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Importance: Breast cancer is one of the leading causes of negligence claims in radiology. The objective of this document is to describe the specific main causes of errors in breast imaging and provide European Society of Breast Imaging (EUSOBI) recommendations to try to minimize these.Technical failures represent 17% of all mammographic diagnostic negligence claims. Mammography quality control protocol and dedicated training for technologists and radiologists are essential. Lack of consideration of the clinical context is a second critical issue, as a clinical abnormality is found in 80% of malpractice claims. EUSOBI emphasizes the importance of communication and clinical examination before the diagnostic investigation. Detection errors or misapplications of the lexicon or Breast Imaging Reporting Data System (BI-RADS) score account for 5% of malpractice claims and should be reduced by limiting radiologists’ distraction or fatigue, and being aware of satisfaction of search errors and the importance of a personal systematic review. Errors related to pathological concordance and MDT review can be limited by the use of markers after biopsy and the use of standardized reports, which can aid communication with other specialities. Finally, errors related to tumor or patient factors should be discussed, considering the use of contrast-enhanced mammography and magnetic resonance imaging.Several factors are responsible for misdiagnosis in breast cancer, including errors in the practice of the technician and/or radiologist (technical failures, lack of consideration of the clinical context, incorrect application of the BI-RADS score, false reassurances), lack of communication with other specialists or with the patient, and the type of tumor and breast parenchyma.QuestionWhat factors most contribute to and what implications stem from misdiagnosis in breast imaging?Findings Ongoing training and education for radiologists and other healthcare providers, as well as interdisciplinary collaboration and communication is paramount.Clinical relevanceMisdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the entire healthcare system.Observations: Breast cancer is one of the leading causes of negligence claims in radiology. The objective of this document is to describe the specific main causes of errors in breast imaging and provide European Society of Breast Imaging (EUSOBI) recommendations to try to minimize these.Technical failures represent 17% of all mammographic diagnostic negligence claims. Mammography quality control protocol and dedicated training for technologists and radiologists are essential. Lack of consideration of the clinical context is a second critical issue, as a clinical abnormality is found in 80% of malpractice claims. EUSOBI emphasizes the importance of communication and clinical examination before the diagnostic investigation. Detection errors or misapplications of the lexicon or Breast Imaging Reporting Data System (BI-RADS) score account for 5% of malpractice claims and should be reduced by limiting radiologists’ distraction or fatigue, and being aware of satisfaction of search errors and the importance of a personal systematic review. Errors related to pathological concordance and MDT review can be limited by the use of markers after biopsy and the use of standardized reports, which can aid communication with other specialities. Finally, errors related to tumor or patient factors should be discussed, considering the use of contrast-enhanced mammography and magnetic resonance imaging.Several factors are responsible for misdiagnosis in breast cancer, including errors in the practice of the technician and/or radiologist (technical failures, lack of consideration of the clinical context, incorrect application of the BI-RADS score, false reassurances), lack of communication with other specialists or with the patient, and the type of tumor and breast parenchyma.QuestionWhat factors most contribute to and what implications stem from misdiagnosis in breast imaging?Findings Ongoing training and education for radiologists and other healthcare providers, as well as interdisciplinary collaboration and communication is paramount.Clinical relevanceMisdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the entire healthcare system.Conclusion: Breast cancer is one of the leading causes of negligence claims in radiology. The objective of this document is to describe the specific main causes of errors in breast imaging and provide European Society of Breast Imaging (EUSOBI) recommendations to try to minimize these.Technical failures represent 17% of all mammographic diagnostic negligence claims. Mammography quality control protocol and dedicated training for technologists and radiologists are essential. Lack of consideration of the clinical context is a second critical issue, as a clinical abnormality is found in 80% of malpractice claims. EUSOBI emphasizes the importance of communication and clinical examination before the diagnostic investigation. Detection errors or misapplications of the lexicon or Breast Imaging Reporting Data System (BI-RADS) score account for 5% of malpractice claims and should be reduced by limiting radiologists’ distraction or fatigue, and being aware of satisfaction of search errors and the importance of a personal systematic review. Errors related to pathological concordance and MDT review can be limited by the use of markers after biopsy and the use of standardized reports, which can aid communication with other specialities. Finally, errors related to tumor or patient factors should be discussed, considering the use of contrast-enhanced mammography and magnetic resonance imaging.Several factors are responsible for misdiagnosis in breast cancer, including errors in the practice of the technician and/or radiologist (technical failures, lack of consideration of the clinical context, incorrect application of the BI-RADS score, false reassurances), lack of communication with other specialists or with the patient, and the type of tumor and breast parenchyma.QuestionWhat factors most contribute to and what implications stem from misdiagnosis in breast imaging?Findings Ongoing training and education for radiologists and other healthcare providers, as well as interdisciplinary collaboration and communication is paramount.Clinical relevanceMisdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the entire healthcare system.Key Points: Breast cancer is one of the leading causes of negligence claims in radiology. The objective of this document is to describe the specific main causes of errors in breast imaging and provide European Society of Breast Imaging (EUSOBI) recommendations to try to minimize these.Technical failures represent 17% of all mammographic diagnostic negligence claims. Mammography quality control protocol and dedicated training for technologists and radiologists are essential. Lack of consideration of the clinical context is a second critical issue, as a clinical abnormality is found in 80% of malpractice claims. EUSOBI emphasizes the importance of communication and clinical examination before the diagnostic investigation. Detection errors or misapplications of the lexicon or Breast Imaging Reporting Data System (BI-RADS) score account for 5% of malpractice claims and should be reduced by limiting radiologists’ distraction or fatigue, and being aware of satisfaction of search errors and the importance of a personal systematic review. Errors related to pathological concordance and MDT review can be limited by the use of markers after biopsy and the use of standardized reports, which can aid communication with other specialities. Finally, errors related to tumor or patient factors should be discussed, considering the use of contrast-enhanced mammography and magnetic resonance imaging.Several factors are responsible for misdiagnosis in breast cancer, including errors in the practice of the technician and/or radiologist (technical failures, lack of consideration of the clinical context, incorrect application of the BI-RADS score, false reassurances), lack of communication with other specialists or with the patient, and the type of tumor and breast parenchyma.QuestionWhat factors most contribute to and what implications stem from misdiagnosis in breast imaging?Findings Ongoing training and education for radiologists and other healthcare providers, as well as interdisciplinary collaboration and communication is paramount.Clinical relevanceMisdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the entire healthcare system. [ABSTRACT FROM AUTHOR]

Importance: Misdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the healthcare system as a whole.Some of the potential implications of misdiagnosis in breast imaging include delayed diagnosis or false reassurance, which can result in a delay in treatment and potentially a worse prognosis. Misdiagnosis can also lead to unnecessary procedures, which can cause physical discomfort, anxiety, and emotional distress for patients, as well as increased healthcare costs. All these events can erode patient trust in the healthcare system and in individual healthcare providers. This can have negative implications for patient compliance with screening and treatment recommendations, as well as overall health outcomes. Moreover, misdiagnosis can also result in legal consequences for healthcare providers, including medical malpractice lawsuits and disciplinary action by licensing boards.To minimize the risk of misdiagnosis in breast imaging, it is important for healthcare providers to use appropriate imaging techniques and interpret images accurately and consistently. This requires ongoing training and education for radiologists and other healthcare providers, as well as collaboration and communication among healthcare providers to ensure that patients receive appropriate and timely care. If a misdiagnosis does occur, it is important for healthcare providers to communicate with patients and provide appropriate follow-up care to minimize the potential implications of the misdiagnosis. This may include repeat imaging, additional biopsies or other procedures, and referral to specialists for further evaluation and management.QuestionWhat factors most contribute to and what implications stem from misdiagnosis in breast imaging?FindingsOngoing training and education for radiologists and other healthcare providers, as well as interdisciplinary collaboration and communication, is paramount.Clinical relevanceMisdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the entire healthcare system.Observations: Misdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the healthcare system as a whole.Some of the potential implications of misdiagnosis in breast imaging include delayed diagnosis or false reassurance, which can result in a delay in treatment and potentially a worse prognosis. Misdiagnosis can also lead to unnecessary procedures, which can cause physical discomfort, anxiety, and emotional distress for patients, as well as increased healthcare costs. All these events can erode patient trust in the healthcare system and in individual healthcare providers. This can have negative implications for patient compliance with screening and treatment recommendations, as well as overall health outcomes. Moreover, misdiagnosis can also result in legal consequences for healthcare providers, including medical malpractice lawsuits and disciplinary action by licensing boards.To minimize the risk of misdiagnosis in breast imaging, it is important for healthcare providers to use appropriate imaging techniques and interpret images accurately and consistently. This requires ongoing training and education for radiologists and other healthcare providers, as well as collaboration and communication among healthcare providers to ensure that patients receive appropriate and timely care. If a misdiagnosis does occur, it is important for healthcare providers to communicate with patients and provide appropriate follow-up care to minimize the potential implications of the misdiagnosis. This may include repeat imaging, additional biopsies or other procedures, and referral to specialists for further evaluation and management.QuestionWhat factors most contribute to and what implications stem from misdiagnosis in breast imaging?FindingsOngoing training and education for radiologists and other healthcare providers, as well as interdisciplinary collaboration and communication, is paramount.Clinical relevanceMisdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the entire healthcare system.Conclusion and relevance: Misdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the healthcare system as a whole.Some of the potential implications of misdiagnosis in breast imaging include delayed diagnosis or false reassurance, which can result in a delay in treatment and potentially a worse prognosis. Misdiagnosis can also lead to unnecessary procedures, which can cause physical discomfort, anxiety, and emotional distress for patients, as well as increased healthcare costs. All these events can erode patient trust in the healthcare system and in individual healthcare providers. This can have negative implications for patient compliance with screening and treatment recommendations, as well as overall health outcomes. Moreover, misdiagnosis can also result in legal consequences for healthcare providers, including medical malpractice lawsuits and disciplinary action by licensing boards.To minimize the risk of misdiagnosis in breast imaging, it is important for healthcare providers to use appropriate imaging techniques and interpret images accurately and consistently. This requires ongoing training and education for radiologists and other healthcare providers, as well as collaboration and communication among healthcare providers to ensure that patients receive appropriate and timely care. If a misdiagnosis does occur, it is important for healthcare providers to communicate with patients and provide appropriate follow-up care to minimize the potential implications of the misdiagnosis. This may include repeat imaging, additional biopsies or other procedures, and referral to specialists for further evaluation and management.QuestionWhat factors most contribute to and what implications stem from misdiagnosis in breast imaging?FindingsOngoing training and education for radiologists and other healthcare providers, as well as interdisciplinary collaboration and communication, is paramount.Clinical relevanceMisdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the entire healthcare system.Key Points: Misdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the healthcare system as a whole.Some of the potential implications of misdiagnosis in breast imaging include delayed diagnosis or false reassurance, which can result in a delay in treatment and potentially a worse prognosis. Misdiagnosis can also lead to unnecessary procedures, which can cause physical discomfort, anxiety, and emotional distress for patients, as well as increased healthcare costs. All these events can erode patient trust in the healthcare system and in individual healthcare providers. This can have negative implications for patient compliance with screening and treatment recommendations, as well as overall health outcomes. Moreover, misdiagnosis can also result in legal consequences for healthcare providers, including medical malpractice lawsuits and disciplinary action by licensing boards.To minimize the risk of misdiagnosis in breast imaging, it is important for healthcare providers to use appropriate imaging techniques and interpret images accurately and consistently. This requires ongoing training and education for radiologists and other healthcare providers, as well as collaboration and communication among healthcare providers to ensure that patients receive appropriate and timely care. If a misdiagnosis does occur, it is important for healthcare providers to communicate with patients and provide appropriate follow-up care to minimize the potential implications of the misdiagnosis. This may include repeat imaging, additional biopsies or other procedures, and referral to specialists for further evaluation and management.QuestionWhat factors most contribute to and what implications stem from misdiagnosis in breast imaging?FindingsOngoing training and education for radiologists and other healthcare providers, as well as interdisciplinary collaboration and communication, is paramount.Clinical relevanceMisdiagnosis in breast imaging can have significant implications for patients, healthcare providers, and the entire healthcare system. [ABSTRACT FROM AUTHOR]

In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting. Key Points • Machine learning is new and rather complex for the radiology community. • Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting. • Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community. [ABSTRACT FROM AUTHOR]

This review explains in simple terms, accessible to the non-statistician, general principles regarding the correct research methods to develop and then evaluate imaging biomarkers in a clinical setting, including radiomic biomarkers. The distinction between diagnostic and prognostic biomarkers is made and emphasis placed on the need to assess clinical utility within the context of a multivariable model. Such models should not be restricted to imaging biomarkers and must include relevant disease and patient characteristics likely to be clinically useful. Biomarker utility is based on whether its addition to the basic clinical model improves diagnosis or prediction. Approaches to both model development and evaluation are explained and the need for adequate amounts of representative data stressed so as to avoid underpowering and overfitting. Advice is provided regarding how to report the research correctly. Key Points: • Imaging biomarker research is common but methodological errors are encountered frequently that may mean the research is not clinically useful. • The clinical utility of imaging biomarkers is best assessed by their additive effect on multivariable models based on clinical factors known to be important. • The data used to develop such models should be sufficient for the number of variables investigated and the model should be evaluated, preferably using data unrelated to development. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the use of reporting checklists and quality scoring tools for self-reporting purposes in radiomics literature. Methods: Literature search was conducted in PubMed (date, April 23, 2023). The radiomics literature was sampled at random after a sample size calculation with a priori power analysis. A systematic assessment for self-reporting, including the use of documentation such as completed checklists or quality scoring tools, was conducted in original research papers. These eligible papers underwent independent evaluation by a panel of nine readers, with three readers assigned to each paper. Automatic annotation was used to assist in this process. Then, a detailed item-by-item confirmation analysis was carried out on papers with checklist documentation, with independent evaluation of two readers. Results: The sample size calculation yielded 117 papers. Most of the included papers were retrospective (94%; 110/117), single-center (68%; 80/117), based on their private data (89%; 104/117), and lacked external validation (79%; 93/117). Only seven papers (6%) had at least one self-reported document (Radiomics Quality Score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), or Checklist for Artificial Intelligence in Medical Imaging (CLAIM)), with a statistically significant binomial test (p < 0.001). Median rate of confirmed items for all three documents was 81% (interquartile range, 6). For quality scoring tools, documented scores were higher than suggested scores, with a mean difference of − 7.2 (standard deviation, 6.8). Conclusion: Radiomic publications often lack self-reported checklists or quality scoring tools. Even when such documents are provided, it is essential to be cautious, as the accuracy of the reported items or scores may be questionable. Clinical relevance statement: Current state of radiomic literature reveals a notable absence of self-reporting with documentation and inaccurate reporting practices. This critical observation may serve as a catalyst for motivating the radiomics community to adopt and utilize such tools appropriately, thereby fostering rigor, transparency, and reproducibility of their research, moving the field forward. Key Points: • In radiomics literature, there has been a notable absence of self-reporting with documentation. • Even if such documents are provided, it is critical to exercise caution because the accuracy of the reported items or scores may be questionable. • Radiomics community needs to be motivated to adopt and appropriately utilize the reporting checklists and quality scoring tools. [ABSTRACT FROM AUTHOR]

Background: Although image-guided interventional procedures of the elbow and wrist are routinely performed, there is poor evidence in the literature concerning such treatments. Our aim was to perform a Delphi-based consensus on published evidence on image-guided interventional procedures around the elbow and wrist and provide clinical indications on this topic.Methods: A board of 45 experts in image-guided interventional musculoskeletal procedures from the European Society of Musculoskeletal Radiology were involved in this Delphi-based consensus study. All panelists reviewed and scored published papers on image-guided interventional procedures around the elbow and wrist updated to September 2018 according to the Oxford Centre for Evidence-based Medicine levels of evidence. Consensus on statements drafted by the panelists about clinical indications was considered as "strong" when more than 95% of panelists agreed and as "broad" if more than 80% agreed.Results: Eighteen statements were drafted, 12 about tendon procedures and 6 about intra-articular procedures. Only statement #15 reached the highest level of evidence (ultrasound-guided steroid wrist injections result in greater pain reduction and greater likelihood of attaining clinically important improvement). Seventeen statements received strong consensus (94%), while one received broad consensus (6%).Conclusions: There is still poor evidence in published papers on image-guided interventional procedures around the elbow and wrist. A strong consensus has been achieved in 17/18 (94%) statements provided by the panel on clinical indications. Large prospective randomized trials are needed to better define the role of these procedures in clinical practice.Key Points: • The panel provided 18 evidence-based statements on clinical indications of image-guided interventional procedures around the elbow and wrist. • Only statement #15 reached the highest level of evidence: ultrasound-guided steroid wrist injections result in greater pain reduction and greater likelihood of attaining clinically important improvement. • Seventeen statements received strong consensus (94%), while broad consensus was obtained by 1 statement (6%). [ABSTRACT FROM AUTHOR]

Objective: To evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for self-reporting through a systematic analysis of its citations. Methods: Google Scholar, Web of Science, and Scopus were used to search for citations (date, 29 April 2023). CLAIM's use for self-reporting with proof (i.e., filled-out checklist) and other potential use cases were systematically assessed in research papers. Eligible papers were evaluated independently by two readers, with the help of automatic annotation. Item-by-item confirmation analysis on papers with checklist proof was subsequently performed. Results: A total of 391 unique citations were identified from three databases. Of the 118 papers included in this study, 12 (10%) provided a proof of self-reported CLAIM checklist. More than half (70; 59%) only mentioned some sort of adherence to CLAIM without providing any proof in the form of a checklist. Approximately one-third (36; 31%) cited the CLAIM for reasons unrelated to their reporting or methodological adherence. Overall, the claims on 57 to 93% of the items per publication were confirmed in the item-by-item analysis, with a mean and standard deviation of 81% and 10%, respectively. Conclusion: Only a small proportion of the publications used CLAIM as checklist and supplied filled-out documentation; however, the self-reported checklists may contain errors and should be approached cautiously. We hope that this systematic citation analysis would motivate artificial intelligence community about the importance of proper self-reporting, and encourage researchers, journals, editors, and reviewers to take action to ensure the proper usage of checklists. Clinical relevance statement: Only a small percentage of the publications used CLAIM for self-reporting with proof (i.e., filled-out checklist). However, the filled-out checklist proofs may contain errors, e.g., false claims of adherence, and should be approached cautiously. These may indicate inappropriate usage of checklists and necessitate further action by authorities. Key Points: • Of 118 eligible papers, only 12 (10%) followed the CLAIM checklist for self-reporting with proof (i.e., filled-out checklist). More than half (70; 59%) only mentioned some kind of adherence without providing any proof. • Overall, claims on 57 to 93% of the items were valid in item-by-item confirmation analysis, with a mean and standard deviation of 81% and 10%, respectively. • Even with the checklist proof, the items declared may contain errors and should be approached cautiously. [ABSTRACT FROM AUTHOR]

Background: Image-guided interventional procedures of the nerves are commonly performed by physicians from different medical specialties, although there is a lack of clinical indications for these types of procedures. This Delphi-based consensus provided a list of indications on image-guided interventional procedures for nerves of the upper limb based on updated published evidence.Methods: An expert panel of 45 members of the Ultrasound and Interventional Subcommittees of the ESSR participated in this Delphi-based consensus study. After revision of the published papers on image-guided interventional procedures for nerves of the upper limb updated to September 2018, the experts drafted a list of statements according to the Oxford Centre for evidence-based medicine levels of evidence. Consensus on statements regarding clinical indications was considered as strong when more than 95% of experts agreed, and broad if more than 80% agreed.Results: Ten statements were drafted on procedures for nerves of the upper limb. Only two statements reached the highest level of evidence (ultrasound guidance is a safe and effective method for brachial plexus block; ultrasound-guided non-surgical approaches are safe and effective methods to treat carpal tunnel syndrome in the short term, but there is sparse evidence on the mid- and long-term effectiveness of these interventions). Strong consensus was obtained on 6/10 statements (60%), while 4/10 statements reached broad consensus (40%).Conclusions: This Delphi-based consensus study reported poor evidence on image-guided interventional procedures for nerves of the upper limb. Sixty percent of statements on clinical indications provided by the expert board reached a strong consensus.Key Points: • An expert panel of the ESSR provided 10 evidence-based statements on clinical indications for image-guided interventional procedures for nerves of the upper limb • Two statements reached the highest level of evidence • Strong consensus was obtained on 6/10 statements (60%), while 4/10 statements reached broad consensus (40%). [ABSTRACT FROM AUTHOR]

Background: Image-guided interventional procedures around the shoulder are commonly performed in clinical practice, although evidence regarding their effectiveness is scarce. We report the results of a Delphi method review of evidence on literature published on image-guided interventional procedures around the shoulder with a list of clinical indications.Methods: Forty-five experts in image-guided musculoskeletal procedures from the ESSR participated in a consensus study using the Delphic method. Peer-reviewed papers regarding interventional procedures around the shoulder up to September 2018 were scored according to the Oxford Centre for Evidence-based Medicine levels of evidence. Statements on clinical indications were constructed. Consensus was considered as strong if more than 95% of experts agreed and as broad if more than 80% agreed.Results: A total of 20 statements were drafted, and 5 reached the highest level of evidence. There were 10 statements about tendon procedures, 6 about intra-articular procedures, and 4 about intrabursal injections. Strong consensus was obtained in 16 of them (80%), while 4 received broad consensus (20%).Conclusions: Literature evidence on image-guided interventional procedures around the shoulder is limited. A strong consensus has been reached for 80% of statements. The ESSR recommends further research to potentially influence treatment options, patient outcomes, and social impact.Key Points: • Expert consensus produced a list of 20 evidence-based statements on clinical indications of image-guided interventional procedures around the shoulder. • The highest level of evidence was reached for five statements. • Strong consensus was obtained for 16 statements (80%), while 4 received broad consensus (20%). [ABSTRACT FROM AUTHOR]

In Europe, lung cancer ranks third among the most common cancers, remaining the biggest killer. Since the publication of the first European Society of Radiology and European Respiratory Society joint white paper on lung cancer screening (LCS) in 2015, many new findings have been published and discussions have increased considerably. Thus, this updated expert opinion represents a narrative, non-systematic review of the evidence from LCS trials and description of the current practice of LCS as well as aspects that have not received adequate attention until now. Reaching out to the potential participants (persons at high risk), optimal communication and shared decision-making will be key starting points. Furthermore, standards for infrastructure, pathways and quality assurance are pivotal, including promoting tobacco cessation, benefits and harms, overdiagnosis, quality, minimum radiation exposure, definition of management of positive screen results and incidental findings linked to respective actions as well as cost-effectiveness. This requires a multidisciplinary team with experts from pulmonology and radiology as well as thoracic oncologists, thoracic surgeons, pathologists, family doctors, patient representatives and others. The ESR and ERS agree that Europe's health systems need to adapt to allow citizens to benefit from organised pathways, rather than unsupervised initiatives, to allow early diagnosis of lung cancer and reduce the mortality rate. Now is the time to set up and conduct demonstration programmes focusing, among other points, on methodology, standardisation, tobacco cessation, education on healthy lifestyle, cost-effectiveness and a central registry.Key Points• Pulmonologists and radiologists both have key roles in the set up of multidisciplinary LCS teams with experts from many other fields.• Pulmonologists identify people eligible for LCS, reach out to family doctors, share the decision-making process and promote tobacco cessation.• Radiologists ensure appropriate image quality, minimum dose and a standardised reading/reporting algorithm, together with a clear definition of a "positive screen".• Strict algorithms define the exact management of screen-detected nodules and incidental findings.• For LCS to be (cost-)effective, it has to target a population defined by risk prediction models. [ABSTRACT FROM AUTHOR]

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the intra- and inter-rater reliability of the total radiomics quality score (RQS) and the reproducibility of individual RQS items' score in a large multireader study. Methods: Nine raters with different backgrounds were randomly assigned to three groups based on their proficiency with RQS utilization: Groups 1 and 2 represented the inter-rater reliability groups with or without prior training in RQS, respectively; group 3 represented the intra-rater reliability group. Thirty-three original research papers on radiomics were evaluated by raters of groups 1 and 2. Of the 33 papers, 17 were evaluated twice with an interval of 1 month by raters of group 3. Intraclass coefficient (ICC) for continuous variables, and Fleiss' and Cohen's kappa (k) statistics for categorical variables were used. Results: The inter-rater reliability was poor to moderate for total RQS (ICC 0.30–055, p < 0.001) and very low to good for item's reproducibility (k − 0.12 to 0.75) within groups 1 and 2 for both inexperienced and experienced raters. The intra-rater reliability for total RQS was moderate for the less experienced rater (ICC 0.522, p = 0.009), whereas experienced raters showed excellent intra-rater reliability (ICC 0.91–0.99, p < 0.001) between the first and second read. Intra-rater reliability on RQS items' score reproducibility was higher and most of the items had moderate to good intra-rater reliability (k − 0.40 to 1). Conclusions: Reproducibility of the total RQS and the score of individual RQS items is low. There is a need for a robust and reproducible assessment method to assess the quality of radiomics research. Clinical relevance statement: There is a need for reproducible scoring systems to improve quality of radiomics research and consecutively close the translational gap between research and clinical implementation. Key Points: • Radiomics quality score has been widely used for the evaluation of radiomics studies. • Although the intra-rater reliability was moderate to excellent, intra- and inter-rater reliability of total score and point-by-point scores were low with radiomics quality score. • A robust, easy-to-use scoring system is needed for the evaluation of radiomics research. [ABSTRACT FROM AUTHOR]

Objective: There has been a large amount of research in the field of artificial intelligence (AI) as applied to clinical radiology. However, these studies vary in design and quality and systematic reviews of the entire field are lacking.This systematic review aimed to identify all papers that used deep learning in radiology to survey the literature and to evaluate their methods. We aimed to identify the key questions being addressed in the literature and to identify the most effective methods employed. Methods: We followed the PRISMA guidelines and performed a systematic review of studies of AI in radiology published from 2015 to 2019. Our published protocol was prospectively registered. Results: Our search yielded 11,083 results. Seven hundred sixty-seven full texts were reviewed, and 535 articles were included. Ninety-eight percent were retrospective cohort studies. The median number of patients included was 460. Most studies involved MRI (37%). Neuroradiology was the most common subspecialty. Eighty-eight percent used supervised learning. The majority of studies undertook a segmentation task (39%). Performance comparison was with a state-of-the-art model in 37%. The most used established architecture was UNet (14%). The median performance for the most utilised evaluation metrics was Dice of 0.89 (range.49–.99), AUC of 0.903 (range 1.00–0.61) and Accuracy of 89.4 (range 70.2–100). Of the 77 studies that externally validated their results and allowed for direct comparison, performance on average decreased by 6% at external validation (range increase of 4% to decrease 44%). Conclusion: This systematic review has surveyed the major advances in AI as applied to clinical radiology. Key Points: • While there are many papers reporting expert-level results by using deep learning in radiology, most apply only a narrow range of techniques to a narrow selection of use cases. • The literature is dominated by retrospective cohort studies with limited external validation with high potential for bias. • The recent advent of AI extensions to systematic reporting guidelines and prospective trial registration along with a focus on external validation and explanations show potential for translation of the hype surrounding AI from code to clinic. [ABSTRACT FROM AUTHOR]

Objectives: Previous attempts at meta-analysis and systematic review have not provided clear recommendations for the clinical application of thermal ablation in metastatic colorectal cancer. Many authors believe that the probability of gathering randomised controlled trial (RCT) data is low. Our aim is to provide a consensus document making recommendations on the appropriate application of thermal ablation in patients with colorectal liver metastases.Methods: This consensus paper was discussed by an expert panel at The Interventional Oncology Sans Frontières 2013. A literature review was presented. Tumour characteristics, ablation technique and different clinical applications were considered and the level of consensus was documented.Results: Specific recommendations are made with regard to metastasis size, number, and location and ablation technique. Mean 31 % 5-year survival post-ablation in selected patients has resulted in acceptance of this therapy for those with technically inoperable but limited liver disease and those with limited liver reserve or co-morbidities that render them inoperable.Conclusions: In the absence of RCT data, it is our aim that this consensus document will facilitate judicious selection of the patients most likely to benefit from thermal ablation and provide a unified interventional oncological perspective for the use of this technology.Key Points: • Best results require due consideration of tumour size, number, volume and location. • Ablation technology, imaging guidance and intra-procedural imaging assessment must be optimised. • Accepted applications include inoperable disease due to tumour distribution or inadequate liver reserve. • Other current indications include concurrent co-morbidity, patient choice and the test-of-time approach. • Future applications may include resectable disease, e.g. for small solitary tumours. [ABSTRACT FROM AUTHOR]

EUSOBI and 30 national breast radiology bodies support mammography for population-based screening, demonstrated to reduce breast cancer (BC) mortality and treatment impact. According to the International Agency for Research on Cancer, the reduction in mortality is 40 % for women aged 50-69 years taking up the invitation while the probability of false-positive needle biopsy is <1 % per round and overdiagnosis is only 1-10 % for a 20-year screening. Mortality reduction was also observed for the age groups 40-49 years and 70-74 years, although with "limited evidence". Thus, we firstly recommend biennial screening mammography for average-risk women aged 50-69 years; extension up to 73 or 75 years, biennially, is a second priority, from 40-45 to 49 years, annually, a third priority. Screening with thermography or other optical tools as alternatives to mammography is discouraged. Preference should be given to population screening programmes on a territorial basis, with double reading. Adoption of digital mammography (not film-screen or phosphor-plate computer radiography) is a priority, which also improves sensitivity in dense breasts. Radiologists qualified as screening readers should be involved in programmes. Digital breast tomosynthesis is also set to become "routine mammography" in the screening setting in the next future. Dedicated pathways for high-risk women offering breast MRI according to national or international guidelines and recommendations are encouraged.Key Points: • EUSOBI and 30 national breast radiology bodies support screening mammography. • A first priority is double-reading biennial mammography for women aged 50-69 years. • Extension to 73-75 and from 40-45 to 49 years is also encouraged. • Digital mammography (not film-screen or computer radiography) should be used. • DBT is set to become "routine mammography" in the screening setting in the next future. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database. Methods: A total of 1254 articles published between January 1, 2021, and December 31, 2022, in leading radiology journals (European Radiology, European Journal of Radiology, Radiology, Radiology: Artificial Intelligence, Radiology: Cardiothoracic Imaging, Radiology: Imaging Cancer) were retrospectively screened, and 257 original research articles were included in this study. The categorical variables were compared using Fisher's exact tests or chi-square test and numerical variables using Student's t test with relation to the year of publication. Results: Half of the articles (128 of 257) shared the model by either including the final model formula or reporting the coefficients of selected radiomics features. A total of 73 (28%) models were validated on an external independent dataset. Only 16 (6%) articles shared the data or used publicly available open datasets. Similarly, only 20 (7%) of the articles shared the code. A total of 7 (3%) articles both shared code and data. All collected data in this study is presented in a radiomics research database (RadBase) and could be accessed at https://github.com/EuSoMII/RadBase. Conclusion: According to the results of this study, the majority of published radiomics models were not technically reproducible since they shared neither model nor code and data. There is still room for improvement in carrying out reproducible and open research in the field of radiomics. Clinical relevance statement: To date, the reproducibility of radiomics research and open science practices within the radiomics research community are still very low. Ensuring reproducible radiomics research with model-, code-, and data-sharing practices will facilitate faster clinical translation. Key Points: • There is a discrepancy between the number of published radiomics papers and the clinical implementation of these published radiomics models. • The main obstacle to clinical implementation is the lack of model-, code-, and data-sharing practices. • In order to translate radiomics research into clinical practice, the radiomics research community should adopt open science practices. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the membranous urethral length (MUL) measurement and its interobserver agreement, and propose literature-based recommendations to standardize MUL measurement for increasing interobserver agreement. MUL measurements based on prostate MRI scans, for urinary incontinence risk assessment before radical prostatectomy (RP), may influence treatment decision-making in men with localised prostate cancer. Before implementation in clinical practise, MRI-based MUL measurements need standardization to improve observer agreement. Methods: Online libraries were searched up to August 5, 2022, on MUL measurements. Two reviewers performed article selection and critical appraisal. Papers reporting on preoperative MUL measurements and urinary continence correlation were selected. Extracted information included measuring procedures, MRI sequences, population mean/median values, and observer agreement. Results: Fifty papers were included. Studies that specified the MRI sequence used T2-weighted images and used either coronal images (n = 13), sagittal images (n = 18), or both (n = 12) for MUL measurements. 'Prostatic apex' was the most common description of the proximal membranous urethra landmark and 'level/entry of the urethra into the penile bulb' was the most common description of the distal landmark. Population mean (median) MUL value range was 10.4–17.1 mm (7.3–17.3 mm), suggesting either population or measurement differences. Detailed measurement technique descriptions for reproducibility were lacking. Recommendations on MRI-based MUL measurement were formulated by using anatomical landmarks and detailed descriptions and illustrations. Conclusions: In order to improve on measurement variability, a literature-based measuring method of the MUL was proposed, supported by several illustrative case studies, in an attempt to standardize MRI-based MUL measurements for appropriate urinary incontinence risk preoperatively. Clinical relevance statement: Implementation of MUL measurements into clinical practise for personalized post-prostatectomy continence prediction is hampered by lack of standardization and suboptimal interobserver agreement. Our proposed standardized MUL measurement aims to facilitate standardization and to improve the interobserver agreement. Key Points: • Variable approaches for membranous urethral length measurement are being used, without detailed description and with substantial differences in length of the membranous urethra, hampering standardization. • Limited interobserver agreement for membranous urethral length measurement was observed in several studies, while preoperative incontinence risk assessment necessitates high interobserver agreement. • Literature-based recommendations are proposed to standardize MRI-based membranous urethral length measurement for increasing interobserver agreement and improving preoperative incontinence risk assessment, using anatomical landmarks on sagittal T2-weighted images. [ABSTRACT FROM AUTHOR]

Objectives: Map the current landscape of commercially available artificial intelligence (AI) software for radiology and review the availability of their scientific evidence.Methods: We created an online overview of CE-marked AI software products for clinical radiology based on vendor-supplied product specifications ( www.aiforradiology.com ). Characteristics such as modality, subspeciality, main task, regulatory information, deployment, and pricing model were retrieved. We conducted an extensive literature search on the available scientific evidence of these products. Articles were classified according to a hierarchical model of efficacy.Results: The overview included 100 CE-marked AI products from 54 different vendors. For 64/100 products, there was no peer-reviewed evidence of its efficacy. We observed a large heterogeneity in deployment methods, pricing models, and regulatory classes. The evidence of the remaining 36/100 products comprised 237 papers that predominantly (65%) focused on diagnostic accuracy (efficacy level 2). From the 100 products, 18 had evidence that regarded level 3 or higher, validating the (potential) impact on diagnostic thinking, patient outcome, or costs. Half of the available evidence (116/237) were independent and not (co-)funded or (co-)authored by the vendor.Conclusions: Even though the commercial supply of AI software in radiology already holds 100 CE-marked products, we conclude that the sector is still in its infancy. For 64/100 products, peer-reviewed evidence on its efficacy is lacking. Only 18/100 AI products have demonstrated (potential) clinical impact.Key Points: • Artificial intelligence in radiology is still in its infancy even though already 100 CE-marked AI products are commercially available. • Only 36 out of 100 products have peer-reviewed evidence of which most studies demonstrate lower levels of efficacy. • There is a wide variety in deployment strategies, pricing models, and CE marking class of AI products for radiology. [ABSTRACT FROM AUTHOR]

Many studies and systematic reviews have been published about MRI of the knee and its structures, discussing detailed anatomy, imaging findings, and correlations between imaging and clinical findings. This paper includes evidence-based recommendations for a general radiologist regarding choice of imaging sequences and reporting basic MRI examinations of the knee. We recommend using clinicians' terminology when it is applicable to the imaging findings, for example, when reporting meniscal, ligament and tendon, or cartilage pathology. The intent is to standardise reporting language and to make reports less equivocal. The aim of the paper is to improve the usefulness of the MRI report by understanding the strengths and limitations of the MRI exam with regard to clinical correlation. We hope the implementation of these recommendations into radiological practice will increase diagnostic accuracy and consistency by avoiding pitfalls and reducing overcalling of pathology on MRI of the knee. Clinical relevance statement: The recommendations presented here are meant to aid general radiologists in planning and assessing studies to evaluate acute and chronic knee findings by advocating the use of unequivocal terminology and discussing the strengths and limitations of MRI examination of the knee. Key Points: • On MRI, the knee should be examined and assessed in three orthogonal imaging planes. • The basic general protocol must yield T2-weighted fluid-sensitive and T1-weighted images. • The radiological assessment should include evaluation of ligamentous structures, cartilage, bony structures and bone marrow, soft tissues, bursae, alignment, and incidental findings. [ABSTRACT FROM AUTHOR]

Creating a patient-centered experience is becoming increasingly important for radiology departments around the world. The goal of patient-centered radiology is to ensure that radiology services are sensitive to patients' needs and desires. This article provides a framework for addressing the patient's experience by dividing their imaging journey into three distinct time periods: pre-exam, day of exam, and post-exam. Each time period has aspects that can contribute to patient anxiety. Although there are components of the patient journey that are common in all regions of the world, there are also unique features that vary by location. This paper highlights innovative solutions from different parts of the world that have been introduced in each of these time periods to create a more patient-centered experience. Clinical relevance statement: Adopting innovative solutions that help patients understand their imaging journey and decrease their anxiety about undergoing an imaging examination are important steps in creating a patient centered imaging experience. Key points: • Patients often experience anxiety during their imaging journey and decreasing this anxiety is an important component of patient centered imaging. • The patient imaging journey can be divided into three distinct time periods: pre-exam, day of exam, and post-exam. • Although components of the imaging journey are common, there are local differences in different regions of the world that need to be considered when constructing a patient centered experience. [ABSTRACT FROM AUTHOR]

Objectives: To assess the weight of imaging and imaging specialists (i.e., affiliated to a radiology/nuclear medicine department) in publications in non-imaging journals. Methods: All articles indexed in English on the Embase database between 1989 and 2019 were extracted. The number and affiliation of authors were determined. A naive Bayesian classifier algorithm was trained to classify abstracts as "imaging" or "non-imaging." The main outcome was the number and position of imaging specialists in the authorship of imaging articles published in non-imaging journals. Analyses per medical specialties and per journal impact factor (IF) were performed. Results: A total of 15,787,825 articles were included with 968,259 (6%) "imaging" articles. The proportion of imaging articles increased over time (+ 370%), quicker than the overall academic output. The proportion of imaging specialist among authors grew from 0.58% in 1989–1994 to 1.54% in 2015–2019. About 20% of imaging articles had ≥ 1 imaging specialist among authors. The proportion of imaging articles decreased with the IF (7.3% for IF 0–2.5 vs. 5.1% for IF > 10, p < 0.001), but the proportion of imaging specialist authors in imaging papers with ≥ 1 imaging specialist author increased with the IF (40% for IF 0–2.5, 53% for IF > 10, p < 0.001). There was significant variability across medical specialties. Conclusions: The weight of imaging articles and imaging specialist among authors in non-imaging journals has increased over time but remains limited. Most of the authors of imaging publications are not imaging specialists. Imaging specialists among authors in imaging papers are associated with a greater IF. Key Points: • The proportion of imaging specialist authors in non-imaging journals, though small, has increased significantly. • Marked differences are observed according to medical specialties and the reputation/impact factor of the journal. • Collaboration between imaging specialists and non-specialists is associated with publication in higher impact journals. [ABSTRACT FROM AUTHOR]

Objectives: Position- and posture-dependent deformation of the vascular system is a relatively unexplored field. The goal of this scoping review was to create an overview of existing vascular imaging modalities in different body positions and postures and address the subsequent changes in vascular anatomy. Methods: Scopus, Medline, and Cochrane were searched for literature published between January 1, 2000, and June 30, 2022, incorporating the following categories: image modality, anatomy, orientation, and outcomes. Results: Out of 2446 screened articles, we included 108. The majority of papers used ultrasound (US, n = 74) in different body positions and postures with diameter and cross-sectional area (CSA) as outcome measures. Magnetic resonance imaging (n = 22) and computed tomography (n = 8) were less frequently used but allowed for investigation of other geometrical measures such as vessel curvature and length. The venous system proved more sensitive to postural changes than the arterial system, which was seen as increasing diameters of veins below the level of the heart when going from supine to prone to standing positions, and vice versa. Conclusions: The influence of body positions and postures on vasculature was predominantly explored with US for vessel diameter and CSA. Posture-induced deformation and additional geometrical features that may be of interest for the (endovascular) treatment of vascular pathologies have been limitedly reported, such as length and curvature of an atherosclerotic popliteal artery during bending of the knee after stent placement. The most important clinical implications of positional changes are found in diagnosis, surgical planning, and follow-up after stent placement. Clinical relevance statement: This scoping review presents the current state and opportunities of position- and posture-dependent imaging of vascular structures using various imaging modalities that are relevant in the fields of clinical diagnosis, surgical planning, and follow-up after stent placement. Key Points: • The influence of body positions and postures on the vasculature was predominantly investigated with US for vessel diameter and cross-sectional area. • Research into geometrical deformation, such as vessel length and curvature adaptation, that may be of interest for the (endovascular) treatment of vascular pathologies is limited in different positions and postures. • The most important clinical implications of postural changes are found in diagnosis, surgical planning, and follow-up after stent placement. [ABSTRACT FROM AUTHOR]

Objective: Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging. Method: A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients. Results: A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5). Conclusion: This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare. Clinical relevance statement: The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice. Key Points: • Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI. [ABSTRACT FROM AUTHOR]

Objectives: To develop a deep learning methodology that distinguishes early from late stages of avascular necrosis of the hip (AVN) to determine treatment decisions. Methods: Three convolutional neural networks (CNNs) VGG-16, Inception ResnetV2, InceptionV3 were trained with transfer learning (ImageNet) and finetuned with a retrospectively collected cohort of (n = 104) MRI examinations of AVN patients, to differentiate between early (ARCO 1–2) and late (ARCO 3–4) stages. A consensus CNN ensemble decision was recorded as the agreement of at least two CNNs. CNN and ensemble performance was benchmarked on an independent cohort of 49 patients from another country and was compared to the performance of two MSK radiologists. CNN performance was expressed with areas under the curve (AUC), the respective 95% confidence intervals (CIs) and precision, and recall and f1-scores. AUCs were compared with DeLong's test. Results: On internal testing, Inception-ResnetV2 achieved the highest individual performance with an AUC of 99.7% (95%CI 99–100%), followed by InceptionV3 and VGG-16 with AUCs of 99.3% (95%CI 98.4–100%) and 97.3% (95%CI 95.5–99.2%) respectively. The CNN ensemble the same AUCs Inception ResnetV2. On external validation, model performance dropped with VGG-16 achieving the highest individual AUC of 78.9% (95%CI 51.6–79.6%) The best external performance was achieved by the model ensemble with an AUC of 85.5% (95%CI 72.2–93.9%). No significant difference was found between the CNN ensemble and expert MSK radiologists (p = 0.22 and 0.092 respectively). Conclusion: An externally validated CNN ensemble accurately distinguishes between the early and late stages of AVN and has comparable performance to expert MSK radiologists. Clinical relevance statement: This paper introduces the use of deep learning for the differentiation between early and late avascular necrosis of the hip, assisting in a complex clinical decision that can determine the choice between conservative and surgical treatment. Key Points: • A convolutional neural network ensemble achieved excellent performance in distinguishing between early and late avascular necrosis. • The performance of the deep learning method was similar to the performance of expert readers. [ABSTRACT FROM AUTHOR]

This document is a reply to a letter to the editor regarding a randomized controlled trial on ultrasound-guided genitofemoral nerve block for femoral arterial access gain and closure. The authors express gratitude for the feedback and address concerns and suggestions. They discuss the amount of anesthetic solution required for nerve blocks and the difficulty in identifying the femoral branch during ultrasound examination. The authors also mention the incidence of peripheral nerve injury and local anesthetic systemic toxicity. They highlight the use of randomized controlled trials in assessing the effectiveness of nerve blocks and discuss the methodology and limitations of their study. The authors conclude by expressing appreciation for the comments and their contribution to the refinement of the paper. [Extracted from the article]

Objective: To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting. Methods: Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders' assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile. Results: From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, −0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0–30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others. Conclusions: The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making. Key Points: • The 63 included studies using noninvasive imaging for ovarian applications were mostly focused on oncologic topic (94%). • The included investigations achieved a median total RQS 6 (IQR, −0.5 to 11), indicating poor methodological rigor. • The RQS was low especially due to the lack of prospective design and formal validation of the results. [ABSTRACT FROM AUTHOR]

The European Society of Cardiovascular Radiology (ESCR) is the European specialist society of cardiac and vascular imaging. This society's highest priority is the continuous improvement, development, and standardization of education, training, and best medical practice, based on experience and evidence. The present intra-society consensus is based on the existing scientific evidence and on the individual experience of the members of the ESCR writing group on carotid diseases, the members of the ESCR guidelines committee, and the members of the executive committee of the ESCR. The recommendations published herein reflect the evidence-based society opinion of ESCR. We have produced a twin-papers consensus, indicated through the documents as respectively "Part I" and "Part II." The first document (Part I) begins with a discussion of features, role, indications, and evidence for CT and MR imaging-based diagnosis of carotid artery disease for risk stratification and prediction of stroke (Section I). It then provides an extensive overview and insight into imaging-derived biomarkers and their potential use in risk stratification (Section II). Finally, detailed recommendations about optimized imaging technique and imaging strategies are summarized (Section III). The second part of this consensus paper (Part II) is focused on structured reporting of carotid imaging studies with CT/MR. Key Points: • CT and MR imaging-based evaluation of carotid artery disease provides essential information for risk stratification and prediction of stroke. • Imaging-derived biomarkers and their potential use in risk stratification are evolving; their correct interpretation and use in clinical practice must be well-understood. • A correct imaging strategy and scan protocol will produce the best possible results for disease evaluation. [ABSTRACT FROM AUTHOR]

Objectives: The revised European Society of Musculoskeletal Radiology (ESSR) consensus guidelines on soft tissue tumor imaging represent an update of 2015 after technical advancements, further insights into specific entities, and revised World Health Organization (2020) and AJCC (2017) classifications. This second of three papers covers algorithms once histology is confirmed: (1) standardized whole-body staging, (2) special algorithms for non-malignant entities, and (3) multiplicity, genetic tumor syndromes, and pitfalls. Materials and methods: A validated Delphi method based on peer-reviewed literature was used to derive consensus among a panel of 46 specialized musculoskeletal radiologists from 12 European countries. Statements that had undergone interdisciplinary revision were scored online by the level of agreement (0 to 10) during two iterative rounds, that could result in 'group consensus', 'group agreement', or 'lack of agreement'. Results: The three sections contain 24 statements with comments. Group consensus was reached in 95.8% and group agreement in 4.2%. For whole-body staging, pulmonary MDCT should be performed in all high-grade sarcomas. Whole-body MRI is preferred for staging bone metastasis, with [18F]FDG-PET/CT as an alternative modality in PET-avid tumors. Patients with alveolar soft part sarcoma, clear cell sarcoma, and angiosarcoma should be screened for brain metastases. Special algorithms are recommended for entities such as rhabdomyosarcoma, extraskeletal Ewing sarcoma, myxoid liposarcoma, and neurofibromatosis type 1 associated malignant peripheral nerve sheath tumors. Satisfaction of search should be avoided in potential multiplicity. Conclusion: Standardized whole-body staging includes pulmonary MDCT in all high-grade sarcomas; entity-dependent modifications and specific algorithms are recommended for sarcomas and non-malignant soft tissue tumors. Clinical relevance statement: These updated ESSR soft tissue tumor imaging guidelines aim to provide support in decision-making, helping to avoid common pitfalls, by providing general and entity-specific algorithms, techniques, and reporting recommendations for whole-body staging in sarcoma and non-malignant soft tissue tumors. Key Points: An early, accurate, diagnosis is crucial for the prognosis of patients with soft tissue tumors. These updated guidelines provide best practice expert consensus for standardized imaging algorithms, techniques, and reporting. Standardization can improve the comparability examinations and provide databases for large data analysis. [ABSTRACT FROM AUTHOR]

In 2012, the European Society of Urogenital Radiology (ESUR) released a foundational paper on how to perform, interpret, and report prostate MRI, with the introduction of Prostate Imagining Reporting and Data System (PI-RADS) [[1]]. Recently, the American College of Radiology (ACR) launched an accreditation programme of imaging centres for not only diagnostic imaging, breast imaging, lung cancer screening, but also for PCa screening [[11]]. [Extracted from the article]

Imaging plays a crucial role in the evaluation of scrotal trauma. Among the imaging modalities, greyscale ultrasound and Colour Doppler ultrasound (CDUS) are the primary techniques with the selective utilisation of advanced techniques such as contrast-enhanced ultrasound (CEUS) and elastography. Despite ultrasound being the mainstay of imaging scrotal trauma, its diagnostic performance is not fully established. Considering these difficulties and their impact on clinical practice, the Scrotal and Penile Imaging Working Group of the European Society of Urogenital Radiology (ESUR-SPIWG) established an expert task force to review the current literature and consolidate their expertise on examination standards and imaging appearances of various entities in scrotal trauma. This paper provides the position statements agreed on by the task force with the aim of providing guidance for the use of imaging especially multiparametric US in scrotal trauma.Key Points• Greyscale and Colour Doppler ultrasound are the mainstay of imaging in patients with scrotal trauma.• Contrast-enhanced ultrasound and elastography are the advanced techniques useful as a problem-solving modality in equivocal cases.• This paper summarises the position statements of the ESUR-SPIWG on the appropriate utilisation of multiparametric ultrasound and other imaging modalities in the evaluation of scrotal trauma. [ABSTRACT FROM AUTHOR]

Objective: To identify adhesive renal venous tumor thrombus (RVTT) of renal cell carcinoma (RCC) by contrast-enhancement CT (CECT). Materials and methods: Our retrospective study included 53 patients who underwent preoperative CECT and pathologically confirmed RCC combined with RVTT. They were divided into two groups based on the intra-operative findings of RVTT adhesion to the venous wall, with 26 cases in the adhesive RVTT group (ARVTT) and 27 cases in the non-adhesive group (NRVTT). The location, maximum diameter (MD) and CT values of tumors, the maximum length (ML) and width (MW) of RVTT, and length of inferior vena cava tumor thrombus were compared between the two groups. The presence of renal venous wall involvement, renal venous wall inflammation, and enlarged retroperitoneal lymph node was compared between the two groups. A receiver operating characteristic curve was used to analyze the diagnostic performance. Results: The MD of RCC and the ML and MW of the RVTT were all larger in the ARVTT group than in the NRVTT group (p = 0.042, p < 0.001, and p = 0.002). The proportion of renal vein wall involvement and renal vein wall inflammation were higher in the ARVTT group than in NRVTT groups (both p < 0.001). The multivariable model including ML and vascular wall inflammation to predict ARVTT could achieve the best diagnostic performance with the area under the curve, sensitivity, specificity, and accuracy of 0.91, 88.5%, 96.3%, and 92.5%, respectively. Conclusion: The multivariable model acquired by CECT images could be used to predict RVTT adhesion. Clinical relevance statement: For RCC patients with tumor thrombus, contrast-enhanced CT could noninvasively predict the adhesion of tumor thrombus, thus predicting the difficulty of surgery and contributing to the selection of an appropriate treatment plan. Key Points: • The length and width of the tumor thrombus could be used to predict its adhesion to the vessel wall. • Adhesion of the tumor thrombus can be reflected by inflammation of the renal vein wall. • The multivariable model from CECT can well predict whether the tumor thrombus adhered to the vein wall. [ABSTRACT FROM AUTHOR]

Objectives: Post-mortem interval (PMI) estimation has long been relying on sequential post-mortem changes on the body as a function of extrinsic, intrinsic, and environmental factors. Such factors are difficult to account for in complicated death scenes; thus, PMI estimation can be compromised. Herein, we aimed to evaluate the use of post-mortem CT (PMCT) radiomics for the differentiation between early and late PMI. Methods: Consecutive whole-body PMCT examinations performed between 2016 and 2021 were retrospectively included (n = 120), excluding corpses without an accurately reported PMI (n = 23). Radiomics data were extracted from liver and pancreas tissue and randomly split into training and validation sets (70:30%). Following data preprocessing, significant features were selected (Boruta selection) and three XGBoost classifiers were built (liver, pancreas, combined) to differentiate between early (< 12 h) and late (> 12 h) PMI. Classifier performance was assessed with receiver operating characteristics (ROC) curves and areas under the curves (AUC), which were compared by bootstrapping. Results: A total of 97 PMCTs were included, representing individuals (23 females and 74 males) with a mean age of 47.1 ± 23.38 years. The combined model achieved the highest AUC reaching 75% (95%CI 58.4–91.6%) (p = 0.03 compared to liver and p = 0.18 compared to pancreas). The liver-based and pancreas-based XGBoost models achieved AUCs of 53.6% (95%CI 34.8–72.3%) and 64.3% (95%CI 46.7–81.9%) respectively (p > 0.05 for the comparison between liver- and pancreas-based models). Conclusion: The use of radiomics analysis on PMCT examinations differentiated early from late PMI, unveiling a novel image-based method with important repercussions in forensic casework. Clinical relevance statement: This paper introduces the employment of radiomics in forensic diagnosis by presenting an effective automated alternative method of estimating post-mortem interval from targeted tissues, thus paving the way for improvement in speed and quality of forensic investigations. Key Points: • A combined liver-pancreas radiomics model differentiated early from late post-mortem intervals (using a 12-h threshold) with an area under the curve of 75% (95%CI 58.4–91.6%). • XGBoost models based on liver-only or pancreas-only radiomics demonstrated inferior performance to the combined model in predicting the post-mortem interval. [ABSTRACT FROM AUTHOR]

Objectives: To analyze the performance of deep learning in isodense/obscure masses in dense breasts. To build and validate a deep learning (DL) model using core radiology principles and analyze its performance in isodense/obscure masses. To show performance on screening mammography as well as diagnostic mammography distribution. Methods: This was a retrospective, single-institution, multi-centre study with external validation. For model building, we took a 3-pronged approach. First, we explicitly taught the network to learn features other than density differences: such as spiculations and architectural distortion. Second, we used the opposite breast to enable the detection of asymmetries. Third, we systematically enhanced each image by piece-wise-linear transformation. We tested the network on a diagnostic mammography dataset (2569 images with 243 cancers, January to June 2018) and a screening mammography dataset (2146 images with 59 cancers, patient recruitment from January to April 2021) from a different centre (external validation). Results: When trained with our proposed technique (and compared with baseline network), sensitivity for malignancy increased from 82.7 to 84.7% at 0.2 False positives per image (FPI) in the diagnostic mammography dataset, 67.9 to 73.8% in the subset of patients with dense breasts, 74.6 to 85.3 in the subset of patients with isodense/obscure cancers and 84.9 to 88.7 in an external validation test set with a screening mammography distribution. We showed that our sensitivity exceeded currently reported values (0.90 at 0.2 FPI) on a public benchmark dataset (INBreast). Conclusion: Modelling traditional mammographic teaching into a DL framework can help improve cancer detection accuracy in dense breasts. Clinical relevance statement: Incorporating medical knowledge into neural network design can help us overcome some limitations associated with specific modalities. In this paper, we show how one such deep neural network can help improve performance on mammographically dense breasts. Key Points: • Although state-of-the-art deep learning networks achieve good results in cancer detection in mammography in general, isodense, obscure masses and mammographically dense breasts posed a challenge to deep learning networks. • Collaborative network design and incorporation of traditional radiology teaching into the deep learning approach helped mitigate the problem. • The accuracy of deep learning networks may be translatable to different patient distributions. We showed the results of our network on screening as well as diagnostic mammography datasets. [ABSTRACT FROM AUTHOR]