Acta radiologica最新文献

Background: You Only Look Once version 5 (YOLOv5), a one-stage deep-learning (DL) algorithm for object detection and classification, offers high speed and accuracy for identifying targets.

Purpose: To investigate the feasibility of using the YOLOv5 algorithm to non-invasively distinguish between aldosterone-producing adenomas (APAs) and non-functional adrenocortical adenomas (NF-ACAs) on computed tomography (CT) images.

Material and methods: A total of 235 patients who were diagnosed with ACAs between January 2011 and July 2022 were included in this study. Of the 215 patients, 81 (37.7%) had APAs and 134 (62.3%) had NF-ACAs' they were randomly divided into either the training set or the validation set at a ratio of 9:1. Another 20 patients, including 8 (40.0%) with APA and 12 (60.0%) with NF-ACA, were collected for the testing set. Five submodels (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) of YOLOv5 were trained and evaluated on the datasets.

Results: In the testing set, the mAP_0.5 value for YOLOv5x (0.988) was higher than the values for YOLOv5n (0.969), YOLOv5s (0.965), YOLOv5m (0.974), and YOLOv5l (0.983). The mAP_0.5:0.95 value for YOLOv5x (0.711) was also higher than the values for YOLOv5n (0.587), YOLOv5s (0.674), YOLOv5m (0.671), and YOLOv5l (0.698) in the testing set. The inference speed of YOLOv5n was 2.4 ms in the testing set, which was the fastest among the five submodels.

Conclusion: The YOLOv5 algorithm can accurately and efficiently distinguish between APAs and NF-ACAs on CT images, especially YOLOv5x has the best identification performance.

Background: Posterior circulation stroke (PCS) accounts for approximately 20% of all acute ischemic strokes. The optimal reperfusion therapy for PCS management remains uncertain.

Purpose: To evaluate the prevalence and outcomes of intravenous thrombolysis (IVT), endovascular thrombectomy (EVT), and bridging therapy in PCS patients.

Material and methods: We conducted a meta-analysis of 19 studies examining reperfusion therapy outcomes in PCS patients, including 9765 individuals. We pooled prevalence data and assessed associations between reperfusion therapies and clinical, safety, and recanalization outcomes using random-effects models.

Results: The pooled prevalence of reperfusion therapies post-acute PCS was 39% for IVT, 54% for EVT, and 48% for bridging therapy. EVT was associated with significantly higher odds of favorable functional outcomes (modified Rankin Score [mRS] 0-3) at 90 days compared to standard medical therapy (odds ratio [OR] = 5.68; 95% confidence interval [CI]=2.07-15.59; P = 0.001). Conversely, bridging therapy was linked to reduced odds of favorable functional outcomes at 90 days compared to EVT (OR = 0.35; 95% CI=0.26-0.47; P < 0.001). Bridging therapy was also significantly associated with lower odds of good functional outcomes (mRS 0-2) (OR = 0.25; 95% CI=0.11-0.54; P < 0.001), reduced risk of symptomatic intracranial hemorrhage (OR = 0.26; 95% CI=0.07-0.68; P = 0.009), lower mortality (OR = 0.13; 95% CI=0.04-0.44; P = 0.001), and less successful recanalization (OR = 0.35; 95% CI=0.13-0.94; P = 0.038) relative to EVT.

Conclusion: Our meta-analysis underscores the favorable outcomes associated with EVT in PCS cases. With notable reperfusion rates, understanding factors influencing PCS outcomes can inform patient selection and prognostic considerations.

Background: The capsular ligaments at the hip joint work in synchrony with the acetabulum and femoral head for articular stability. There is a lack of understanding about ischiofemoral ligament (ISFL) anatomy and function.

Purpose: To assess the insertion of the ISFL in non-arthritic adult hips.

Material and methods: A retrospective analysis was performed in 72 patients who underwent magnetic resonance arthrogram (MRA) for the assessment of hip pain. The distribution of the ISFL components, the thickness, and the insertion site were assessed by concomitantly using the axial oblique, coronal, and sagittal MRA images.

Results: Two insertions of the ISFL anterior to the center of the femoral head were identified in 71 (99%) hips: (i) predominant anterior merging with the iliofemoral ligament as continuation of zona orbicularis, observed in all hips; and (ii) anterolateral junction of femoral neck and greater trochanter. Two ISFL parts (proximal and distal) were identified in 70 (97%) of the 72 studied hips. The proximal part was always thinner (mean 2.6 ± 0.7 mm) and originated from the ischium at the acetabular rim. The distal part was a continuation of the zona orbicularis, and the mean thickness was 6.7 ± 1.6 mm. Both parts merged as they coursed over the superior portion of the femoral head.

Conclusion: The predominant insertion of the ischiofemoral ligament is a merging to the iliofemoral ligament anteriorly. Surgical procedures such as hip arthroscopy involving the ISFL will affect the function of the iliofemoral ligament, and vice versa.

Radiographic measurements play a crucial role in evaluating the alignment of distal radius fractures (DRFs). Various manual methods have been used to perform the measurements, but they are susceptible to inaccuracies. Recently, computer-aided methods have become available. This review explores the methods commonly used to assess DRFs. The review introduces the different measurement techniques, discusses the sources of measurement errors and measurement reliability, and provides a recommendation for their use. Radiographic measurements used in the evaluation of DRFs are not reliable. Standardizing the measurement techniques is crucial to address this and automated image analysis could help improve accuracy and reliability.

Background: The fatty infiltration and atrophy in the muscle after a rotator cuff (RC) tear are important in surgical decision-making and are linked to poor clinical outcomes after rotator cuff repair. An accurate and reliable quantitative method should be developed to assess the entire RC muscles.

Purpose: To develop a fully automated approach based on a deep neural network to segment RC muscles from clinical magnetic resonance imaging (MRI) scans.

Material and methods: In total, 94 shoulder MRI scans (mean age = 62.3 years) were utilized for the training and internal validation datasets, while an additional 20 MRI scans (mean age = 62.6 years) were collected from another institution for external validation. An orthopedic surgeon and a radiologist manually segmented muscles and bones as reference masks. Segmentation performance was evaluated using the Dice score, sensitivities, precision, and percent difference in muscle volume (%). In addition, the segmentation performance was assessed based on sex, age, and the presence of a RC tendon tear.

Results: The average Dice score, sensitivities, precision, and percentage difference in muscle volume of the developed algorithm were 0.920, 0.933, 0.912, and 4.58%, respectively, in external validation. There was no difference in the prediction of shoulder muscles, with the exception of teres minor, where significant prediction errors were observed (0.831, 0.854, 0.835, and 10.88%, respectively). The segmentation performance of the algorithm was generally unaffected by age, sex, and the presence of RC tears.

Conclusion: We developed a fully automated deep neural network for RC muscle and bone segmentation with excellent performance from clinical MRI scans.

Background: The optimal primary debulking surgery outcome of serous ovarian carcinoma (SOC) is greatly affected by primary ovarian neoplasm or metastatic lesion close to the rectum.

Purpose: To study the risk factors affecting postoperative residual primary ovarian neoplasm or metastatic lesion close to the rectum of SOC.

Material and methods: The clinical and MRI data of 164 patients with SOC eligible from institution A (training and test groups) and 36 patients with SOC eligible from institution B (external validation group) were collected and retrospectively analyzed. The clinical data included age, serum carbohydrate antigen 125 (CA-125), human epididymis protein 4, and neutrophil-to-lymphocyte ratio (NLR). Magnetic resonance imaging (MRI) data included ovarian mass distribution, maximum diameter of ovarian mass, ovarian mass features, degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion, and amount of ascites. A model was established using multivariate logistic regression.

Results: By univariate and multivariate logistic regressions, CA-125 (P = 0.024, odds ratio [OR] = 3.798, 95% confidence interval [CI] = 1.24-13.32), NLR (P = 0.037, OR = 3.543, 95% CI = 1.13-12.72), and degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion (P < 0.001, OR = 37.723, 95% CI = 7.46-266.88) were screened as independent predictors. The area under the curve values of the model in the training, test, and external validation groups were 0.860, 0.764, and 0.778, respectively.

Conclusion: The clinical-radiological model based on T1-weighted dual-echo MRI can be used non-invasively to predict postoperative residual ovarian neoplasm or metastasis close to SOC in the rectum.

Spinal bone lesions encompass a wide array of pathologies, spanning from benign abnormalities to aggressive malignancies, such as diffusely localized metastases. Early detection and accurate differentiation of the underlying diseases is crucial for every patient's clinical treatment and outcome, with radiological imaging being a core element in the diagnostic pathway. Across numerous pathologies and imaging techniques, deep learning (DL) models are progressively considered a valuable resource in the clinical setting. This review describes not only the diagnostic performance of these models and the differing approaches in the field of spinal bone malignancy recognition, but also the lack of standardized methodology and reporting that we believe is currently hampering this newly founded area of research. In line with their established and reliable role in lesion detection, this publication focuses on both computed tomography and magnetic resonance imaging, as well as various derivative modalities (i.e. SPECT). After conducting a systematic literature search and subsequent analysis for applicability and quality using a modified QUADAS-2 scoring system, we confirmed that most of the 14 identified studies were plagued by major limitations, such as insufficient reporting of model statistics and data acquisition, a lacking external validation dataset, and potentially biased annotation. Although we experienced these limitations, we nonetheless conclude that the potential of these methods shines through in the presented results. These findings underline the need for more stringent quality controls in DL studies, as well as model development to afford increased insight and progress in this promising novel field.

Background: The impact of excluding intrahepatic segmental vessels from regions of interest (ROIs) on liver stiffness measurement (LSM) via magnetic resonance elastography (MRE) remains uncertain.

Purpose: To determine the effect of excluding intrahepatic segmental vessels from ROIs on LSM obtained from MRE.

Material and methods: This retrospective analysis included 95 participants who underwent successful two-dimensional gradient recalled-echo MRE before hepatic tumor resection (n = 49) or living liver donation (n = 46). The conventional LSM was determined by manually drawing ROIs on the elastogram within the 95% confidence region, staying 1 cm within the liver capsule and excluding large hilar vessels, the gallbladder, hepatic lesions, and artifacts. In addition, the modified LSM was determined by excluding intrahepatic segmental vessels. LSMs obtained by the two methods were compared with paired sample signed-rank test. Diagnostic performance for advanced fibrosis was calculated and compared using McNemar's test and Delong's test. The stage of hepatic fibrosis was assessed using surgical specimens by the METAVIR system.

Results: The modified LSM was larger than the conventional LSM (2.4 kPa vs. 2.2 kPa in reader 1; 2.7 kPa vs. 2.4 kPa in reader 2; P < 0.001). The modified LSM showed superior sensitivity (0.841 vs. 0.659 in reader 1; 0.864 vs. 0.705 in reader 2; P < 0.05) and area under the curve (0.901 vs. 0.820 in reader 1; 0.912 vs. 0.843 in reader 2; P < 0.05) for detecting advanced fibrosis (≥F3) than conventional LSM.

Conclusion: The exclusion of intrahepatic segmental vessels from ROIs in MRE affected the LSM and enhanced diagnostic performance for advanced fibrosis.

Background: Positron emission tomography (PET) has been reported as effective in diagnosing peripheral nerve injury (PNI). However, there is a lack of studies evaluating different degrees of PNI using PET within the same individual to reduce errors due to interindividual differences.

Purpose: To evaluate the recovery process in the same rat after sciatic nerve injury using PET/magnetic resonance imaging (MRI).

Material and methods: Crushing nerve injuries were induced in the left sciatic nerves of six male rats, preserving the right ones. The degree of nerve damage was measured at one, two, three, four, and five weeks postoperatively using three assessment methods: paw withdrawal threshold test (RevWT); PET (SUVR); and MRI (MRSIR). All the representing values of each method are presented as ratio values of the right and left sides in each rat.

Results: Significant gradual recovery of all rats was observed over time in all the methods. No significant differences in RevWT and MRSIR were observed between before and more than four weeks after injury, whereas a significant difference in SUVR was still observed between before and five weeks after injury (P = 0.0007). The parameters of all methods decreased significantly over time (P = 0.000, all), and the explanatory power was significant in RevWT, SUVR, and MRSIR.

Conclusion: PET and MRI could be valuable non-invasive techniques for diagnosing neuropathic pain resulting from PNI. PET/MRI would be expected to be a more accurate and informative diagnostic tool for PNI than MRI alone.

Background: The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined.

Purpose: To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI.

Material and methods: The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity.

Results: The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010).

Conclusion: DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.