Machine learning solutions in radiology: does the emperor have no clothes?

Cuocolo, Renato; Imbriaco, Massimo

doi:10.1007/s00330-021-07895-w

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Nevertheless, after nearly a decade of research, translation of radiomics into clinical practice remains a distant prospect, and there are many unanswered questions about the potential availability of commercial radiomics tools [10]. Additionally, reasonable concerns have also been raised that we might be overlooking negative, unpublished, but potentially valuable results, i.e., publication bias [11].…”

Section: Introductionmentioning

confidence: 99%

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative

et al. 2022

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Objective The main aim of the present systematic review was a comprehensive overview of the Radiomics Quality Score (RQS)–based systematic reviews to highlight common issues and challenges of radiomics research application and evaluate the relationship between RQS and review features. Methods The literature search was performed on multiple medical literature archives according to PRISMA guidelines for systematic reviews that reported radiomic quality assessment through the RQS. Reported scores were converted to a 0–100% scale. The Mann-Whitney and Kruskal-Wallis tests were used to compare RQS scores and review features. Results The literature research yielded 345 articles, from which 44 systematic reviews were finally included in the analysis. Overall, the median of RQS was 21.00% (IQR = 11.50). No significant differences of RQS were observed in subgroup analyses according to targets (oncological/not oncological target, neuroradiology/body imaging focus and one imaging technique/more than one imaging technique, characterization/prognosis/detection/other). Conclusions Our review did not reveal a significant difference of quality of radiomic articles reported in systematic reviews, divided in different subgroups. Furthermore, low overall methodological quality of radiomics research was found independent of specific application domains. While the RQS can serve as a reference tool to improve future study designs, future research should also be aimed at improving its reliability and developing new tools to meet an ever-evolving research space. Key Points • Radiomics is a promising high-throughput method that may generate novel imaging biomarkers to improve clinical decision-making process, but it is an inherently complex analysis and often lacks reproducibility and generalizability. • The Radiomics Quality Score serves a necessary role as the de facto reference tool for assessing radiomics studies. • External auditing of radiomics studies, in addition to the standard peer-review process, is valuable to highlight common limitations and provide insights to improve future study designs and practical applicability of the radiomics models.

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Section: Introductionmentioning

confidence: 99%

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative

et al. 2022

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“…Ideally, the final aim of scientific investigation on radiomics and DL should overall be to enable to creation of certified and reliable software cleared for medical use and capable of improving patient outcomes. Indeed, commercially available products for bringing AI in radiology are overall increasing in number, but these are often supported by low methodological quality studies and limited evidence of clinical benefit [ 76 , 77 ]. Possibly as a consequence of what discussed in this section, and to the best of our knowledge, there are currently no commercially available solutions at present [ 78 ].…”

Section: Still More Challenges Than Opportunitiesmentioning

confidence: 99%

Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows

Leo,

Stanzione,

Miele

et al. 2023

JCM

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Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.

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“…The benefits that other domains have obtained from such large-scale dataset building efforts [ 44 ] indeed seem to support this concept. Even though still in their infancy, efforts are being made both to raise awareness on methodological issues affecting current radiomics research [ 38 , 45 ], raise awareness in editors and reviewers to pose greater attention to technical aspects and clinical relevance of these papers [ 46 , 47 ], increase awareness of potential buyers of commercial solutions based on radiomics [ 48 , 49 ], and collect curated, open medical imaging datasets [ 50 , 51 ].…”

Section: Limitationsmentioning

confidence: 99%

Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization

Ugga

Spadarella

Pinto

et al. 2022

Cancers

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Meningiomas are the most common extra-axial tumors of the central nervous system (CNS). Even though recurrence is uncommon after surgery and most meningiomas are benign, an aggressive behavior may still be exhibited in some cases. Although the diagnosis can be made by radiologists, typically with magnetic resonance imaging, qualitative analysis has some limitations in regard to outcome prediction and risk stratification. The acquisition of this information could help the referring clinician in the decision-making process and selection of the appropriate treatment. Following the increased attention and potential of radiomics and artificial intelligence in the healthcare domain, including oncological imaging, researchers have investigated their use over the years to overcome the current limitations of imaging. The aim of these new tools is the replacement of subjective and, therefore, potentially variable medical image analysis by more objective quantitative data, using computational algorithms. Although radiomics has not yet fully entered clinical practice, its potential for the detection, diagnostic, and prognostic characterization of tumors is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging.

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Machine learning solutions in radiology: does the emperor have no clothes?

Cited by 5 publications

References 9 publications

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative

Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows

Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization

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