Kang

Yoon

et al. 2021

Sci Rep

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Although metabolic intratumoral heterogeneity (ITH) gives important value on treatment responses and prognoses, its association with treatment outcomes have not been reported in gastric cancer (GC). We aimed to evaluate temporal changes in metabolic ITH and the associations with treatment responses, progression-free survival (PFS), and overall survival (OS) in advanced GC patients. Eighty-five patients with unresectable, locally advanced, or metastatic GC were prospectively enrolled before the first-line palliative chemotherapy and underwent [18F]FDG PET at baseline (TP1) and the first response follow-up evaluation (TP2). Standardized uptake values (SUVs), volumetric parameters, and textural features were evaluated in primary gastric tumor at TP1 and TP2. Of 85 patients, 44 had partial response, 33 had stable disease, and 8 progressed. From TP1 to TP2, metabolic ITH was significantly reduced (P < 0.01), and the degree of the decrease was greater in responders than in non-responders (P < 0.01). Using multiple Cox regression analyses, a low SUVmax at TP2, a high kurtosis at TP2 and larger decreases in the coefficient of variance were associated with better PFS. A low SUVmax at TP2, larger decreases in the metabolic tumor volume and larger decreased in the energy were associated with better OS. Age older than 60 years and responders also showed better OS. An early reduction in metabolic ITH is useful to predict treatment outcomes in advanced GC patients.

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“…(41-44) Radiomics features may be prone to biological validation for their correlation with disease pathology. (45) This observation is based on the hypothesis that radiomics features, especially second-order features, capture the spatial variation in signal intensity that may re ect the deposition of Aβ plaques. Further, it may extract different biological information from volume, (43,46) which is the traditional imaging biomarker of AD.…”

Section: Discussionmentioning

confidence: 99%

Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

Park

Choi

Park

et al. 2021

JAD

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Background: Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients. Objective: To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ42 status when integrated with clinical profiles. Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampus were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated. Results: The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set. Conclusion: Radiomics models from MRI can help predict CSF Aβ42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.

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A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features

Cited by 93 publications

References 66 publications

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review

Prospective evaluation of metabolic intratumoral heterogeneity in patients with advanced gastric cancer receiving palliative chemotherapy

Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

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