Radiologic Evaluation of Uncinate Processes of the Cervical Spine and the Relationship Between the Uncinate Process and Vertebral Artery: Implication in Anterior Cervical Spine Surgery.

OBJECTIVE: To determine the relationship between the uncinate process (UP) and vertebral artery (VA) from a radiologic view and to confirm the surgical safety margin to minimize the risk of VA injury during anterior cervical approaches. METHODS: We retrospectively reviewed computed tomography angiography of 205 patients by using a contrast-enhanced computed tomography angiography protocol of the VA. Four kinds of images were simultaneously reconstructed to measure all the parameters associated with VA and UP of cervical spine. RESULTS: The shortest distance from the UP's tip to the VA's medial border (P < 0.001) was at the C-6 level (2.9 ± 0.9 mm on the left and 3.2 ± 1.3 mm on the right), and the longest distance (P < 0.001) was at the C-3 level on both sides. The distance between UP's tip and the medial border of the ipsilateral VA was statistically significantly different at each cervical level, and the right distance was larger than the left (P < 0.05). We found the height of UP gradually increased from C-3 to C5-level and then decreased from C-5 to C-7 level for both sides. The mean distance between the medial borders of left UP and left VA was on average 7.5 ± 1.4 mm. The diameter of VA was on average 3.4 ± 0.6 mm on the left side and 3.2 ± 0.7 mm on the right. The diameter of the VA was statistically significantly different on both sides, and the left side was larger than the right (P < 0.05). CONCLUSIONS: Detailed radiologic anatomy of VA and UP was reviewed in this study. A deep understanding of the correlation between the UP and VA is essential to perform anterior cervical spine surgery safely and ensure adequate spinal canal decompression.

A Clinical Classification of Cervical Ossification of the Posterior Longitudinal Ligament to Guide Surgical Strategy.

STUDY DESIGN: A clinical classification of cervical ossification of the posterior longitudinal ligament (COPLL) was developed based on imaging findings. OBJECTIVE: This study aimed to establish a clinical classification for COPLL and provide corresponding surgery strategies for each subtype. SUMMARY OF BACKGROUND DATA: A practical and reliable classification is needed to guide the treatment of COPLL. MATERIALS AND METHODS: This study retrospectively reviewed plain radiographs, computed tomography scans, and magnetic resonance images of patients diagnosed with COPLL between 2018 and 2022 at Shanghai Changzheng Hospital. The types of COPLL were classified according to the location, morphology, and canal-occupying ratio (OR) of the ossification mass. Interobserver and intraobserver reliability were evaluated using Cohen's kappa. RESULTS: A total of 1000 cases were included, which were classified into five types: focal type (F type), short-sequential type (S type), long-sequential type (L type), high type (H type), and mixed type (M type). In addition, each type could be classified into subtype 1 or subtype 2 according to the canal-OR. Then each type could be further classified into other subtypes according to location and morphology. The interobserver reliabilities in the first and second rounds were 0.853 and 0.887, respectively. The intraobserver reliability was 0.888. CONCLUSION: The authors classified COPLL into a system comprised of five types and several subtypes according to canal-OR, location, and morphology. Surgical strategies for each subtype are also suggested. This provides a theoretical guide for the description and surgical management of COPLL.

Correlation Between Intranodular Vessels and Tumor Invasiveness of Lung Adenocarcinoma Presenting as Ground-glass Nodules: A Deep Learning 3-Dimensional Reconstruction Algorithm-based Quantitative Analysis on Noncontrast Computed Tomography Images.

PURPOSE: To evaluate the role of quantitative features of intranodular vessels based on deep learning in distinguishing pulmonary adenocarcinoma invasiveness. MATERIALS AND METHODS: This retrospective study included 512 confirmed ground-glass nodules from 474 patients with 241 precursor glandular lesions (PGL), 126 minimally invasive adenocarcinomas (MIA), and 145 invasive adenocarcinomas (IAC). The pulmonary blood vessels were reconstructed on noncontrast computed tomography images using deep learning-based region-segmentation and region-growing techniques. The presence of intranodular vessels was evaluated based on the automatic calculation of vessel prevalence, vascular categories, and vessel volume percentage. Further comparisons were made between different invasive groups by the Mantel-Haenszel χ 2 test, χ 2 test, and analysis of variance. RESULTS: The detection rate of intranodular vessels in PGL (33.2%) was significantly lower than that of MIA (46.8%, P = 0.011) and IAC (55.2%, P < 0.001), while the vascular categories were similar (all P > 0.05). Vascular changes were more common in IAC and MIA than in PGL, mainly in increased vessel volume percentage (12.4 ± 19.0% vs. 6.3 ± 13.1% vs. 3.9 ± 9.4%, P < 0.001). The average intranodular artery and vein volume percentage of IAC (7.5 ± 14.0% and 5.0 ± 10.1%) was higher than that of PGL (2.1 ± 6.9% and 1.7 ± 5.8%) and MIA (3.2 ± 9.1% and 3.1 ± 8.7%), with statistical significance (all P < 0.05). CONCLUSIONS: The quantitative analysis of intranodular vessels on noncontrast computed tomography images demonstrated that the ground-glass nodules with increased internal vessel prevalence and volume percentages had higher possibility of tumor invasiveness.

An AI-Based Image Quality Control Framework for Knee Radiographs.

Image quality control (QC) is crucial for the accurate diagnosis of knee diseases using radiographs. However, the manual QC process is subjective, labor intensive, and time-consuming. In this study, we aimed to develop an artificial intelligence (AI) model to automate the QC procedure typically performed by clinicians. We proposed an AI-based fully automatic QC model for knee radiographs using high-resolution net (HR-Net) to identify predefined key points in images. We then performed geometric calculations to transform the identified key points into three QC criteria, namely, anteroposterior (AP)/lateral (LAT) overlap ratios and LAT flexion angle. The proposed model was trained and validated using 2212 knee plain radiographs from 1208 patients and an additional 1572 knee radiographs from 753 patients collected from six external centers for further external validation. For the internal validation cohort, the proposed AI model and clinicians showed high intraclass consistency coefficients (ICCs) for AP/LAT fibular head overlap and LAT knee flexion angle of 0.952, 0.895, and 0.993, respectively. For the external validation cohort, the ICCs were also high, with values of 0.934, 0.856, and 0.991, respectively. There were no significant differences between the AI model and clinicians in any of the three QC criteria, and the AI model required significantly less measurement time than clinicians. The experimental results demonstrated that the AI model performed comparably to clinicians and required less time. Therefore, the proposed AI-based model has great potential as a convenient tool for clinical practice by automating the QC procedure for knee radiographs.

Evaluating coronary artery calcification with low-dose chest CT reconstructed by different kernels.

PURPOSE: To understand the reliability of low-dose chest computed tomography (LDCT) in coronary artery calcification (CAC) assessment and evaluate the performance of different reconstruction kernels against the standard cardiac computed tomography (CaCT) as reference. MATERIALS AND METHODS: Patients from the NELCIN-B3 screening program who underwent CaCT and LDCT scans were analyzed retrospectively. LDCT were reconstructed with smooth, standard, and sharp kernels (Group B1, B2 and B3) to compare against standard CaCT (Group A). The image quality was evaluated by noise value, signal-to-noise ratio (SNR), and contrast to noise ratio (CNR); moreover, radiation dose was recorded for both scans. Coronary artery calcification scores (CACS) were measured with volume, mass and Agatston standards. Agatston score was divided into four cardiovascular risk categories (0, 1-99, 100-399, and >400). The agreement in CACS and risk classification between LDCT and CaCT was analyzed by intra-group correlation coefficient (ICC) and Kappa test. RESULTS: The sensitivity of diagnosing CAC with LDCT was 98.5% (330/335) regardless of reconstruction kernels. Group B1 demonstrated the highest agreement in raw CACS (ICC volume 0.932; mass 0.904; Agatston 0.906; all p < 0.001) and risk classification (kappa 0.757, 95% CI 0.70-0.82). Smooth-kernel reconstruction achieved lower image noise, better SNR and CNR than other kernels. The effective radiation dose in of LDCT was 41.2% lower than that of the calcium scan (p < 0.001). CONCLUSION: Reconstructing LDCT with a smooth kernel in LDCT could provide a reliable imaging method to detect and quantitatively evaluate CAC, potentially expanding the application of LDCT lung screening to incidental findings of cardiovascular disease.

Medical gases for stroke therapy: summary of progress 2015-2016.

Stroke is a cerebrovascular disease with high mortality and morbidity. Despite extensive research, there are only a very limited number of therapeutic approaches suitable for treatment of stroke patients as yet. Mounting evidence has demonstrated that such gases as oxygen, hydrogen and hydrogen sulfide are able to provide neuroprotection after stroke. In this paper, we will focus on the recent two years' progress in the development of gas therapies of stroke and in understanding the molecular mechanisms underlying protection induced by medical gases. We will also discuss the advantages and challenges of these approaches and provide information for future study.