Thymic Imaging Pitfalls and Strategies for Optimized Diagnosis.

Thymic imaging is challenging because the imaging appearance of a variety of benign and malignant thymic conditions are similar. CT is the most commonly used modality for mediastinal imaging, while MRI and fluorine 18 fluorodeoxyglucose (FDG) PET/CT are helpful when they are tailored to the correct indication. Each of these imaging modalities has limitations and technical pitfalls that may lead to an incorrect diagnosis and mismanagement. CT may not be sufficient for the characterization of cystic thymic processes and differentiation between thymic hyperplasia and thymic tumors. MRI can be used to overcome these limitations but is subject to other potential pitfalls such as an equivocal decrease in signal intensity at chemical shift imaging, size limitations, unusual signal intensity for cysts, subtraction artifacts, pseudonodularity on T2-weighted MR images, early imaging misinterpretation, flow and spatial resolution issues hampering assessment of local invasion, and the overlap of apparent diffusion coefficients between malignant and benign thymic entities. FDG PET/CT is not routinely indicated due to some overlap in FDG uptake between thymomas and benign thymic processes. However, it is useful for staging and follow-up of aggressive tumors (eg, thymic carcinoma), particularly for detection of occult metastatic disease. Pitfalls in imaging after treatment of thymic malignancies relate to technical challenges such as postthymectomy sternotomy streak metal artifacts, differentiation of postsurgical thymic bed changes from tumor recurrence, or human error with typical "blind spots" for identification of metastatic disease. Understanding these pitfalls enables appropriate selection of imaging modalities, improves diagnostic accuracy, and guides patient treatment. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Retrospectively assessing evaluation and management of artificial-intelligence detected nodules on uninterpreted chest radiographs in the era of radiologists shortage.

PURPOSE: High volumes of chest radiographs (CXR) remain uninterpreted due to severe shortage of radiologists. These CXRs may be informally reported by non-radiologist physicians, or not reviewed at all. Artificial intelligence (AI) software can aid lung nodule detection. Our aim was to assess evaluation and management by non-radiologists of uninterpreted CXRs with AI detected nodules, compared to retrospective radiology reports. MATERIALS AND METHODS: AI detected nodules on uninterpreted CXRs of adults, performed 30/6/2022-31/1/2023, were evaluated. Excluded were patients with known active malignancy and duplicate CXRs of the same patient. The electronic medical records (EMR) were reviewed, and the clinicians' notes on the CXR and AI detected nodule were documented. Dedicated thoracic radiologists retrospectively interpreted all CXRs, and similarly to the clinicians, they had access to the AI findings, prior imaging and EMR. The radiologists' interpretation served as the ground truth, and determined if the AI-detected nodule was a true lung nodule and if further workup was required. RESULTS: A total of 683 patients met the inclusion criteria. The clinicians commented on 386 (56.5%) CXRs, identified true nodules on 113 CXRs (16.5%), incorrectly mentioned 31 (4.5%) false nodules as real nodules, and did not mention the AI detected nodule on 242 (35%) CXRs, of which 68 (10%) patients were retrospectively referred for further workup by the radiologist. For 297 patients (43.5%) there were no comments regarding the CXR in the EMR. Of these, 77 nodules (11.3%) were retrospectively referred for further workup by the radiologist. CONCLUSION: AI software for lung nodule detection may be insufficient without a formal radiology report, and may lead to over diagnosis or misdiagnosis of nodules.

Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know.

Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies. Because of the central role of imaging in cancer diagnosis and therapy, radiologists need to be familiar with the basic concepts of genomics, which are now becoming the new norm in oncologic clinical practice. By incorporating these concepts into clinical practice, radiologists can make their imaging interpretations more meaningful and specific, facilitate multidisciplinary clinical dialogue and interventions, and provide better patient-centric care. This review article highlights basic concepts of genomics and epigenomics, reviews the most common genetic alterations in cancer, and discusses the implications of these concepts on imaging by organ system in a case-based manner. This information will help stimulate new innovations in imaging research, accelerate the development and validation of new imaging biomarkers, and motivate efforts to bring new molecular and functional imaging methods to clinical radiology. Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Markers Supplemental material is available for this article. © RSNA, 2023.

Prevalence and Characteristics of Cervical Ossified Posterior Longitudinal Ligament in the Jewish Population.

BACKGROUND: Numerous studies have demonstrated an association between ethnic identity and the prevalence rate of cervical ossified posterior longitudinal ligament (C-OPLL). To date, its prevalence rate in the Jewish population has not been determined. The aim of this historical prospective study is to evaluate the prevalence and characteristics of C-OPLL in the Jewish population. METHODS: We performed a retrospective evaluation of imaging studies of all adult patients who underwent both cervical computed tomography and magnetic resonance imaging for all clinical indications within a span of 36 months between January 2017 and July 2020 at a single tertiary referral hospital located in central Israel. Identified C-OPLL carriers were interviewed by telephone. All the patients provided informed consent and then were questioned for current symptoms and demographics, including religion, Jewish ethnic identity, birthplace, parental birthplace and ethnic identity, and family history of spinal disorders. RESULTS: Overall, 440 participants were radiographically evaluated. The prevalence of C-OPLL in the Jewish population was 7.5% (33 of 440). The mean age of the C-OPLL carriers was 65.8 years. All the C-OPLL carriers were symptomatic at analysis. The carriers had an increased proportion with a Sephardic Jewish ethnic identity (65.4%), with a significantly high rate of homogeneous parental Jewish identity (92.4%), suggesting a prominent genetic contribution to the development of this condition. CONCLUSIONS: The prevalence of C-OPLL in the Jewish population in central Israel was 7.5%. This rate is significantly higher than that in other previously studied populations. To the best of our knowledge, this is the first study to identify the Jewish population as experiencing an increased prevalence of C-OPLL.

The impact on clinical outcomes after 1 year of implementation of an artificial intelligence solution for the detection of intracranial hemorrhage.

BACKGROUND: To assess the effect of a commercial artificial intelligence (AI) solution implementation in the emergency department on clinical outcomes in a single level 1 trauma center. METHODS: A retrospective cohort study for two time periods-pre-AI (1.1.2017-1.1.2018) and post-AI (1.1.2019-1.1.2020)-in a level 1 trauma center was performed. The ICH algorithm was applied to 587 consecutive patients with a confirmed diagnosis of ICH on head CT upon admission to the emergency department. Study variables included demographics, patient outcomes, and imaging data. Participants admitted to the emergency department during the same time periods for other acute diagnoses (ischemic stroke (IS) and myocardial infarction (MI)) served as control groups. Primary outcomes were 30- and 120-day all-cause mortality. The secondary outcome was morbidity based on Modified Rankin Scale for Neurologic Disability (mRS) at discharge. RESULTS: Five hundred eighty-seven participants (289 pre-AI-age 71 ± 1, 169 men; 298 post-AI-age 69 ± 1, 187 men) with ICH were eligible for the analyzed period. Demographics, comorbidities, Emergency Severity Score, type of ICH, and length of stay were not significantly different between the two time periods. The 30- and 120-day all-cause mortality were significantly reduced in the post-AI group when compared to the pre-AI group (27.7% vs 17.5%; p = 0.004 and 31.8% vs 21.7%; p = 0.017, respectively). Modified Rankin Scale (mRS) at discharge was significantly reduced post-AI implementation (3.2 vs 2.8; p = 0.044). CONCLUSION: The added value of this study emphasizes the introduction of artificial intelligence (AI) computer-aided triage and prioritization software in an emergent care setting that demonstrated a significant reduction in a 30- and 120-day all-cause mortality and morbidity for patients diagnosed with intracranial hemorrhage (ICH). Along with mortality rates, the AI software was associated with a significant reduction in the Modified Ranking Scale (mRs).

Magnetic resonance imaging for prospective assessment of local recurrence of non-small cell lung cancer after stereotactic body radiation therapy.

OBJECTIVES: To evaluate multi-parametric MRI for distinguishing stereotactic body radiation therapy (SBRT) induced pulmonary fibrosis from local recurrence (LR). MATERIALS AND METHODS: SBRT treated non-small cell lung cancer (NSCLC) patients suspected of LR by conventional imaging underwent MRI: T2 weighted, diffusion weighted imaging, dynamic contrast enhancement (DCE) with a 5-minute delayed sequence. MRI was reported as high or low suspicion of LR. Follow-up imaging ≥12 months or biopsy defined LR status as proven LR, no-LR or not-verified. RESULTS: MRI was performed between 10/2017 and 12/2021, at a median interval of 22.5 (interquartile range 10.5-32.75) months after SBRT. Of the 20 lesions in 18 patients: 4 had proven LR, 10 did not have LR and 6 were not verified for LR due to subsequent additional local and/or systemic therapy. MRI correctly identified as high suspicion LR in all proven LR lesions and low suspicion LR in all confirmed no-LR lesions. All proven LR lesions (4/4) showed heterogeneous enhancement and heterogeneous T2 signal, as compared to the proven no-LR lesions in which 7/10 had homogeneous enhancement and homogeneous T2 signal. DCE kinetic curves could not predict LR status. Although lower apparent diffusion coefficient (ADC) values were seen in proven LR lesions, no absolute cut-off ADC value could determine LR status. CONCLUSION: In this pilot study of NSCLC patients after SBRT, multi-parametric chest MRI was able to correctly determine LR status, with no single parameter being diagnostic by itself. Further studies are warranted.

Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel.

BACKGROUND: AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty. PURPOSE: To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness. METHODS: Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends. RESULTS: A total of 4052 records were screened, and 233 full-text articles were selected for content analysis. Twenty-one papers described FDA-approved commercial tools, and 212 reported algorithm prototypes. Works ranged from foundational research to multi-reader multi-case trials with heterogeneous external data. Scalable convolutional neural network-based implementations increased steeply after 2016 and were used in all commercial products; however, options for explainability were narrow. Of FDA-approved tools, 9/10 performed detection tasks. Dataset sizes ranged from < 100 to > 500,000 patients, and commercialization coincided with public dataset availability. Cross-sectional torso datasets were uniformly small. Data curation methods with ground truth labeling by independent readers were uncommon. No papers assessed user acceptance, and no method included human-computer interaction. The USA and China had the highest research output and frequency of research funding. CONCLUSIONS: Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier.

Liver Attenuation Assessment in Reduced Radiation Chest Computed Tomography.

OBJECTIVE: This study aimed to evaluate the reliability of liver and spleen Hounsfield units (HU) measurements in reduced radiation computed tomography (RRCT) of the chest within the sub-millisievert range. METHODS: We performed a prospective, institutional review board-approved study of accrued patients who underwent unenhanced normal-dose chest CT (NDCT) and with an average radiation dose of less than 5% of NDCT. In-house artificial intelligence-based denoising methods produced 2 denoised RRCT (dRRCT) series. Hepatic and splenic attenuations were measured on all 4 series: NDCT, RRCT, dRRCT1, and dRRCT2. Statistical analyses assessed the differences between the HU measurements of the liver and spleen in RRCTs and NDCT. As a test case, we assessed the performance of RRCTs for fatty liver detection, considering NDCT to be the reference standard. RESULTS: Wilcoxon test compared liver and spleen attenuation in the 72 patients included in our cohort. The liver attenuation in NDCT (median, 59.38 HU; interquartile range, 55.00-66.06 HU) was significantly different from the attenuation in RRCT, dRRCT1, and dRRCT2 (median, 63.63, 42.00, and 33.67 HU; interquartile range, 56.19-67.19, 37.33-45.83, and 30.33-38.50 HU, respectively), all with a P value <0.01. Six patients (8.3%) were considered to have fatty liver on NDCT. The specificity, sensitivity, and accuracy of fatty liver detection by RRCT were greater than 98.5%, 50%, and 94.3%, respectively. CONCLUSIONS: Attenuation measurements were significantly different between NDCT and RRCTs, but may still have diagnostic value in appreciating hepatosteastosis. Abdominal organ attenuation on RRCT protocols may differ from attenuation on NDCT and should be validated when new low-dose protocols are used.

Diagnostic and Prognostic Value of Chest Radiographs for COVID-19 at Presentation.

INTRODUCTION: Pulmonary opacities in COVID-19 increase throughout the illness and peak after ten days. The radiological literature mainly focuses on CT findings. The purpose of this study was to assess the diagnostic and prognostic value of chest radiographs (CXR) for coronavirus disease 2019 (COVID-19) at presentation. METHODS: We retrospectively identified consecutive reverse transcription polymerase reaction-confirmed COVID-19 patients (n = 104, 75% men) and patients (n = 75, 51% men) with repeated negative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests. Two radiologists blindly and independently reviewed the CXRs, documented findings, assigned radiographic assessment of lung edema (RALE) scores, and predicted the patients' COVID-19 status. We calculated interobserver reliability. The score use for diagnosis and prognosis of COVID-19 was evaluated with the area under the receiver operating characteristic curve. RESULTS: The overall RALE score failed to identify COVID-19 patients at presentation. However, the score was inversely correlated with a COVID-19 diagnosis within ≤2 days, and a positive correlation was found six days after symptom onset.Interobserver agreement with regard to separating normal from abnormal CXRs was moderate (k = 0.408) with low specificity (25% and 27%). Definite pleural effusion had almost perfect agreement (k = 0.833) and substantially reduced the odds of a COVID-19 diagnosis. Disease distribution and experts' opinion on COVID-19 status had only fair interobserver agreement. The RALE score interobserver reliability was moderate to good (intraclass correlation coefficient = 0.745). A high RALE score predicted a poor outcome (intensive care unit hospitalization, intubation, or death) in COVID-19 patients; a score of ≥5 substantially increased the odds of having a poor outcome. CONCLUSION: Chest radiography was found not to be a valid diagnostic tool for COVID-19, as normal or near-normal CXRs are more likely early in the disease course. Pleural effusions at presentation suggest a diagnosis other than COVID-19. More extensive lung opacities at presentation are associated with poor outcome in COVID-19 patients. Thus, patients with more than minimal opacities should be monitored closely for clinical deterioration. This clinical application of CXR is its greatest strength in COVID-19 as it impacts patient care.

Promoting head CT exams in the emergency department triage using a machine learning model.

PURPOSE: In this study, we aimed to develop a novel prediction model to identify patients in need of a non-contrast head CT exam during emergency department (ED) triage. METHODS: We collected data of all adult ED visits in our institution for five consecutive years (1/2013-12/2017). Retrieved variables included the following: demographics, mode of arrival to the ED, comorbidities, home medications, structured and unstructured chief complaints, vital signs, pain scale score, emergency severity index, ED wing assignment, documentation of previous ED visits, hospitalizations and CTs, and current visit non-contrast head CT usage. A machine learning gradient boosting model was trained on data from the years 2013-2016 and tested on data from 2017. Area under the curve (AUC) was used as metrics. Single-variable AUCs were also determined. Youden's index evaluated optimal sensitivity and specificity of the models. RESULTS: The final cohort included 595,561 ED visits. Non-contrast head CT usage rate was 11.8%. Each visit was coded into an input vector of 171 variables. Single-variable analysis showed that chief complaint had the best single predictive analysis (AUC = 0.87). The best model showed an AUC of 0.93 (95% CI 0.931-0.936) for predicting non-contrast head CT usage at triage level. The model had a sensitivity of 88.1% and specificity of 85.7% for non-contrast head CT utilization. CONCLUSION: The developed model can identify patients that need to undergo head CT exam already in the ED triage level and by that allow faster diagnosis and treatment.

A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score.

BACKGROUND: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications. OBJECTIVE: Evaluate a state-of-the-art machine learning model for predicting mortality at the triage level and, by validating this automatic tool, improve the categorization of patients in the ED. DESIGN: An institutional review board (IRB) approval was granted for this retrospective study. Information of consecutive adult patients (ages 18-100) admitted at the emergency department (ED) of one hospital were retrieved (January 1, 2012-December 31, 2018). Features included the following: demographics, admission date, arrival mode, referral code, chief complaint, previous ED visits, previous hospitalizations, comorbidities, home medications, vital signs, and Emergency Severity Index (ESI). The following outcomes were evaluated: early mortality (up to 2 days post ED registration) and short-term mortality (2-30 days post ED registration). A gradient boosting model was trained on data from years 2012-2017 and examined on data from the final year (2018). The area under the curve (AUC) for mortality prediction was used as an outcome metric. Single-variable analysis was conducted to develop a nine-point triage score for early mortality. KEY RESULTS: Overall, 799,522 ED visits were available for analysis. The early and short-term mortality rates were 0.6% and 2.5%, respectively. Models trained on the full set of features yielded an AUC of 0.962 for early mortality and 0.923 for short-term mortality. A model that utilized the nine features with the highest single-variable AUC scores (age, arrival mode, chief complaint, five primary vital signs, and ESI) yielded an AUC of 0.962 for early mortality. CONCLUSION: The gradient boosting model shows high predictive ability for screening patients at risk of early mortality utilizing data available at the time of triage in the ED.

Monosodium glutamate intake affect the function of the kidney through NMDA receptor.

AIMS: We investigated whether the chronic intake of monosodium glutamate (MSG) with food affects kidney function, and renal response to glycine. We also established if the NMDA receptors are involved in the changes observed. MAIN METHODS: Male Wistar rats (5weeks old) were fed a diet supplemented with MSG (3g/kg b.w./day), five days a week, and spontaneous ingestion of a 1% MSG solution during 16weeks. NaCl rats were fed a diet with NaCl (1g/kg b.w./day) and 0.35% NaCl solution at the same frequency and time. Control group was fed with normal chow and tap water. We utilized clearance techniques to examine glomerular filtration rate (GFR) and cortical renal plasma flow (CRPF) response to glycine and glycine+MK-801 (antagonist NMDA-R), and we determined NMDA-R1 in kidney by immunohistochemistry. KEY FINDINGS: The addition of MSG in the diet of rats increased both GFR and CRPF with an increase of absolute sodium reabsorption. However, hyperfiltration was accompanied with a normal response to glycine infusion. Immunostain of kidney demonstrate that the NMDA receptor is upregulated in rats fed with MSG diet. NMDA-R antagonist MK-801 significantly reduced both the GFR and CRPF; however the percentage of reduction was significantly higher in the group MSG. MK-801 also reduces fractional excretion of water, sodium and potassium in the three groups. SIGNIFICANCE: Renal NMDAR may be conditioned by the addition of MSG in the diet, favoring the hyperfiltration and simultaneously Na retention in the body.