Radiotherapy and thermal ablation are covered, in addition to systemic therapies like conventional chemotherapy, targeted therapy, and immunotherapy.

The Editorial Comment by Hyun Soo Ko provides context on this article. The abstract for this article is available in Chinese (audio/PDF) and Spanish (audio/PDF) translations. The key to optimal clinical outcomes in patients with acute pulmonary embolism (PE) is the timely application of interventions like anticoagulation. Our goal is to quantify the effect of artificial intelligence-driven radiologist worklist prioritization on the time taken to generate reports for CT pulmonary angiography (CTPA) cases with positive findings for acute pulmonary embolism. This retrospective, single-center study examined patients who underwent CT pulmonary angiography (CTPA) both prior to (October 1, 2018 – March 31, 2019; pre-artificial intelligence period) and subsequent to (October 1, 2019 – March 31, 2020; post-artificial intelligence period) the implementation of an AI system that prioritized CTPA cases, featuring acute pulmonary embolism (PE) detection, at the top of radiologists' reading lists. To ascertain examination wait time (the time between examination completion and report initiation), read time (the time between report initiation and report availability), and report turnaround time (the sum of wait and read times), examination timestamps from the EMR and dictation system were used. To ascertain differences, reporting times for positive pulmonary embolism cases, using the final radiology reports as a reference, were compared across each time period. CADD522 The examinations encompassed 2501 instances, affecting 2197 patients (average age, 57.417 years; 1307 females, 890 males), inclusive of 1166 pre-AI and 1335 post-AI evaluations. The frequency of acute pulmonary embolisms, as documented by radiology, was 151% (201 cases out of 1335) during the pre-artificial intelligence era, contrasting with 123% (144 cases out of 1166) in the post-artificial intelligence period. After the AI phase, the AI device reorganized the priority list of 127% (148 out of 1166) of the exams. A comparison of the post-AI and pre-AI periods revealed a statistically significant reduction in the mean report turnaround time for PE-positive examinations. The turnaround time decreased from 599 to 476 minutes (mean difference, 122 minutes; 95% CI, 6-260 minutes). Routine-priority examinations during standard business hours experienced a dramatic reduction in waiting time post-AI, shrinking from 437 minutes pre-AI to 153 minutes post-AI (mean difference 284 minutes, 95% CI 22–647 minutes). Stat or urgent priority examinations, however, showed no comparable decrease. AI's impact on worklist prioritization resulted in faster report turnaround times and decreased wait times, notably for PE-positive CPTA examinations. The AI instrument, by supporting rapid diagnostic capabilities for radiologists, could potentially lead to earlier interventions for acute pulmonary embolism.

Previously known as pelvic congestion syndrome, pelvic venous disorders (PeVD) have been a historically underdiagnosed condition contributing to chronic pelvic pain (CPP), a substantial health problem negatively impacting quality of life. Progress in the field has facilitated a sharper comprehension of definitions related to PeVD, and the evolution of PeVD workup and treatment algorithms has unveiled novel insights into the causes of pelvic venous reservoirs and their concomitant symptoms. Both ovarian and pelvic vein embolization, and the endovascular stenting of common iliac venous compression, are current methods of consideration for PeVD treatment. The efficacy and safety of both treatments have been consistently demonstrated in patients with CPP of venous origin, irrespective of age. PeVD treatment protocols display significant heterogeneity, attributable to the limited availability of prospective, randomized data and the evolving understanding of variables related to favorable treatment outcomes; forthcoming clinical trials are poised to improve the comprehension of venous-origin CPP and refine management approaches. This AJR Expert Panel Narrative Review offers a contemporary account of PeVD, including its current classification, diagnostic approach, endovascular procedures, strategies for handling persistent/recurrent symptoms, and future research considerations.

Studies have shown the ability of Photon-counting detector (PCD) CT to decrease radiation dose and improve image quality in adult chest CT, but its potential in pediatric CT is not fully understood. The purpose of this study is to determine the comparative radiation dose and image quality (both objective and subjective) between PCD CT and energy-integrating detector (EID) CT in children undergoing high-resolution chest computed tomography (HRCT). A retrospective analysis of 27 children (median age 39 years; 10 female, 17 male) who underwent PCD CT scans from March 1st, 2022 to August 31st, 2022, and 27 more children (median age 40 years; 13 female, 14 male) who underwent EID CT scans between August 1st, 2021, and January 31st, 2022, was conducted. All these examinations included a clinically necessary HRCT of the chest. Patients in the two groups were coordinated based on their age and water-equivalent diameter. The parameters of the radiation dose were documented. Using regions of interest (ROIs), an observer determined the objective parameters of lung attenuation, image noise, and signal-to-noise ratio (SNR). Two radiologists independently evaluated the subjective attributes of overall image quality and motion artifacts, employing a 5-point Likert scale, whereby 1 signifies the highest quality. A comparison of the groups was undertaken. CADD522 Results from PCD CT showed a lower median CTDIvol (0.41 mGy) than EID CT (0.71 mGy), with a statistically significant difference (P < 0.001) apparent in the comparison. The difference in DLP (102 vs 137 mGy*cm, p = .008) and size-specific dose estimate (82 vs 134 mGy, p < .001) is statistically evident. mAs levels varied considerably between 480 and 2020 (P < 0.001), demonstrating a statistically significant difference. No statistically significant difference was observed between PCD CT, EID CT, and the right upper lobe (RUL) lung attenuation values (-793 vs -750 HU, P = .09), right lower lobe (RLL) lung attenuation (-745 vs -716 HU, P = .23), RUL image noise (55 vs 51 HU, P = .27), RLL image noise (59 vs 57 HU, P = .48), RUL signal-to-noise ratio (-149 vs -158, P = .89), or RLL signal-to-noise ratio (-131 vs -136, P = .79) when comparing PCD CT and EID CT. Comparing PCD CT and EID CT, no noteworthy difference was found in the median overall image quality for reader 1 (10 vs 10, P = .28), or for reader 2 (10 vs 10, P = .07). Likewise, the median motion artifacts did not show a substantial distinction for reader 1 (10 vs 10, P = .17) or reader 2 (10 vs 10, P = .22). Compared to EID CT, PCD CT yielded demonstrably lower radiation doses, maintaining comparable image quality metrics, both objective and subjective. These data concerning PCD CT's performance in children provide a broader understanding, highlighting its suitability for routine application.

The advanced artificial intelligence (AI) models, large language models (LLMs), including ChatGPT, are specifically created to process and comprehend the nuances of human language. Automating clinical histories and impressions, producing layperson summaries of radiology reports, and facilitating patient-relevant questions and answers are potential ways that LLMs can boost the quality of radiology reporting and patient engagement. However, large language models are not without their errors, and careful human review is required to lessen the chances of patient injury.

The preliminary circumstances. AI-based tools for clinical image analysis need to handle the variability in examination settings, which is anticipated. The primary objective remains. This investigation aimed to assess the technical reliability of a selection of automated AI abdominal CT body composition tools on a varied sample of external CT examinations conducted outside the authors' hospital system, while also exploring potential factors leading to tool failure. A range of methods is being implemented to complete the mission. In this retrospective study, 8949 patients (4256 men and 4693 women; average age, 55.5 ± 15.9 years) underwent 11,699 abdominal CT scans at 777 diverse external institutions. These scans, acquired with 83 different scanner models from six manufacturers, were later transferred to the local Picture Archiving and Communication System (PACS) for clinical applications. To assess body composition, including bone attenuation, the amount and attenuation of muscle, and the amounts of visceral and subcutaneous fat, three autonomous AI tools were implemented. Evaluations were conducted on a single axial series per examination instance. Empirically derived reference spans determined the technical adequacy of the tool's output measurements. A review of instances where tool output lay outside the prescribed reference range was carried out to identify potential causes of failures. This JSON schema produces a list containing sentences. Across 11431 of 11699 examinations, all three tools performed within acceptable technical standards. A failure of at least one tool occurred in 268, or 23%, of the examinations. Bone tools boasted an individual adequacy rate of 978%, muscle tools 991%, and fat tools a rate of 989%. A critical image processing error, anisotropic in nature and stemming from incorrect DICOM header voxel dimension specifications, resulted in the failure of all three tools in 81 of 92 (88%) cases, implying a strong correlation between this particular error and complete tool failure. CADD522 Among all types of tools (bone, 316%; muscle, 810%; fat, 628%), anisometry error was the most prevalent cause of failure. In a single manufacturer's line of scanners, anisometry errors were extraordinarily prevalent, affecting 79 of 81 units (97.5%). The investigation into the failure of 594% of bone tools, 160% of muscle tools, and 349% of fat tools did not uncover a reason for the failures. Consequently, The automated AI body composition tools performed with high technical adequacy in a heterogeneous sample of external CT scans, signifying their broad applicability and generalizability across diverse patient populations.