Quality assessment of meningioma radiomics studies: Bridging the gap between exploratory research and clinical applications

Won, So Yeon; Park, Yae Won; Ahn, Sung Soo; Moon, Ju Hyung; Kim, Eui Hyun; Kang, Seok‐Gu; Chang, Jong Hee; Kim, Se Hoon; Lee, Seung Koo

doi:10.1016/j.ejrad.2021.109673

Cited by 22 publications

(17 citation statements)

References 46 publications

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“…Adherence to the nomenclature of standard radiomics features and calculations described by the IBSI are important for improving the reproducibility of studies [ 52 55 ]. However, only a few studies have reported the quality of radiomics studies that assessed adherence to the IBSI of each journal [ 44 56 ]. In our study, we focused on the pre-processing steps in IBSI and reported the number of published radiomics studies on brain metastases following the guidelines.…”

Section: Discussionmentioning

confidence: 99%

“…The radiomics quality score (RQS) is determined using a scoring system that incorporates the crucial aspects of radiomics studies and assesses their quality [ 43 44 ]. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist is also a well-known tool for assessing the quality of prediction models [ 45 ].…”

Section: Introductionmentioning

confidence: 99%

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Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

Park

Ahn

et al. 2022

Korean J Radiol

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Objective Our study aimed to evaluate the quality of radiomics studies on brain metastases based on the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guidelines. Materials and Methods PubMed MEDLINE, and EMBASE were searched for articles on radiomics for evaluating brain metastases, published until February 2021. Of the 572 articles, 29 relevant original research articles were included and evaluated according to the RQS, TRIPOD checklist, and IBSI guidelines. Results External validation was performed in only three studies (10.3%). The median RQS was 3.0 (range, -6 to 12), with a low basic adherence rate of 50.0%. The adherence rate was low in comparison to the “gold standard” (10.3%), stating the potential clinical utility (10.3%), performing the cut-off analysis (3.4%), reporting calibration statistics (6.9%), and providing open science and data (3.4%). None of the studies involved test-retest or phantom studies, prospective studies, or cost-effectiveness analyses. The overall rate of adherence to the TRIPOD checklist was 60.3% and low for reporting title (3.4%), blind assessment of outcome (0%), description of the handling of missing data (0%), and presentation of the full prediction model (0%). The majority of studies lacked pre-processing steps, with bias-field correction, isovoxel resampling, skull stripping, and gray-level discretization performed in only six (20.7%), nine (31.0%), four (3.8%), and four (13.8%) studies, respectively. Conclusion The overall scientific and reporting quality of radiomics studies on brain metastases published during the study period was insufficient. Radiomics studies should adhere to the RQS, TRIPOD, and IBSI guidelines to facilitate the translation of radiomics into the clinical field.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

Park

Ahn

et al. 2022

Korean J Radiol

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“…In meningioma attempts have focused on diagnosis and grading especially in the context of radiomics and surgical resection [21] and linked analysis to extent of tumor and brain or bone invasion [82][83][84][85]. If analysed in conjunction with biomarkers and RT dosimetry, these endeavors could prove highly relevant to RT volumes and dose as well patterns of recurrence [86][87][88] but clinical applications are only starting to emerge [89]. Attempts to alter RT volumes in future trials should focus on optimizing RT treatment planning in a manner consistent with radiogenomic driven pattern of failure analysis [90].…”

Section: Radiogenomic Advances In Rare Cns Histologies Craniospinal A...mentioning

confidence: 99%

Molecular Biology in Treatment Decision Processes—Neuro-Oncology Edition

Krauze

Camphausen

2021

IJMS

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Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.

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“…MRI is the key imaging modality for diagnosis and characterization of meningioma and treatment decision 5 . Several studies applying radiomics, which translates radiological images into high-dimensional mineable imaging data 6 , have shown promising results in predicting meningioma grade 7 – 12 . However, majority of them did not perform external validation 7 .…”

Section: Introductionmentioning

confidence: 99%

Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation

Park

Shin

Eom

et al. 2022

Sci Rep

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The heterogeneity of MRI is one of the major reasons for decreased performance of a radiomics model on external validation, limiting the model’s generalizability and clinical application. We aimed to establish a generalizable radiomics model to predict meningioma grade on external validation through leveraging Cycle-Consistent Adversarial Networks (CycleGAN). In this retrospective study, 257 patients with meningioma were included in the institutional training set. Radiomic features (n = 214) were extracted from T2-weighted (T2) and contrast-enhanced T1 (T1C) images. After radiomics feature selection, extreme gradient boosting classifiers were developed. The models were validated in the external validation set consisting of 61 patients with meningiomas. To reduce the gap in generalization associated with the inter-institutional heterogeneity of MRI, the smaller image set style of the external validation was translated into the larger image set style of the institutional training set using CycleGAN. On external validation before CycleGAN application, the performance of the combined T2 and T1C models showed an area under the curve (AUC), accuracy, and F1 score of 0.77 (95% confidence interval 0.63–0.91), 70.7%, and 0.54, respectively. After applying CycleGAN, the performance of the combined T2 and T1C models increased, with an AUC, accuracy, and F1 score of 0.83 (95% confidence interval 0.70–0.97), 73.2%, and 0.59, respectively. Quantitative metrics (by Fréchet Inception Distance) showed that CycleGAN can decrease inter-institutional image heterogeneity while preserving predictive information. In conclusion, leveraging CycleGAN may be helpful to increase the generalizability of a radiomics model in differentiating meningioma grade on external validation.

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Quality assessment of meningioma radiomics studies: Bridging the gap between exploratory research and clinical applications

Cited by 22 publications

References 46 publications

Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation

Molecular Biology in Treatment Decision Processes—Neuro-Oncology Edition

Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation

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