Literature DB >> 32531722

Prostate MRI radiomics: A systematic review and radiomic quality score assessment.

Arnaldo Stanzione1, Michele Gambardella1, Renato Cuocolo2, Andrea Ponsiglione1, Valeria Romeo1, Massimo Imbriaco1.   

Abstract

BACKGROUND: Radiomics have the potential to further increase the value of MRI in prostate cancer management. However, implementation in clinical practice is still far and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the literature to assess the quality of prostate MRI radiomic studies using the radiomics quality score (RQS).
METHODS: Multiple medical literature archives (PubMed, Web of Science and EMBASE) were searched to retrieve original investigations focused on prostate MRI radiomic approaches up to the end of June 2019. Three researchers independently assessed each paper using the RQS. Data from the most experienced researcher were used for descriptive analysis. Inter-rater reproducibility was assessed using the intraclass correlation coefficient (ICC) on the total RQS score.
RESULTS: 73 studies were included in the analysis. Overall, the average RQS total score was 7.93 ± 5.13 on a maximum of 36 points, with a final average percentage of 23 ± 13%. Among the most critical items, the lack of feature robustness testing strategies and external validation datasets. The ICC resulted poor to moderate, with an average value of 0.57 and 95% Confidence Intervals between 0.44 and 0.69.
CONCLUSIONS: Current studies on prostate MRI radiomics still lack the quality required to allow their introduction in clinical practice.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  MRI; Machine learning; Prostate cancer; Radiomics quality score; Systematic review

Year:  2020        PMID: 32531722     DOI: 10.1016/j.ejrad.2020.109095

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


  25 in total

1.  Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions.

Authors:  Linghao Li; Lili Gu; Bin Kang; Jiaojiao Yang; Ying Wu; Hao Liu; Shasha Lai; Xueting Wu; Jian Jiang
Journal:  Front Oncol       Date:  2022-07-05       Impact factor: 5.738

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

Review 4.  A primer on texture analysis in abdominal radiology.

Authors:  Natally Horvat; Joao Miranda; Maria El Homsi; Jacob J Peoples; Niamh M Long; Amber L Simpson; Richard K G Do
Journal:  Abdom Radiol (NY)       Date:  2021-11-26

5.  Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis.

Authors:  Lorenzo Ugga; Teresa Perillo; Renato Cuocolo; Arnaldo Stanzione; Valeria Romeo; Roberta Green; Valeria Cantoni; Arturo Brunetti
Journal:  Neuroradiology       Date:  2021-03-02       Impact factor: 2.804

6.  A Meta-Analysis for Using Radiomics to Predict Complete Pathological Response in Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiation.

Authors:  Yung-Shuo Kao; Yen Hsu
Journal:  In Vivo       Date:  2021 May-Jun       Impact factor: 2.406

7.  Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review.

Authors:  Meilinuer Abdurixiti; Mayila Nijiati; Rongfang Shen; Qiu Ya; Naibijiang Abuduxiku; Mayidili Nijiati
Journal:  Br J Radiol       Date:  2021-05-12       Impact factor: 3.629

8.  The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment.

Authors:  Gaia Spadarella; Lorenzo Ugga; Giuseppina Calareso; Rossella Villa; Serena D'Aniello; Renato Cuocolo
Journal:  Neuroradiology       Date:  2022-04-23       Impact factor: 2.995

9.  Use of Radiomics to Improve Diagnostic Performance of PI-RADS v2.1 in Prostate Cancer.

Authors:  Mou Li; Ling Yang; Yufeng Yue; Jingxu Xu; Chencui Huang; Bin Song
Journal:  Front Oncol       Date:  2021-02-17       Impact factor: 6.244

10.  MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance.

Authors:  Nikita Sushentsev; Leonardo Rundo; Oleg Blyuss; Vincent J Gnanapragasam; Evis Sala; Tristan Barrett
Journal:  Sci Rep       Date:  2021-06-21       Impact factor: 4.379

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