The 50 most-cited articles in Radiology, 2024

Members of the Fleischner Society have compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984, 1996, and 2008, respectively. The impetus to update the previous version arose from multiple considerations. These include an awareness that new terms and concepts have emerged, others have become obsolete, and the usage of some terms has either changed or become inconsistent to a degree that warranted a new definition. This latest glossary is focused on terms of clinical importance and on those whose meaning may be perceived as vague or ambiguous. As with previous versions, the aim of the present glossary is to establish standardization of terminology for thoracic radiology and, thereby, to facilitate communications between radiologists and clinicians. Moreover, the present glossary aims to contribute to a more stringent use of terminology, increasingly required for structured reporting and accurate searches in large databases. Compared with the previous version, the number of images (chest radiography and CT) in the current version has…

Although chatbots have existed for decades, the emergence of transformer-based large language models (LLMs) has captivated the world through the most recent wave of artificial intelligence chatbots, including ChatGPT. Transformers are a type of neural network architecture that enables better contextual understanding of language and efficient training on massive amounts of unlabeled data, such as unstructured text from the internet. As LLMs have increased in size, their improved performance and emergent abilities have revolutionized natural language processing. Since language is integral to human thought, applications based on LLMs have transformative potential in many industries. In fact, LLM-based chatbots have demonstrated human-level performance on many professional benchmarks, including in radiology. LLMs offer numerous clinical and research applications in radiology, several of which have been explored in the literature with encouraging results. Multimodal LLMs can simultaneously interpret text and images to generate reports, closely mimicking current diagnostic pathways in…

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations.…

= -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.

See also the editorial by Rahsepar in this issue.

The pooled #HOPE4LIVER trials demonstrated the safety and technical success of the HistoSonics System for the destruction of liver tumors using histotripsy, a non-thermal, mechanical process of focused US.

According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology. This is due to the explosive increase in big data and artificial intelligence (AI) applications that have resulted in large energy requirements for developing and deploying AI models. However, AI also has the potential to improve environmental sustainability in medical imaging. For example, use of AI can shorten MRI scan times with accelerated acquisition times, improve the scheduling efficiency of scanners, and optimize the use of decision-support tools to reduce low-value imaging. The purpose of this Radiology in Focus article is to discuss this duality at the intersection of environmental sustainability and AI in radiology. Further discussed…

Visual 68 Ga-PSMA-11 PET/CT eligibility criteria helped identify participants with metastatic castration-resistant prostate cancer who benefitted from 177 Lu-PSMA-617 therapy in the VISION trial, with higher 68 Ga-PSMA-11 uptake associated with improved outcomes.

Artificial intelligence (AI) models for medical imaging tasks, such as classification or segmentation, require large and diverse datasets of images. However, due to privacy and ethical issues, as well as data sharing infrastructure barriers, these datasets are scarce and difficult to assemble. Synthetic medical imaging data generated by AI from existing data could address this challenge by augmenting and anonymizing real imaging data. In addition, synthetic data enable new applications, including modality translation, contrast synthesis, and professional training for radiologists. However, the use of synthetic data also poses technical and ethical challenges. These challenges include ensuring the realism and diversity of the synthesized images while keeping data unidentifiable, evaluating the performance and generalizability of models trained on synthetic data, and high computational costs. Since existing regulations are not sufficient to guarantee the safe and ethical use of synthetic images, it becomes evident that updated laws and more rigorous oversight are needed. Regulatory…

Background Large language models (LLMs) hold substantial promise for medical imaging interpretation. However, there is a lack of studies on their feasibility in handling reasoning questions associated with medical diagnosis. Purpose To investigate the viability of leveraging three publicly available LLMs to enhance consistency and diagnostic accuracy in medical imaging based on standardized reporting, with pathology as the reference standard. Materials and Methods US images of thyroid nodules with pathologic results were retrospectively collected from a tertiary referral hospital between July 2022 and December 2022 and used to evaluate malignancy diagnoses generated by three LLMs-OpenAI's ChatGPT 3.5, ChatGPT 4.0, and Google's Bard. Inter- and intra-LLM agreement of diagnosis were evaluated. Then, diagnostic performance, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), was evaluated and compared for the LLMs and three interactive approaches: human reader combined with LLMs, image-to-text model combined with LLMs, and an…

The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations also increase the likelihood of unanticipated problems. Standards-based interoperability facilitates AI integration with systems from different vendors into a single environment by enabling seamless exchange between information systems in the radiology workflow. Integrating the Healthcare Enterprise (IHE) is an initiative to improve how computer systems share information across health care domains, including radiology. IHE integrates existing standards-such as Digital Imaging and Communications in Medicine, Health Level Seven, and health care lexicons and ontologies (ie, LOINC, RadLex, SNOMED Clinical Terms)-by mapping data elements from one standard to another. IHE Radiology manages profiles (standards-based implementation guides) for departmental workflow and information sharing across care sites, including profiles for scaling AI…

= .02). Radiologists (range, 45%-88%) outperformed the LLMs at T1 (range, 24%-75%) in most subspecialties. Conclusion Using direct radiologic image inputs, GPT-4V and Gemini Pro Vision showed improved diagnostic accuracy with increasing temperature settings. Although GPT-4V slightly underperformed compared with radiologists, it nonetheless demonstrated promising potential as a supportive tool in diagnostic decision-making. © RSNA, 2024 See also the editorial by Nishino and Ballard in this issue.

Full implementation of an artificial intelligence system considerably reduced the workload of breast radiologists while improving screening performance in a regional Danish population-based breast cancer screening program.

Based on breast imaging reports written in three languages, GPT-3.5, GPT-4, and Google Bard achieved moderate agreement with human reader–assigned Breast Imaging Reporting and Data System categories but also yielded a high percentage of discordant category assignments that would negatively impact clinical management.

The performance of a deep learning model was not different from that of radiologists in the detection of clinically significant prostate cancer at MRI, and gradient-weighted class activation maps localized the tumor.

Background Coronary CT angiography is a first-line test in coronary artery disease but is limited by severe calcifications. Photon-counting–detector (PCD) CT improves spatial resolution. Purpose To investigate the effect of improved spatial resolution on coronary stenosis assessment and reclassification. Materials and Methods Coronary stenoses were evaluated prospectively in a vessel phantom (in vitro) containing two stenoses (25%, 50%), and retrospectively in patients (in vivo) who underwent ultrahigh-spatial-resolution cardiac PCD CT (from July 2022 to April 2023). Images were reconstructed at standard resolution (section thickness, 0.6 mm; increment, 0.4 mm; Bv44 kernel), high spatial resolution (section thickness, 0.4 mm; increment, 0.2 mm; Bv44 kernel), and ultrahigh spatial resolution (section thickness, 0.2; increment, 0.1 mm; Bv64 kernel). Percentages of diameter stenosis (DS) were compared between reconstructions. In vitro values were compared with the manufacturer specifications of the phantom and patient results were assessed regarding effects on Coronary Artery Disease…

Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help…

Background The specialization and complexity of radiology makes the automatic generation of radiologic impressions (ie, a diagnosis with differential diagnosis and management recommendations) challenging. Purpose To develop a large language model (LLM) that generates impressions based on imaging findings and to evaluate its performance in professional and linguistic dimensions. Materials and Methods Six radiologists recorded imaging examination findings from August 2 to 31, 2023, at Shanghai General Hospital and used the developed LLM before routinely writing report impressions for multiple radiologic modalities (CT, MRI, radiography, mammography) and anatomic sites (cranium and face, neck, chest, upper abdomen, lower abdomen, vessels, bone and joint, spine, breast), making necessary corrections and completing the radiologic impression. A subset was defined to investigate cases where the LLM-generated impressions differed from the final radiologist impressions by excluding identical and highly similar cases. An expert panel scored the LLM-generated impressions on a five-point Likert…

(ICD-10), including the anatomic location (hereafter, laterality). Results In the zero-shot setting, in the task of detecting ICD-10 codes alone, GPT-4V attained an average positive predictive value (PPV) of 12.3%, average true-positive rate (TPR) of 5.8%, and average F1 score of 7.3% on the NIH data set, and an average PPV of 25.0%, average TPR of 16.8%, and average F1 score of 18.2% on the MIDRC data set. When both the ICD-10 codes and their corresponding laterality were considered, GPT-4V produced an average PPV of 7.8%, average TPR of 3.5%, and average F1 score of 4.5% on the NIH data set, and an average PPV of 10.9%, average TPR of 4.9%, and average F1 score of 6.4% on the MIDRC data set. With few-shot learning, GPT-4V showed improved performance on both data sets. When contrasting zero-shot and few-shot learning, there were improved average TPRs and F1 scores in the few-shot setting, but there was not a substantial increase in the average PPV. Conclusion Although GPT-4V has shown promise in understanding natural images, it had limited effectiveness in interpreting real-world…

With the rising incidence of hepatocellular carcinoma, there has been increasing use of local-regional therapy (LRT) to downstage or bridge to transplant, for definitive treatment, and for palliation. The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Assessment (TRA) algorithm provides guidance for step-by-step tumor assessment after LRT and standardized reporting. Current evidence suggests that the algorithm performs well in the assessment of tumor response to arterial embolic and loco-ablative therapies and fair when assessing response to radiation-based therapies, with limited data to validate the latter. Both evidence-based and expert-based refinements of the algorithm are needed to improve its diagnostic accuracy after varying types of LRT. This review provides an overview of the challenges and limitations of the LI-RADS TRA algorithm version 2017 and discusses the refinements introduced in the updated 2024 LI-RADS algorithm for CT/MRI.

Endometriosis is a prevalent and potentially debilitating condition that mostly affects individuals of reproductive age, and often has a substantial diagnostic delay. US is usually the first-line imaging modality used when patients report chronic pelvic pain or have issues of infertility, both common symptoms of endometriosis. Other than the visualization of an endometrioma, sonologists frequently do not appreciate endometriosis on routine transvaginal US images. Given a substantial body of literature describing techniques to depict endometriosis at US, the Society of Radiologists in Ultrasound convened a multidisciplinary panel of experts to make recommendations aimed at improving the screening process for endometriosis. The panel was composed of experts in the imaging and management of endometriosis, including radiologists, sonographers, gynecologists, reproductive endocrinologists, and minimally invasive gynecologic surgeons. A comprehensive literature review combined with a modified Delphi technique achieved a consensus. This statement defines the targeted screening population,…

Patients with differing severities of obstructive sleep apnea (OSA) exhibited differences in MRI indexes of the perivascular space network, and those with moderate OSA treated with uvulopalatopharyngoplasty showed improvements in these indexes and in cognitive performance.

GPT-4 enabled near-perfect creation of pancreatic ductal adenocarcinoma synoptic reports from original reports and accurate resectability categorization using chain-of-thought prompting, which improved surgeon accuracy and efficiency in interpreting reports for resectability categorization.

Default GPT-3.5 and GPT-4 were reliably accurate over three attempts at answering radiology board–style multiple-choice questions but had poor repeatability and robustness and were frequently overconfident, limiting usability without domain-specific optimization.

Immediate action is needed to sustain planetary health by implementing mitigation strategies to reduce greenhouse gas emissions in radiology and adaptation strategies that build resiliency to the effects of climate change.

This systematic review and meta-analysis provides an evidence synthesis of prostate MRI diagnostic performance based on PI-RADS version 2.1 for benchmarking different diagnostic metrics and outcome thresholds.

Endometriosis is a common condition impacting approximately 190 million individuals and up to 50% of women with infertility globally. The disease is characterized by endometrial-like tissue located outside of the uterine corpus, which causes cyclical hemorrhage, inflammation, and fibrosis. Based on clinical suspicion or findings at routine transvaginal pelvic US or other prior imaging, dedicated imaging for endometriosis may be warranted with MRI or advanced transvaginal US. Deep endometriosis (DE) in the pelvis includes evaluation for stromal and fibrotic components and architectural distortion resulting from fibrosis and tethering. It is a disease requiring a compartment-based, pattern-recognition approach. MRI has the benefit of global assessment of the pelvis and is effective in assessing for features of malignancy and for evaluating extrapelvic locations. Transvaginal US has the advantage of dynamic maneuvers to assess for adhesions and may achieve higher spatial resolution for assessing the depth of bowel wall invasion. T1-weighted MRI evaluation increases the specificity of…

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist’s workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities. © RSNA, 2024 Supplemental material is available…

By using data manually extracted by a radiologist as the ground truth, GPT-4 more frequently extracted correct data from free-text reports on mechanical thrombectomy in patients with ischemic stroke compared with GPT-3.5.

Time-dependent diffusion MRI–based microstructural mapping was shown to be an effective method in helping to identify molecular subtypes and predict pathologic complete response to neoadjuvant chemotherapy in participants with breast cancer.

values of .27 to >.99. Conclusion While GPT-4V demonstrated a level of competence in text-based questions, it showed deficits interpreting radiologic images. © RSNA, 2024 See also the editorial by Deng in this issue.

The apparent diffusion coefficient (ADC) provides a quantitative measure of water mobility that can be used to probe alterations in tissue microstructure due to disease or treatment. Establishment of the accepted level of variance in ADC measurements for each clinical application is critical for its successful implementation. The Diffusion-Weighted Imaging Biomarker Committee of the Quantitative Imaging Biomarkers Alliance (QIBA) has recently advanced the ADC Profile from the consensus to clinically feasible stage for the brain, liver, prostate, and breast. This profile distills multiple studies on ADC repeatability and describes detailed procedures to achieve stated performance claims on an observed ADC change within acceptable confidence limits. In addition to reviewing the current ADC Profile claims, this report has used recent literature to develop proposed updates for establishing metrology benchmarks for mean lesion ADC change that account for measurement variance. Specifically, changes in mean ADC exceeding 8% for brain lesions, 27% for liver lesions, 27% for prostate…

Background Mirai, a state-of-the-art deep learning–based algorithm for predicting short-term breast cancer risk, outperforms standard clinical risk models. However, Mirai is a black box, risking overreliance on the algorithm and incorrect diagnoses. Purpose To identify whether bilateral dissimilarity underpins Mirai’s reasoning process; create a simplified, intelligible model, AsymMirai, using bilateral dissimilarity; and determine if AsymMirai may approximate Mirai’s performance in 1–5-year breast cancer risk prediction. Materials and Methods This retrospective study involved mammograms obtained from patients in the EMory BrEast imaging Dataset, known as EMBED, from January 2013 to December 2020. To approximate 1–5-year breast cancer risk predictions from Mirai, another deep learning–based model, AsymMirai, was built with an interpretable module: local bilateral dissimilarity (localized differences between left and right breast tissue). Pearson correlation coefficients were computed between the risk scores of Mirai and those of AsymMirai. Subgroup analysis was performed in patients…

A life cycle assessment of a diagnostic radiology department’s environmental footprint identified the energy consumption from imaging equipment as more than 50% of its greenhouse gas emissions, as well as opportunities to improve its sustainability.

In this multisite prospective study of simulated artificial intelligence (AI)–assisted chest radiograph diagnosis involving 220 physicians, AI explanation type (local vs global) differentially impacted physician diagnostic performance and trust in AI advice.

Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from conventional CT in its ability to directly convert incident x-ray photon energies into electrical signals. The detector design also permits substantial improvements in spatial resolution and radiation dose efficiency and allows for concurrent high-pitch and high-temporal-resolution multienergy imaging. This review summarizes (a) key differences in PCCT image acquisition and image reconstruction compared with conventional CT; (b) early evidence for the clinical benefit of PCCT for high-spatial-resolution diagnostic tasks in thoracic imaging, such as assessment of airway and parenchymal diseases, as well as benefits of high-pitch and multienergy scanning; (c) anticipated radiation dose reduction, depending on the diagnostic task, and increased utility for routine low-dose thoracic CT imaging; (d) adaptations for thoracic imaging in children; (e) potential for further quantitation of thoracic diseases; and (f) limitations and trade-offs. Moreover, important points for conducting and interpreting clinical…

Background Deep learning (DL)–accelerated MRI can substantially reduce examination times. However, studies prospectively evaluating the diagnostic performance of DL-accelerated MRI reconstructions in acute suspected stroke are lacking. Purpose To investigate the interchangeability of DL-accelerated MRI with conventional MRI in patients with suspected acute ischemic stroke at 1.5 T. Materials and Methods In this prospective study, 211 participants with suspected acute stroke underwent clinically indicated MRI at 1.5 T between June 2022 and March 2023. For each participant, conventional MRI (including T1-weighted, T2-weighted, T2*-weighted, T2 fluid-attenuated inversion-recovery, and diffusion-weighted imaging; 14 minutes 18 seconds) and DL-accelerated MRI (same sequences; 3 minutes 4 seconds) were performed. The primary end point was the interchangeability between conventional and DL-accelerated MRI for acute ischemic infarction detection. Secondary end points were interchangeability regarding the affected vascular territory and clinically relevant secondary findings (eg,…

Abbreviated MRI is an umbrella term, defined as a focused MRI examination tailored to answer a single specific clinical question. For abbreviated breast MRI, this question is: “Is there evidence of breast cancer?” Abbreviated MRI of the breast makes maximum use of the fact that the kinetics of breast cancers and of benign tissue differ most in the very early postcontrast phase; therefore, abbreviated breast MRI focuses on this period. The different published approaches to abbreviated MRI include the following three subtypes: (a) short protocols, consisting of a precontrast and either a single postcontrast acquisition (first postcontrast subtracted [FAST]) or a time-resolved series of postcontrast acquisitions with lower spatial resolution (ultrafast [UF]), obtained during the early postcontrast phase immediately after contrast agent injection; (b) abridged protocols, consisting of FAST or UF acquisitions plus selected additional pulse sequences; and (c) noncontrast protocols, where diffusion-weighted imaging replaces the contrast information. Abbreviated MRI was proposed to increase…

Background Patients have the highest risk of subsequent fractures in the first few years after an initial fracture, yet models to predict short-term subsequent risk have not been developed. Purpose To develop and validate a deep learning prediction model for subsequent fracture risk using digitally reconstructed radiographs from hip CT in patients with recent hip fractures. Materials and Methods This retrospective study included adult patients who underwent three-dimensional hip CT due to a fracture from January 2004 to December 2020. Two-dimensional frontal, lateral, and axial digitally reconstructed radiographs were generated and assembled to construct an ensemble model. DenseNet modules were used to calculate risk probability based on extracted image features and fracture-free probability plots were output. Model performance was assessed using the C index and area under the receiver operating characteristic curve (AUC) and compared with other models using the paired t test. Results The training and validation set included 1012 patients (mean age, 74.5 years ± 13.3 [SD]; 706…

This prospective study defines expected US elastography measurement variability to guide clinical interpretation, enable diagnostic enrichment in clinical trials, and support U.S. Food and Drug Administration biomarker validation for nonalcoholic fatty liver disease.

Performance of deep learning models for predicting preinvasive, minimally invasive, or invasive adenocarcinoma was improved by combining binary and ternary classification models for predicting invasiveness and adjudication of discordant classification.

Chronic liver disease is highly prevalent and often leads to fibrosis or cirrhosis and complications such as liver failure and hepatocellular carcinoma. The diagnosis and staging of liver fibrosis is crucial to determine management and mitigate complications. Liver biopsy for histologic assessment has limitations such as sampling bias and high interreader variability that reduce precision, which is particularly challenging in longitudinal monitoring. MR elastography (MRE) is considered the most accurate noninvasive technique for diagnosing and staging liver fibrosis. In MRE, low-frequency vibrations are applied to the abdomen, and the propagation of shear waves through the liver is analyzed to measure liver stiffness, a biomarker for the detection and staging of liver fibrosis. As MRE has become more widely used in clinical care and research, different contexts of use have emerged. This review focuses on the latest developments in the use of MRE for the assessment of liver fibrosis; provides guidance for image acquisition and interpretation; summarizes diagnostic performance, along…

< .001). Text input length affected LLM accuracy (odds ratio range, 3.2 [95% CI: 1.9, 5.5] to 6.6 [95% CI: 3.7, 12.0]). Conclusion LLMs demonstrated substantial accuracy with text and image inputs, outperforming a medical student. However, their accuracy decreased with shorter text lengths, regardless of image input. © RSNA, 2024

statistic, and meta-regression to explore potential sources of heterogeneity. When available, mean liver attenuation, liver-spleen attenuation difference, liver to spleen attenuation ratio, and the DECT-derived fat fraction for hepatic steatosis diagnosis were assessed. Results Forty-two studies (14 186 participants) were included. NCCT had a sensitivity and specificity of 72% and 88%, respectively, for steatosis (>5% fat at biopsy) detection and 82% and 94% for at least moderate steatosis (over 20%-33% fat at biopsy) detection. CECT had a sensitivity and specificity of 66% and 90% for steatosis detection and 68% and 93% for at least moderate steatosis detection. DECT had a sensitivity and specificity of 85% and 88% for steatosis detection. In the subgroup analysis, the sensitivity and specificity for detecting steatosis were 80% and 99% for CECT and 84% and 93% for DECT. There was heterogeneity among studies focusing on CECT and DECT. Liver attenuation less than 40-45 HU, liver-spleen attenuation difference less than -5 to 0 HU, and liver to spleen attenuation ratio less than 0.9-1…

Systemic immunotherapies have led to tremendous progress across the cancer landscape. However, several challenges exist, potentially limiting their efficacy in the treatment of solid tumors. Direct intratumoral injection can increase the therapeutic index of immunotherapies while overcoming many of the barriers associated with systemic administration, including limited bioavailability to tumors and potential systemic safety concerns. However, challenges remain, including the lack of standardized approaches for administration, issues relating to effective drug delivery, logistical hurdles, and safety concerns specific to this mode of administration. This article reviews the biologic rationale for the localized injection of immunotherapeutic agents into tumors. It also addresses the existing limitations and practical considerations for safe and effective implementation and provide recommendations for optimizing logistics and treatment workflows. It also highlights the critical role that radiologists, interventional radiologists, and medical physicists play in intratumoral…

A 10-center study found no evidence of a significant difference in progression-free survival rate between microwave ablation (MWA) and surgical resection for US-detected multifocal T1N0M0 papillary thyroid carcinoma, with MWA resulting in fewer complications.

With the COVID-19 pandemic having lasted more than 3 years, concerns are growing about prolonged symptoms and respiratory complications in COVID-19 survivors, collectively termed post-COVID-19 condition (PCC). Up to 50% of patients have residual symptoms and physiologic impairment, particularly dyspnea and reduced diffusion capacity. Studies have also shown that 24%-54% of patients hospitalized during the 1st year of the pandemic exhibit radiologic abnormalities, such as ground-glass opacity, reticular opacity, bronchial dilatation, and air trapping, when imaged more than 1 year after infection. In patients with persistent respiratory symptoms but normal results at chest CT, dual-energy contrast-enhanced CT, xenon 129 MRI, and low-field-strength MRI were reported to show abnormal ventilation and/or perfusion, suggesting that some lung injury may not be detectable with standard CT. Histologic patterns in post-COVID-19 lung disease include fibrosis, organizing pneumonia, and vascular abnormality, indicating that different pathologic mechanisms may contribute to PCC. Therefore, a…

A Food and Drug Administration (FDA)-cleared artificial intelligence (AI) algorithm misdiagnosed a finding as an intracranial hemorrhage in a patient, who was finally diagnosed with an ischemic stroke. This scenario highlights a notable failure mode of AI tools, emphasizing the importance of human-machine interaction. In this report, the authors summarize the review processes by the FDA for software as a medical device and the unique regulatory designs for radiologic AI/machine learning algorithms to ensure their safety in clinical practice. Then the challenges in maximizing the efficacy of these tools posed by their clinical implementation are discussed.

Y resin microspheres, 88% and 72% achieved a per-lesion and overall duration of response of 1 year or greater, respectively, with one grade 3 adverse event. In patients whose tumors received 300 Gy or more according to posttreatment dosimetry, a disease progression benefit was noted. © RSNA, 2024