Thermal ablation, radiotherapy, and systemic therapies—including conventional chemotherapy, targeted therapy, and immunotherapy—constitute the covered treatments.

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. This study seeks to evaluate the effect of utilizing AI for reordering radiologist worklists on the speed of reporting CT pulmonary angiography (CTPA) examinations confirming acute pulmonary embolism. A single-center, retrospective study investigated patients undergoing CT pulmonary angiography (CTPA) prior to (October 1, 2018, to March 31, 2019; pre-AI phase) and subsequent to (October 1, 2019 to March 31, 2020; post-AI phase) the introduction of an AI tool that ranked CTPA exams with detected acute pulmonary embolism (PE) highest on radiologists' reading lists. Examination wait time, read time, and report turnaround time were ascertained by leveraging the timestamps from the EMR and dictation system. This calculation considered the interval from examination completion to report initiation, report initiation to report availability, and the combined duration of the two, respectively. Utilizing final radiology reports as a point of reference, the reporting times for positive PE cases were contrasted for each of the specified time periods. DSS Crosslinker The 2501 examinations in the study encompassed 2197 patients (mean age 57.417 years, including 1307 women and 890 men). The data comprised 1166 examinations from the pre-AI period and 1335 from the post-AI period. Radiological data revealed a pre-AI rate of acute pulmonary embolism at 151% (201/1335), subsequently declining to 123% (144/1166) post-artificial intelligence implementation. After the AI phase, the AI device reorganized the priority list of 127% (148 out of 1166) of the exams. PE-positive examinations, assessed post-AI integration, manifested a drastically reduced average report turnaround time (476 minutes) in contrast to the pre-AI era (599 minutes). The mean difference amounted to 122 minutes (95% CI, 6-260 minutes). Routine examination wait times during operating hours saw a striking decrease in the post-AI period compared to the pre-AI era, dropping from 437 minutes to 153 minutes (mean difference: 284 minutes; 95% CI: 22-647 minutes). However, wait times for stat or urgent priority examinations remained unchanged. AI-driven reprioritization of worklists contributed to a decrease in both report turnaround time and wait time for PE-positive CPTA examinations. The AI tool has the potential to support faster diagnoses by radiologists, thereby enabling earlier interventions in cases of acute pulmonary embolism.

Pelvic venous disorders (PeVD), formerly known by imprecise terms like pelvic congestion syndrome, have historically been under-recognized as a cause of chronic pelvic pain (CPP), a significant health issue that diminishes 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. Currently, endovascular stenting of common iliac venous compression, combined with ovarian and pelvic vein embolization, are important management options for PeVD. Across all age groups, patients with venous origin CPP have shown both treatments to be both safe and effective. There's substantial heterogeneity in current PeVD therapeutic approaches, driven by the limited availability of prospective, randomized trials and ongoing refinement of factors contributing to positive outcomes; upcoming clinical trials are anticipated to improve our understanding of venous-origin CPP and develop more effective management strategies for PeVD. The AJR Expert Panel Narrative Review gives a current assessment of PeVD, covering its current classification, diagnostic methods, endovascular procedures, management of ongoing or recurring symptoms, and future research priorities.

The use of Photon-counting detector (PCD) CT for adult chest CT scans has shown promise in terms of reduced radiation dose and improved image quality; however, its efficacy in pediatric CT applications has yet to be extensively documented. This study aims to evaluate radiation exposure, picture quality objectively and subjectively, using PCD CT versus EID CT, in children undergoing high-resolution chest computed tomography (HRCT). The retrospective analysis included 27 children (median age 39 years; 10 girls, 17 boys) who had PCD CT between March 1, 2022, and August 31, 2022, and 27 additional children (median age 40 years; 13 girls, 14 boys) who had EID CT examinations from August 1, 2021 to January 31, 2022. Chest HRCT was performed in all cases, dictated by clinical necessity. Matching criteria for patients in the two groups included age and water-equivalent diameter. A comprehensive account of the radiation dose parameters was made. To obtain objective measurements of lung attenuation, image noise, and signal-to-noise ratio (SNR), an observer designated specific regions of interest (ROIs). The subjective qualities of overall image quality and motion artifacts were independently assessed by two radiologists, who used a 5-point Likert scale where a score of 1 signified the best possible quality. Comparative metrics were applied to the groups. DSS Crosslinker Compared to EID CT, PCD CT results exhibited a lower median CTDIvol (0.41 mGy versus 0.71 mGy), demonstrating a statistically significant difference (P < 0.001). There is a notable disparity in DLP values (102 vs 137 mGy*cm, p = .008) and corresponding size-specific dose estimates (82 vs 134 mGy, p < .001). mAs values displayed a substantial variation when comparing 480 to 2020, with statistical significance (P < 0.001). There was no statistically significant divergence between PCD CT and EID CT scans in the parameters of lung attenuation (right upper lobe -793 vs -750 HU, P = .09; right lower lobe -745 vs -716 HU, P = .23), image noise (RUL 55 vs 51 HU, P = .27; RLL 59 vs 57 HU, P = .48), or signal-to-noise ratio (RUL -149 vs -158, P = .89; RLL -131 vs -136, P = .79) for the right upper and lower lobes. 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. Clinical implications: These data augment our comprehension of PCD CT's potential and advocate for its regular use in pediatric patients.

The advanced artificial intelligence (AI) models, large language models (LLMs), including ChatGPT, are specifically created to process and comprehend the nuances of human language. Improved radiology reporting and increased patient engagement are attainable through LLM-powered automation of clinical history and impression generation, the creation of easily comprehensible patient reports, and the provision of pertinent questions and answers regarding radiology report findings. Errors in LLMs are a concern, and the need for human review remains to reduce the risk of patient safety issues.

The foundational elements. AI-based tools for clinical image analysis need to handle the variability in examination settings, which is anticipated. Objectively speaking, the goal is. 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. Our approach utilizes diverse methods to attain our targets. This study, a retrospective review, involved 8949 patients (4256 men and 4693 women; average age, 55.5 ± 15.9 years) who underwent 11,699 abdominal CT scans at 777 different external institutions. The scans utilized 83 unique scanner models from six different manufacturers, and the images were subsequently processed for clinical use via a local Picture Archiving and Communication System (PACS). In assessing body composition, three AI tools, operating autonomously, were deployed to measure bone attenuation, the quantity and attenuation of muscle, and the quantities of visceral and subcutaneous fat. An evaluation was performed on one axial series per examination. Tool output values were considered technically adequate when situated within empirically derived reference intervals. An investigation into failures, which included tool output diverging from the established reference parameters, was undertaken to identify possible contributing factors. A list of sentences is the output of this JSON schema. A significant 11431 out of 11699 assessments confirmed the technical adequacy of all three instruments (97.7%). Failures in at least one tool were observed in 268 examinations, representing 23% of the total. Individual adequacy rates for bone tools, muscle tools, and fat tools were 978%, 991%, and 989%, respectively. A single, anisotropic image processing error—stemming from the DICOM header's inaccurate voxel dimensions—accounted for a substantial 81 of 92 (88%) examinations, each exhibiting failure across all three tools. The simultaneous failure of all three tools was invariably linked to this specific error type. DSS Crosslinker The primary reason for tool failures, as identified across three tissues (bone, 316%; muscle, 810%; fat, 628%), was anisometry error. Scans from a single manufacturer were found to have an alarming 97.5% (79 out of 81) incidence of anisometry errors. Among 594% of bone tool failures, 160% of muscle tool failures, and 349% of fat tool failures, an underlying reason for failure was not established. In summary, A diverse sample of external CT scans yielded high technical performance for the automated AI body composition tools, showcasing their generalizability and wide potential for use.