Literature DB >> 33774441

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

So Yeon Won1, Yae Won Park2, Sung Soo Ahn1, Ju Hyung Moon3, Eui Hyun Kim3, Seok-Gu Kang3, Jong Hee Chang3, Se Hoon Kim4, Seung-Koo Lee1.   

Abstract

PURPOSE: To evaluate the quality of radiomics studies on meningiomas, using a radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), and the Image Biomarker Standardization Initiative (IBSI).
METHODS: PubMed MEDLINE and Embase were searched to identify radiomics studies on meningiomas. Of 138 identified articles, 25 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and items in IBSI.
RESULTS: Only four studies (16 %) performed external validation. The mean RQS was 5.6 out of 36 (15.4 %), and the basic adherence rate was 26.8 %. The adherence rate was low for stating biological correlation (4%), conducting calibration statistics (12 %), multiple segmentation (16 %), and stating potential clinical utility (16 %). None of the studies conducted a test‒retest or phantom study, stated a comparison to a 'gold standard', conducted prospective studies or cost-effectivity analysis, or opened code and data to the public, resulting in low RQS. The overall adherence rate for TRIPOD was 54.1 %, with low scores for reporting the title (4%), abstract (0%), blind assessment of the outcome (8%), and explaining the sample size (0%). According to IBSI items, only 6 (24 %), 6 (24 %), and 3 (12 %) studies performed N4 bias-field correction, isovoxel resampling, and grey-level discretization, respectively. No study performed skull stripping.
CONCLUSION: The quality of radiomics studies for meningioma is insufficient. Acknowledgement of RQS, TRIPOD, and IBSI reporting guidelines may improve the quality of meningioma radiomics studies and enable their clinical application.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Machine learning; Meningioma; Quality improvement; Radiomics; Radiomics quality score

Mesh:

Year:  2021        PMID: 33774441     DOI: 10.1016/j.ejrad.2021.109673

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  11 in total

1.  Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review.

Authors:  Suyon Chang; Kyunghwa Han; Young Joo Suh; Byoung Wook Choi
Journal:  Eur Radiol       Date:  2022-03-01       Impact factor: 5.315

2.  Diagnosis of Idiopathic Pulmonary Fibrosis in High-Resolution Computed Tomography Scans Using a Combination of Handcrafted Radiomics and Deep Learning.

Authors:  Turkey Refaee; Zohaib Salahuddin; Anne-Noelle Frix; Chenggong Yan; Guangyao Wu; Henry C Woodruff; Hester Gietema; Paul Meunier; Renaud Louis; Julien Guiot; Philippe Lambin
Journal:  Front Med (Lausanne)       Date:  2022-06-23

Review 3.  Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment.

Authors:  Andrea Ponsiglione; Arnaldo Stanzione; Renato Cuocolo; Raffaele Ascione; Michele Gambardella; Marco De Giorgi; Carmela Nappi; Alberto Cuocolo; Massimo Imbriaco
Journal:  Eur Radiol       Date:  2021-11-23       Impact factor: 7.034

4.  Three-dimensional fractal dimension and lacunarity features may noninvasively predict TERT promoter mutation status in grade 2 meningiomas.

Authors:  So Yeon Won; Jun Ho Lee; Narae Lee; Yae Won Park; Sung Soo Ahn; Jinna Kim; Jong Hee Chang; Se Hoon Kim; Seung-Koo Lee
Journal:  PLoS One       Date:  2022-10-20       Impact factor: 3.752

Review 5.  Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.

Authors:  Paul Windisch; Carole Koechli; Susanne Rogers; Christina Schröder; Robert Förster; Daniel R Zwahlen; Stephan Bodis
Journal:  Cancers (Basel)       Date:  2022-05-27       Impact factor: 6.575

Review 6.  A Spotlight on the Role of Radiomics and Machine-Learning Applications in the Management of Intracranial Meningiomas: A New Perspective in Neuro-Oncology: A Review.

Authors:  Lara Brunasso; Gianluca Ferini; Lapo Bonosi; Roberta Costanzo; Sofia Musso; Umberto E Benigno; Rosa M Gerardi; Giuseppe R Giammalva; Federica Paolini; Giuseppe E Umana; Francesca Graziano; Gianluca Scalia; Carmelo L Sturiale; Rina Di Bonaventura; Domenico G Iacopino; Rosario Maugeri
Journal:  Life (Basel)       Date:  2022-04-14

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

Authors:  Chae Jung Park; Yae Won Park; Sung Soo Ahn; Dain Kim; Eui Hyun Kim; Seok-Gu Kang; Jong Hee Chang; Se Hoon Kim; Seung-Koo Lee
Journal:  Korean J Radiol       Date:  2022-01       Impact factor: 3.500

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

Authors:  Yae Won Park; Seo Jeong Shin; Jihwan Eom; Heirim Lee; Seng Chan You; Sung Soo Ahn; Soo Mee Lim; Rae Woong Park; Seung-Koo Lee
Journal:  Sci Rep       Date:  2022-04-29       Impact factor: 4.996

Review 9.  Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition.

Authors:  Andra V Krauze; Kevin Camphausen
Journal:  Int J Mol Sci       Date:  2021-12-10       Impact factor: 5.923

10.  Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results.

Authors:  Chansik An; Yae Won Park; Sung Soo Ahn; Kyunghwa Han; Hwiyoung Kim; Seung-Koo Lee
Journal:  PLoS One       Date:  2021-08-12       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.