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

Stanzione, Arnaldo; Gambardella, Michele; Cuocolo, Renato; Ponsiglione, Andrea; Romeo, Valeria; Imbriaco, Massimo

doi:10.1016/j.ejrad.2020.109095

Cited by 98 publications

(85 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It could help identifying high-quality results among the large number of publications in this field as well as issues limiting their value and applicability. The average RQS of the articles included in our systematic review was low (6.96, [22,51,52]. Therefore, the problems affecting radiomics studies and limiting the RQS score seem to be general and not restricted to a specific application.…”

Section: Discussionmentioning

confidence: 89%

“…Continuous variables are presented as mean ± standard deviation. Following previous experiences both with RQS and other scoring systems [22,23], inter-reader reproducibility was evaluated by calculating the intraclass correlation coefficient (ICC) for the total RQS score obtained by each study. In accordance with recent guidelines, a two-way, random-effects, single-rater, absolute agreement ICC model was used [24].…”

Section: Discussionmentioning

confidence: 99%

“…The current situation can be at least in part explained by an exponential growth in interest and number of papers submitted using radiomics, a dynamic also experienced in the wider field of ML [7]. On the other hand, the RQS scoring system is relatively new and has been used in a limited number of occasions [18,22,[51][52][53]. Therefore, further revisions and improvements after initial feedback may produce a different weighting of each item and/or modifications in the items themselves.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice.

show abstract

Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The quality of the selected literature was evaluated by both Authors. The inter-rater intra-class correlation was calculated based on the total RQS in order to evaluate interrater reliability (13).…”

Section: Risk Of Biasmentioning

confidence: 99%

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

Kao

Hsu

2021

In Vivo

View full text Add to dashboard Cite

Background: Preservation of organ function is important in cancer treatment. The 'watch-and-wait' strategy is an important approach in management of esophageal cancer. However, clinical imaging cannot accurately evaluate the presence or absence of residual tumor after neoadjuvant chemoradiation. As a result, using radiomics to predict complete pathological response in esophageal cancer has gained in popularity in recent years. Given that the characteristics of patients and sites vary considerably, a meta-analysis is needed to investigate the predictive power of radiomics in esophageal cancer. Patients and Methods: PRISMA guidelines were used to conduct this study. PubMed, Cochrane, and Embase were searched for literature review. The quality of the selected studies was evaluated by the radiomics quality score. I 2 score and Cochran's Q test were used to evaluate heterogeneity between studies. A funnel plot was used for evaluation of publication bias. Results: A total of seven articles were collected for this meta-analysis. The pooled area under the receiver operating characteristics curve of the seven selected articles for predicting pathological complete response in eosphageal cancer patient was quite high, achieving a pooled value of 0.813 (95% confidence intervaI=0.761-0.866). The radiomics quality score ranged from −2 to 16 (maximum score: 36 points). Three out of the seven studies used machine learning algorithms, while the others used traditional biostatistics methods. One of the seven studies used morphology class features, while four studies used first-order features, and five used second-order features. Conclusion: Using radiomics to predict complete pathological response after neoadjuvant chemoradiotherapy in esophageal cancer is feasible. In the future, prospective, multicenter studies should be carried out for predicting pathological complete response in patients with esophageal cancer.

show abstract

“…In the relatively recent radiomics approach, quantitative analysis of radiological images (mainly CT [37][38][39], magnetic resonance imaging (MRI) [40][41][42], and positron emission tomography (PET) [43] images, but also ultrasounds [44], mammograms [45], and radiography) by extraction of a large number of image features (up to a few hundred or thousands) can be combined with ML classifiers to produce prognostic and predictive models [39].…”

Section: Imagingmentioning

confidence: 99%

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

et al. 2021

View full text Add to dashboard Cite

: Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

show abstract

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

Cited by 98 publications

References 88 publications

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

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

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

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

Contact Info

Product

Resources

About