Spectrum of Cognitive Biases in Diagnostic Radiology.

Cognitive biases are systematic thought processes involving the use of a filter of personal experiences and preferences arising from the tendency of the human brain to simplify information processing, especially when taking in vast amounts of data such as from imaging studies. These biases encompass a wide spectrum of thought processes and frequently overlap in their concepts, with multiple biases usually in operation when interpretive and perceptual errors occur in radiology. The authors review the gamut of cognitive biases that occur in radiology. These biases are organized according to their expected stage of occurrence while the radiologist reads and interprets an imaging study. In addition, the authors propose several additional cognitive biases that have not yet, to their knowledge, been defined in the radiologic literature but are applicable to diagnostic radiology. Case examples are used to illustrate potential biases and their impact, with emergency radiology serving as the clinical paradigm, given the associated high imaging volumes, wide diversity of imaging examinations, and rapid pace, which can further increase a radiologist's reliance on biases and heuristics. Potential strategies to recognize and overcome one's personal biases at each stage of image interpretation are also discussed. Awareness of such biases and their unintended effects on imaging interpretations and patient outcomes may help make radiologists cognizant of their own biases that can result in diagnostic errors. Identification of cognitive bias in departmental and systematic quality improvement practices may represent another tool to prevent diagnostic errors in radiology. ©RSNA, 2024 See the invited commentary by Larson in this issue.

Violencia y maltrato infantil. El aporte del médico radiólogo al abordaje integral multidisciplinario / Child maltreatment and violence. Contributions from radiologists to a comprehensive multidisciplinary approach

El maltrato infantil es definido por la Organización Mundial de la Salud (OMS) como "el abuso y la desatención que sufren los niños menores de 18 años. Incluye todo tipo de maltrato físico y/o emocional […] que resulte en un daño real o potencial para la salud, la supervivencia, el desarrollo o la dignidad del niño". Al examinar los rastros corporales del maltrato físico, siguiendo los mecanismos de lesión más frecuentemente implicados, es posible detectar patrones radiológicos típicos. La evaluación imagenológica del hueso en reparación permite inferir cronologías para correlacionar con los datos obtenidos en la anamnesis. Los profesionales de la salud deben detectar oportunamente lesiones radiológicas sospechosas y activar de forma temprana el resguardo del menor. Nuestro propósito es realizar una revisión sobre las publicaciones recientes referidas al estudio imagenológico en niños de quienes se sospeche que puedan ser víctimas de violencia física.

The World Health Organization (WHO) defines child maltreatment as "the abuse and neglect that occurs to children under 18 years of age. It includes all types of physical and/or emotional ill-treatment [...], which results in actual or potential harm to the child's health, survival, development or dignity." By examining the bodily traces of physical abuse, following the most frequently involved mechanisms of injury, it is possible to identify typical radiological patterns. The imaging studies of the bone under repair allows inferring a timeline that may be correlated to the data obtained during history taking. Health care providers should detect suspicious radiological lesions in a timely manner and promptly activate the safeguarding of the child. Our objective was to review recent publications on the imaging studies of children suspected of being victims of physical violence.

Doug Altman, medical statistician par excellence: What can radiologists learn from his legacy?

This narrative review describes our experience of working with Doug Altman, the most highly cited medical statistician in the world. Doug was particularly interested in diagnostics, and imaging studies in particular. We describe how his insights helped improve our own radiological research studies and we provide advice for other researchers hoping to improve their own research practice.

Familiarity, confidence and preference of artificial intelligence feedback and prompts by Australian breast cancer screening readers.

Objectives This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection. Methods Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants' responses to questions were compared using Pearson's χ 2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons. Results Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were 'Displaying suspicious areas on mammograms with the percentage of cancer possibility' (67.8%) and 'Automatic mammogram classification (normal, benign, cancer, uncertain)' (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying 'somewhat happy' to 'extremely happy') over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001). Conclusion The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike.

RASopathies for Radiologists.

RASopathies are a heterogeneous group of genetic syndromes caused by germline mutations in a group of genes that encode components or regulators of the Ras/mitogen-activated protein kinase (MAPK) signaling pathway. RASopathies include neurofibromatosis type 1, Legius syndrome, Noonan syndrome, Costello syndrome, cardiofaciocutaneous syndrome, central conducting lymphatic anomaly, and capillary malformation-arteriovenous malformation syndrome. These disorders are grouped together as RASopathies based on our current understanding of the Ras/MAPK pathway. Abnormal activation of the Ras/MAPK pathway plays a major role in development of RASopathies. The individual disorders of RASopathies are rare, but collectively they are the most common genetic condition (one in 1000 newborns). Activation or dysregulation of the common Ras/MAPK pathway gives rise to overlapping clinical features of RASopathies, involving the cardiovascular, lymphatic, musculoskeletal, cutaneous, and central nervous systems. At the same time, there is much phenotypic variability in this group of disorders. Benign and malignant tumors are associated with certain disorders. Recently, many institutions have established multidisciplinary RASopathy clinics to address unique therapeutic challenges for patients with RASopathies. Medications developed for Ras/MAPK pathway-related cancer treatment may also control the clinical symptoms due to an abnormal Ras/MAPK pathway in RASopathies. Therefore, radiologists need to be aware of the concept of RASopathies to participate in multidisciplinary care. As with the clinical manifestations, imaging features of RASopathies are overlapping and at the same time diverse. As an introduction to the concept of RASopathies, the authors present major representative RASopathies, with emphasis on their imaging similarities and differences. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.

BACKGROUND: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. METHODS: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. RESULTS: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. CONCLUSIONS: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

High sensitivity methods for automated rib fracture detection in pediatric radiographs.

Rib fractures are highly predictive of non-accidental trauma in children under 3 years old. Rib fracture detection in pediatric radiographs is challenging because fractures can be obliquely oriented to the imaging detector, obfuscated by other structures, incomplete, and non-displaced. Prior studies have shown up to two-thirds of rib fractures may be missed during initial interpretation. In this paper, we implemented methods for improving the sensitivity (i.e. recall) performance for detecting and localizing rib fractures in pediatric chest radiographs to help augment performance of radiology interpretation. These methods adapted two convolutional neural network (CNN) architectures, RetinaNet and YOLOv5, and our previously proposed decision scheme, "avalanche decision", that dynamically reduces the acceptance threshold for proposed regions in each image. Additionally, we present contributions of using multiple image pre-processing and model ensembling techniques. Using a custom dataset of 1109 pediatric chest radiographs manually labeled by seven pediatric radiologists, we performed 10-fold cross-validation and reported detection performance using several metrics, including F2 score which summarizes precision and recall for high-sensitivity tasks. Our best performing model used three ensembled YOLOv5 models with varied input processing and an avalanche decision scheme, achieving an F2 score of 0.725 ± 0.012. Expert inter-reader performance yielded an F2 score of 0.732. Results demonstrate that our combination of sensitivity-driving methods provides object detector performance approaching the capabilities of expert human readers, suggesting that these methods may provide a viable approach to identify all rib fractures.

Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy.

Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories, and reading time were assessed using Wald χ2 tests and paired-sample t tests. Results GPT-4 (detection rate, 82.7%;124 of 150; 95% CI: 75.8, 87.9) matched the average detection performance of radiologists independent of their experience (senior radiologists, 89.3% [134 of 150; 95% CI: 83.4, 93.3]; attending physicians, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; residents, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; P value range, .522-.99). One senior radiologist outperformed GPT-4 (detection rate, 94.7%; 142 of 150; 95% CI: 89.8, 97.3; P = .006). GPT-4 required less processing time per radiology report than the fastest human reader in the study (mean reading time, 3.5 seconds ± 0.5 [SD] vs 25.1 seconds ± 20.1, respectively; P < .001; Cohen d = -1.08). The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist ($0.03 ± 0.01 vs $0.42 ± 0.41; P < .001; Cohen d = -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.

Service Improvements and Workload Increases: Royal College of Radiologists (RCR) Re-Audit of Curative Intent Radiotherapy for Non-Small Cell Lung Cancer (NSCLC).

AIMS: The Royal College of Radiologists (RCR) audit of radical radiotherapy (RR) for patients with non-small cell lung cancer (NSCLC) in 2013 concluded that there was under-treatment compared to international comparators and marked variability between cancer networks. Elderly patients were less likely to receive guideline recommended treatments. Access to technological developments was low. Various national and local interventions have since taken place. This study aims to re-assess national practice. MATERIALS AND METHODS: Radiotherapy departments completed one questionnaire for each patient started on RR for 4 weeks in January 2023. RESULTS: Ninety-three percent of centres returned data on 295 patients. RR has increased 70% since 2013 but patients on average wait 20% longer to start treatment (p = 0.02). Staging investigations were often outside a desirable timeframe (79% of PET/CT scans). Advanced planning techniques are used more frequently: 4-dimensional planning increased from 33% to 90% (P < 0.001), cone beam imaging from 67% to 97% (p < 0.001) and colleague led peer review increased from 41% to 73% (P < 0.001). CONCLUSION: There have been significant improvements in care. There has been a considerable increase in clinical oncology workload with evidence of stress on the system that requires additional resourcing.

How does deep learning/machine learning perform in comparison to radiologists in distinguishing glioblastomas (or grade IV astrocytomas) from primary CNS lymphomas?: a meta-analysis and systematic review.

BACKGROUND: Several studies have been published comparing deep learning (DL)/machine learning (ML) to radiologists in differentiating PCNSLs from GBMs with equivocal results. We aimed to perform this meta-analysis to evaluate the diagnostic accuracy of ML/DL versus radiologists in classifying PCNSL versus GBM using MRI. METHODOLOGY: The study was performed in accordance with PRISMA guidelines. Data was extracted and interpreted by two researchers with 12 and 23 years' experience, respectively, and QUADAS-2 tool was used for quality and risk-bias assessment. We constructed contingency tables to derive sensitivity, specificity accuracy, summary receiver operating characteristic (SROC) curve, and the area under the curve (AUC). RESULTS: Our search identified 11 studies, of which 8 satisfied our inclusion criteria and restricted the analysis in each study to reporting the model showing highest accuracy, with a total sample size of 1159 patients. The random effects model showed a pooled sensitivity of 0.89 [95% CI:0.84-0.92] for ML and 0.82 [95% CI:0.76-0.87] for radiologists. Pooled specificity was 0.88 [95% CI: 0.84-0.91] for ML and 0.90 [95% CI: 0.81-0.95] for radiologists. Pooled accuracy was 0.88 [95% CI: 0.86-0.90] for ML and 0.86 [95% CI: 0.78-0.91] for radiologists. Pooled AUC of ML was 0.94 [95% CI:0.92-0.96]and for radiologists, it was 0.90 [95% CI: 0.84-0.93]. CONCLUSIONS: MRI-based ML/DL techniques can complement radiologists to improve the accuracy of classifying GBMs from PCNSL, possibly reduce the need for a biopsy, and avoid any unwanted neurosurgical resection of a PCNSL.

Society of Radiologists in Ultrasound Consensus on Routine Pelvic US for Endometriosis.

Endometriosis is a prevalent and potentially debilitating condition that mostly affects individuals of reproductive age, and often has a substantial diagnostic delay. US is usually the first-line imaging modality used when patients report chronic pelvic pain or have issues of infertility, both common symptoms of endometriosis. Other than the visualization of an endometrioma, sonologists frequently do not appreciate endometriosis on routine transvaginal US images. Given a substantial body of literature describing techniques to depict endometriosis at US, the Society of Radiologists in Ultrasound convened a multidisciplinary panel of experts to make recommendations aimed at improving the screening process for endometriosis. The panel was composed of experts in the imaging and management of endometriosis, including radiologists, sonographers, gynecologists, reproductive endocrinologists, and minimally invasive gynecologic surgeons. A comprehensive literature review combined with a modified Delphi technique achieved a consensus. This statement defines the targeted screening population, describes techniques for augmenting pelvic US, establishes direct and indirect observations for endometriosis at US, creates an observational grading and reporting system, and makes recommendations for additional imaging and patient management. The panel recommends transvaginal US of the posterior compartment, observation of the relative positioning of the uterus and ovaries, and the uterine sliding sign maneuver to improve the detection of endometriosis. These additional techniques can be performed in 5 minutes or less and could ultimately decrease the delay of an endometriosis diagnosis in at-risk patients.

Chronic Nonspinal Osteomyelitis in Adults: Consensus Recommendations on Percutaneous Bone Biopsies from the Society of Academic Bone Radiologists.

The diagnosis and management of chronic nonspinal osteomyelitis can be challenging, and guidelines regarding the appropriateness of performing percutaneous image-guided biopsies to acquire bone samples for microbiological analysis remain limited. An expert panel convened by the Society of Academic Bone Radiologists developed and endorsed consensus statements on the various indications for percutaneous image-guided biopsies to standardize care and eliminate inconsistencies across institutions. The issued statements pertain to several commonly encountered clinical presentations of chronic osteomyelitis and were supported by a literature review. For most patients, MRI can help guide management and effectively rule out osteomyelitis when performed soon after presentation. Additionally, in the appropriate clinical setting, open wounds such as sinus tracts and ulcers, as well as joint fluid aspirates, can be used for microbiological culture to determine the causative microorganism. If MRI findings are positive, surgery is not needed, and alternative sites for microbiological culture are not available, then percutaneous image-guided biopsies can be performed. The expert panel recommends that antibiotics be avoided or discontinued for an optimal period of 2 weeks prior to a biopsy whenever possible. Patients with extensive necrotic decubitus ulcers or other surgical emergencies should not undergo percutaneous image-guided biopsies but rather should be admitted for surgical debridement and intraoperative cultures. Multidisciplinary discussion and approach are crucial to ensure optimal diagnosis and care of patients diagnosed with chronic osteomyelitis.

Multiple and hereditary renal tumors: a review for radiologists.

80% of renal carcinomas (RC) are diagnosed incidentally by imaging. 2-4% of "sporadic" multifocality and 5-8% of hereditary syndromes are accepted, probably with underestimation. Multifocality, young age, familiar history, syndromic data, and certain histologies lead to suspicion of hereditary syndrome. Each tumor must be studied individually, with a multidisciplinary evaluation of the patient. Nephron-sparing therapeutic strategies and a radioprotective diagnostic approach are recommended. Relevant data for the radiologist in major RC hereditary syndromes are presented: von-Hippel-Lindau, Chromosome-3 translocation, BRCA-associated protein-1 mutation, RC associated with succinate dehydrogenase deficiency, PTEN, hereditary papillary RC, Papillary thyroid cancer- Papillary RC, Hereditary leiomyomatosis and RC, Birt-Hogg-Dubé, Tuberous sclerosis complex, Lynch, Xp11.2 translocation/TFE3 fusion, Sickle cell trait, DICER1 mutation, Hereditary hyperparathyroidism and jaw tumor, as well as the main syndromes of Wilms tumor predisposition. The concept of "non-hereditary" familial RC and other malignant and benign entities that can present as multiple renal lesions are discussed.

Clinical utility, reproducibility, and radiologist acceptance of ILD-RADS.

PURPOSE: To investigate the clinical utility, reproducibility, and radiologists' acceptance of the Interstitial Lung Disease Imaging-Reporting and Data System (ILD-RADS). METHOD: In this single-institutional retrospective study, three radiologists independently reviewed the chest high-resolution CT (HRCT) scans of 111 consecutive patients diagnosed with ILDs. They assessed the HRCT pulmonary features using the ILD-RADS template and assigned an ILD-RADS category (1-4) to each scan based on the identified imaging pattern. Patients were classified into idiopathic pulmonary fibrosis (IPF) (n = 14) and non-IPF ILD (n = 97) groups based on clinical diagnoses determined by multidisciplinary discussion. Association between ILD-RADS categories and clinical diagnoses was assessed using the Chi-square test for trend. Reproducibility was evaluated using kappa (k) scores, and radiologists' acceptance of the ILD-RADS was evaluated with a questionnaire. RESULTS: We found a significant association between the ILD-RADS categories and patients' clinical diagnoses (P ≤ 0.0001) for the three readers, with a trend toward increased assignment of ILD-RADS-1 to IPF patients (50 %-57.1 %), and ILD-RADS-4 to non-IPF patients (46.4 %-49.5 %). The ILD-RADS categories showed excellent intra-reader agreement (k = 0.873) and moderate inter-reader agreement (k = 0.440). ILD-RADS-1 and -4 categories showed the highest inter-reader agreement (k = 0.681 and 0.481, respectively). Radiologists gave a positive response to using the ILD-RADS in daily practice. CONCLUSIONS: The clinical utility of the ILD-RADS was demonstrated by the significant association between the ILD-RADS categories and patients' clinical diagnoses, particularly the ILD-RADS-1 and -4 categories. Excellent intra-reader and moderate inter-reader reproducibility was observed. ILD-RADS has the potential to be widely accepted for standardized HRCT reporting among radiologists.

Implementing supplementary breast cancer screening in women with dense breasts: Insights from European radiographers and radiologists.

INTRODUCTION: In response to the critical need for enhancing breast cancer screening for women with dense breasts, this study explored the understanding of challenges and requirements for implementing supplementary breast cancer screening for such women among clinical radiographers and radiologists in Europe. METHOD: Fourteen (14) semi-structured online interviews were conducted with European clinical radiologists (n = 5) and radiographers (n = 9) specializing in breast cancer screening from 8 different countries: Denmark, Finland, Greece, Italy, Malta, the Netherlands, Switzerland, United Kingdom. The interview schedule comprised questions regarding professional background and demographics and 13 key questions divided into six subgroups, namely Supplementary Imaging, Training, Resources and Guidelines, Challenges, Implementing supplementary screening and Women's Perspective. Data analysis followed the six phases of reflexive thematic analysis. RESULTS: Six significant themes emerged from the data analysis: Understanding and experiences of supplementary imaging for women with dense breasts; Challenges and requirements related to training among clinical radiographers and radiologists; Awareness among radiographers and radiologists of guidelines on imaging women with dense breasts; Challenges to implement supplementary screening; Predictors of Implementing Supplementary screening; Views of radiologists and radiographers on women's perception towards supplementary screening. CONCLUSION: The interviews with radiographers and radiologists provided valuable insights into the challenges and potential strategies for implementing supplementary breast cancer screening. These challenges included patient and staff related challenges. Implementing multifaceted solutions such as Artificial Intelligence integration, specialized training and resource investment can address these challenges and promote the successful implementation of supplementary screening. Further research and collaboration are needed to refine and implement these strategies effectively. IMPLICATIONS FOR PRACTICE: This study highlights the urgent need for specialized training programs and dedicated resources to enhance supplementary breast cancer screening for women with dense breasts in Europe. These resources include advanced imaging technologies, such as MRI or ultrasound, and specialized software for image analysis. Moreover, further research is imperative to refine screening protocols and evaluate their efficacy and cost-effectiveness, based on the findings of this study.