Chronic Chest CT Findings following COVID-19 pneumonia.

Respiratory symptoms are a frequent manifestation of patients with post-acute sequela of SARS-CoV-2 (PASC), also known as long-COVID. Many cohorts of predominantly hospitalized patients have shown that a significant subset may have persistent chest CT findings for more than 12 months after the acute infection. Proper understanding of the evolving long-term imaging findings and terminology is crucial for accurate imaging interpretation and patient care. The goal of this article is to review the chronic chest CT findings of patients with PASC and common pitfalls.

Appropriateness and imaging outcomes of ultrasound, CT, and MR in the emergency department: a retrospective analysis from an urban academic center.

PURPOSE: To evaluate the appropriateness and outcomes of ultrasound (US), computed tomography (CT), and magnetic resonance (MR) orders in the ED. METHODS: We retrospectively reviewed consecutive US, CT, and MR orders for adult ED patients at a tertiary care urban academic center from January to March 2019. The American College of Radiology Appropriateness Criteria (ACRAC) guidelines were primarily used to classify imaging orders as "appropriate" or "inappropriate". Two radiologists in consensus judged specific clinical scenarios that were unavailable in the ACRAC. Final imaging reports were compared with the initial clinical suspicion for imaging and categorized into "normal", "compatible with initial diagnosis", "alternative diagnosis", or "inconclusive". The sample was powered to show a prevalence of inappropriate orders of 30% with a margin of error of 5%. RESULTS: The rate of inappropriate orders was 59.4% for US, 29.1% for CT, and 33.3% for MR. The most commonly imaged systems for each modality were neuro (130/330) and gastrointestinal (95/330) for CT, genitourinary (132/330) and gastrointestinal (121/330) for US, neuro (273/330) and gastrointestinal (37/330) for MR. Compared to inappropriately ordered tests, the final reports of appropriate orders were nearly three times more likely to demonstrate findings compatible with the initial diagnosis for all modalities: US (45.5 vs. 14.3%, p < 0.001), CT (46.6 vs. 14.6%, p < 0.001), and MR (56.3 vs. 21.8%, p < 0.001). Inappropriate orders were more likely to show no abnormalities compared to appropriate orders: US (65.8 vs. 38.8%, p < 0.001), CT (62.5 vs. 34.2%, p < 0.001), and MR (61.8 vs. 38.7%, p < 0.001). CONCLUSION: The prevalence of inappropriate imaging orders in the ED was 59.4% for US, 29.1% for CT, and 33.3% for MR. Appropriately ordered imaging was three times more likely to yield findings compatible with the initial diagnosis across all modalities.

Diagnostic Performance of MRI for the Detection of Pulmonary Nodules: A Systematic Review and Meta-Analysis.

Purpose To perform a meta-analysis of the diagnostic performance of MRI for the detection of pulmonary nodules, with use of CT as the reference standard. Materials and Methods PubMed, Embase, Scopus, and other databases were systematically searched for studies published from January 2000 to March 2023 evaluating the performance of MRI for diagnosis of lung nodules measuring 4 mm or larger, with CT as reference. Studies including micronodules, nodules without size stratification, or those from which data for contingency tables could not be extracted were excluded. Primary outcomes were the per-lesion sensitivity of MRI and the rate of false-positive nodules per patient (FPP). Subgroup analysis by size and meta-regression with other covariates were performed. The study protocol was registered in the International Prospective Register of Systematic Reviews, or PROSPERO (no. CRD42023437509). Results Ten studies met inclusion criteria (1354 patients and 2062 CT-detected nodules). Overall, per-lesion sensitivity of MRI for nodules measuring 4 mm or larger was 87.7% (95% CI: 81.1, 92.2), while the FPP rate was 12.4% (95% CI: 7.0, 21.1). Subgroup analyses demonstrated that MRI sensitivity was 98.5% (95% CI: 90.4, 99.8) for nodules measuring at least 8-10 mm and 80.5% (95% CI: 71.5, 87.1) for nodules less than 8 mm. Conclusion MRI demonstrated a good overall performance for detection of pulmonary nodules measuring 4 mm or larger and almost equal performance to CT for nodules measuring at least 8-10 mm, with a low rate of FPP. Systematic review registry no. CRD42023437509 Keywords: Lung Nodule, Lung Cancer, Lung Cancer Screening, MRI, CT Supplemental material is available for this article. © RSNA, 2024.

Evaluating Acute Pulmonary Changes in Coronavirus Disease 2019: A Comparative Analysis of Computed Tomography, Chest Radiography, Lung Ultrasound, Magnetic Resonance Imaging, and Positron Emission Tomography with Fluorodeoxyglucose Modalities.

This review explores imaging's crucial role in acute Coronavirus Disease 2019 (COVID-19) assessment. High Resolution Computer Tomography is especially effective in detection of lung abnormalities. Chest radiography has limited utility in the initial stages of COVID-19 infection. Lung Ultrasound has emerged as a valuable, radiation-free tool in critical care, and Magnetic Resonance Imaging shows promise as a Computed Tomography alternative. Typical and atypical findings of COVID-19 by each of these modalities are discussed with emphasis on their prognostic value. Considerations for pediatric and immunocompromised cases are outlined. A comprehensive diagnostic approach is recommended, as radiological diagnosis remains challenging in the acute phase.

Lung cancer screening in Brazil: recommendations from the Brazilian Society of Thoracic Surgery, Brazilian Thoracic Association, and Brazilian College of Radiology and Diagnostic Imaging.

Although lung cancer (LC) is one of the most common and lethal tumors, only 15% of patients are diagnosed at an early stage. Smoking is still responsible for more than 85% of cases. Lung cancer screening (LCS) with low-dose CT (LDCT) reduces LC-related mortality by 20%, and that reduction reaches 38% when LCS by LDCT is combined with smoking cessation. In the last decade, a number of countries have adopted population-based LCS as a public health recommendation. Albeit still incipient, discussion on this topic in Brazil is becoming increasingly broad and necessary. With the aim of increasing knowledge and stimulating debate on LCS, the Brazilian Society of Thoracic Surgery, the Brazilian Thoracic Association, and the Brazilian College of Radiology and Diagnostic Imaging convened a panel of experts to prepare recommendations for LCS in Brazil. The recommendations presented here were based on a narrative review of the literature, with an emphasis on large population-based studies, systematic reviews, and the recommendations of international guidelines, and were developed after extensive discussion by the panel of experts. The following topics were reviewed: reasons for screening; general considerations about smoking; epidemiology of LC; eligibility criteria; incidental findings; granulomatous lesions; probabilistic models; minimum requirements for LDCT; volumetric acquisition; risks of screening; minimum structure and role of the multidisciplinary team; practice according to the Lung CT Screening Reporting and Data System; costs versus benefits of screening; and future perspectives for LCS.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model: A cohort study.

BACKGROUND: Liver transplant (LT) patients have become older and sicker. The rate of post-LT major adverse cardiovascular events (MACE) has increased, and this in turn raises 30-d post-LT mortality. Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients. AIM: To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort. METHODS: This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center. We developed a predictive model for post-LT MACE (defined as a composite outcome of stroke, new-onset heart failure, severe arrhythmia, and myocardial infarction) using the extreme gradient boosting (XGBoost) machine learning model. We addressed missing data (below 20%) for relevant variables using the k-nearest neighbor imputation method, calculating the mean from the ten nearest neighbors for each case. The modeling dataset included 83 features, encompassing patient and laboratory data, cirrhosis complications, and pre-LT cardiac assessments. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). We also employed Shapley additive explanations (SHAP) to interpret feature impacts. The dataset was split into training (75%) and testing (25%) sets. Calibration was evaluated using the Brier score. We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting. Scikit-learn and SHAP in Python 3 were used for all analyses. The supplementary material includes code for model development and a user-friendly online MACE prediction calculator. RESULTS: Of the 537 included patients, 23 (4.46%) developed in-hospital MACE, with a mean age at transplantation of 52.9 years. The majority, 66.1%, were male. The XGBoost model achieved an impressive AUROC of 0.89 during the training stage. This model exhibited accuracy, precision, recall, and F1-score values of 0.84, 0.85, 0.80, and 0.79, respectively. Calibration, as assessed by the Brier score, indicated excellent model calibration with a score of 0.07. Furthermore, SHAP values highlighted the significance of certain variables in predicting postoperative MACE, with negative noninvasive cardiac stress testing, use of nonselective beta-blockers, direct bilirubin levels, blood type O, and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level. These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE, making it a valuable tool for clinical practice. CONCLUSION: Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE, using both cardiovascular and hepatic variables. The model demonstrated impressive performance, aligning with literature findings, and exhibited excellent calibration. Notably, our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data, reinforcing the model's value as a reliable tool for predicting post-LT MACE in clinical practice.

Evaluating ChatGPT's Accuracy in Providing Screening Mammography Recommendations among Older Women: Artificial Intelligence and Cancer Communication.

Abstract Objective: The U.S. Preventive Services Task Force (USPSTF) recommends biennial screening mammography through age 74. Guidelines vary as to whether or not they recommended mammography screening to women aged 75 and older. This study aims to determine the ability of ChatGPT to provide appropriate recommendations for breast cancer screening in patients aged 75 years and older. Methods: 12 questions and 4 clinical vignettes addressing fundamental concepts about breast cancer screening and prevention in patients aged 75 years and older were created and asked to ChatGPT three consecutive times to generate 3 sets of responses. The responses were graded by a multi-disciplinary panel of experts in the intersection of breast cancer screening and aging . The responses were graded as 'appropriate', 'inappropriate', or 'unreliable' based on the reviewer's clinical judgment, content of the response, and whether the content was consistent across the three responses . Appropriateness was determined through a majority consensus. Results: The responses generated by ChatGPT were appropriate for 11/17 questions (64%). Three questions were graded as inappropriate (18%) and 2 questions were graded as unreliable (12%). A consensus was not reached on one question (6%) and was graded as no consensus. Conclusions: While recognizing the limitations of ChatGPT, it has potential to provide accurate health care information and could be utilized by healthcare professionals to assist in providing recommendations for breast cancer screening in patients age 75 years and older. Physician oversight will be necessary, due to the possibility of ChatGPT to provide inappropriate and unreliable responses, and the importance of accuracy in medicine.

MRI with DWI improves detection of liver metastasis and selection of surgical candidates with pancreatic cancer: a systematic review and meta-analysis.

OBJECTIVE: To perform a systematic review and meta-analysis to evaluate if magnetic resonance imaging (MRI) with diffusion weighted imaging (DWI) adds value compared to contrast-enhanced computed tomography (CECT) alone in the preoperative evaluation of pancreatic cancer. METHODS: MEDLINE, EMBASE, and Cochrane databases were searched for relevant published studies through October 2022. Studies met eligibility criteria if they evaluated the per-patient diagnostic performance of MRI with DWI in the preoperative evaluation of newly diagnosed pancreatic cancer compared to CECT. Our primary outcome was the number needed to treat (NNT) to prevent one futile surgery using MRI with DWI, defined as those in which CECT was negative and MRI with DWI was positive for liver metastasis (i.e., surgical intervention in metastatic disease missed by CECT). The secondary outcomes were to determine the diagnostic performance and the NNT of MRI with DWI to change management in pancreatic cancer. RESULTS: Nine studies met the inclusion criteria with a total of 1121 patients, of whom 172 had liver metastasis (15.3%). The proportion of futile surgeries reduced by MRI with DWI was 6.0% (95% CI, 3.0-11.6%), yielding an NNT of 16.6. The proportion of cases that MRI with DWI changed management was 18.1% (95% CI, 9.9-30.7), corresponding to an NNT of 5.5. The per-patient sensitivity and specificity of MRI were 92.4% (95% CI, 87.4-95.6%) and 97.3% (95% CI, 96.0-98.1). CONCLUSION: MRI with DWI may prevent futile surgeries in pancreatic cancer by improving the detection of occult liver metastasis on preoperative CECT with an NNT of 16.6. CLINICAL RELEVANCE STATEMENT: MRI with DWI complements the standard preoperative CECT evaluation for liver metastasis in pancreatic cancer, improving the selection of surgical candidates and preventing unnecessary surgeries. KEY POINTS: • The NNT of MRI with DWI to prevent potential futile surgeries due to occult liver metastasis on CECT, defined as those in which CECT was negative and MRI with DWI was positive for liver metastasis, in patients with pancreatic cancer was 16.6. • The higher performance of MRI with DWI to detect liver metastasis occult on CECT can be attributed to an increased detection of subcentimeter liver metastasis.

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Open-Source Deep Learning and Quantitative Measurement Initiative.

INTRODUCTION: With global adoption of computed tomography (CT) lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open-source, cloud-based, globally distributed, screening CT imaging data set and computational environment that are compliant with the most stringent international privacy regulations that also protect the intellectual properties of researchers, the International Association for the Study of Lung Cancer sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be used for clinically relevant AI research. METHODS: In this second phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans. RESULTS: A total of 1394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness more than or equal to 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high-quality CT scans. CONCLUSIONS: These initial experiments revealed that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based data sets.

Lung cancer screening in Brazil: recommendations from the Brazilian Society of Thoracic Surgery, Brazilian Thoracic Association, and Brazilian College of Radiology and Diagnostic Imaging / Recomendações da Sociedade Brasileira de Cirurgia Torácica, Sociedade Brasileira de Pneumologia e Tisiologia e Colégio Brasileiro de Radiologia e Diagnóstico por Imagem para o rastreamento do câncer de pulmão no Brasil

ABSTRACT Although lung cancer (LC) is one of the most common and lethal tumors, only 15% of patients are diagnosed at an early stage. Smoking is still responsible for more than 85% of cases. Lung cancer screening (LCS) with low-dose CT (LDCT) reduces LC-related mortality by 20%, and that reduction reaches 38% when LCS by LDCT is combined with smoking cessation. In the last decade, a number of countries have adopted population-based LCS as a public health recommendation. Albeit still incipient, discussion on this topic in Brazil is becoming increasingly broad and necessary. With the aim of increasing knowledge and stimulating debate on LCS, the Brazilian Society of Thoracic Surgery, the Brazilian Thoracic Association, and the Brazilian College of Radiology and Diagnostic Imaging convened a panel of experts to prepare recommendations for LCS in Brazil. The recommendations presented here were based on a narrative review of the literature, with an emphasis on large population-based studies, systematic reviews, and the recommendations of international guidelines, and were developed after extensive discussion by the panel of experts. The following topics were reviewed: reasons for screening; general considerations about smoking; epidemiology of LC; eligibility criteria; incidental findings; granulomatous lesions; probabilistic models; minimum requirements for LDCT; volumetric acquisition; risks of screening; minimum structure and role of the multidisciplinary team; practice according to the Lung CT Screening Reporting and Data System; costs versus benefits of screening; and future perspectives for LCS.

RESUMO O câncer de pulmão (CP) é uma das neoplasias mais comuns e letais no Brasil, e apenas 15% dos pacientes são diagnosticados nos estágios iniciais. O tabagismo persiste como o responsável por mais de 85% de todos os casos. O rastreamento do CP (RCP) por meio da TC de baixa dosagem de radiação (TCBD) reduz a mortalidade do CP em 20%, e, quando combinado com a cessação do tabagismo, essa redução chega a 38%. Na última década, diversos países adotaram o RCP como recomendação de saúde populacional. No Brasil, embora ainda incipiente, a discussão sobre o tema é cada vez mais ampla e necessária. Com o intuito de aumentar o conhecimento e estimular o debate sobre o RCP, a Sociedade Brasileira de Cirurgia Torácica, a Sociedade Brasileira de Pneumologia e Tisiologia e o Colégio Brasileiro de Radiologia e Diagnóstico por Imagem constituíram um painel de especialistas para elaborar as recomendações para o RCP. As recomendações aqui apresentadas foram baseadas em revisão narrativa da literatura, com ênfase em grandes estudos populacionais, em revisões sistemáticas e em recomendações de diretrizes internacionais, sendo construídas após ampla discussão pelo grupo de especialistas. Os temas revisados foram os seguintes: porque rastrear, considerações gerais sobre tabagismo, epidemiologia do CP, critérios de elegibilidade, achados incidentais, lesões granulomatosas, modelos probabilísticos, requisitos mínimos da TCBD, aquisições volumétricas, riscos do rastreamento, estrutura mínima e papel da equipe multidisciplinar, conduta segundo o Lung CT Screening Reporting and Data System (Lung-RADS), custos vs. benefícios e perspectivas do rastreamento.

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges.

INTRODUCTION: Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential. AREAS COVERED: The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential. EXPERT OPINION: There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.

Detection of Liver Lesions in Colorectal Cancer Patients Using 18F-FDG PET/CT Dual-Time-Point Scan Imaging.

OBJECTIVE: The aim of this study was to evaluate the diagnostic performance of dual-time-point fluorine-18-fluorodeoxyglucose positron emission computed tomography/computed tomography (18F-FDG PET/CT) compared to conventional early imaging for detecting colorectal liver metastases (CRLM) in colorectal cancer (CRC) patients. METHODS: One hundred twenty-four consecutive CRC patients underwent dual-time-point imaging scans on a retrospective basis. Histopathological confirmation and/or clinical follow-up were accepted as the gold standard. Standard uptake values (SUV), signal-to-noise ratio (SNR), retention index (RI), tumor-to-normal liver ratio (TNR), and lesion sizes were measured for early and delayed PET scans. The diagnostic performance of early and delayed images was calculated on a per-patient basis and compared using McNemar's test. RESULTS: Among the 124 patients, 57 (46%) had CRLM, 6 (4.8%) had benign lesions, and 61 (49.2%) had no concerning lesions detected. Smaller CRLM lesions (<5 cm3) showed significantly higher uptake in the delayed scans relative to early imaging (p < 0.001). The SUV and TNR increased significantly in delayed imaging of all metastatic lesions (p < 0.001). The retention index of all CRLM was high (40.8%), especially for small lesions (54.8%). A total of 177 lesions in delayed images and 124 in standard early images were identified. In a per-patient analysis, delayed imaging had significantly higher sensitivity (100% vs. 87.7%) and specificity (91.0% vs. 94.0%) compared to early imaging (p-value = 0.04). CONCLUSIONS: The detection of liver lesions using dual-time-point PET/CT scan improves the sensitivity and specificity for the detection of colorectal liver metastasis.

A New (Digital) Era in Medical Journalism: Leveraging Social Media and Other Online Tools to Increase Reach and Engagement.

Social media is a popular communication and marketing tool in modern society, with the power to reach and engage large audiences. Many members of the medical and radiology communities have embraced social media platforms, particularly X (formerly known as Twitter), as an efficient and economic means for performing patient outreach, disseminating research and educational materials, building networks, and promoting diversity. Editors of medical journals with a clear vision and relevant expertise can leverage social media and other digital tools to advance the journal's mission, further their interests, and directly benefit journal authors and readers. For editors, social media offers a means to increase article visibility and downloads, expand awareness of volunteer opportunities, and use metrics and other feedback to inform future initiatives. Authors benefit from broader dissemination of their work, which aids establishment of a national or international reputation. Readers can receive high-quality high-yield content in a digestible format directly on their devices while actively engaging with journal editors and authors in the online community. The authors highlight the multifaceted benefits of social media engagement and digital tool implementation in the context of medical journalism and summarize the activities of the RadioGraphics Social Media and Digital Innovation Team. By enumerating the social media activities of RadioGraphics and describing the underlying rationale for each activity, the authors present a blueprint for other medical journals considering similar initiatives. ©RSNA, 2023.