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In 3D‐analysis of the calcaneus, a consistent coordinate system aligned with the original anatomical directions is crucial for pre‐ and postoperative analysis. This importance stems from the calcaneus's key role in weight‐bearing and biomechanical alignment. However, defining a reliable coordinate system based solely on fractured or surgically reconstructed calcanei presents significant challenges. Given its anatomical prominence and consistent orientation, the talus offers a potential solution to this challenge. Our work explores the feasibility of talus‐derived coordinate systems for 3D‐modeling of the calcaneus across its various conditions. Four methods were tested on nonfractured, fractured and surgically reconstructed calcanei, utilizing Principal Component Analysis, anatomical landmarks, bounding box, and an atlas‐based approach. The methods were compared with a self‐defined calcaneus reference coordinate system. Additionally, the impact of deviation of the coordinate system on morphological measurements was investigated. Among methods for constructing nonfractured calcanei coordinate systems, the atlas‐based method displayed the lowest Root Mean Square value in comparison with the reference coordinate system. For morphological measures like Böhler's Angle and the Critical angle of Gissane, the atlas talus‐based system closely aligned with ground truth, yielding differences of 0.6° and 1.2°, respectively, compared to larger deviations seen in other talus‐based coordinate systems. In conclusion, all tested methods were feasible for creating a talus derived coordinate system. A talus derived coordinate system showed potential, offering benefits for morphological measurements and clinical scenarios involving fractured and surgically reconstructed calcanei. Further research is recommended to assess the impact of these coordinate systems on surgical planning and outcomes. [ABSTRACT FROM AUTHOR]

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aims to improve the preoperative classification of nerve sheath tumors using radiomics, a method that extracts quantitative data from medical images. By analyzing MRI scans, we seek to develop a more accurate way to distinguish between different types of nerve sheath tumors before surgery. Our findings could lead to better treatment planning and outcomes for patients with these tumors. This research has the potential to enhance the diagnostic process and contribute to more personalized care for individuals with nerve sheath tumors, ultimately benefiting the medical community and patients alike. Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Current magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasive biopsies. This study aims to develop a radiomics model using quantitative imaging features and machine learning to distinguish MPNSTs from BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000–2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi-automatically segmented on MRI scans, and radiomics features were extracted using the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100× random-split cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1-weighted (T1w) MRI radiomics model outperformed others with an area under the curve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance performance. Combining T1w MRI with clinical features achieved an AUC of 0.74. Experienced radiologists achieved AUCs of 0.75 and 0.66, respectively. Radiomics based on T1w MRI scans and clinical features show some ability to distinguish MPNSTs from BPNSTs, potentially aiding in the management of these tumors. [ABSTRACT FROM AUTHOR]

The physical and social distancing measures that have been adopted worldwide because of COVID-19 will probably remain in place for a long time, especially for senior adults, people with chronic conditions, and other at-risk populations. Teleconsultations can be useful in ensuring that patients continue to receive clinical care while reducing physical crowding and avoiding unnecessary exposure of health care staff. Implementation processes that typically take months of planning, budgeting, pilot testing, and education were compressed into days. However, in the urgency to deal with the present crisis, we may be forgetting that the introduction of digital health is not exclusively a technological issue, but part of a complex organizational change problem. This viewpoint offers insight regarding issues that rapidly adopted teleconsultation systems may face in a post–COVID-19 world.

Objective: To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology. Methods: This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value. Results: RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring. Conclusion: The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology. Critical relevance statement: The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation. Keypoints: • Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap. [ABSTRACT FROM AUTHOR]

Background: Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). Objectives: To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. Methods: The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. Results: A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. Conclusion: We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans. [ABSTRACT FROM AUTHOR]

The article discusses the relationship between artificial intelligence (AI) and structured reporting in radiology. Structured reporting, which has not been widely adopted, aims to standardize and improve the quality of radiology reports. AI has the potential to enhance the quality and efficiency of radiology by improving sensitivity and specificity, automating time-consuming tasks, and improving processing times. The article suggests that AI development should align with the information needs of radiologists and that AI-generated outputs can be integrated into structured radiology reports to improve workflow integration. Additionally, AI can help in template development, quality control, and measuring AI maturity. The article emphasizes the mutual benefits of AI and structured reporting in radiology. [Extracted from the article]

There is no clear evidence regarding the benefit of restaging for distant metastases after neoadjuvant radiotherapy (RTX) in patients with soft tissue sarcoma (STS) of the extremities and trunk wall. This study aimed to determine how often restaging of the chest identified metastatic disease that altered management in these patients. We performed a single-centre retrospective study from 2010 to 2020. All patients with non-metastatic STS of the extremities and trunk wall who were treated with neoadjuvant RTX and received a staging and restaging chest CT scan or X-ray for distant metastasis were included. The outcome of interest was change in treatment strategy due to restaging after neoadjuvant RTX. Within the 144 patients who were staged and treated with neoadjuvant RTX, a restaging chest CT or X-ray was performed in 134 patients (93%). A change in treatment strategy due to new findings at restaging after RTX was observed in 26 out of 134 patients (19%). In 24 patients the scheduled resection of the primary STS was cancelled at restaging (24/134, 18%), given the findings at restaging. The other two patients did receive the intended local resection, but either with palliative intent, or as a part of a previously unplanned multimodality treatment. In approximately one in five patients restaging results in a change in treatment strategy. This underlines the added value of routine restaging for distant metastases with chest CT or X-ray after neoadjuvant RTX in patients with STS. [ABSTRACT FROM AUTHOR]

Approximately 25% of the patients with muscle-invasive bladder cancer (MIBC) who are clinically node negative have occult lymph node metastases at radical cystectomy (RC) and pelvic lymph node dissection. The aim of this study was to evaluate preoperative CT-based radiomics to differentiate between pN+ and pN0 disease in patients with clinical stage cT2-T4aN0-N1M0 MIBC. Patients with cT2-T4aN0-N1M0 MIBC, of whom preoperative CT scans and pathology reports were available, were included from the prospective, multicenter CirGuidance trial. After manual segmentation of the lymph nodes, 564 radiomics features were extracted. A combination of different machine-learning methods was used to develop various decision models to differentiate between patients with pN+ and pN0 disease. A total of 209 patients (159 pN0; 50 pN+) were included, with a total of 3153 segmented lymph nodes. None of the individual radiomics features showed significant differences between pN+ and pN0 disease, and none of the radiomics models performed substantially better than random guessing. Hence, CT-based radiomics does not contribute to differentiation between pN+ and pN0 disease in patients with cT2-T4aN0-N1M0 MIBC. [ABSTRACT FROM AUTHOR]

Background: The value‐based healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. Methods, findings and interpretation: This multi‐society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia and New Zealand, describes the place of radiology in VBH models and the healthcare value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined. [ABSTRACT FROM AUTHOR]

Background: The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. Methods, findings and interpretation: This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the healthcare value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined. [ABSTRACT FROM AUTHOR]

Musculoskeletal imaging is mainly based on the subjective and qualitative analysis of imaging examinations. However, integration of quantitative assessment of imaging data could increase the value of imaging in both research and clinical practice. Some imaging modalities, such as perfusion magnetic resonance imaging (MRI), diffusion MRI, or T2 mapping, are intrinsically quantitative. But conventional morphological imaging can also be analyzed through the quantification of various parameters. The quantitative data retrieved from imaging examinations can serve as biomarkers and be used to support diagnosis, determine patient prognosis, or monitor therapy.We focus on the value, or clinical utility, of quantitative imaging in the musculoskeletal field. There is currently a trend to move from volume- to value-based payments. This review contains definitions and examines the role that quantitative imaging may play in the implementation of value-based health care. The influence of artificial intelligence on the value of quantitative musculoskeletal imaging is also discussed. [ABSTRACT FROM AUTHOR]

In the era of value-based health care, adding value is a key element in providing care. The choice of appropriate imaging modality and protocol should be based on consideration of patients' values, health care outcomes, and cost-effectiveness, taking into account the perspective of the decision maker, the health care system, and society at large. This article provides an overview of the available tools to measure value, outcomes, and cost-effectiveness in musculoskeletal radiology, illustrated with relevant examples. [ABSTRACT FROM AUTHOR]

The article presents a study that evaluates guidelines for cardiovascular risk assessment to give clinicians insights on which screening intervention to use in health check. It notes that the guidelines assessed were taken from various sources such as National Library for Health, National Guideline Clearing House and G-I-N International Library. It mentions that 16 out of 27 guidelines were traced with conflicts. It suggests that physicians should rely on rigorously developed guidelines.

The authors describe developments in Dutch society during the second half of the twentieth century in relation to legislative measures concerning medical decisions toward the end of life. Euthanasia and physician-assisted death are matters of such criticality that they have been debated heavily for more than thirty years. Organizations, groups, and individuals from all levels in society have discussed not only the medical and moral aspects or the patients' perspective but also have demanded transparency through research and fact-finding. Also the legal, political, and even international ramifications were taken into consideration. The role of the government was to facilitate the ongoing debate until parties reached some form of consensus. In 2002, it finally culminated in a law that legalized the detailed procedures that had immersed over time and were agreed upon by all those involved. [ABSTRACT FROM AUTHOR]

Patients with BRAF mutated (BRAF-mt) metastatic melanoma benefit significantly from treatment with BRAF inhibitors. Currently, the BRAF status is determined on archival tumor tissue or on fresh tumor tissue from an invasive biopsy. The aim of this study was to evaluate whether radiomics can predict the BRAF status in a non-invasive manner. Patients with melanoma lung metastases, known BRAF status, and a pretreatment computed tomography scan were included. After semi-automatic annotation of the lung lesions (maximum two per patient), 540 radiomics features were extracted. A chest radiologist scored all segmented lung lesions according to the Lung Image Database Consortium (LIDC) criteria. Univariate analysis was performed to assess the predictive value of each feature for BRAF mutation status. A combination of various machine learning methods was used to develop BRAF decision models based on the radiomics features and LIDC criteria. A total of 169 lung lesions from 103 patients (51 BRAF-mt; 52 BRAF wild type) were included. There were no features with a significant discriminative value in the univariate analysis. Models based on radiomics features and LIDC criteria both performed as poorly as guessing. Hence, the BRAF mutation status in melanoma lung metastases cannot be predicted using radiomics features or visually scored LIDC criteria. [ABSTRACT FROM AUTHOR]

Purpose: The American College of Radiology (ACR) Actionable Reporting Work Group defined three categories of imaging findings that require additional, nonroutine communication with the referring physician because of their urgency or unexpectedness. The objective of this study was to determine the prevalence of actionable findings in radiology reports, and to assess how well radiologists agree on the categorisation of actionable findings.Method: From 124,909 consecutive radiology reports stored in the electronic health record system of a large university hospital, 1000 reports were randomly selected. Two radiologists independently annotated all actionable findings according to the three categories of urgency defined by the ACR Work Group. Annotation differences were resolved in a consensus meeting and a final category was established for each report. Interannotator agreement was measured by accuracy and the kappa coefficient.Results: The prevalence of the three categories of actionable findings together was 32.5 %. Of all reports, 10.9 % were from patients seen in the emergency department. Prevalence of actionable findings for these patients (45.9 %) was considerably higher than for patients in routine clinical care (30.9 %). Interannotator agreement scores on the categorisation of actionable findings were 0.812 for accuracy and 0.616 for kappa coefficient.Conclusions: The prevalence of actionable findings in radiology reports is high. The interannotator agreement scores are moderate, indicating that categorisation of actionable findings is a difficult task. To avoid unneeded increase in the workload of radiologists, in particular in routine practice, clinical context may need to be considered in deciding whether a finding is actionable. [ABSTRACT FROM AUTHOR]

B Background: b Gastrointestinal stromal tumors (GISTs) are rare mesenchymal tumors of the gastrointestinal (GI) tract. Predicting the c-KIT mutational status of GISTs led to an AUC of 0.52 (95% CI 0.32-0.72) for predicting all c-KIT mutations, an AUC of 0.51 (95% CI 0.29-0.72) for predicting a c-KIT exon 9 mutation, and an AUC of 0.61 (95% CI 0.47-0.74) for predicting a c-KIT exon 11 mutation. B Conclusions b : The results of this pilot study showed the potential of radiomics to distinguish GIST from other GI tumors, but no potential in predicting c-KIT mutational status of GISTs. [Extracted from the article]

AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large number of developed AI tools and those translated into daily clinical practice, which can be primarily attributed to limited usefulness and trust in current AI tools. Instead of technically driven development, little effort has been put into value-based development to ensure AI tools will have a clinically relevant impact on patient care.An iterative comprehensive value evaluation process covering the complete AI tool lifecycle should be part of radiology AI development. For value assessment of health technologies, health technology assessment (HTA) is an extensively used and comprehensive method. While most aspects of value covered by HTA apply to radiology AI, additional aspects, including transparency, explainability, and robustness, are unique to radiology AI and crucial in its value assessment. Additionally, value assessment should already be included early in the design stage to determine the potential impact and subsequent requirements of the AI tool. Such early assessment should be systematic, transparent, and practical to ensure all stakeholders and value aspects are considered. Hence, early value-based development by incorporating early HTA will lead to more valuable AI tools and thus facilitate translation to clinical practice.This paper advocates for the use of early value-based assessments. These assessments promote a comprehensive evaluation on how an AI tool in development can provide value in clinical practice and thus help improve the quality of these tools and the clinical process they support.Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains.Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools.Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains.Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools.Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.Clinical relevance statement: AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large number of developed AI tools and those translated into daily clinical practice, which can be primarily attributed to limited usefulness and trust in current AI tools. Instead of technically driven development, little effort has been put into value-based development to ensure AI tools will have a clinically relevant impact on patient care.An iterative comprehensive value evaluation process covering the complete AI tool lifecycle should be part of radiology AI development. For value assessment of health technologies, health technology assessment (HTA) is an extensively used and comprehensive method. While most aspects of value covered by HTA apply to radiology AI, additional aspects, including transparency, explainability, and robustness, are unique to radiology AI and crucial in its value assessment. Additionally, value assessment should already be included early in the design stage to determine the potential impact and subsequent requirements of the AI tool. Such early assessment should be systematic, transparent, and practical to ensure all stakeholders and value aspects are considered. Hence, early value-based development by incorporating early HTA will lead to more valuable AI tools and thus facilitate translation to clinical practice.This paper advocates for the use of early value-based assessments. These assessments promote a comprehensive evaluation on how an AI tool in development can provide value in clinical practice and thus help improve the quality of these tools and the clinical process they support.Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains.Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools.Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains.Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools.Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.Key Points: AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large number of developed AI tools and those translated into daily clinical practice, which can be primarily attributed to limited usefulness and trust in current AI tools. Instead of technically driven development, little effort has been put into value-based development to ensure AI tools will have a clinically relevant impact on patient care.An iterative comprehensive value evaluation process covering the complete AI tool lifecycle should be part of radiology AI development. For value assessment of health technologies, health technology assessment (HTA) is an extensively used and comprehensive method. While most aspects of value covered by HTA apply to radiology AI, additional aspects, including transparency, explainability, and robustness, are unique to radiology AI and crucial in its value assessment. Additionally, value assessment should already be included early in the design stage to determine the potential impact and subsequent requirements of the AI tool. Such early assessment should be systematic, transparent, and practical to ensure all stakeholders and value aspects are considered. Hence, early value-based development by incorporating early HTA will lead to more valuable AI tools and thus facilitate translation to clinical practice.This paper advocates for the use of early value-based assessments. These assessments promote a comprehensive evaluation on how an AI tool in development can provide value in clinical practice and thus help improve the quality of these tools and the clinical process they support.Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains.Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools.Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption.Value in radiology AI should be perceived as a comprehensive term including health technology assessment domains and AI-specific domains.Incorporation of an early health technology assessment for radiology AI during development will lead to more valuable radiology AI tools.Comprehensive and transparent value assessment of radiology AI tools is essential for their widespread adoption. [ABSTRACT FROM AUTHOR]

Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline—such as image, application of image filters, and selection of feature extraction parameters—can greatly affect the values of radiomic features. Moreover, common errors in data partitioning, model comparison, fine-tuning, assessment, and calibration can reduce reproducibility and impede clinical translation. Clinical adoption of radiomics also requires a deep understanding of model explainability and the development of intuitive interpretations of radiomic features. To address these challenges, it is essential for radiomics model developers and clinicians to be well-versed in current best practices. Proper knowledge and application of these practices is crucial for accurate radiomics feature extraction, robust model development, and thorough assessment, ultimately increasing reproducibility, generalizability, and the likelihood of successful clinical translation. In this article, we have provided researchers with our recommendations along with practical examples to facilitate good research practices in radiomics.Radiomics’ inherent methodological complexity should be understood to ensure rigorous radiomic model development to improve clinical decision-making.Adherence to radiomics-specific checklists and quality assessment tools ensures methodological rigor.Use of standardized radiomics tools and best practices enhances clinical translation of radiomics models.Radiomics’ inherent methodological complexity should be understood to ensure rigorous radiomic model development to improve clinical decision-making.Adherence to radiomics-specific checklists and quality assessment tools ensures methodological rigor.Use of standardized radiomics tools and best practices enhances clinical translation of radiomics models.Key Points: Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline—such as image, application of image filters, and selection of feature extraction parameters—can greatly affect the values of radiomic features. Moreover, common errors in data partitioning, model comparison, fine-tuning, assessment, and calibration can reduce reproducibility and impede clinical translation. Clinical adoption of radiomics also requires a deep understanding of model explainability and the development of intuitive interpretations of radiomic features. To address these challenges, it is essential for radiomics model developers and clinicians to be well-versed in current best practices. Proper knowledge and application of these practices is crucial for accurate radiomics feature extraction, robust model development, and thorough assessment, ultimately increasing reproducibility, generalizability, and the likelihood of successful clinical translation. In this article, we have provided researchers with our recommendations along with practical examples to facilitate good research practices in radiomics.Radiomics’ inherent methodological complexity should be understood to ensure rigorous radiomic model development to improve clinical decision-making.Adherence to radiomics-specific checklists and quality assessment tools ensures methodological rigor.Use of standardized radiomics tools and best practices enhances clinical translation of radiomics models.Radiomics’ inherent methodological complexity should be understood to ensure rigorous radiomic model development to improve clinical decision-making.Adherence to radiomics-specific checklists and quality assessment tools ensures methodological rigor.Use of standardized radiomics tools and best practices enhances clinical translation of radiomics models. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to critically appraise guidelines on imaging of asymptomatic coronary artery disease (CAD). Background: Various imaging tests exist to detect CAD in asymptomatic persons. Because randomized controlled trials are lacking, guidelines that address the use of CAD imaging tests may disagree. Methods: Guidelines in English published between January 1, 2003, and February 26, 2010, were retrieved using MEDLINE, Cumulative Index to Nursing and Allied Health Literature, the National Guideline Clearinghouse, the National Library for Health, the Canadian Medication Association Infobase, and the Guidelines International Network International Guideline Library. Guidelines developed by national and international medical societies from Western countries, containing recommendations on imaging of asymptomatic CAD were included. Rigor of development was scored by 2 independent reviewers using the Appraisal of Guidelines Research and Evaluation (AGREE) instrument. One reviewer performed full extraction of recommendations, which was checked by a second reviewer. Results: Of 2,415 titles identified, 14 guidelines met our inclusion criteria. Eleven of 14 guidelines reported relationship with industry. The AGREE scores varied across guidelines from 21% to 93%. Two guidelines considered cost effectiveness. Eight guidelines recommended against or found insufficient evidence for testing of asymptomatic CAD. The other 6 guidelines recommended imaging patients at intermediate or high CAD risk based on the Framingham risk score, and 5 considered computed tomography calcium scoring useful for this purpose. Conclusions: Guidelines on risk assessment by imaging of asymptomatic CAD contain conflicting recommendations. More research, including randomized controlled trials, evaluating the impact of imaging on clinical outcomes and costs is needed. [ABSTRACT FROM AUTHOR]

Purpose: Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types.Methods: Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1-weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset.Results: The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74.Conclusions: Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status. [ABSTRACT FROM AUTHOR]