Thin-slice Computed Tomography Research Articles

Reproducibility of Facial Information in Three-Dimensional Reconstructed Head Images: An Exploratory Study.

Facial information acquired via three-dimensional reconstruction of head computed tomography (CT) data may be considered personal information, which can be problematic for neuroimaging studies. However, no study has verified the relationship between slice thickness and face reproducibility. This study determined the relationship and match rate between image slice thickness and face detection accuracy of face-recognition software in facial reconstructed models. Head CT data of 60 cases comprising entire faces obtained under conditions of non-contrast and 1-mm slice thickness were resampled to obtain 2-10-mm slice-thickness data. Facial models, reconstructed by image thresholding, were acquired from the data. We performed face detection tests per slice thickness on the models and calculated the face detection rate. The reconstructed facial models created from 1-mm slice-thickness data and other slice thicknesses were used as training and test data, respectively. Match confidence scores were obtained via three programs, match rates were calculated per slice thickness, and generalized estimating equations were used to evaluate the match rate trend. In general, the face detection rates for the 1-10-mm slice thicknesses were 100, 100, 98.3, 98.3, 95.0, 91.7, 86.7, 78.3, 68.3, and 61.7 %, respectively. The match rates for the 2-10-mm slice thicknesses were 100, 98.3, 98.3, 95.0, 85.0, 71.7, 53.3, 28.3, and 16.7 %, respectively. The reconstructed models tended to have higher match rates as the slice thickness decreased. Thus, thin-slice head CT imaging data may increase the possibility of the information becoming personally identifiable health information.

Longitudinal Radiomics-Based Prediction of Local Recurrence after Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer

Computed tomography (CT)-based radiomic analysis is an emerging tool that can help predict recurrence after radiation in non-small cell lung cancer (NSCLC). Most studies have focused on using radiomic features obtained from pre-treatment CT scans but not on post-treatment CTs. Our goal was to determine if radiomic features derived from post-treatment CTs perform better than those from pre-treatment CTs in predicting local recurrence after stereotactic body radiation therapy (SBRT) in patients with early-stage NSCLC. Patients with T1/T2 N0 early-stage NSCLC were retrospectively reviewed. Criteria for selection included patients with biopsy-proven NSCLC, non-ground glass solid lesions, 4-5 fraction SBRT treatments, and non-contrast thin-slice CTs available at pre-treatment baseline and at 3 and 6 months post-SBRT. Clinical and tumor characteristics were compared using Chi-square and Student's t-tests. CT image pre-processing was performed and 107 radiomic features were extracted using 3D Slicer and open-source software. Univariate analysis for local recurrence was determined by Cox regression with death as a competing risk. The Benjamini-Hochberg Procedure was applied to control the false discovery rate. Pearson correlation analysis was used to exclude redundant features (r > 0.8), and multivariate analysis was conducted with Random Forest on the top-performing features found on univariate logistic regression. Models were trained on a class-balanced loss to account for class imbalance. Twenty iterations of stratified 3 k-fold cross-validation were used to select the best model by the area under the receiver operating characteristic curve (AUC). We identified and analyzed 86 patient tumors, including 76 non-local recurrences and 10 local recurrences (49 males, 37 females; age, median 72, range 52-91 years). No differences in age, histology, standardized uptake values on positron emission tomography scans, gross tumor volume, and radiation dose were found between patients with and without local recurrence (all p >0.05). The median time to local recurrence was 18.9 months (range 5.5-45.6 months). At the baseline, 3-month, and 6-month timepoints, 2, 10, and 10 radiomic features predicted local recurrence on Cox regression univariate analysis, respectively (all q <0.01). On multivariate analysis of top-performing radiomic features, the 3-month timepoint performed the best with a mean (±standard deviation) AUC score of 0.82 (±0.13) compared to a mean AUC of 0.73 (±0.086) at baseline and a mean AUC of 0.74 (±0.10) at 6 months. Post-treatment radiomic features at 3 months outperformed pre-treatment radiomic features in predicting local recurrence after SBRT for NSCLC. These results suggest radiomic data from follow-up CTs may be helpful when developing radiomic models to predict local recurrence in patients with NSCLC.

Computed Tomography-derived intratumoral and peritumoral radiomics in predicting EGFR mutation in lung adenocarcinoma

ObjectiveTo investigate the value of Computed Tomography (CT) radiomics derived from different peritumoral volumes of interest (VOIs) in predicting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients.Materials and methodsA retrospective cohort of 779 patients who had pathologically confirmed lung adenocarcinoma were enrolled. 640 patients were randomly divided into a training set, a validation set, and an internal testing set (3:1:1), and the remaining 139 patients were defined as an external testing set. The intratumoral VOI (VOI_I) was manually delineated on the thin-slice CT images, and seven peritumoral VOIs (VOI_P) were automatically generated with 1, 2, 3, 4, 5, 10, and 15 mm expansion along the VOI_I. 1454 radiomic features were extracted from each VOI. The t-test, the least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) algorithm were used for feature selection, followed by the construction of radiomics models (VOI_I model, VOI_P model and combined model). The performance of the models were evaluated by the area under the curve (AUC).Results399 patients were classified as EGFR mutant (EGFR+), while 380 were wild-type (EGFR−). In the training and validation sets, internal and external testing sets, VOI4 (intratumoral and peritumoral 4 mm) model achieved the best predictive performance, with AUCs of 0.877, 0.727, and 0.701, respectively, outperforming the VOI_I model (AUCs of 0.728, 0.698, and 0.653, respectively).ConclusionsRadiomics extracted from peritumoral region can add extra value in predicting EGFR mutation status of lung adenocarcinoma patients, with the optimal peritumoral range of 4 mm.

Prevalence and risk factors for retropharyngeal and retro-styloid lymph node metastasis in hypopharyngeal carcinoma

BackgroundWe evaluated the prevalence and identified the risk factors for retropharyngeal and retro-styloid lymph node metastasis (LNM) in patients with hypopharyngeal carcinoma (HPC). This was achieved using a combination of magnetic resonance (MR) and [18 F]-fluoro-2-deoxy-D-glucose (FDG)–positron emission tomography (PET)/computed tomography (CT) images.MethodsTwo board-certified radiation oncologists retrospectively reviewed pretreatment FDG–PET/CT images and contrast-enhanced thin-slice CT and MR images of 155 patients with HPC who underwent radiotherapy. Fisher’s exact tests and logistic regression analyses were performed to assess the risk factors for LNM.ResultsRetropharyngeal LNM (RPLNM) was confirmed in 20 (13%) patients. Posterior wall (PW) tumors (odds ratio [OR]: 4.128, 95% confidence interval [CI]: 1.339–12.727; p = 0.014) and bilateral or contralateral cervical LNM (OR: 11.577, 95% CI: 2.135–62.789; p = 0.005) were significantly correlated with RPLNM. The RPLNM was found in 9 (32%) of the 28 patients with PW tumors. Of these 9 patients, 2 (7%) had ipsilateral RPLNM, 3 (11%) had contralateral RPLNM, and 4 (14%) had bilateral RPLNM. The PW tumors were significantly associated with contralateral RPLNM (p < 0.001). Retro-styloid LNM (RSLNM) was confirmed in two (1%) patients, both of whom had ipsilateral RSLNM with lymph nodes (LNs) of ≥ 15 mm in the upper limit of ipsilateral level II. A significant association was found between LNs of ≥ 15 mm in the upper limit of ipsilateral level II and ipsilateral RSLNM (p = 0.001).ConclusionsThe RPLNM was identified in 13% of patients with HPC. The PW tumors and bilateral or contralateral cervical LNM were risk factors for RPLNM; particularly, PW tumors were a specific risk factor for contralateral RPLNM. Although the RSLNM was rare, LNs of ≥ 15 mm in the upper limit of ipsilateral level II were a risk factor for ipsilateral RSLNM.

Image Quality and Lesion Detectability of Pancreatic Phase Thin-Slice Computed Tomography Images With a Deep Learning-Based Reconstruction Algorithm.

To evaluate the image quality and lesion detectability of pancreatic phase thin-slice computed tomography (CT) images reconstructed with a deep learning-based reconstruction (DLR) algorithm compared with filtered-back projection (FBP) and hybrid iterative reconstruction (IR) algorithms. Fifty-three patients who underwent dynamic contrast-enhanced CT including pancreatic phase were enrolled in this retrospective study. Pancreatic phase thin-slice (0.625 mm) images were reconstructed with each FBP, hybrid IR, and DLR. Objective image quality and signal-to-noise ratio of the pancreatic parenchyma, and contrast-to-noise ratio of pancreatic lesions were compared between the 3 reconstruction algorithms. Two radiologists independently assessed the image quality of all images. The diagnostic performance for the detection of pancreatic lesions was compared among the reconstruction algorithms using jackknife alternative free-response receiver operating characteristic analysis. Deep learning-based reconstruction resulted in significantly lower image noise and higher signal-to-noise ratio and contrast-to-noise ratio than hybrid IR and FBP ( P < 0.001). Deep learning-based reconstruction also yielded significantly higher visual scores than hybrid IR and FBP ( P < 0.01). The diagnostic performance of DLR for detecting pancreatic lesions was highest for both readers, although a significant difference was found only between DLR and FBP in one reader ( P = 0.02). Deep learning-based reconstruction showed improved objective and subjective image quality of pancreatic phase thin-slice CT relative to other reconstruction algorithms and has potential for improving lesion detectability.

An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT

Lung cancer screening using computed tomography (CT) has increased the detection rate of small pulmonary nodules and early-stage lung adenocarcinoma. It would be clinically meaningful to accurate assessment of the nodule histology by CT scans with advanced deep learning algorithms. However, recent studies mainly focus on predicting benign and malignant nodules, lacking of model for the risk stratification of invasive adenocarcinoma. We propose an ensemble multi-view 3D convolutional neural network (EMV-3D-CNN) model to study the risk stratification of lung adenocarcinoma. We include 1075 lung nodules (≤30 mm and ≥4 mm) with preoperative thin-section CT scans and definite pathology confirmed by surgery. Our model achieves a state-of-art performance of 91.3% and 92.9% AUC for diagnosis of benign/malignant and pre-invasive/invasive nodules, respectively. Importantly, our model outperforms senior doctors in risk stratification of invasive adenocarcinoma with 77.6% accuracy [i.e., Grades 1, 2, 3]). It provides detailed predictive histological information for the surgical management of pulmonary nodules. Finally, for user-friendly access, the proposed model is implemented as a web-based system (https://seeyourlung.com.cn).

CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion.

Secondary embolization (SE) during mechanical thrombectomy (MT) for cerebral large vessel occlusion (LVO) could reduce the anterior blood flow and worsen clinical outcomes. The current SE prediction tools have limited accuracy. In this study, we aimed to develop a nomogram to predict SE following MT for LVO based on clinical features and radiomics extracted from computed tomography (CT) images. A total of 61 patients with LVO stroke treated by MT at Beijing Hospital were included in this retrospective study, of whom 27 developed SE during the MT procedure. The patients were randomly divided (7:3) into training (n = 42) and testing (n = 19) cohorts. The thrombus radiomics features were extracted from the pre-interventional thin-slice CT images, and the conventional clinical and radiological indicators associated with SE were recorded. A support vector machine (SVM) learning model with 5-fold cross-verification was used to obtain the radiomics and clinical signatures. For both signatures, a prediction nomogram for SE was constructed. The signatures were then combined using the logistic regression analysis to construct a combined clinical radiomics nomogram. In the training cohort, the area under the receiver operating characteristic curve (AUC) of the nomograms was 0.963 for the combined model, 0.911 for the radiomics, and 0.891 for the clinical model. Following validation, the AUCs were 0.762 for the combined model, 0.714 for the radiomics model, and 0.637 for the clinical model. The combined clinical and radiomics nomogram had the best prediction accuracy in both the training and test cohort. This nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE.

Thin-slice computed tomography enables to classify pulmonary subsolid nodules into pre-invasive lesion/minimally invasive adenocarcinoma and invasive adenocarcinoma: a retrospective study

The aim was to investigate the ability of thin-slice computed tomography (TSCT) to differentiate invasive pulmonary adenocarcinomas (IACs) from pre-invasive/minimally invasive adenocarcinoma (AAH-MIAs), manifesting as subsolid nodules (SSNs) of diameter less than 30 mm. The CT findings of 810 patients with single subsolid nodules diagnosed by pathology of resection specimens were analyzed (atypical adenomatous hyperplasia, n = 13; adenocarcinoma in situ, n = 175; minimally invasive adenocarcinoma, n = 285; and invasive adenocarcinoma, n = 337). According to the classification of lung adenocarcinoma published by WHO classification of thoracic tumors in 2015, TSCT features of 368 pure ground-glass nodules (pGGN) and 442 part-solid nodules (PSNs) were compared AAH-MIAs with IACs. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed. In pGGNs, multivariate analysis of factors found to be significant by univariate analysis revealed that higher mean-CT values (p = 0.006, OR 1.006, 95% CI 1.002–1.010), larger tumor size (p < 0.001, OR 1.483, 95% CI 1.304–1.688) with air bronchogram and non-smooth margins were significantly associated with IACs. The optimal cut-off tumor diameter for AAH-MIAs lesions was less than 10.75 mm (sensitivity, 82.8%; specificity, 80.6%) and optimal cut-off mean-CT value − 629HU (sensitivity, 78.1%; specificity, 50.7%). In PSNs, multivariate analysis of factors found to be significant by univariate analysis revealed that smaller tumor diameter (p < 0.001, OR 0.647, 95% CI 0.481–0.871), smaller size of solid component (p = 0.001, OR 83.175, 95% CI 16.748–413.079),and lower mean-CT value of solid component (p < 0.001, OR 1.009, 95% CI 1.004–1.014) were significantly associated with AAH-MIAs (p < 0.05). The optimal cut-off tumor diameter, size of solid component, and mean-CT value of solid component for AAH-MIAs lesions were less than 14.595 mm (sensitivity, 71.1%; specificity, 83.4%), 4.995 mm (sensitivity, 97.8%; specificity, 92.3%) and − 227HU (sensitivity, 65.6%; specificity, 76.3%), respectively. In subsolid nodules, whether pGGN or PSNs, the characteristics of TSCT can help in distinguishing IACs from AAH-MIAs.

Enhanced Recognition Confidence of Millimeter-Sized Intrahepatic Targets by Real-Time Virtual Sonography.

Real-time virtual sonography (RVS) is an artificial-intelligence-assisted ultrasonographic navigation system that displays synchronized preoperative computed tomography (CT) images corresponding to real-time intraoperative ultrasonograms (IOUS). This study aimed to investigate whether RVS can enhance IOUS identification of small intrahepatic targets found in preoperative CT. Patients with small intrahepatic targets detected by preoperative thin-slice dynamic CT before liver resection were included. The targets included millimeter-sized liver tumors or a third-order or more distal portal branch and were marked on CT images using 3D simulation software. After laparotomy, the targets were searched using fundamental IOUS, and participating liver surgeons subjectively scored the target identifying confidence on a scale of 1-5 (5 points for detection with the highest confidence and one point for undetectable). Then, the search procedure was repeated using the RVS, and the scores were compared. Totally, 55 patients with 117 small targets were investigated. The median target size was 6.0 mm, and the median registration time was 3.6 seconds. The target identification confidence score significantly increased from 2.78 to 4.52 points after using RVS. Seventeen targets (14.5%) were undetectable in fundamental IOUS, and 14 of them were identified by RVS. The detectability of small liver tumors (2-5 points of identification confidence) by IOUS was 81.1 and 96.7% by RVS. RVS enhanced surgeons' confidence in identifying millimeter-sized intrahepatic targets found in preoperative CT.

CT Radiomics for Predicting Pathological Complete Response of Axillary Lymph Nodes in Breast Cancer After Neoadjuvant Chemotherapy: A Prospective Study.

The diagnostic effectiveness of traditional imaging techniques is insufficient to assess the response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC), especially for pathological complete response (pCR). A radiomics model based on computed tomography (CT) could be helpful. Prospective consecutive breast cancer patients with positive axillary LNs initially were enrolled, who received NAC prior to surgery. Chest contrast-enhanced thin-slice CT scan was performed both before and after the NAC (recorded as the first and the second CT respectively), and on both of them, the target metastatic axillary LN was identified and demarcated layer by layer. Using pyradiomics-based software that was independently created, radiomics features were retrieved. A pairwise machine learning workflow based on Sklearn (https://scikit-learn.org/) and FeAture Explorer was created to increase diagnostic effectiveness. An effective pairwise auto encoder model was developed by the improvement of data normalization, dimensionality reduction, and features screening scheme as well as the comparison of the prediction effectiveness of the various classifiers. A total of 138 patients were enrolled, and 77 (58.7%) in the overall group achieved pCR of LN after NAC. Nine radiomics features were finally chosen for modeling. The AUCs of the training group, validation group, and test group were 0.944 (0.919-0.965), 0.962 (0.937-0.985), and 1.000 (1.000-1.000), respectively, and the corresponding accuracies were 0.891, 0.912, and 1.000. The pCR of axillary LNs in breast cancer following NAC can be precisely predicted using thin-sliced enhanced chest CT-based radiomics.

Generating High-Resolution CT Slices from Two Image Series Using Deep-Learning-Based Resolution Enhancement Methods.

Medical image super-resolution (SR) has mainly been developed for a single image in the literature. However, there is a growing demand for high-resolution, thin-slice medical images. We hypothesized that fusing the two planes of a computed tomography (CT) study and applying the SR model to the third plane could yield high-quality thin-slice SR images. From the same CT study, we collected axial planes of 1 mm and 5 mm in thickness and coronal planes of 5 mm in thickness. Four SR algorithms were then used for SR reconstruction. Quantitative measurements were performed for image quality testing. We also tested the effects of different regions of interest (ROIs). Based on quantitative comparisons, the image quality obtained when the SR models were applied to the sagittal plane was better than that when applying the models to the other planes. The results were statistically significant according to the Wilcoxon signed-rank test. The overall effect of the enhanced deep residual network (EDSR) model was superior to those of the other three resolution-enhancement methods. A maximal ROI containing minimal blank areas was the most appropriate for quantitative measurements. Fusing two series of thick-slice CT images and applying SR models to the third plane can yield high-resolution thin-slice CT images. EDSR provides superior SR performance across all ROI conditions.

Prevalence of ground glass nodules in preschool children: a cross-sectional study.

Following increased screening efforts and the use of thin-slice computed tomography (CT), there has been a considerable increase in the incidence of ground-glass nodules (GGNs) in adults. As a result, we have more and more treatments for ground-glass nodules in adults, but few in children. Most think development pattern of pulmonary GGNs is lung inflammation, tumor, or tuberculosis that are more related to acquired or environmental factors. By studying the incidence of pulmonary GGNs in preschool children, we sought to determine whether we had ground glass nodules in the lung before we were teenagers, but we didn't pay attention to them until later. If the hypothesis holds, we may change the cognition and treatment strategies of ground glass nodules. Even not, there are few epidemiological studies with big data that can fill this gap. We retrospectively collected the data of all preschool children who had undergone CT at the Children's Hospital of Zhejiang University School of Medicine from 2013 to 2020. These data were filtered according to the following exclusion criteria: severe artifacts, data with identical names to the original data; and patients without follow-up records (≥3 months). Inclusion criteria: must have undergone thin-slice CT (≤1.25 mm) at the first and last follow-up. Two thoracic radiologists with 5 years of experience and another senior one assessed the images. There were a total of 13,361 cases after relevant exclusions, 311 patients were finally enrolled. Clinical features: age at diagnosis (year): 3.56±1.84, female: 147, male: 164, follow-up interval (month): 6.90±4.74, leukemia: 99, pneumonia: 21, lung cyst: 8, space-occupying lesions outside the lungs: 69, foreign body in respiratory tract: 6. After manual screening and reading, only 1 patient meets all requirements. The results showed that between 2013 and 2020, the incidence of GGNs that could be basically determined in the Children's Hospital of Zhejiang University School of Medicine was 0.32%. There have been few previous studies of GGNs in children, and based on our study, we found that there is still some associated morbidity for preschool children, it is rarely found when they are young.

Automated 3D segmentation of the aorta and pulmonary artery for predicting outcomes after thoracoscopic lobectomy in lung cancer patients.

Preoperative two-dimensional manual measurement of pulmonary artery diameter in a single-cut axial view computed tomography (CT) image is a commonly used non-invasive prediction method for pulmonary hypertension. However, the accuracy may be unreliable. Thus, this study aimed to evaluate the correlation of short-term surgical outcomes and pulmonary artery/aorta (PA/Ao) diameter ratio measured by automated three-dimensional (3D) segmentation in lung cancer patients who underwent thoracoscopic lobectomy. We included 383 consecutive lung cancer patients with thin-slice CT images who underwent lobectomy at a single institute between January 1, 2011 and December 31, 2019. Automated 3D segmentation models were used for 3D vascular reconstruction and measurement of the average diameters of Ao and PA. Propensity-score matching incorporating age, Charlson comorbidity index, and lobectomy performed by uniportal VATS was used to compare clinical outcomes in patients with PA/Ao ratio ≥1 and those <1. Our segmentation method measured 29 (7.57%) patients with a PA/Ao ratio ≥1. After propensity-score matching, a higher overall postoperative complication classified by the Clavien-Dindo classification (p = 0.016) were noted in patients with 3D PA/Ao diameter ratio ≥1 than those of <1. By multivariate logistic regression, patients with a 3D PA/Ao ratio ≥ 1 (p = 0.013) and tumor diameter > 3cm (p = 0.002) both significantly predict the incidence of postoperative complications. Pulmonary artery/aorta diameter ratio ≥ 1 measured by automated 3D segmentation may predict postoperative complications in lung cancer patients who underwent lobectomy.

Does thrombus imaging characteristics predict the degree of recanalisation after endovascular thrombectomy in acute ischaemic stroke?

BackgroundIdentification of computed tomography (CT) thrombus imaging characteristics can predict the degree of recanalization and outcome after endovascular thrombectomy (EVT) in patients with acute ischaemic stroke and large vessel occlusion. AimWe analyzed the thrombus imaging characteristics and procedural factors and correlated with the degree of recanalization and functional outcome after EVT. MethodsWe evaluated the thrombus imaging characteristics (hyperdense MCA sign, thrombus location, length and thrombus permeability) from thin slice CT and CT angiogram. In addition, groin to recanalization time, number of passes, and EVT technique were documented. The primary outcome was degree of recanalization (mTICI score) and secondary outcome was modified Rankin scale (mRS) at 3 months. ResultsThe mean age of 102 patients was 60.5±11.8 years. Patients with hyperdense MCA sign (90 % vs 75%, p=0.07) and permeable thrombus (86 % vs 70 %, p=0.09) had good recanalization (mTICI grade 2b,2c or 3). The requirement of <3 passes (90 % vs 62 %, p= 0.001) was associated with good recanalization. Multiple logistic regression analysis showed thrombus permeability (OR 5.9; 95% CI 1.3-26.6, p=0.02), use of stent retreiver alone (without aspiration) (OR 5.4; 95% CI 1.3-22.5, p=0.02) and a puncture to recanalization ≤60 minutes (OR 7.9; 95% CI 1.7-36.8; p=0.008) were associated with good recanalization. The requirement of ≥3 passes was associated with poor functional outcome (OR 3.4;95% CI 1.2-9.8; p=0.02). ConclusionsThrombus permeability was a predictor of successful recanalization after EVT. The requirement of three or more passes during EVT was associated with poor recanalization and poor functional outcome.

Thoracoscopic lobectomy for an aberrant pulmonary artery of mediastinal inferior lobar branch: A case report

We report on a rare case of thoracoscopic surgery for a patient with an aberrant pulmonary artery of the inferior lobar branch (A7) in the right lower lobe. A 69-year-old woman presented with a chief complaint of an abnormal shadow on chest radiography. Computed tomography (CT) revealed a 14-mm solid nodule in the right lower lobe. The patient was diagnosed with lung cancer by bronchoscopic biopsy, and a surgery was planned. Preoperative thin-slice CT and three-dimensional CT showed an aberrant A7 branching from the right main pulmonary artery. The right lower lobe and lymph node dissection was performed safely under a video view. Preoperative evaluation of this anomaly is important for a safe surgery.

Radiography Suggests a Fibrous Tubular Sleeve Distal to a Cartilaginous Anlage in a Limb with ‘Absent Fibula’ Represents a Traction

BackgroundDissections of congenitally shortened limbs displaying congenital fibular deficiency have revealed pathologic cartilaginous bodies and fragments along the course of the “absent” fibula consistent with remnants of a fibular anlage (Wagstaffe, 1875; Freund, 1936; Harmon &amp; Fahey, 1937; Thompson et al., 1957). Bands distal to the anlagen have traditionally been reported as fibular calcaneal collateral ligaments.ObjectiveThis project reinterprets the composition of the posterolateral ankle band frequently found in patients with congenital shortened limb (CSL) with fibular deficiency based on advanced radiographic criteria.Patient and MethodBilateral thin slice computed tomography (TSCT) imaging was performed on the distal lower extremities of a 43‐year‐old female patient with unilateral congenital shortened limb with fibular “absence” for operative planning. The images were imported into virtual modeling system 3D Slicer and various tissues in the region were measured and compared for attenuation using the Hounsfield unit (HU) scale.ResultsThe radiological study of the posterolateral ankle band in the presence of a limb with a diminutive fibular anlage exhibiting a normal medullary density revealed a tubular structure sharing attenuation readings with nearby adipose tissue at its core and fibrous tissue at its periphery.ConclusionThe tubular structure of the posterolateral ankle band with an adipose core, surrounded by fibrous tissue in the presence of a retained primitive fibular anlage, supports the assertion that fibular absence in this congenitally shorted limb is a radiographic misnomer, since a dystrophic anlage persists in its place. Additionally, previously identified fibulocalcaneal structures (or bands) may actually represent a stretched periosteal sleeve caused by traction exerted on the anlage by the paired lower tibia.These findings support the hypothesis that this “ligament” structure is the distalmost tubular remnant of an otherwise normally formed cartilaginous scaffold of the fibular anlage. The anlage has failed to ossify to form a normal fibular long bone structure, due to embryonic arterial dysgenesis and failed vascular invasion during the sixth‐seventh weeks of embryonic development.

Quantitative thrombus characteristics on thin-slice computed tomography improve prediction of thrombus histopathology: results of the MR CLEAN Registry

ObjectivesThrombus computed tomography (CT) characteristics might be used to assess histopathologic thrombus composition in patients treated with endovascular thrombectomy (EVT) for acute ischemic stroke (AIS). We aimed to assess the variability in thrombus composition that could be predicted with combined thrombus CT characteristics.MethodsThrombi of patients enrolled in the MR CLEAN Registry between March 2014 and June 2016 were histologically analyzed with hematoxylin-eosin staining and quantified for percentages of red blood cells (RBCs) and fibrin/platelets. We estimated the association between general qualitative characteristics (hyperdense artery sign [HAS], occlusion location, clot burden score [CBS]) and thrombus composition with linear regression, and quantified RBC variability that could be explained with individual and combined characteristics with R2. For patients with available thin-slice (≤ 2.5 mm) imaging, we performed similar analyses for general and quantitative characteristics (HAS, occlusion location, CBS, [relative] thrombus density, thrombus length, perviousness, distance from ICA-terminus).ResultsIn 332 included patients, the presence of HAS (aβ 7.8 [95% CI 3.9–11.7]) and shift towards a more proximal occlusion location (aβ 3.9 [95% CI 0.6–7.1]) were independently associated with increased RBC and decreased fibrin/platelet content. With general characteristics, 12% of RBC variability could be explained; HAS was the strongest predictor. In 94 patients with available thin-slice imaging, 30% of RBC variability could be explained; thrombus density and thrombus length were the strongest predictors.ConclusionsQuantitative thrombus CT characteristics on thin-slice admission CT improve prediction of thrombus composition and might be used to further guide clinical decision-making in patients treated with EVT for AIS in the future.Key Points• With hyperdense artery sign and occlusion location, 12% of variability in thrombus RBC content can be explained.• With hyperdense artery sign, occlusion location, and quantitative thrombus characteristics on thin-slice (≤ 2.5 mm) non-contrast CT and CTA, 30% of variability in thrombus RBC content can be explained.• Absolute thrombus density and thrombus length were the strongest predictors for thrombus composition.

Aortic Elongation in Bicuspid Aortic Valve with Aortic Stenosis Assessed by Thin-Slice Electrocardiogram-Gated Computed Tomography.

Bicuspid aortic valve (BAV) patients with aortic stenosis (AS) are known to develop dilatation of the ascending aorta at a younger age, but the morphology of the aorta in these patients is yet to be investigated. Thus, in this study, we aim to evaluate the aortic morphology of BAV patients with severe AS using thin-slice electrocardiogram (ECG) -gated computed tomography (CT) and identify the possible contributing effect of age.In this retrospective study, 122 BAV and 154 tricuspid aortic valve (TAV) patients who received aortic valve replacement for severe AS were assessed by thin-slice ECG-gated CT and three-dimensional reconstruction. The morphology of the ascending aorta was also evaluated among BAV patients aged < 70 (n = 72) and ≥ 70 (n = 50) years old. As per our findings, BAV patients with severe AS had significantly greater diameter (P < 0.01), elongation (P < 0.01), and tortuosity (P = 0.03) of the ascending aorta; minimum aortic arch angle (P < 0.01); and significantly lower calcified plaque (P < 0.01) compared with those of TAV patients even after adjusting for background. Multiple regression analysis showed that standardized partial regression coefficients (β) of dilatation (0.5) and elongation (0.35) were higher among other measurements of aortic morphology for BAV patients. BAV patients with severe AS aged ≥ 70 years had significantly greater diameter (42.0 [37.2-46.1] mm versus 40.4 [35.2-44.2] mm, P = 0.049) and elongation (133.8 [123.5-147.3] mm versus 127.0 [111.0-140.0] mm, P = 0.01) of the ascending aorta than those aged < 70 years.BAV patients with severe AS were determined to have greater dilatation and elongation of the ascending aorta. Moreover, BAV patients older than 70 years had greater diameter and elongation of the ascending aorta.

Growth Curves for Intracranial Volume and Two-dimensional Parameters for Japanese Children without Cranial Abnormality: Toward Treatment of Craniosynostosis.

In the management of patients with craniosynostosis, it is important to understand growth curve of the normal cranium. Although three-dimensional (3D) computed tomography (CT) images taken in thin slices are easily available nowadays, data on the growth curves of intracranial volume (ICV), cranial length, cranial width, and cranial height in the normal cranium are mainly based on older studies using radiography, and there are insufficient reports using CT images especially taken in thin slices. The purpose of this study was to establish growth curves in the normal cranium of Japanese children using thin-slice images. Cranial images of 106 subjects (57 males, 49 females; aged 0–83 months) without significant cranial abnormalities were retrospectively analyzed. Using thin-slice CT images, the ICV and two-dimensional parameters such as cranial length, cranial width, and cranial height were measured by iPlan, followed by generating growth curves and calculating cephalic index (CI). ICV calculated from thin-slice CT images was compared with that obtained by substituting two-dimensional parameters into Mackinnon formula. The ICV growth curves for males and females were similar in shape. As with the ICV, the two-dimensional parameters increased most rapidly in the first year after birth. There was no significant difference in CI between the sexes or among any age groups. ICV calculated from thin-slice 3D CT images was 60% of that obtained from Mackinnon formula. These data will enable us to compare these specific measurements in craniosynostosis patients directly with those of normal children, which will hopefully help in managing these patients.

Preliminary Study of CT Three-dimensional Reconstruction Combined with Ground Glass Nodules of Natural Lung Collapse in Thoracoscopic Pulmonary Segmental Resection

背景与目的计算机断层扫描（computed tomography, CT）三维重建技术越来越多地被应用于肺磨玻璃结节（ground glass nodule, GGN）肺癌患者术前规划中，但术中如何准确定位结节和保证安全切除边缘依然是临床医生面对的难题。本研究旨在探讨全胸腔镜下肺段切除术中，CT三维重建联合术中肺自然萎陷定位方法的准确性、便捷性及切缘的安全性。方法选取2019年7月-2019年12月收治入院的45例影像学表现有肺GGN的患者为研究组，45例患者均接受薄层CT扫描并术前进行三维重建，于麻醉后快速打开胸腔小操作口和患者气道，利用压强差使肺快速自然萎陷，根据自然标志线进行GGN定位，3-0 prolene线标记，然后行三维重建指导下的胸腔镜肺段切除术。标本摘除后测量GGN与缝线标识的距离、GGN与切缘的距离并常规送检切缘。统计患者一般临床资料、病理资料与术后并发症，并与同期采用hookwire定位针进行定位的连续45例患者进行比较。结果CT三维重建联合术中无创式肺自然萎陷GGN定位，平均定位时间为6.9 min，定位准确率为90.6%。术中发现广泛胸腔粘连2例，肺气肿1例。术后病理均证实为肺腺癌，切缘送检均呈阴性。肺段切除后均无严重并发症的发生。结论CT三维重建联合术中肺自然萎陷GGN定位，缩短了术中寻找GGN的时间，保障了切缘的安全性，是一种更经济、实惠、便捷的定位方法，使肺段切除更加精准。