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Autoimmune encephalitis is a relatively novel nosological entity characterized by an immune-mediated damage of the central nervous system. While originally described as a paraneoplastic inflammatory phenomenon affecting limbic structures, numerous instances of non-paraneoplastic pathogenesis, as well as extra-limbic involvement, have been characterized. Given the wide spectrum of insidious clinical presentations ranging from cognitive impairment to psychiatric symptoms or seizures, it is crucial to raise awareness about this disease category. In fact, an early diagnosis can be dramatically beneficial for the prognosis both to achieve an early therapeutic intervention and to detect a potential underlying malignancy. In this scenario, the radiologist can be the first to pose the hypothesis of autoimmune encephalitis and refer the patient to a comprehensive diagnostic work-up - including clinical, serological, and neurophysiological assessments.In this article, we illustrate the main radiological characteristics of autoimmune encephalitis and its subtypes, including the typical limbic presentation, the features of extra-limbic involvement, and also peculiar imaging findings. In addition, we review the most relevant alternative diagnoses that should be considered, ranging from other encephalitides to neoplasms, vascular conditions, and post-seizure alterations. Finally, we discuss the most appropriate imaging diagnostic work-up, also proposing a suggested MRI protocol.

The cancer in lungs is termed as a severe disease that can lead to huge deaths globally. The timely identification can be useful to increase rate of survival. A Computed Tomography is used to identify position of tumor and discover level of cancer. The present study provides an innovative model with CT to identify lung cancer. The lung CT is used for analyzing the cancer levels. These images undergoes pre-processing wiener filter to eradicate the noise. Subsequently, the lung lobe segmentation is attained with LadderNet whose training is executed with Archimedes Flow Regime Optimization (AFRO). The detection of nodule is performed using grid based strategy followed by extraction of statistical, GLCM and texture features. Finally, lung cancer detection are performed using DBN and the hyper parameters of DBN are optimally fine tuned using devised AFRO. The AFRO-based DBN provided finest efficiency with accuracy of 95.8%, F-measure of 92.6% and precision of 93.6%.The finest outcomes revealed that the technique has feasible robustness in classifying the nodules. The technique poses the ability to help the radiologists to interpret the data more effective and distinguish the lung nodules with CT images. [ABSTRACT FROM AUTHOR]

Palpable calvarial lesions in children may require multi-modality imaging for adequate characterization due to non-specific clinical features. Causative lesions range from benign incidental lesions to highly aggressive pathologies. While tissue sampling may be required for some lesions, others have a typical imaging appearance, and an informed imaging approach facilitates diagnosis. This review illustrates imaging findings of common and clinically important focal pediatric calvarial bulges to aid the radiologist in narrowing the differential diagnosis and directing appropriate referral. We focus on birth-related lesions, congenital abnormalities, and modeling disturbances (i.e., those that produce a change in calvarial contour early in development), normal variants, and neoplastic lesions with their mimics. [ABSTRACT FROM AUTHOR]

Purpose: To assess the reproducibility of radiomic features (RFs) extracted from dynamic contrast-enhanced computed tomography (DCE-CT) scans of patients diagnosed with hepatocellular carcinoma (HCC) with regards to inter-observer variability and acquisition timing after contrast injection. The predictive ability of reproducible RFs for differentiating between the degrees of HCC differentiation is also investigated. Methods: We analyzed a set of DCE-CT scans of 39 patients diagnosed with HCC. Two radiologists independently segmented the scans, and RFs were extracted from each sequence of the DCE-CT scans. The same lesion was segmented across the DCE-CT sequences of each patient's scan. From each lesion, 127 commonly used RFs were extracted. The reproducibility of RFs was assessed with regard to (i) inter-observer variability, by evaluating the reproducibility of RFs between the two radiologists; and (ii) timing of acquisition following contrast injection (inter- and intra-imaging phase). The reproducibility of RFs was assessed using the concordance correlation coefficient (CCC), with a cut-off value of 0.90. Reproducible RFs were used for building XGBoost classification models for the differentiation of HCC differentiation. Results: Inter-observer analyses across the different contrast-enhancement phases showed that the number of reproducible RFs was 29 (22.8%), 52 (40.9%), and 36 (28.3%) for the non-contrast enhanced, late arterial, and portal venous phases, respectively. Intra- and inter-sequence analyses revealed that the number of reproducible RFs ranged between 1 (0.8%) and 47 (37%), inversely related with time interval between the sequences. XGBoost algorithms built using reproducible RFs in each phase were found to be high predictive ability of the degree of HCC tumor differentiation. Conclusions: The reproducibility of many RFs was significantly impacted by inter-observer variability, and a larger number of RFs were impacted by the difference in the time of acquisition after contrast injection. Our findings highlight the need for quality assessment to ensure that scans are analyzed in the same physiologic imaging phase in quantitative imaging studies, or that phase-wide reproducible RFs are selected. Overall, the study emphasizes the importance of reproducibility and quality control when using RFs as biomarkers for clinical applications. [ABSTRACT FROM AUTHOR]

Background An adequate workforce is needed to guarantee optimal kidney care. We used the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA) to provide an assessment of the global kidney care workforce. Methods We conducted a multinational cross-sectional survey to evaluate the global capacity of kidney care and assessed data on the number of adult and paediatric nephrologists, the number of trainees in nephrology and shortages of various cadres of the workforce for kidney care. Data are presented according to the ISN region and World Bank income categories. Results Overall, stakeholders from 167 countries responded to the survey. The median global prevalence of nephrologists was 11.75 per million population (pmp) (interquartile range [IQR] 1.78–24.76). Four regions had median nephrologist prevalences below the global median: Africa (1.12 pmp), South Asia (1.81 pmp), Oceania and Southeast Asia (3.18 pmp) and newly independent states and Russia (9.78 pmp). The overall prevalence of paediatric nephrologists was 0.69 pmp (IQR 0.03–1.78), while overall nephrology trainee prevalence was 1.15 pmp (IQR 0.18–3.81), with significant variations across both regions and World Bank income groups. More than half of the countries reported shortages of transplant surgeons (65%), nephrologists (64%), vascular access coordinators (59%), dialysis nurses (58%) and interventional radiologists (54%), with severe shortages reported in low- and lower-middle-income countries. Conclusions There are significant limitations in the available kidney care workforce in large parts of the world. To ensure the delivery of optimal kidney care worldwide, it is essential to develop national and international strategies and training capacity to address workforce shortages. [ABSTRACT FROM AUTHOR]

Multi-sequence magnetic resonance imaging is crucial in accurately identifying knee abnormalities but requires substantial expertise from radiologists to interpret. Here, we introduce a deep learning model incorporating co-plane attention across image sequences to classify knee abnormalities. To assess the effectiveness of our model, we collected the largest multi-sequence knee magnetic resonance imaging dataset involving the most comprehensive range of abnormalities, comprising 1748 subjects and 12 types of abnormalities. Our model achieved an overall area under the receiver operating characteristic curve score of 0.812. It achieved an average accuracy of 0.78, outperforming junior radiologists (accuracy 0.65) and remains competitive with senior radiologists (accuracy 0.80). Notably, with the assistance of model output, the diagnosis accuracy of all radiologists was improved significantly (p < 0.001), elevating from 0.73 to 0.79 on average. The interpretability analysis demonstrated that the model decision-making process is consistent with the clinical knowledge, enhancing its credibility and reliability in clinical practice. The authors present a deep learning model that incorporates co-plane attention across image sequences with a performance comparable to senior radiologists in classifying 12 knee abnormalities from MRI. The model significantly improves diagnostic performance and aligns with clinical observations. [ABSTRACT FROM AUTHOR]

Interventional radiologists mainly rely on visual feedback via imaging modalities to steer a needle toward a tumor during biopsy and ablation procedures. In the case of CT-guided procedures, there is a risk of exposure to hazardous X-ray-based ionizing radiation. Therefore, CT scans are usually not used continuously, which increases the chances of a misplacement of the needle and the need for reinsertion, leading to more tissue trauma. Interventionalists also encounter haptic feedback via needle–tissue interaction forces while steering a needle. These forces are useful but insufficient to clearly perceive and identify deep-tissue structures such as tumors. The objective of this paper was to investigate the effect of enhanced force feedback for sensing interaction forces and guiding the needle when applied individually and simultaneously during a virtual CT-guided needle insertion task. We also compared the enhanced haptic feedback to enhanced visual feedback. We hypothesized that enhancing the haptic feedback limits the time needed to reach the target accurately and reduces the number of CT scans, as the interventionalist depends more on real-time enhanced haptic feedback. To test the hypothesis, a simulation environment was developed to virtually steer a needle in five degrees of freedom (DoF) to reach a tumor target embedded in a liver model. Twelve participants performed in the experiment with different feedback conditions where we measured their performance in terms of the following: targeting accuracy, trajectory tracking, number of CT scans required, and the time needed to finish the task. The results suggest that the combination of enhanced haptic feedback for guidance and sensing needle–tissue interaction forces significantly reduce the number of scans and the duration required to finish the task by 32.1% and 46.9%, respectively, when compared to nonenhanced haptic feedback. The other feedback modalities significantly reduced the duration to finish the task by around 30% compared to nonenhanced haptic feedback. [ABSTRACT FROM AUTHOR]

ABSTRACT Objectives Methods Results Conclusions To transform the standardized descriptions of the ultrasound characteristics of endometrial and intrauterine lesions devised by the International Endometrial Tumor Analysis (IETA) group into a practical scoring method and to investigate whether application of this method enhances the diagnostic accuracy of ultrasound radiologists with different levels of experience in detecting malignancy compared with subjective assessment.This was a retrospective study of 855 patients with endometrial and/or intrauterine lesions, who were divided into a training (n = 600) and a validation (n = 255) set. Ultrasound radiologists with varying levels of experience (expert, intermediate and junior) evaluated all lesions by subjective assessment and according to IETA rules. Using IETA rules, the experts identified signs of malignancy in the training set, assigned scores for each indicator and validated the scoring method in the validation set. The intermediate‐level and junior ultrasound radiologists reassessed the malignancy of the lesions using the IETA scoring method and compared their classifications with those made previously by subjective assessment. Postsurgical pathological evaluation was used as the reference standard.Using subjective assessment, the experts demonstrated the highest level of diagnostic accuracy, with a sensitivity of 85.0%, specificity of 94.3% and an area under the receiver‐operating‐characteristics curve (AUC) of 0.897. Applying the IETA scoring method (comprising eight ultrasound characteristics that contributed to the total score) with a threshold of > 25 points for the diagnosis of malignancy achieved a sensitivity of 84.7%, specificity of 94.7% and AUC of 0.9533 in the training set, with similar performance in the validation set, when performed by experts. Using the IETA scoring method, both junior and intermediate ultrasound radiologists showed improvement in sensitivity (from 55.5% to 74.8% and from 70.2% to 77.1%, respectively), specificity (from 88.4% to 91.5% and from 87.4% to 92.2%, respectively) and AUC (from 0.704 to 0.827 and from 0.793 to 0.841, respectively) for diagnosing malignant lesions.The IETA scoring method exhibits high diagnostic efficacy for malignant endometrial and intrauterine lesions. This method compensates for the lack of experience among junior and intermediate‐level ultrasound radiologists, enhancing their diagnostic skill to a level nearing that of experienced senior ultrasound radiologists. Further research is essential to validate the practicality of implementing this method and to confirm its clinical value. © 2024 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

The future will bring increasing demands for specialty training of MSK radiologists. These will include requirements for further standardization of curriculum, educational methods, and documentation. This will place further burdens (both temporal and financial) on the fellowship programs and may lead to termination of some programs. There is a risk that increasingly bureaucratic training requirements may "crowd out" the basic conceptual education necessary for the continued growth and relevance of the subspecialty. Educators must find ways to incorporate these aspects into programs even when they are not mandated. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the utility of the #Enzian classification in magnetic resonance imaging (MRI) for endometriosis assessment, focusing on inter-reader agreement, diagnostic accuracy, and the correlation of adenomyosis with deep endometriosis (DE). Methods: This IRB- approved retrospective single-center study included 412 women who underwent MRI evaluation for endometriosis between February 2017 and June 2022. Two experienced radiologists independently analyzed MRI images using the #Enzian classification and assessed the type of adenomyosis, if any. The surgical #Enzian classification served as the gold standard for evaluating preoperative MRI results of 45 patients. Statistical analysis was performed to assess inter-reader agreement and diagnostic accuracy. Results: Inter-reader agreement was substantial to excellent (Cohen's kappa 0.75–0.96) for most compartments except peritoneal involvement (0.39). The preoperative MRI showed mostly substantial to excellent accuracy (0.84–0.98), sensitivity (0.62–1.00), specificity (0.87–1.00), positive (0.58–1.00) and negative predictive values (0.86–1.00) for most compartments, except for peritoneal lesions (0.36, 0.17, 1.00, 1.00, 0.26 respectively). A trend with a higher prevalence of concordant DE in women with MR features of external adenomyosis compared to those with internal adenomyosis was visible (p = 0.067). Conclusions: The mr#Enzian showed mostly high inter-reader agreement and good diagnostic accuracy for various endometriosis compartments. MRI's role is particularly significant in the context of the current paradigm shift towards medical endometriosis treatment. The inclusion of information about the type of adenomyosis in the mr#Enzian classification could enhance diagnostic accuracy and inform treatment planning. [ABSTRACT FROM AUTHOR]

Purpose: To determine the role of deep learning-based arterial subtraction images in viability assessment on extracellular agents-enhanced MRI using LR-TR algorithm. Methods: Patients diagnosed with HCC who underwent locoregional therapy were retrospectively collected. We constructed a deep learning-based subtraction model and automatically generated arterial subtraction images. Two radiologists evaluated LR-TR category on ordinary images and then evaluated again on ordinary images plus arterial subtraction images after a 2-month washout period. The reference standard for viability was tumor stain on the digital subtraction hepatic angiography within 1 month after MRI. Results: 286 observations of 105 patients were ultimately enrolled. 157 observations were viable and 129 observations were nonviable according to the reference standard. The sensitivity and accuracy of LR-TR algorithm for detecting viable HCC significantly increased with the application of arterial subtraction images (87.9% vs. 67.5%, p < 0.001; 86.4% vs. 75.9%, p < 0.001). And the specificity slightly decreased without significant difference when the arterial subtraction images were added (84.5% vs. 86.0%, p = 0.687). The AUC of LR-TR algorithm significantly increased with the addition of arterial subtraction images (0.862 vs. 0.768, p < 0.001). The arterial subtraction images also improved inter-reader agreement (0.857 vs. 0.727). Conclusion: Extended application of deep learning-based arterial subtraction images on extracellular agents-enhanced MRI can increase the sensitivity of LR-TR algorithm for detecting viable HCC without significant change in specificity. [ABSTRACT FROM AUTHOR]

Purpose To measure radiologic science professionals' current attitudes toward older adults. Methods The Geriatrics Attitude Scale (GAS) paper survey was distributed to radiology and radiation oncology personnel in a large, single teaching hospital system. The GAS provides a global measure of ageist attitudes using 14 questions and 4 subscales. Demographic information also was collected. Results The total sample (N = 74) comprised radiology and radiation oncology practitioners from rural and nonrural facilities in the health system. Of the 14 questions, 3 yielded significant differences between medical imaging and radiation therapy practitioners. There were no significant effects for gender, race, years of experience, or facility type. Discussion Although significant differences were found between medical imaging and radiation therapy professionals for some of the constructs, both groups had positive attitudes overall toward older adults as measured by the GAS and subscales. Interventions to disrupt ageism should be introduced to make positive shifts in attitudes. Conclusion The United Nations has declared 2021 to 2030 the Decade of Healthy Ageing; people are challenged to change how they think, feel, and act toward aging and older adults. This pilot study provides a timely baseline for further research as older adults continue to challenge the U.S. health care system for the near future. [ABSTRACT FROM AUTHOR]

In this study, the development of a deep learning approach for distinguishing cardiomegaly in chest X-ray images and its validation process are presented. Typically, radiologists diagnose cardiomegaly by examining X-ray images. However, their interpretations can vary owing to subjective judgments, and mild cardiomegaly can be missed. For this reason, there is ongoing research into the use of AI-based deep learning algorithms as an adjunct to X-ray interpretation. In this study, radiologists collected 10,000 public images, from which 718 useful images were selected to create a dataset. A DenseNet121 algorithm was then used to develop an AI model for cardiomegaly detection. The results demonstrate an accuracy of 0.95, a recall of 0.91, and an F1 score of 0.94. Additional validation was performed to ensure the accuracy of the cardiomegaly detection model. The validation methods included saliency maps and guided backpropagation, which indicated the significance of the model. In conclusion, this study demonstrates the potential to develop a high-quality AI model with fewer data than previous studies, suggesting its applicability in the medical field. [ABSTRACT FROM AUTHOR]

(1) Background: The Kaiser score is a user-friendly tool that evaluates lesions on breast MRI and has been studied in the general population and a few specific clinical scenarios. We aim to evaluate the performance of the Kaiser score in the characterization of additional lesions identified on staging breast MRI. (2) Methods: The Kaiser score of the biopsied additional lesions identified on staging MRI in recently diagnosed breast cancer patients was retrospectively determined. Statistical analysis was performed to evaluate the diagnostic capability of the Kaiser score and whether it is affected by different imaging and pathological parameters of the additional and the index lesion. (3) Results: Seventy-six patients with ninety-two MRI-detected lesions constitute the studied population. There was a statistically significant difference in the Kaiser score between benign and malignant lesions, irrespective of the pathology of the index cancer (p = 0.221) or the size and the imaging features of the additional lesion. Using a cutoff of 5 and above for suspicious lesions, biopsy could have been avoided in 34/92 lesions. (4) Conclusions: The Kaiser score can assist radiologists in the evaluation of additional MRI lesions identified in recently diagnosed breast cancer patients, thus decreasing the number of unneeded biopsies and delays in definitive surgical management. [ABSTRACT FROM AUTHOR]

The aim of the present study was to determine the morphometric characteristics of the fibular notch (FN). This study was carried out with 76 dry adult tibial bone specimens (right 38, left 38) with unknown age and sex collected from the Department of Anatomy, Cukurova University, Adana. The mean width of the FN was 23.04 ± 2.02 mm; the mean depth of the FN 3.63 ± 0.83 mm; the mean height of the FN was 41.76 ± 4.01 mm. The mean anterior facet length and posterior facet length was found to be 10.44 ± 1.94 mm and 13.93 ±1.63 mm, respectively. The mean value of the angle between the anterior and posterior facets was found to be 140.56° ± 11.72. The mean value of the angle between the anterior surface of the tibia and the intertubercular line was 75.5° ± 5.47. No statistically significant differences were detected between the right and left sides for all measurements. It is considered that knowing the morphometric and anatomical characteristics of the fibular notch in detail will help radiologists evaluate the talocrural region. It is also considered that these data will guide surgeons and help determine the appropriate size for ankle reconstruction operations. [ABSTRACT FROM AUTHOR]

Background: Accurate assessment of the bone supporting the implant is crucial. Early detection of bone defects around the implant can prevent the loss of bone support that ultimately leads to the loss of the implant. Therefore, the purpose of this study is to check the accuracy of CBCT in detecting peri-implant fenestrations around the implant. Materials & methods: In this laboratory study, healthy beef ribs were used. The ribs were divided into three groups of 12 (control group, 1–2 mm fenestration group, and 2–3 mm fenestration group). The blocks were cut to a length of 20 mm and 36 osteotomies with dimensions of 4 × 12 mm were made by the periodontist in order to place the implant in these bone blocks. Then the titanium implant was placed in the holes and the initial scan was performed with CBCT. In the second group, fenestration-like lesions were created on the same buccal side at a distance of 10 mm from the crest with a diameter of 1–2 mm and in the third group with a diameter of 2–3 mm, and the CBCT scan was performed again with the same parameters. Two radiologists evaluated the images twice for the presence and absence of fenestration. Results: There was no statistically significant difference between direct measurements and CBCT in the fenestration group of 1–2 mm (p < 0.05), but there was a significant difference between direct measurements and CBCT in the fenestration group of 2–3 mm and underestimation was observed in CBCT measurements. Conclusion: The findings of this study showed that CBCT radiography has a higher accuracy in measuring the fenestration around the implant with a smaller diameter and has an acceptable diagnostic value in detecting bone loss around the implant. [ABSTRACT FROM AUTHOR]

Light sheet fluorescence microscopy (LSFM) is a transformative imaging method that enables the visualization of non-dissected specimen in real-time 3D. Optical clearing of tissues is essential for LSFM, typically employing toxic solvents. Here, we test the applicability of a non-hazardous alternative, ethyl cinnamate (ECi). We comprehensively characterized autofluorescence (AF) spectra in diverse murine tissues—ocular globe, knee, and liver—employing LSFM under various excitation wavelengths (405–785 nm) to test the feasibility of unstained samples for diagnostic purposes, in particular regarding percutaneous biopsies, as they constitute to most harvested type of tissue sample in clinical routine. Ocular globe structures were best discerned with 640 nm excitation. Knee tissue showed complex variation in AF spectra variation influenced by tissue depth and structure. Liver exhibited a unique AF pattern, likely linked to vasculature. Hepatic tissue samples were used to demonstrate the compatibility of our protocol for antibody staining. Furthermore, we employed machine learning to augment raw images and segment liver structures based on AF spectra. Radiologists rated representative samples transferred to the clinical assessment software. Learning-generated images scored highest in quality. Additionally, we investigated an actual murine biopsy. Our study pioneers the application of AF spectra for tissue characterization and diagnostic potential of optically cleared unstained percutaneous biopsies, contributing to the clinical translation of LSFM. [ABSTRACT FROM AUTHOR]

Objective: We aimed to evaluate ovarian vascularity with superb microvascular imaging (SMI) and to compare it with other conventional Doppler imaging methods in girls with premature thelarche, precocious puberty, and those at puberty. Methods: A total of 133 ovaries from 69 patients were evaluated. Among the 69 subjects, 50 girls applied with the preliminary diagnosis of precocious puberty, and 19 of them were pubertal adolescent girls. The color Doppler imaging (CDI), power Doppler imaging (PDI), color SMI (cSMI), and monochrome SMI (mSMI) techniques were performed, and the images at the same site of the ovary were obtained. The images were evaluated by 2 pediatric radiologists using a 4-level grading system to evaluate the degree of vascularity. Results: A total of 69 patients were evaluated, including 19 who were pubertal, 39 with premature thelarche (PT), and 11 with precocious puberty (PP) were evaluated. Among the 50 patients, 11 were diagnosed with precocious puberty, while 39 received a diagnosis of premature thelarce. The sensitivity of the techniques according to vascularity grading was interpreted as mSMI > cSMI > PDI > CDI. The interrater agreement of vascularity grading among the two observers according to their κ values was almost perfect in the left CDI, left PDI, and right PDI (κ > 0.92), strong in the right CDI, left and right cSMI, left and right mSMI (κ > 0.80), and moderate in the right CDI (κ > 0.60). There was a significant difference between the vascularity of pubertal and prepubertal ovaries, which correlated with their volumes (P < .001). Ovarian vascularity was similar in precocious puberty and premature thelarche groups. Conclusion: The SMI is superior to other Doppler methods, such as PDI and CDI, in the evaluation of ovarian vascularity. It is useful in evaluating parenchymal vascularity, especially in pediatric patients. [ABSTRACT FROM AUTHOR]

Objectives: This study assesses the perceptions and attitudes of Chinese radiologists concerning the application of artificial intelligence (AI) in the diagnosis of lung nodules. Material and Methods: An anonymous questionnaire, consisting of 26 questions addressing the usability of AI systems and comprehensive evaluation of AI technology, was distributed to all radiologists affiliated with Beijing Anzhen Hospital and Beijing Tsinghua Changgung Hospital. The data collection was conducted between July 19, and 21, 2023. Results: Of the 90 respondents, the majority favored the AI system's convenience and usability, reflected in "good" system usability scale (SUS) scores (Mean ± standard deviation [SD]: 74.3 ± 11.9). General usability was similarly well-received (Mean ± SD: 76.0 ± 11.5), while learnability was rated as "acceptable" (Mean ± SD: 67.5 ± 26.4). Most radiologists noted increased work efficiency (Mean Likert scale score: 4.6 ± 0.6) and diagnostic accuracy (Mean Likert scale score: 4.2 ± 0.8) with the AI system. Views on AI's future impact on radiology careers varied (Mean ± SD: 3.2 ± 1.4), with a consensus that AI is unlikely to replace radiologists entirely in the foreseeable future (Mean ± SD: 2.5 ± 1.1). Conclusion: Radiologists at two leading Beijing hospitals generally perceive the AI-assisted lung nodule diagnostic system positively, citing its user-friendliness and effectiveness. However, the system's learnability requires enhancement. While AI is seen as beneficial for work efficiency and diagnostic accuracy, its long-term career implications remain a topic of debate. [ABSTRACT FROM AUTHOR]

Persistent sciatic artery (PSA) is a rare congenital vascular anomaly resulting from embryologic axial artery malformation in the lower limb. This case report presents three patients aged 45–60, each with bilateral PSA presenting with symptoms indicative of PSA complications, including aneurysmal degeneration, limb ischemia, thromboembolism, or neuralgia from nerve compression. It highlights the diagnostic process, management strategies, and clinical outcomes observed at a tertiary referral hospital. Treatment involved a collaborative, multidisciplinary approach with vascular surgeons, internists, and radiologists tailoring interventions to individual patient findings and disease progression. This report aims to provide insights into the diverse presentations and management of PSA in a resource limited setting, encouraging further reporting and case studies to enhance understanding of therapeutic outcomes. [ABSTRACT FROM AUTHOR]

Background/Objectives: Imaging studies are often an integral part of patient evaluation and serve as the primary means of communication between radiologists and referring physicians. This study aimed to evaluate brain Magnetic Resonance Imaging (MRI) reports and to determine whether these reports follow a standardized or narrative format. Methods: A series of 466 anonymized MRI reports from an academic hospital were downloaded from the Picture Archiving and Communication System (PACS) in portable document format (pdf) for the period between August 2017 and March 2018. Two hundred brain MRI reports, written by four radiologists, were compared to a structured report template from the Radiology Society of North America (RSNA) and were included, whereas MR-modified techniques, such as MRI orbits and MR venography reports, were excluded (n = 266). All statistical analyses were conducted using Statistical Package for the Social Sciences (SPSS) statistical software (version 16.4.1, MedCalc Software). Results: None of the included studies used the RSNA template for structured reports (SRs). The highest number of brain-reported pathologies was for vascular disease (24%), while the lowest was for infections (3.5%) and motor dysfunction (5.5%). Radiologists specified the Technique (n = 170, 85%), Clinical Information (n = 187, 93.5%), and Impression (n = 197, 98.5%) in almost all reports. However, information in the Findings section was often missing. As hypothesized, radiologists with less experience showed a greater commitment to reporting additional elements than those with more experience. Conclusions: The SR template for medical imaging has been accessible online for over a decade. However, many hospitals and radiologists still use the free-text style for reporting. Our study was conducted in an academic hospital with a fellowship program, and we found that structured reporting had not yet been implemented. As the health system transitions towards teleservices and teleradiology, more efforts need to be put into advocating standardized reporting in medical imaging. [ABSTRACT FROM AUTHOR]

Purpose: Septic arthritis is a dangerous medical condition requiring prompt diagnosis, often via arthrocentesis. A "dry tap" occurs when no fluid is aspirated. We hypothesized that the absence of a joint effusion on pre-procedure advanced imaging would reliably predict a dry tap and exclude septic arthritis. Methods: A cohort of 217 arthrocentesis cases of large joints (hips, shoulders, knees) from our institution, with pre-procedure advanced imaging (CT, MR, US) of the same joint performed within the previous 48 h, was analyzed. Exclusion criteria included non-native joints or inadequate imaging of the affected joint. These cases underwent blinded review by 4 radiologists who measured the deepest pocket of joint fluid on the pre-procedure imaging. Wilcoxon rank-sum test was performed comparing joint fluid pocket size to outcomes of successful aspiration and final diagnosis. Results: A smaller average joint pocket fluid size was present on advanced imaging in both dry taps compared with successful arthrocenteses (p <.0001), and in uninfected joints compared with septic joints (p =.0001). However, the overlap of values was too great to allow for a perfectly predictive cutoff. 29% (5/17) of patients with no visible joint fluid on pre-aspiration imaging underwent successful arthrocentesis, one case representing septic arthritis. Conclusion: Volume of joint fluid on advanced pre-arthrocentesis imaging cannot reliably predict subsequent dry tap nor exclude septic arthritis. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the multisystem factors contributing to redundant neurovascular orders in the ED. Methods: This was an IRB-approved, retrospective study, performed at a single institution examining a 5-year history of redundant CTA/MRA head and neck (HN) exams performed in the ED for patients with no documented clinical change in mental status/neurological exam necessitating additional imaging. Factors contributing to redundant ordering including provider experience, synchronous order placement, and radiologist recommendations were examined. Additionally, the impact of duplicative imaging in terms of medical cost and ED length of stay was evaluated. Results: 250 patients met inclusion criteria with both CTA/MRA of the HN performed during a single ED encounter (total 500 exams). 190 (76%) redundant exams were not recommended by a radiologist and contributed to an added ED length of stay of 3.6 h on average. Provider experience was not a significant contributing factor. 60 (24%) of redundant exams were recommended by a radiologist and were most frequently CTAs needed to clarify an area of artifact/high-grade stenosis/occlusion on a primary MRA exam. Conclusion: Evaluation of contributing factors to redundant CTA/MRA HN exams ordering has highlighted multiple associated factors including provider experience, recommendations by radiologists for clarification of MRA findings, as well as systems processes related to synchronous CTA/MRA order placement. [ABSTRACT FROM AUTHOR]

Purpose: We hypothesize that delayed phase imaging does not provide additional diagnostic information in patients who undergo multi-phasic CTA for suspected active bleeding. Methods: Data on patients who underwent multiphasic CTA (pre-contrast, arterial, porto-venous, and delayed phases) for suspected acute bleed were retrospectively collected between January 2019 and November 2021. CTA images were reviewed by a general radiologist, an interventional radiologist, and a body imaging radiologist independently. Each reader evaluated if delayed phase images provided additional information that would change the final impression of the CTA report. Additional information regarding bleeding location, time needed for delayed image acquisition, and radiation exposure were also obtained. Results: A total of 104 patients with CTAs were analyzed with an average age of 58 years ± 22. Studies rated with absent additional findings on delayed images were 102 (98.1%) by the interventional radiologist, 101 (97.1%) by the body imaging radiologist, and 100 (96.1%) by the general radiologist with percent agreement of 96.15% (kappa 0.54, p < 0.001). All the findings were characterized as unlikely to be clinically significant. Mean time added to complete a delayed phase images was 3.61 ± 3.4 min. The average CT dose length product (DLP) for the total exam was 3621.78 ± 2129.57 mGy.cm with delayed acquisition adding a mean DLP of 847.75 ± 508.8 mGy.cm. Conclusion: Delayed phase imaging does not provide significant additional diagnostic information in evaluating patients with suspected active bleeding but is associated with increased examination time and radiation exposure. [ABSTRACT FROM AUTHOR]

Atraumatic muscle disorders comprise a very wide range of skeletal muscle diseases, including metabolic, inflammatory, autoimmune, infectious, ischemic, and neoplastic involvement of the muscles. Therefore, one must take clinical and laboratory data into consideration to elucidate the differential diagnoses, as well as the distribution of the muscle compromise along the body—whether isolated or distributed along the body in a symmetric or asymmetrical fashion. Assessment of muscular disorders often requires imaging investigation before image-guided biopsy or more invasive procedures; therefore, radiologists should understand the advantages and limitations of imaging methods for proper lesion evaluation and be aware of the imaging features of such disorders, thus contributing to proper decision-making and good patient outcomes. In this review, we propose a systematic approach for the assessment of muscle disorders based on their main imaging presentation, dividing them into patterns that can be easily recognized. [ABSTRACT FROM AUTHOR]

Alignment, longitudinal growth, and function of the musculoskeletal unit of the pediatric hand is complex due to the combination of open growth plates, ossification variations, and their relationship with finger tendinous and ligamentous attachments. This review presents the basics of normal development, acquired and congenital variations, and traumatic conditions of the pediatric hand from the perspective of the pediatric musculoskeletal radiologist and hand surgeon. This multimodality imaging article focuses on non-oncologic and non-rheumatologic conditions of the pediatric hand, inclusive of fingers, thumb, and metacarpal bones. [ABSTRACT FROM AUTHOR]

Gender-affirming surgery (GAS) is increasingly being performed. GAS is tailored to the patient leading to a diverse spectrum of radiologic post-operative findings. Radiologists who are unfamiliar with expected anatomic alterations after GAS may misdiagnose important complications leading to adverse patient outcomes. This collaborative multi-institutional review aims to: Describe relevant embryology and native anatomy. Describe relevant Gender-Affirming Surgery (GAS) techniques and expected neo-anatomy with associated complications, including common terminology. Review expected imaging appearance of neo-anatomy/postoperative findings. Review multi-modality [ultrasound, plain film, retrograde urethrogram, computed tomography] emergent imaging findings. Understand unique patient evaluation and imaging protocol considerations in the GAS population. Discuss pearls and pitfalls of imaging in the acute post-GAS setting. [ABSTRACT FROM AUTHOR]

Devices for the gastrointestinal tract are widely available and constantly advancing with less invasive techniques. They play a crucial role in diagnostic and therapeutic interventions and are commonly placed by interventional radiologists, gastroenterologists, and surgeons. These devices frequently appear in imaging studies, which verify their proper placement, identify any complications, or may be incidentally detected. Radiologists must be able to identify these devices at imaging and understand their intended purpose to assess their efficacy, detect complications such as incorrect positioning, and avoid misinterpreting them as abnormalities. Furthermore, many patients with these devices may require MRI, making assessing compatibility essential for safe patient care. This review seeks to provide a succinct and practical handbook for radiologists regarding both common and uncommon gastrointestinal devices. In addition to textual descriptions of clinical indications, imaging findings, complications, and MRI compatibility, the review incorporates a summary table as a quick reference point for key information and illustrative images for each device. [ABSTRACT FROM AUTHOR]

Portal venous gas on abdominal ultrasound classically represents an indirect indicator of bowel ischemia, a critical condition which poses a high patient mortality and therefore warrants emergent corrective action. While the classic appearance of portal venous gas on ultrasound is well-described in the literature, the characteristic descriptors are nonspecific and may actually represent other less emergent mimics. Therefore, while radiologists should remain vigilant for the detection of findings corresponding to portal venous gas, they should also be aware of similar-appearing entities in order to provide the most accurate diagnosis. This pictorial essay will open with imaging examples of true portal venous gas attributable to bowel ischemia and describe the classic features which should alert radiologists to this specific diagnosis. Subsequently, this pictorial essay will provide imaging examples of other various other clinical entities which on ultrasound may share similar imaging characteristics. An important objective of this pictorial essay is to highlight distinguishing imaging features along with specific clinical circumstances for each pathological entity which can direct radiologists into identifying the correct diagnosis. [ABSTRACT FROM AUTHOR]

Despite the increased use of diagnostic imaging in Africa, the completion of the Radiology Request Form (RRF) remains suboptimal, often relying on paper-based communication. To examine the practices surrounding RRF completion in the African continent, on 25 March 2024, we conducted a systematic review of peer-reviewed articles describing the practice in African settings. Non-African studies, studies involving non-human subjects, and articles examining the practice of the RRF for interventional usage were excluded. Our search involves PubMed/MEDLINE, ScienceDirect, Scopus, Web of Science, Google Scholar, and African Journals Online. The included studies were 3004, of which 30 met the inclusion criteria. These studies span eight countries and highlighted several shortcomings, including the usage of informal forms, unconventional abbreviations, illegibility, inaccuracy, and the lack of important fields from institutional forms, commonly the last menstrual period and the referrer's contact details. We also found widespread non-compliance in all RRF fields; half of the studies did not have an adequately filled form. Incomplete RRFs lead to delayed imaging, increased workloads for radiographers and radiologists, and potential misdiagnoses due to insufficient information. It will also impede the application of radiation protection principles. To address these challenges, empowering radiographers and radiologists and encouraging best practices is essential. Regular audits and educational initiatives aimed at clinicians are recommended. While transitioning to a paperless communication system might help, implementing nationwide quality improvement projects to standardise radiology request forms is currently more feasible. [ABSTRACT FROM AUTHOR]

To determine the diagnostic performance of simulated reduced-dose chest CT scans regarding pulmonary T1 tumors and assess the potential impact on patient management, a repository of 218 patients with histologically proven pulmonary T1 tumors was used. Virtual reduced-dose images were simulated at 25%- and 5%-dose levels. Tumor size, attenuation, and localization were scored by two experienced chest radiologists. The impact on patient management was assessed by comparing hypothetical LungRADS scores. The study included 210 patients (41% females, mean age 64.5 ± 9.2 years) with 250 eligible T1 tumors. There were differences between the original and the 5%—but not the 25%—dose simulations, and LungRADS scores varied between the dose levels with no clear trend. Sensitivity of Reader 1 was significantly lower using the 5%-dose vs. 25%-dose vs. original dose for size categorization (0.80 vs. 0.85 vs. 0.84; p = 0.007) and segmental localization (0.81 vs. 0.86 vs. 0.83; p = 0.018). Sensitivities of Reader 2 were unaffected by a dose reduction. A CT dose reduction may affect the correct categorization and localization of pulmonary T1 tumors and potentially affect patient management. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion Medical imaging's critical role in diagnosis requires prompt and precise image interpretation. Numerous radiology departments, especially in low‐resourced settings, encounter challenges such as a shortage of radiologists in their operational setup. This study explored the perceptions of radiographers and radiologists from low‐resourced departments in a single country regarding operational challenges and potential solutions in image interpretation.A qualitative approach was utilised, involving heads of departments, senior radiographers, and radiologists with a minimum of 5 years of experience, from three major state referral hospitals. Face‐to‐face, semi‐structured interviews were conducted in November 2022, using an interview guide that included questions on the challenges encountered during image interpretation and the proposed solutions. Data analysis was conducted using Atlas.ti version 9.0, following the four‐step content analysis method. All participants willingly provided consent to participate in the study.Ten participants, comprising two radiologists and eight radiographers participated in the study. The research identified three main themes: image interpretation pathways, image interpretation operational challenges and proposed solutions for image interpretation. In addition, a total of 10 subthemes were generated from the three main themes.The study revealed critical challenges and the need to explore the formal inclusion of radiographers in image interpretation, as a way to improve efficiency. However, a comprehensive assessment of the image interpretation system, encompassing radiographers' knowledge and competence, is recommended for context‐specific, empirical‐based modifications to enhance service provision. [ABSTRACT FROM AUTHOR]

Objectives: To compare diagnostic accuracy of a deep learning artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT to attending radiologists and assess which undetected fractures were injuries in need of stabilising therapy (IST). Methods: This single-centre, retrospective diagnostic accuracy study included consecutive patients (age ≥18 years; 2007–2014) screened for C-spine fractures with CT. To validate ground truth, one radiologist and three neurosurgeons independently examined scans positive for fracture. Negative scans were followed up until 2022 through patient files and two radiologists reviewed negative scans that were flagged positive by AI. The neurosurgeons determined which fractures were ISTs. Diagnostic accuracy of AI and attending radiologists (index tests) were compared using McNemar. Results: Of the 2368 scans (median age, 48, interquartile range 30–65; 1441 men) analysed, 221 (9.3%) scans contained C-spine fractures with 133 IST. AI detected 158/221 scans with fractures (sensitivity 71.5%, 95% CI 65.5–77.4%) and 2118/2147 scans without fractures (specificity 98.6%, 95% CI 98.2–99.1). In comparison, attending radiologists detected 195/221 scans with fractures (sensitivity 88.2%, 95% CI 84.0–92.5%, p < 0.001) and 2130/2147 scans without fracture (specificity 99.2%, 95% CI 98.8–99.6, p = 0.07). Of the fractures undetected by AI 30/63 were ISTs versus 4/26 for radiologists. AI detected 22/26 fractures undetected by the radiologists, including 3/4 undetected ISTs. Conclusion: Compared to attending radiologists, the artificial intelligence has a lower sensitivity and a higher miss rate of fractures in need of stabilising therapy; however, it detected most fractures undetected by the radiologists, including fractures in need of stabilising therapy. Clinical relevance statement The artificial intelligence algorithm missed more cervical spine fractures on CT than attending radiologists, but detected 84.6% of fractures undetected by radiologists, including fractures in need of stabilising therapy. Key Points: The impact of artificial intelligence for cervical spine fracture detection on CT on fracture management is unknown. The algorithm detected less fractures than attending radiologists, but detected most fractures undetected by the radiologists including almost all in need of stabilising therapy. The artificial intelligence algorithm shows potential as a concurrent reader. [ABSTRACT FROM AUTHOR]

Objectives: To assess radiologists' current use of, and opinions on, structured reporting (SR) in oncologic imaging, and to provide recommendations for a structured report template. Materials and methods: An online survey with 28 questions was sent to European Society of Oncologic Imaging (ESOI) members. The questionnaire had four main parts: (1) participant information, e.g., country, workplace, experience, and current SR use; (2) SR design, e.g., numbers of sections and fields, and template use; (3) clinical impact of SR, e.g., on report quality and length, workload, and communication with clinicians; and (4) preferences for an oncology-focused structured CT report. Data analysis comprised descriptive statistics, chi-square tests, and Spearman correlation coefficients. Results: A total of 200 radiologists from 51 countries completed the survey: 57.0% currently utilized SR (57%), with a lower proportion within than outside of Europe (51.0 vs. 72.7%; p = 0.006). Among SR users, the majority observed markedly increased report quality (62.3%) and easier comparison to previous exams (53.5%), a slightly lower error rate (50.9%), and fewer calls/emails by clinicians (78.9%) due to SR. The perceived impact of SR on communication with clinicians (i.e., frequency of calls/emails) differed with radiologists' experience (p < 0.001), and experience also showed low but significant correlations with communication with clinicians (r = − 0.27, p = 0.003), report quality (r = 0.19, p = 0.043), and error rate (r = − 0.22, p = 0.016). Template use also affected the perceived impact of SR on report quality (p = 0.036). Conclusion: Radiologists regard SR in oncologic imaging favorably, with perceived positive effects on report quality, error rate, comparison of serial exams, and communication with clinicians. Clinical relevance statement: Radiologists believe that structured reporting in oncologic imaging improves report quality, decreases the error rate, and enables better communication with clinicians. Implementation of structured reporting in Europe is currently below the international level and needs society endorsement. Key Points: • The majority of oncologic imaging specialists (57% overall; 51% in Europe) use structured reporting in clinical practice. • The vast majority of oncologic imaging specialists use templates (92.1%), which are typically cancer-specific (76.2%). • Structured reporting is perceived to markedly improve report quality, communication with clinicians, and comparison to prior scans. [ABSTRACT FROM AUTHOR]

[Display omitted] • The suspicious ultrasonography features are related to immune response in thyroid cancer. • The features were also related to epithelial to mesenchymal transition (EMT). • Immune response and EMT were related to aggressive clinical behaviors. • Immune response and EMT reflected in the decision by radiologists and convolutional neural networks. Ultrasonography (US) features of papillary thyroid cancers (PTCs) are used to select nodules for biopsy due to their association with tumor behavior. However, the molecular biological mechanisms that lead to the characteristic US features of PTCs are largely unknown. This study aimed to investigate the molecular biological mechanisms behind US features assessed by radiologists and three convolutional neural networks (CNN) through transcriptome analysis. Transcriptome data from 273 PTC tissue samples were generated and differentially expressed genes (DEGs) were identified according to US feature. Pathway enrichment analyses were also conducted by gene set enrichment analysis (GSEA) and ClusterProfiler according to assessments made by radiologists and three CNNs - CNN1 (ResNet50), CNN2 (ResNet101) and CNN3 (VGG16). Signature gene scores for PTCs were calculated by single-sample GSEA (ssGSEA). Individual suspicious US features consistently suggested an upregulation of genes related to immune response and epithelial-mesenchymal transition (EMT). Likewise, PTCs assessed as positive by radiologists and three CNNs showed the coordinate enrichment of similar gene sets with abundant immune and stromal components. However, PTCs assessed as positive by radiologists had the highest number of DEGs, and those assessed as positive by CNN3 had more diverse DEGs and gene sets compared to CNN1 or CNN2. The percentage of PTCs assessed as positive or negative concordantly by radiologists and three CNNs was 85.6% (231/273) and 7.1% (3/273), respectively. US features assessed by radiologists and CNNs revealed molecular biologic features and tumor microenvironment in PTCs. [ABSTRACT FROM AUTHOR]

Existing magnetic resonance imaging translation models rely on generative adversarial networks, primarily employing simple convolutional neural networks. Unfortunately, these networks struggle to capture global representations and contextual relationships within magnetic resonance images. While the advent of Transformers enables capturing long-range feature dependencies, they often compromise the preservation of local feature details. To address these limitations and enhance both local and global representations, we introduce DBGAN, a novel dual-branch generative adversarial network. In this framework, the Transformer branch comprises sparse attention blocks and dense self-attention blocks, allowing for a wider receptive field while simultaneously capturing local and global information. The convolutional neural network branch, built with integrated residual convolutional layers, enhances local modeling capabilities. Additionally, we propose a fusion module that cleverly integrates features extracted from both branches. Extensive experimentation on two public datasets and one clinical dataset validates significant performance improvements with DBGAN. On Brats2018, it achieves a 10% improvement in MAE, 3.2% in PSNR, and 4.8% in SSIM for image generation tasks compared to RegGAN. Notably, the generated MRIs receive positive feedback from radiologists, underscoring the potential of our proposed method as a valuable tool in clinical settings. [ABSTRACT FROM AUTHOR]

Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans. [ABSTRACT FROM AUTHOR]

Dr. Tariq Mahmood, a Professor Emeritus of Radiation Oncology at Jinnah Postgraduate Medical Centre in Karachi, has been awarded a Fellowship by the Royal College of Radiologists in the UK. The presentation ceremony took place in London and recognized Dr. Mahmood's work in the field of radiology and radiation oncology in Pakistan. The Royal College of Radiologists is the largest organization of its kind and awards the Honorary Fellowship to a select number of individuals worldwide. Dr. Mahmood has also received other prestigious honors for his contributions to medicine, including the Tamgha-e-Imtiaz for public service and an honorary Fellowship from the Royal College of Edinburgh. The Cyber knife- Radiation Oncology services at JPMC Karachi, where Dr. Mahmood works, have also been recognized by the Royal College of Radiologists. This recognition is significant for the province of Sindh and Pakistan as it acknowledges the free cancer treatment facility provided through a public-private partnership. The JPMC is the only center in the world to offer free treatment of cancer with Cyber knife and Tom therapy, benefiting patients from various countries and cities. The Radiation Oncology department at JPMC is equipped with advanced technology and provides precise treatments for various types of cancer. Additionally, the department offers free PET-CT scans and plans to conduct thousands more in the coming year. Another recipient of the Fellowship, Prof. Yan Chen, has made significant contributions to mammographic screening and AI performance evaluation in the UK. Her [Extracted from the article]

Background: This study is to propose a clinically applicable 2-echelon (2e) diagnostic criteria for the analysis of thyroid nodules such that low-risk nodules are screened off while only suspicious or indeterminate ones are further examined by histopathology, and to explore whether artificial intelligence (AI) can provide precise assistance for clinical decision-making in the real-world prospective scenario. Methods: In this prospective study, we enrolled 1036 patients with a total of 2296 thyroid nodules from three medical centers. The diagnostic performance of the AI system, radiologists with different levels of experience, and AI-assisted radiologists with different levels of experience in diagnosing thyroid nodules were evaluated against our proposed 2e diagnostic criteria, with the first being an arbitration committee consisting of 3 senior specialists and the second being cyto- or histopathology. Results: According to the 2e diagnostic criteria, 1543 nodules were classified by the arbitration committee, and the benign and malignant nature of 753 nodules was determined by pathological examinations. Taking pathological results as the evaluation standard, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the AI systems were 0.826, 0.815, 0.821, and 0.821. For those cases where diagnosis by the Arbitration Committee were taken as the evaluation standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.946, 0.966, 0.964, and 0.956. Taking the global 2e diagnostic criteria as the gold standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.868, 0.934, 0.917, and 0.901, respectively. Under different criteria, AI was comparable to the diagnostic performance of senior radiologists and outperformed junior radiologists (all P < 0.05). Furthermore, AI assistance significantly improved the performance of junior radiologists in the diagnosis of thyroid nodules, and their diagnostic performance was comparable to that of senior radiologists when pathological results were taken as the gold standard (all p > 0.05). Conclusions: The proposed 2e diagnostic criteria are consistent with real-world clinical evaluations and affirm the applicability of the AI system. Under the 2e criteria, the diagnostic performance of the AI system is comparable to that of senior radiologists and significantly improves the diagnostic capabilities of junior radiologists. This has the potential to reduce unnecessary invasive diagnostic procedures in real-world clinical practice. [ABSTRACT FROM AUTHOR]

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. • We describe how working with the statistician Doug Altman improved our research. • Medical statisticians should be consulted when preparing for research. • Medical statisticians help design unbiased, adequately powered studies. • High quality medical research is unlikely without statistical help. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging—Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale. In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5–10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4–6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov , NCT05489341. Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87–0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83–0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6–63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3–92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3–72·4] vs 69·0% [65·5–72·5]) at the same sensitivity (96·1%, 94·0–98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (−0·04) was greater than the non-inferiority margin (−0·05) and a p value below the significance threshold was reached (p<0·001). An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicability of this system. Health~Holland and EU Horizon 2020. [ABSTRACT FROM AUTHOR]

Study Design. Retrospective cohort. Objective. To evaluate the clinical relevance, usefulness, and financial implications of intraoperative radiograph interpretation by radiologists in spine surgery. Summary of Background Data. Due to rising health care costs, spine surgery is under scrutiny to maximize value-based care. Formal radiographic analysis remains a potential source of unnecessary health care costs, especially for intraoperative radiographs. Materials and Methods. A retrospective cohort analysis was performed on all adult elective spine surgeries at a single institution between July 2020 and July 2021. Demographic and radiographic data were collected, including intraoperative localization and post-instrumentation radiographs. Financial data were obtained through the institution's price estimator. Radiographic characteristics included time from radiographic imaging to completion of radiologist interpretation report, completion of radiologist interpretation report before the conclusion of surgical procedure, clinical relevance, and clinical usefulness. Reports were considered clinically relevant if the spinal level of the procedure was described and clinically useful if completed before the conclusion of the procedure and deemed clinically relevant. Results. Four hundred eighty-one intraoperative localization and post-instrumentation radiographs from 360 patients revealed a median delay of 128 minutes between imaging and completion of the interpretive report. Only 38.9% of reports were completed before the conclusion of surgery. There were 79.4% deemed clinically relevant and only 33.5% were clinically useful. Localization reports were completed more frequently before the conclusion of surgery (67.2% vs. 34.4%) but with lower clinical relevance (90.1% vs. 98.5%) and clinical usefulness (60.3% vs. 33.6%) than post-instrumentation reports. Each patient was charged $32 to $34 for the interpretation fee, cumulating a minimum total cost of $15,392. Conclusions. Formal radiographic interpretation of intraoperative spine radiographs was of low clinical utility for spine surgeons. Institutions should consider optimizing radiology workflows to improve timeliness and clinical relevance or evaluate the necessity of reflexive consultation to radiology for intraoperative imaging interpretation to ensure that value-based care is maximized during spine surgeries. [ABSTRACT FROM AUTHOR]

• The presence and quantification of moderate to severe incidental CAC was underreported at a major academic center. Failure to report incidental CAC represents a missed opportunity to initiate preventive therapies. • Education of reporting radiologists and changes to the reporting templates significantly improved the reporting and quantification of incidental CAC. • Hospital systems interested in improving the quality of their radiology reporting procedures should examine their practices to assure that CAC quantification is routinely performed. Current guidelines recommend the reporting of incidental coronary artery calcification (CAC) on non-electrocardigram-gated computed tomography (CT) scans of the chest. The finding of incidental moderate or severe CAC on non-cardiac non-contrast chest CT correlates with a CAC score ≥ 100 Agatston units, a guideline-based indication for a clinician-patient discussion regarding the initiation of statin therapy. In contemporary practice, whether the presence and severity of incidental CAC are routinely reported on such CT scans of the chest is unknown. At a major university hospital, we collected a one-month convenience sample of 297 patients who had chest CT imaging for indications other than lung cancer screening (OICT) and 42 patients who underwent lung cancer chest CT screening (LSCT). We evaluated reporting patterns of incidental CAC in the body and impression of the reports as compared to the overreading of such studies by a board-certified CT chest radiologist. We hypothesized and demonstrated that there was underreporting of incidental CAC on these scans. We then undertook an initiative to educate reporting radiologists on the importance of reporting CAC and implemented a reporting template change to encourage routine reporting. Then we repeated another one-month sample (n= 363 for the OICT and n= 63 for the LSCT groups) to evaluate reporting patterns following our intervention. The presence of incidental moderate and severe CAC was systematically underreported in the OICT group (0 and 4.8 %) and the severity was never mentioned in the impression of reports. In the LSCT group, the presence of incidental moderate and severe CAC was also underreported (66.7 % and 75 %) and the severity of CAC was mentioned 50 % of the time in the impression of the reports. Following the initiation of an educational program and radiology reporting template change, there was a significant increase in reporting of moderate or severe CAC in the OICT group (0 vs. 80.0 %, p < 0.001) and (4.8 vs. 93.5 %, p < 0.001) respectively and a significant increase in the reporting of the severity of incidental CAC for those with severe CAC in the LSCT group (50 vs. 94.1 %, p=0.006). Despite guideline recommendations, incidental CAC was underreported at a large academic center. We implemented a system that significantly improved reporting patterns of incidental CAC. Failure to report incidental CAC represents a missed opportunity to initiate preventive therapies. Hospital systems interested in improving the quality of their radiology reporting procedures should examine their practices to assure that CAC quantification is routinely performed. [ABSTRACT FROM AUTHOR]

Background. The use of radiological approaches in forensic investigations is rapidly emerging as an alternative to traditional autopsies. Here, we present the experience of the Sicilian and Calabrian forensic medical departments over the past five years, highlighting the application of PMCT to support autopsies in cases involving gunshot wound victims. Materials and Methods. From January 2020 to March 2024, thirty cadavers who died from gunshot wounds underwent total body PMCT examinations followed by autopsies. The examinations included multi-planar and 3D reconstructions. Data collected for each subject included gender, age, mode of death, type of weapon, number of shots, body sites affected, and presence of retained bullets. A team of two radiologists and two forensic physicians evaluated the images and compared them with the autopsy findings. Conclusion. PMCT and 3D reconstruction enabled evaluation of entry wounds, fracture margins, injury sites, fragments, and anatomical trajectories. The results from PMCT were compared with autopsy findings in terms of entry wounds, wound channels, foreign bodies, organ and skeletal injuries, and causes of death. PMCT showed concordance with autopsy findings in determining the cause of death in all cases. The speed, non-invasiveness, and ability to manipulate and reconstruct data make PMCT an ideal technique for supporting forensic investigations and autopsies. PMCT is highly reliable for accurately identifying lethal wounds and determining the cause of death. It excels at tracking wound paths, distinguishing entrance and exit wounds, locating retained bullets, and detecting fragmentation and dispersion of ballistic components. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China. Methods: This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP. Results: The study included 452 valid questionnaires. The mean knowledge score was 9.01± 4.87, the attitude score was 48.96± 4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5– 10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P< 0.001), and engaging in AI diagnosis-related research (OR=3.178, P< 0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5– 10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P< 0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P< 0.001), an associate senior professional title (OR=4.267, P=0.026), 5– 10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P< 0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (β=0.481, P< 0.001) and practice (β=0.412, P< 0.001), and attitude had a direct effect on practice (β=0.135, P< 0.001). Conclusion: Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary. [ABSTRACT FROM AUTHOR]

Background: Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype than PKD1 mutations. However, pancreatic cysts are commonly overlooked by radiologists. Here, we automate the detection of pancreatic cysts on abdominal MRI in ADPKD. Methods: Eight nnU-Net-based segmentation models with 2D or 3D configuration and various loss functions were trained on positive-only or positive-and-negative datasets, comprising axial and coronal T2-weighted MR images from 254 scans on 146 ADPKD patients with pancreatic cysts labeled independently by two radiologists. Model performance was evaluated on test subjects unseen in training, comprising 40 internal, 40 external, and 23 test–retest reproducibility ADPKD patients. Results: Two radiologists agreed on 52% of cysts labeled on training data, and 33%/25% on internal/external test datasets. The 2D model with a loss of combined dice similarity coefficient and cross-entropy trained with the dataset with both positive and negative cases produced an optimal dice score of 0.7 ± 0.5/0.8 ± 0.4 at the voxel level on internal/external validation and was thus used as the best-performing model. In the test–retest, the optimal model showed superior reproducibility (83% agreement between scan A and B) in segmenting pancreatic cysts compared to six expert observers (77% agreement). In the internal/external validation, the optimal model showed high specificity of 94%/100% but limited sensitivity of 20%/24%. Conclusions: Labeling pancreatic cysts on T2 images of the abdomen in patients with ADPKD is challenging, deep learning can help the automated detection of pancreatic cysts, and further image quality improvement is warranted. [ABSTRACT FROM AUTHOR]

Background: There is little information regarding the size measurement differences in gallbladder (GB) polyps performed by different radiologists on abdominal ultrasonography (US). Aim: To reveal the differences in GB polyp size measurements performed by different radiologists on abdominal US. Methods: From June to September 2022, the maximum diameter of 228 GB polyps was measured twice on abdominal US by one of three radiologists (a third-year radiology resident [reader A], a radiologist with 7 years of experience in abdominal US [reader B], and an abdominal radiologist with 8 years of experience in abdominal US [reader C]). Intra-reader agreements for polyp size measurements were assessed by intraclass correlation coefficient (ICC). A Bland–Altman plot was used to visualize the differences between the first and second size measurements in each reader. Results: Reader A, reader B, and reader C evaluated 65, 77, and 86 polyps, respectively. The mean size of measured 228 GB polyps was 5.0 ± 1.9 mm. Except for the case where reader A showed moderate intra-reader agreement (0.726) for polyps with size ≤ 5 mm, all readers showed an overall high intra-reader reliability (reader A, ICC = 0.859; reader B, ICC = 0.947, reader C, ICC = 0.948), indicative of good and excellent intra-reader agreements. The 95% limit of agreement of reader A, B, and C was 1.9 mm of the mean in all three readers. Conclusions: GB polyp size measurement on abdominal US showed good or excellent intra-reader agreements. However, size changes of approximately less than 1.9 mm should be interpreted carefully because these may be within the measurement error. [ABSTRACT FROM AUTHOR]

Introduction: Ultrasonography (US) features of papillary thyroid cancers (PTCs) are used to select nodules for biopsy due to their association with tumor behavior. However, the molecular biological mechanisms that lead to the characteristic US features of PTCs are largely unknown. Objectives: This study aimed to investigate the molecular biological mechanisms behind US features assessed by radiologists and three convolutional neural networks (CNN) through transcriptome analysis. Methods: Transcriptome data from 273 PTC tissue samples were generated and differentially expressed genes (DEGs) were identified according to US feature. Pathway enrichment analyses were also conducted by gene set enrichment analysis (GSEA) and ClusterProfiler according to assessments made by radiologists and three CNNs - CNN1 (ResNet50), CNN2 (ResNet101) and CNN3 (VGG16). Signature gene scores for PTCs were calculated by single-sample GSEA (ssGSEA). Results: Individual suspicious US features consistently suggested an upregulation of genes related to immune response and epithelial-mesenchymal transition (EMT). Likewise, PTCs assessed as positive by radiologists and three CNNs showed the coordinate enrichment of similar gene sets with abundant immune and stromal components. However, PTCs assessed as positive by radiologists had the highest number of DEGs, and those assessed as positive by CNN3 had more diverse DEGs and gene sets compared to CNN1 or CNN2. The percentage of PTCs assessed as positive or negative concordantly by radiologists and three CNNs was 85.6% (231/273) and 7.1% (3/273), respectively. Conclusion: US features assessed by radiologists and CNNs revealed molecular biologic features and tumor microenvironment in PTCs.