Obsolescence of nomograms in radiomics research

D’Antonoli, Tugba Akinci; Mercaldo, Nathaniel D.

doi:10.1007/s00330-023-09728-4

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…These efforts should also include altering the attitudes of academic journals towards negative results, i.e., statistically non-significant results [ 3 , 6 ], promoting the use of checklists and quality scoring tools [ 44 ], and fostering the conduct of reproducibility studies. Particular attention should also be posed to trending research practices which may not only be unrealistic for the clinical setting but also methodologically inappropriate, such as the use of radiomics-based nomograms [ 45 ]. Finally, METRICS is explicitly targeted at the research setting, while commercially available products based on radiomics and machine learning have to account for further issues such as regulatory demands and liability for potential mistakes, which are outside the scope of the tool we developed.…”

Section: Discussionmentioning

confidence: 99%

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Kocak,

Akinci D’Antonoli,

Mercaldo

et al. 2024

Insights Imaging

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Purpose To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. Methods We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. Result In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. Conclusion In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. Critical relevance statement A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. Key points • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score (https://metricsscore.github.io/metrics/METRICS.html) and a repository created to collect feedback from the radiomics community (https://github.com/metricsscore/metrics). Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Kocak,

Akinci D’Antonoli,

Mercaldo

et al. 2024

Insights Imaging

Self Cite

View full text Add to dashboard Cite

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“…However, nomograms come with several drawbacks. They are frequently employed in radiology research to handle complex tasks that exceed the capabilities of a simple linear model; however, they oversimplify these tasks and lack reliability since they demand numerous user-driven decisions [38]. In their study, Ye et al provided their algorithm with approximately twice as many positive cases as negative ones, deviating considerably from the reported disease prevalence relative to mechanical causes [16].…”

Section: Discussionmentioning

confidence: 99%

Radiomics for the Detection of Active Sacroiliitis Using MR Imaging

Triantafyllou,

Klontzas,

Koltsakis

et al. 2023

Diagnostics

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Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these methods primarily identify chronic structural lesions. Conversely, Magnetic Resonance Imaging (MRI) is the preferred technique for detecting bone marrow edema, although it is a complex process requiring extensive expertise. Additionally, ascertaining the origin of lesions can be challenging, even for experienced medical professionals. Machine learning (ML) has showcased its proficiency in various fields by uncovering patterns that are not easily perceived from multi-dimensional datasets derived from medical imaging. The aim of this study is to develop a radiomic signature to aid clinicians in diagnosing active sacroiliitis. A total of 354 sacroiliac joints were segmented from axial fluid-sensitive MRI images, and their radiomic features were extracted. After selecting the most informative features, a number of ML algorithms were utilized to identify the optimal method for detecting active sacroiliitis, leading to the selection of an Extreme Gradient Boosting (XGBoost) model that accomplished an Area Under the Receiver-Operating Characteristic curve (AUC-ROC) of 0.71, thus further showcasing the potential of radiomics in the field.

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“…A more holistic approach appears desirable since it combines the strength of both quantitative as well as qualitative evaluation, and the study also has the merit of presenting an external validation. However, it has been highlighted that nomograms do not represent the most appropriate tool to handle radiomics models, as it will be further detailed later in the review [ 30 ].…”

Section: Prognostic Factors Related To Tumour Stagementioning

confidence: 99%

“…It also interesting to note that most of the reviewed studies propose the clinical implementation of their AI/radiomics models by means of a nomogram. However, the suitability of these static graphics to represent dynamic models such as those generated by ML has been questioned and many consider them to be obsolete [ 30 ]. To bring these technologies to the next step, all the above-mentioned shortcomings need to be properly addressed.…”

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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Obsolescence of nomograms in radiomics research

Cited by 10 publications

References 13 publications

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

Radiomics for the Detection of Active Sacroiliitis Using MR Imaging

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

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