Radiology最新文献

Background Radiofrequency ablation (RFA) has comparable clinical outcomes to surgical resection (SR) for treating small recurrent hepatocellular carcinoma (HCC). However, whether combined transarterial chemoembolization (TACE) with RFA (hereafter, TACE-RFA) outperforms SR for treating small late-recurrence HCCs remains unknown. Purpose To compare the clinical outcome of TACE-RFA with that of SR in patients with small late-recurrence HCCs. Materials and Methods This randomized clinical trial recruited patients between July 2013 and March 2019. Patients with small late-recurrence HCCs (a single recurrent HCC nodule [≤ 5 cm in diameter] or three or fewer nodules [each ≤ 3 cm in diameter] and recurrence at least 12 months after radical therapy of primary HCC) were randomly assigned to receive TACE-RFA or SR. The primary end point was overall survival (OS). The secondary end points included recurrence-free survival (RFS) and the incidence of complications. OS and RFS were assessed using the Kaplan-Meier method and log-rank test. Results In the intention-to-treat analysis, 210 patients (mean age, 52 years ± 12 [SD]; 194 male) were included, with 105 patients in each group. The 1-, 3-, and 5-year OS rates were 99%, 81%, and 69%, respectively, in the TACE-RFA group and 96%, 81%, and 76%, respectively, in the SR group (hazard ratio [HR], 1.34; 95% CI: 0.81, 2.23; P = .26). The 1-, 3-, and 5-year RFS rates were 71%, 38%, and 24%, respectively, in the TACE-RFA group and 73%, 43%, and 29%, respectively, in the SR group (HR, 1.05; 95% CI: 0.76, 1.45; P = .78). The incidence of complications was greater in the SR group than in the TACE-RFA group (41% [43 of 104] vs 24% [23 of 96]; P = .01). Conclusion For patients with small late-recurrence HCCs, TACE-RFA did not yield better survival outcomes than SR. However, the incidence of complications was lower in patients who received TACE-RFA therapy. ClinicalTrials.gov Identifier: NCT01833286 © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Ronot in this issue.

Background Since the introduction of TotalSegmentator CT, there has been demand for a similar robust automated MRI segmentation tool that can be applied across all MRI sequences and anatomic structures. Purpose To develop and evaluate an automated MRI segmentation model for robust segmentation of major anatomic structures independent of MRI sequence. Materials and Methods In this retrospective study, an nnU-Net model (TotalSegmentator MRI) was trained on MRI and CT scans to segment 80 anatomic structures relevant for use cases such as organ volumetry, disease characterization, surgical planning, and opportunistic screening. Images were randomly sampled from routine clinical studies to represent real-world examples. Dice scores were calculated between the predicted segmentations and expert radiologist segmentations to evaluate model performance on an internal test set and two external test sets and against two publicly available models and TotalSegmentator CT. The Wilcoxon signed rank test was used to compare model performance. The proposed model was applied to a separate internal dataset containing abdominal MRI scans to investigate age-dependent volume changes. Results A total of 1143 scans (616 MRI, 527 CT; median patient age, 61 years [IQR, 50-72 years]) were split into a training set (n = 1088; CT and MRI) and an internal test set (n = 55; MRI only). The two external test sets (AMOS and CHAOS) contained 20 MRI scans each, and the aging-study dataset contained 8672 abdominal MRI scans (median patient age, 59 years [IQR, 45-70 years]). The proposed model had a Dice score of 0.839 for the 80 anatomic structures in the internal test set and outperformed two other models (Dice score of 0.862 vs 0.759 for 40 anatomic structures and 0.838 vs 0.560 for 13 anatomic structures; P < .001 for both). On the TotalSegmentator CT test set (89 CT scans), the performance of the proposed model almost matched that of TotalSegmentator CT (Dice score, 0.966 vs 0.970; P < .001). The aging study demonstrated a strong correlation between age and organ volume (eg, age and liver volume: ρ = -0.096; P < .0001). Conclusion The proposed open-source, easy-to-use model allows for automatic, robust segmentation of 80 structures, extending the capabilities of TotalSegmentator to MRI scans from any MRI sequence. The ready-to-use online tool is available at https://totalsegmentator.com; the model, at https://github.com/wasserth/TotalSegmentator; and the dataset, at http://zenodo.org/records/14710732. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Kitamura in this issue.

Background Clinical information improves imaging interpretation, but physician-provided histories on requisitions for oncologic imaging often lack key details. Purpose To evaluate large language models (LLMs) for automatically generating clinical histories for oncologic imaging requisitions from clinical notes and compare them with original requisition histories. Materials and Methods In total, 207 patients with CT performed at a cancer center from January to November 2023 and with an electronic health record clinical note coinciding with ordering date were randomly selected. A multidisciplinary team informed selection of 10 parameters important for oncologic imaging history, including primary oncologic diagnosis, treatment history, and acute symptoms. Clinical notes were independently reviewed to establish the reference standard regarding presence of each parameter. After prompt engineering with seven patients, GPT-4 (version 0613; OpenAI) was prompted on April 9, 2024, to automatically generate structured clinical histories for the 200 remaining patients. Using the reference standard, LLM extraction performance was calculated (recall, precision, F1 score). LLM-generated and original requisition histories were compared for completeness (proportion including each parameter), and 10 radiologists performed pairwise comparison for quality, preference, and subjective likelihood of harm. Results For the 200 LLM-generated histories, GPT-4 performed well, extracting oncologic parameters from clinical notes (F1 = 0.983). Compared with original requisition histories, LLM-generated histories more frequently included parameters critical for radiologist interpretation, including primary oncologic diagnosis (99.5% vs 89% [199 and 178 of 200 histories, respectively]; P < .001), acute or worsening symptoms (15% vs 4% [29 and seven of 200]; P < .001), and relevant surgery (61% vs 12% [122 and 23 of 200]; P < .001). Radiologists preferred LLM-generated histories for imaging interpretation (89% vs 5%, 7% equal; P < .001), indicating they would enable more complete interpretation (86% vs 0%, 15% equal; P < .001) and have a lower likelihood of harm (3% vs 55%, 42% neither; P < .001). Conclusion An LLM enabled accurate automated clinical histories for oncologic imaging from clinical notes. Compared with original requisition histories, LLM-generated histories were more complete and were preferred by radiologists for imaging interpretation and perceived safety. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Tavakoli and Kim in this issue.

Background Type 2 lateral spinal cerebrospinal fluid (CSF) leakage occurs in approximately 20% of cases of spontaneous intracranial hypotension (SIH); however, the underlying pathologic mechanism remains ambiguous. Purpose To characterize MRI features of type 2 leaks, correlate them with intraoperative observations, and evaluate their diagnostic value. Materials and Methods Patients with SIH and type 2 leaks diagnosed between January 2021 and February 2023 were retrospectively identified. Characteristic imaging features from heavily T2-weighted MR myelography (T2-MRM) images were reevaluated (independently and blinded) in the type 2 leak sample mixed with a sample of 40 patients with SIH and type 1 (ventral) leaks. Available intraoperative data were reviewed for lateral dural tears, arachnoid outpouching, and ruptured spinal meningeal diverticula. Results Twenty-eight patients with SIH (mean age, 37.3 years ± 8.2 [SD]; 22 [79%] female patients) had 29 type 2 leaks between the T7 and L2 levels without side predominance. Characteristic cystic lesions with a broad dural base on the exiting nerve root sleeve were identified at T2-MRM; this "bud-on-branch" sign reflects an arachnoid outpouching herniating through a lateral dural tear, distinct from a meningeal diverticulum, which yielded a sensitivity of 79% (22 of 28; 95% CI: 59, 92) and a specificity of 100% (40 of 40; 95% CI: 91, 100) for leak location. Arachnoid outpouching was confirmed intraoperatively in 23 of 25 patients (92%; 95% CI: 81, 100), originating from the nerve root sleeve axilla in most patients (19 of 25, 76%; 95% CI: 59, 93); two of 25 patients (8%; 95% CI: 0, 19) had a dural tear only, and none had an underlying ruptured meningeal diverticulum. Conclusion This study showed that type 2 leaks are actually due to a lateral dural nerve root sleeve tear through which the arachnoid herniates, which contrasted the common perception that these leaks result from ruptured meningeal diverticula. These leaks had a characteristic anatomic distribution and MRI appearance with substantially facilitated leak localization in patients with SIH. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Rovira and Torres-Ferrús in this issue.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Background Diagnostic error rates for detecting small lung nodules on chest CT scans remain high at 50%, despite advances in imaging technology and radiologist training. These failure rates may stem from limitations in conscious recognition processes. However, successful visual processes may be detecting the nodule independent of the radiologist's report. Purpose To investigate visual processing in radiologists during the assessment of chest nodules to determine if radiologists have successful non-conscious processes that detect lung nodules on chest CT examinations even when not consciously recognized or considered, as evidenced by changes in how long they look (dwell time) and pupil size to missed nodules. Materials and Methods This prospective study, conducted from [8/14] to [09/23], compared 6 experienced radiologists with 6 medically naïve control participants. Participants viewed 18 chest CTs (9 abnormal with 16 nodules, 9 normal) to detect lung nodules. High-speed video eye-tracking measured gaze duration and pupil size (indicating physiological arousal) at missed nodule locations and same locations on normal CTs. The reference standard was the known presence or absence of nodules (as determined by a 4-radiologist consensus panel) in abnormal and normal CTs, respectively. Primary outcome measures were detection rates of nodules, dwell time and pupil size at nodule locations versus normal tissue. Paired t-tests were used for statistical analysis. Results Twelve participants (6 radiologists [9.3 average years of radiological experience]) 6 controls (with no radiological experience) were evaluated. Radiologists missed on average 59% of these lung nodules. For missed nodules, radiologists exhibited longer dwell times (Mean: 228 milliseconds vs 175 milliseconds, P=.005) and larger pupil area (Mean: 1446 pixels vs. 1349 pixels, P=.04.) than normal tissue. Control participants showed no differences in dwell time (Mean: 197 milliseconds vs 180 milliseconds, P= .64) or pupil size (Mean: 1426 pixels vs. 1714 pixels, P=.23) for missed nodules than normal tissue locations. Conclusion Radiologists non-conscious processes during visual assessment of a CT examination can detect lung nodules on chest CTs even when conscious recognition fails, as evidenced by increased dwell time and larger pupil size. This successful non-conscious detection is a result of general radiology training.

Background Deep learning (DL) methods enable faster shoulder MRI than conventional methods, but arthroscopy-validated evidence of good diagnostic performance is scarce. Purpose To validate the clinical efficacy of 7-minute threefold parallel imaging (PIx3)-accelerated DL super-resolution shoulder MRI against arthroscopic findings. Materials and Methods Adults with painful shoulder conditions who underwent PIx3-accelerated DL super-resolution 3-T shoulder MRI and arthroscopy between March and November 2023 were included in this retrospective study. Seven radiologists independently evaluated the MRI scan quality parameters and the presence of artifacts (Likert scale rating ranging from 1 [very bad/severe] to 5 [very good/absent]) as well as the presence of rotator cuff tears, superior and anteroinferior labral tears, biceps tendon tears, cartilage defects, Hill-Sachs lesions, Bankart fractures, and subacromial-subdeltoid bursitis. Interreader agreement based on κ values was evaluated, and diagnostic performance testing was conducted. Results A total of 121 adults (mean age, 55 years ± 14 [SD]; 75 male) who underwent MRI and arthroscopy within a median of 39 days (range, 1-90 days) were evaluated. The overall image quality was good (median rating, 4 [IQR, 4-4]), with high reader agreement (κ ≥ 0.86). Motion artifacts and image noise were minimal (rating of 4 [IQR, 4-4] for each), and reconstruction artifacts were absent (rating of 5 [IQR, 5-5]). Arthroscopy-validated abnormalities were detected with good or better interreader agreement (κ ≥ 0.68). The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were 89%, 90%, 89%, and 0.89, respectively, for supraspinatus-infraspinatus tendon tears; 82%, 63%, 68%, and 0.68 for subscapularis tendon tears; 93%, 73%, 86%, and 0.83 for superior labral tears; 100%, 100%, 100%, and 1.00 for anteroinferior labral tears; 68%, 90%, 82%, and 0.80 for biceps tendon tears; 42%, 93%, 81%, and 0.64 for cartilage defects; 93%, 99%, 98%, and 0.94 for Hill-Sachs deformities; 100%, 99%, 99%, and 1.00 for osseous Bankart lesions; and 97%, 63%, 92%, and 0.80 for subacromial-subdeltoid bursitis. Conclusion Seven-minute PIx3-accelerated DL super-resolution 3-T shoulder MRI has good diagnostic performance for diagnosing tendinous, labral, and osteocartilaginous abnormalities. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Tuite in this issue.