Computed Tomography Lesions Research Articles

The relationship between lung CT features and serum cryptococcal antigen titers in localized pulmonary cryptococcosis patients

BackgroundTo explore the associations of computed tomography (CT) image features with the serum cryptococcal antigen (CrAg) titers measured by the lateral flow assay (LFA) in localized pulmonary cryptococcosis patients.MethodsA retrospective analysis of patients with pathologically confirmed pulmonary cryptococcosis admitted to the First Affiliated Hospital of Xiamen University from January 2016 to December 2022 was performed. Clinical data, CT results, serum CrAg-LFA test results, and follow-up data were collected and analyzed.ResultsA total of 107 patients with localized pulmonary cryptococcosis were included, of which 31 had a single lesion in chest CT and the other 76 had multiple lesions. The positivity rate was (94.74% vs 64.52%) and titers of serum CrAg-LFA (1.77 ± 0.87 vs 0.91 ± 0.98) in the multiple lesion group were higher than those in the single lesion group, respectively. Multivariate linear regression analysis showed that the serum CrAg titers were positively associated with the number of lesions (β, 0.08; 95% CI, 0.05 to 0.12) and the lesion size (β, 0.40; 95% CI, 0.31 to 0.50) after adjusting other covariates. The serum CrAg-LFA titers of 60 pulmonary cryptococcosis patients showed a decreasing trend with the reduction in pulmonary lesion size after effective therapy.ConclusionIn pulmonary cryptococcosis patients, the number and size of lung lesions are positively correlated with the titers of the serum CrAg-LFA test. The CrAg-LFA test could be a useful tool for the diagnosis, severity assessment, and therapeutic monitoring of localized pulmonary cryptococcosis patients.

Diagnostic Performance of GFAP, UCH-L1, and MAP-2 Within 30 and 60 Minutes of Traumatic Brain Injury.

Data on the performance of traumatic brain injury (TBI) biomarkers within minutes of injury are lacking. To examine the performance of glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and microtubule-associated protein 2 (MAP-2) within 30 and 60 minutes of TBI in identifying intracranial lesions on computed tomography (CT) scan, need for neurosurgical intervention (NSI), and clinically important early outcomes (CIEO). This cohort study is a biomarker analysis of a multicenter prehospital TBI cohort from the Prehospital Tranexamic Acid Use for TBI clinical trial conducted across 20 centers and 39 emergency medical systems in North America from May 2015 to March 2017. Prehospital hemodynamically stable adult patients with traumatic injury and suspected moderate to severe TBI were included. Blood samples were measured for GFAP, UCH-L1, and MAP-2. Data were analyzed from December 1, 2023, to March 15, 2024. The presence of CT lesions, diffuse injury severity on CT, NSI within 24 hours of injury, and CIEO (composite outcome including early death, neurosurgery, or prolonged mechanical ventilation ≥7 days) within 7 days of injury. Of 966 patients enrolled, 804 patients (mean [SD] age, 41 [19] years; 418 [74.2%] male) had blood samples, including 563 within 60 minutes and 375 within 30 minutes of injury. Among patients with blood drawn within 30 minutes of injury, 212 patients (56.5%) had CT lesions, 61 patients (16.3%) had NSI, and 112 patients (30.0%) had CIEO. Among those with blood drawn within 60 minutes, 316 patients (56.1%) had CT lesions, 95 patients (16.9%) had NSI, and 172 patients (30.6%) had CIEO. All biomarkers showed significant elevations with worsening diffuse injury on CT within 30 and 60 minutes of injury. Among blood samples taken within 30 minutes, GFAP had the highest area under the receiver operating characteristic curve (AUC) to detect CT lesions, at 0.88 (95% CI, 0.85-0.92), followed by MAP-2 (AUC, 0.78; 95% CI, 0.73-0.83) and UCH-L1 (AUC, 0.75; 95% CI, 0.70-0.80). Among blood samples taken within 60 minutes, AUCs for CT lesions were 0.89 (95% CI, 0.86-0.92) for GFAP, 0.76 (95% CI, 0.72-0.80) for MAP-2, and 0.73 (95% CI, 0.69-0.77) for UCH-L1. Among blood samples taken within 30 minutes, AUCs for NSI were 0.78 (95% CI, 0.72-0.84) for GFAP, 0.75 (95% CI, 0.68-0.81) for MAP-2, and 0.69 (95% CI, 0.63-0.75) for UCH-L1; and for CIEO, AUCs were 0.89 (95% CI, 0.85-0.93) for GFAP, 0.83 (95% CI, 0.78-0.87) for MAP-2, and 0.77 (95% CI, 0.72-0.82) for UCH-L1. Combining the biomarkers was no better than GFAP alone for all outcomes. At GFAP of 30 pg/mL within 30 minutes, sensitivity for CT lesions was 98.1% (95% CI, 94.9%-99.4%) and specificity was 34.4% (95% CI, 27.2%-42.2%). GFAP levels greater than 6200 pg/mL were associated with high risk of NSI and CIEO. In this cohort study of prehospital patients with TBI, GFAP, UCH-L1, and MAP-2 measured within 30 and 60 minutes of injury were significantly associated with traumatic intracranial lesions and diffuse injury severity on CT scan, 24-hour NSI, and 7-day CIEO. GFAP was the strongest independent marker associated with all outcomes. This study sets a precedent for the early utility of GFAP in the first 30 minutes from injury in future clinical and research endeavors.

Comparison of foci of active inflammation and fatty degeneration according to MRI and CT data of syndesmophytes in patients with ankylosing spondylitis

Introduction. Syndesmophyte, which is the hallmark of ankylosing spondylitis (AS), is a vertically oriented ossification located outside the fibrous ring of the intervertebral disc. In recent decades, the most widespread theory is that after inflammation in the area of attachment of the fibrous ring to the vertebral body, fatty degeneration occurs with reparative processes, as a result of which the growth of new bone – syndesmophyte – begins. It is believed that the likelihood of developing syndesmophyte is two to three times higher in the angles of those vertebrae in which active inflammation or fatty changes were detected on magnetic resonance tomography (MRI) in the previous 2 years, the latter having a stronger association with subsequent pathological bone formation. However, this relationship is ambiguous according to different studies.The aim – comparison of the exact localization of magnetic resonance tomography and computed tomography (CT) signs of lesions on the vertebral endplates, characteristic of axial spondyloarthritis, in patients with ankylosing spondylitis.Material and methods. The cross-sectional study included 10 patients with AS who met the inclusion criteria.All patients underwent a standard examination for AS, as well as MRI and CT examination of the lumbar spine.A special technique for joint assessment of changes detected during MRI and CT examination has been developed.For the primary analysis, changes found on the upper endplates of 5 lumbar vertebrae were taken.Results. On the upper endplates of 5 lumbar vertebrae, a CT examination revealed 35 syndesmophytes (from 1 to 7 syndesmophytes in 1 patient), and MRI revealed 9 foci of acute inflammation and 24 of chronic inflammation (fatty degeneration). When jointly analyzing the MRI foci of detectable inflammation and syndesmophytes transferred to the vertebral diagram, it turned out that in the majority of cases (68%), the latter were not associated with sites of active and/or chronic inflammation. Correlation analysis showed a certain direct relationship only between the number of syndesmophytes and foci of chronic inflammation (r=0.68).Discussion. Preliminary data show that the proposed method for comparing MRI and CT lesions in AS allows us to jointly study the localization of MRI and CT spinal injuries in patients with AS using multidirectional sections. The first results of our one-time study showed that in most cases the localization of developing syndesmophytes does not coincide with the foci of inflammation detected by MRI (both acute and chronic).

Association of Prenatal Congenital Lung Malformation Volume Ratio to Postnatal Computed Tomography Characteristics

IntroductionCongenital lung malformations (CLMs) are diverse and readily diagnosed on prenatal ultrasound. Postnatal computed tomography (CT) characteristics, including volume, are used in centers for the clinical decision-making of asymptomatic CLM. We aim to evaluate the relationship of prenatal CLM volume ratio (CVR) to postnatal CT characteristics by suspected prenatal diagnosis and postnatal radiological diagnosis. MethodsWe performed a single-center retrospective review of all patients evaluated with prenatally diagnosed CLM (May 2015-December 2022). Demographics, prenatal imaging findings at initial evaluation, and postnatal radiological diagnosis/imaging findings were analyzed with descriptive statistics. Pairwise correlation coefficient tests were performed to analyze the correlation between prenatal CVR and postnatal CT lesion size stratified by diagnosis. ResultsOf 123 patients referred and evaluated, suspected prenatal diagnoses consisted of 68 bronchial atresia (BA), 20 intralobar bronchopulmonary sequestration (iBPS), 20 extralobar bronchopulmonary sequestration (eBPS), and nine congenital pulmonary airway malformation (CPAM). Postnatal radiological diagnoses consisted of 53 BA, 22 iBPS, 14 eBPS, and 20 CPAM. Overall correlation coefficient of prenatal CVR to postnatal CT lesion size volume was 0.56. By suspected prenatal diagnosis, correlation coefficients were 0.61 (BA), 0.59 (iBPS), 0.29 (eBPS), and 0.51 (CPAM). For postnatal radiological diagnosis, correlation coefficients were 0.58 (BA), 0.56 (iBPS), 0.33 (eBPS), and 0.62 (CPAM). ConclusionsOur results indicate that initial CVR is overall consistent with the postnatal CT lesion size. This correlation is present in suspected prenatal diagnoses of BA and iBPS and postnatal radiological diagnoses of BA, iBPS, and CPAM. Additional studies analyzing long-term follow-up should be conducted to specify the safety of patients who undergo observation rather than surgical intervention.

MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3-D CT Lesions.

With the renaissance of deep learning, automatic diagnostic algorithms for computed tomography (CT) have achieved many successful applications. However, they heavily rely on lesion-level annotations, which are often scarce due to the high cost of collecting pathological labels. On the other hand, the annotated CT data, especially the 3-D spatial information, may be underutilized by approaches that model a 3-D lesion with its 2-D slices, although such approaches have been proven effective and computationally efficient. This study presents a multiview contrastive network (MVCNet), which enhances the representations of 2-D views contrastively against other views of different spatial orientations. Specifically, MVCNet views each 3-D lesion from different orientations to collect multiple 2-D views; it learns to minimize a contrastive loss so that the 2-D views of the same 3-D lesion are aggregated, whereas those of different lesions are separated. To alleviate the issue of false negative examples, the uninformative negative samples are filtered out, which results in more discriminative features for downstream tasks. By linear evaluation, MVCNet achieves state-of-the-art accuracies on the lung image database consortium and image database resource initiative (LIDC-IDRI) (88.62%), lung nodule database (LNDb) (76.69%), and TianChi (84.33%) datasets for unsupervised representation learning. When fine-tuned on 10% of the labeled data, the accuracies are comparable to the supervised learning models (89.46% versus 85.03%, 73.85% versus 73.44%, 83.56% versus 83.34% on the three datasets, respectively), indicating the superiority of MVCNet in learning representations with limited annotations. Our findings suggest that contrasting multiple 2-D views is an effective approach to capturing the original 3-D information, which notably improves the utilization of the scarce and valuable annotated CT data.

Segmentation of COVID-19 CT lesions in CT scans through transfer learning

Since its emergence at the end of 2019, SARS-CoV-2 has infected millions of people worldwide, putting healthcare systems to the test. This has led many researchers to investigate how machine learning can assist clinicians in diagnosing infections caused by SARS-CoV-2. Building upon prior studies, we propose a novel deep learning framework designed for the segmentation of lesions evident in Computed Tomography (CT) scans. For this work, we utilized a dataset consisting of 20 CT scans annotated by experts and conducted training, validation and external evaluation of the deep learning models we implemented, employing a 5-fold cross-validation scheme. When splitting data by slice, our optimal model achieved noteworthy performance metrics, attaining a Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) score of 0.8644 and 0.7612, respectively, during the validation phase. In the external evaluation phase, the model maintained a strong performance with a DSC and IoU score of 0.7211 and 0.5641, respectively. When splitting data by patient, our optimal model achieved a DSC and IoU score of 0.7889 and 0.6540, respectively, during the validation phase. In the external evaluation phase, the model maintained a strong performance with a DSC and IoU score of 0.7079 and 0.5480, respectively. The results, obtained from this research, suggest that the incorporation of transfer learning, combined with appropriate pre-processing techniques, can contribute to achieving state-of-the-art performance in the segmentation of lesions associated with SARS-CoV-2 infections.

Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity

To precisely determine the severity of COVID-19-related pneumonia, computed tomography (CT) is an imaging modality beneficial for patient monitoring and therapy planning. Thus, we aimed to develop a deep learning-based image segmentation model to automatically assess lung lesions related to COVID-19 infection and calculate the total severity score (TSS). The entire dataset consisted of 124 COVID-19 patients acquired from Chulabhorn Hospital, divided into 28 cases without lung lesions and 96 cases with lung lesions categorized severity by radiologists regarding TSS. The model used a 3D-UNet along with DenseNet and ResNet models that had already been trained to separate the lobes of the lungs and figure out the percentage of lung involvement due to COVID-19 infection. It also used the Dice similarity coefficient (DSC) to measure TSS. Our final model, consisting of 3D-UNet integrated with DenseNet169, achieved segmentation of lung lobes and lesions with the Dice similarity coefficients of 91.52% and 76.89%, respectively. The calculated TSS values were similar to those evaluated by radiologists, with an R2 of 0.842. The correlation between the ground-truth TSS and model prediction was greater than that of the radiologist, which was 0.890 and 0.709, respectively.

Diagnostic consistency between computed tomography and adrenal vein sampling of primary aldosteronism: leading to successful curative outcome after adrenalectomy; a retrospective study.

Adrenal computed tomography (CT) is a useful tool for locating adrenal lesion in primary aldosteronism (PA) patients. However, adrenal vein sampling (AVS) is considered as a gold standard for subtype diagnosis of PA. The aim of this study was to investigate the consistency of CT and AVS for the diagnosis of PA subtypes and evaluate the concordance of surgical outcomes. This retrospective study included 264 PA patients having both CT and AVS. Diagnostic consistency between CT and AVS was accessed, and clinical and biochemical outcomes were evaluated at 6 months after adrenalectomy. Of all, 207 (78%) had a CT unilateral lesion, 31 (12%) CT bilateral lesion, and 26 (10%) CT bilateral normal findings. Among the CT unilateral lesion group, 138 (67%) had ipsilateral AVS lateralization. For CT bilateral lesion and bilateral normal, AVS unilateral lateralization was found in 17 (55%) and 2 (8%), respectively. The consistency between CT lesion and AVS lateralization including CT unilateral with AVS ipsilateral, and CT bilateral lesion with AVS bilateral patients was 63.8% (152/238). Of 77 patients with available data out of 138 patients who underwent adrenalectomy with consistency between CT and AVS, the clinical success rate was 96%, for 17 inconsistency patients out of 22 patients who underwent adrenalectomy, the clinical success rate was 94% after adrenalectomy following the lateralization result of AVS. CT is a useful tool to diagnose the adrenal lesion in PA patients. However, AVS is more sufficient to detect the unilateral PA subtype, which could provide curable treatment to surgical candidates of PA such that AVS can identify patients with contralateral PA in CT unilateral lesion and unilateral PA in CT bilateral lesion. The surgical outcome was successful when an adrenalectomy was performed according to the AVS lateralization result.

A Bi-LSTM and multihead attention-based model incorporating radiomics signatures and radiological features for differentiating the main subtypes of lung adenocarcinoma.

The radiological features of computed tomography (CT) images and the sequence of radiomics signatures in continuous slices of lung CT lesions are helpful in identifying subtypes of lung adenocarcinoma. A model based on bidirectional long short-term memory (Bi-LSTM) and multihead attention can learn the rules of this sequence well. In this study, 421 patients with 427 lesions confirmed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC) were recruited from three hospitals. The radiomics signatures of the identified lesion regions in each CT image were extracted using 'PyRadiomics' software, and the corresponding radiological features were subsequently documented and collected. Then, the top 100 features were extracted by the minimum redundancy maximum relevance (mRMR) feature ranking method. A model based on the radiological and imaging features was established to classify the lesions using Bi-LSTM and multihead attention. The diagnostic performance of the model was measured by the area under the curve (AUC) of the receiver operating characteristic (ROC). The model combined radiological features and radiomics signatures. The AUCs of the model in the training, testing, and validation groups were 0.985, 0.94 and 0.981, respectively, and the accuracy was 0.92, 0.976 and 0.91, respectively. In addition, we trained two other models [convolutional neural network (CNN) + multihead attention, long short-term memory (LSTM) + multihead attention] and compared them using the testing dataset. The precision of the two models was 0.89 and 0.88, respectively, and the accuracy was 0.88 and 0.87, respectively. Bi-LSTM and multihead attention based on radiomics signatures and radiological features provide a way to distinguish AIS, MIA, and IAC.

Computed tomography findings in patients with pulmonary tuberculosis and diabetes at an infectious disease hospital in China: a retrospective cross-sectional study

BackgroundThis study aimed to investigate the relationship between active pulmonary tuberculosis (TB) and type 2 diabetes mellitus (T2DM) by analysing the clinical features and computed tomography (CT) findings of patients with active pulmonary TB and comorbid T2DM (TB-DM) in the LiangShan Yi regions.MethodsWe collected data from 154 hospitalised patients with TB-DM initially confirmed at an infectious disease hospital in the Liangshan Yi Autonomous Prefecture between 1 and 2019, and 31 December 2021. These were matched by sex and age ± 3 years to 145 hospitalised patients with initially confirmed pulmonary TB without comorbid T2DM (TB-NDM) over the same period. The clinical characteristics of the two groups were analysed separately. Three group-blinded radiologists independently analysed the CT findings and classified them into mild-to-moderate and severe groups. Severe chest CT lesion refers to a lesion that is less diffused or moderately dense and either exceeds the total volume of one lung, a high-density fused lesion greater than one-third of the volume of one lung, or a cavitary lesion with a maximum diameter ≥ 4 cm.ResultsNo significant differences were observed in the presentation of clinical features. Regarding the severity of chest CT manifestation, patients with TB-DM had significantly more severe TB than those with TB-NDM (89.61% vs. 68.97%, P < 0.0001). Regarding CT findings, patients with TB-DM had higher proportions of consolidation (79.22% vs. 52.41%, P < 0.0001), cavitary lesions (85.06% vs. 59.31%, P < 0.0001), bronchiectasis (71.43% vs. 31.03%, P < 0.0001), exudative lesions (88.96% vs. 68.28%, P < 0.0001), and fibrous lesions (93.51% vs. 68.97%, P < 0.0001) than patients with TB-NDM. In conclusion, patients with TB-DM have more severe pulmonary TB CT findings than those without. There were no significant differences in the distribution of lesions in the lung lobes between TB-DM and TB-NDM patients.ConclusionsAmong patients hospitalised with pulmonary TB, those with T2DM had more severe findings on chest CT than those without T2DM. However, the clinical presentation was not significantly different.

CT-guided biopsies of unspecified suspect intrahepatic lesions: pre-procedure Lipiodol-marking improves the biopsy success rate.

While computed tomography (CT)-guided liver biopsies are commonly performed using unenhanced images, contrast-enhanced images are beneficial for challenging puncture pathways and lesion locations. This study aimed to evaluate the accuracy of CT-guided biopsies for intrahepatic lesions using unenhanced, intravenous (IV)-enhanced, or intra-arterial Lipiodol-marked CT for lesion marking. Six-hundred-seven patients (men: 358 [59.0%], mean age 61 years; SD ±12.04) with suspect hepatic lesions and CT-guided liver biopsies were retrospectively evaluated. Successful biopsies were histopathological findings other than typical liver tissue or non-specific findings. Data was ascertained regarding the use of contrast medium for the biopsy-planning CT, unenhanced (group 1) vs. Lipiodol (group 2) vs. IV contrast (group 3). Technical success and influencing factors were insulated. Complications were noted. The results were analyzed using the Wilcoxon-Man-Whitney t-test, Chi-square test, and Spearman-Rho. Overall lesion hitting rate was 73.1%, with significantly better rates using Lipiodol-marked lesions (79.3%) compared to group 1 (73.8%) and group 3 (65.2%) (p = 0.037). Smaller lesions (<20 mm diameter) benefited significantly from Lipiodol-marking with 71.2% successful biopsy rate compared to group 1 (65.5%) and group 3 (47.7%) (p = 0.021). Liver cirrhosis (p = 0.94) and entity of parenchymal lesions (p = 0.78) had no impact on the hitting rate between the groups. No major complications occurred during the interventions. Pre-biopsy Lipiodol marking of suspect hepatic lesions significantly increases the lesion-hitting rate and is especially beneficial for biopsy of smaller targets below 20 mm diameter. Further, Lipiodol marking is superior to IV contrast for non-visible lesions in unenhanced CT. Target lesion entity has no impact on the hitting rate.

Anatomically guided reconstruction improves lesion quantitation and detectability in bone SPECT/CT.

Bone single-photon emission computed tomography (SPECT)/computed tomography (CT) imaging suffers from poor spatial resolution, but the image quality can be improved during SPECT reconstruction by using anatomical information derived from CT imaging. The purpose of this work was to compare two different anatomically guided SPECT reconstruction methods to ordered subsets expectation maximization (OSEM) which is the most commonly used reconstruction method in nuclear medicine. The comparison was done in terms of lesion quantitation and lesion detectability. Anatomically guided Bayesian reconstruction (AMAP) and kernelized ordered subset expectation maximization (KEM) algorithms were implemented and compared against OSEM. Artificial lesions with a wide range of lesion-to-background contrasts were added to normal bone SPECT/CT studies. The quantitative accuracy was assessed by the error in lesion standardized uptake values and lesion detectability by the area under the receiver operating characteristic curve generated by a non-prewhitening matched filter. AMAP and KEM provided significantly better quantitative accuracy than OSEM at all contrast levels. Accuracy was the highest when SPECT lesions were matched to a lesion on CT. Correspondingly, AMAP and KEM also had significantly better lesion detectability than OSEM at all contrast levels and reconstructions with matching CT lesions performed the best. Quantitative differences between AMAP and KEM algorithms were minor. Visually AMAP and KEM images looked similar. Anatomically guided reconstruction improves lesion quantitation and detectability markedly compared to OSEM. Differences between AMAP and KEM algorithms were small and thus probably clinically insignificant.

Intraprocedural computed tomography-guided navigation with ventilatory strategy for atelectasis (ICNVA): a modified electromagnetic navigation bronchoscopy.

Computed tomography (CT)-body divergence limits the accuracy of electromagnetic navigation bronchoscopy (ENB) in peripheral lung lesions diagnosis. We developed intraprocedural CT-guided navigation with ventilatory strategy for atelectasis (ICNVA) ENB for patients with peripheral lung lesions. Retrospective observational study in which ten consecutive patients with pulmonary lesions (without bronchial direct connection) underwent ICNVA-ENB was conducted. During ICNVA-ENB, intraoperative CT data were used for ENB path planning, and a new ventilation strategy were employed to help maintain the pulmonary region in a static and inflation state which reduce CT to body divergence. We collected three sets of CT data: preENB CT, post-anesthesia intubation CT, and postENB CT. To evaluate the accuracy of ICNVA-ENB, we measured the distance between the ENB probe and the actual lesion location, but also recorded the results of rapid on-site evaluation (ROSE), and postoperative pathology. To evaluate the impact of CT-body divergence induced by atelectasis, we calculated the mutual position distance of target lesions in preENB CT, post-anesthesia intubation CT and postENB CT. Furthermore, ENB operation time and operative complications were recorded. Our analysis revealed that the distance between the navigation probe with the actual location of lesion center was 4-10 (5.90±1.73) mm. The ROSE results were consistent with the postoperative pathological diagnosis in 9 out of 10 patients (90%). The ICNVA-ENB atelectasis CT-body divergence was smaller than traditional ENB (12.10±3.67 vs. 6.60±2.59 mm, P<0.01). The ENB operation time was 20-53 (29.30±10.14) minutes and one patient developed slight intrapulmonary hemorrhage. ICNVA-ENB can reduce the CT-body divergence and appears to be safe and accurate for patients with peripheral lung lesions.

Characterizing incidental mass lesions in abdominal dual-energy CT compared to conventional contrast-enhanced CT.

Incidental findings are common in abdominal computed tomography (CT) and often warrant further investigations with economic implications as well as implications for patients. To evaluate the potential of dual-energy CT (DECT) in the identification and/or characterization of abdominal incidental mass lesions compared to conventional contrast-enhanced CT. This retrospective study from a major tertiary hospital included 96 patients, who underwent contrast-enhanced abdominal DECT. Incidental lesions in adrenals, kidneys, liver, and pancreas were evaluated by two board-certified abdominal radiologists. Observer 1 only had access to standard CT reconstructions, while observer 2 had access to standard CT as well as DECT reconstructions. Disagreements were resolved by consensus review and used as a reference for observers using McNemar's test. Observers 1 and 2 identified a total of 40 and 34 findings, respectively. Furthermore, observer 1 registered 13 lesions requiring follow-up, of which seven (two renal and five adrenal lesions) were resolved following consensus review using DECT (P = 0.008). The inter-observer agreement was near perfect (κ = 0.82). DECT has the potential to improve the immediate characterization of incidental findings when compared to conventional CT for abdominal imaging.

Video Endoscopy-Guided Intrabronchial Spray Inoculation of Mycobacterium bovis in Goats and Comparative Assessment of Lung Lesions With Various Imaging Methods.

Bovine tuberculosis (bTB) not only poses a zoonotic threat to humans but also has a significant economic impact on livestock production in many areas of the world. Effective vaccines for humans, livestock, and wildlife are highly desirable to control tuberculosis. Suitable large animal models are indispensable for meaningful assessment of vaccine candidates. Here, we describe the refinement of an animal model for bTB in goats. Intrabronchial inoculation procedure via video-guided endoscopy in anesthetized animals, collection of lungs after intratracheal fixation in situ, and imaging of lungs by computed tomography (CT) were established in three goats using barium sulfate as surrogate inoculum. For subsequent infection experiments, four goats were infected with 4.7 × 102 colony-forming units of M. bovis by intrabronchial inoculation using video-guided endoscopy with spray catheters. Defined amounts of inoculum were deposited at five sites per lung. Four age-matched goats were mock-inoculated. None of the goats developed clinical signs until they were euthanized 5 months post infection, but simultaneous skin testing confirmed bTB infection in all goats inoculated with M. bovis. In tissues collected at necropsy, M. bovis was consistently re-isolated from granulomas in lymph nodes, draining the lungs of all the goats infected with M. bovis. Further dissemination was observed in one goat only. Pulmonary lesions were quantified by CT and digital 2D radiography (DR). CT revealed mineralized lesions in all the infected goats ranging from <5 mm to >10 mm in diameter. Small lesions <5 mm predominated. The DR failed to detect small lesions and to determine the exact location of lesions because of overlapping of pulmonary lobes. Relative volume of pulmonary lesions was low in three but high in one goat that also had extensive cavitation. CT lesions could be correlated to gross pathologic findings and histologic granuloma types in representative pulmonary lobes. In conclusion, video-guided intrabronchial inoculation with spray catheters, mimicking the natural way of infection, resulted in pulmonary infection of goats with M. bovis. CT, but not DR, presented as a highly sensitive method to quantify the extent of pulmonary lesions. This goat model of TB may serve as a model for testing TB vaccine efficacy.

Does Vestibulo-Ocular Reflex (VOR) Gain Correlate With Radiological Findings in the Semi-Circular Canals in Patients Carrying the p.Pro51Ser (P51S) COCH Variant Causing DFNA9? Relationship Between the Three-Dimensional Video Head Impulse Test (vHIT) and MR/CT Imaging.

The primary aim was to determine whether 3D video-head-impulse-test vestibulo-ocular reflex (vHIT VOR)-gains correlate with computed tomography (CT) and magnetic resonance (MR) lesions in a series of carriers of the p.(Pro51Ser)-variant (P51S) in the COCH-gene (DFNA9). Secondary aim was to compare routine imaging with second peer review radiologic lecture. Analytical cross-sectional study. Secondary referral center. Twenty-four p.P51S carriers with MR and CT images. Eighteen carriers were selected of whom both 3D-vHIT and imaging data were available within a time interval of 24 months. All imaging data were reassessed by two independent neuroradiologists. vHIT VOR-gains were correlated with semi-circular canal (SCC) lesions. Correlation between vHIT VOR-gains and SCC lesions, and additional lesions detected during scientific lecture of imaging data. The average gain of the ipsilateral labyrinth was significantly lower when positive CT (0.3215; p = 0.0122) and MR results (0.3215; p = 0.0134).92% of ears presented MR lesions on at least one SCC, whereas this was 75% on CT. The posterior SCC is the most frequently affected on MR and CT. Second lecture led to nine additional MR and 16 CT lesions. Significant correlation was observed between radiological lesions at any SCC and lower average gain of the three ipsilateral SCC. The substantially larger number of lesions during scientific assessment stresses the need to fully inform radiologists concerning differential diagnosis to facilitate accurate diagnosis when planning imaging. Focal sclerosis and narrowing of SCC in DFNA9 represent a possible biomarker of advanced stages of otovestibular deterioration.

Automatic liver and tumour segmentation from CT images using Deep learning algorithm

The diagnosis and treatment of liver diseases from computed tomography (CT) images is an indispensable task for segmentation of Liver & its tumours. Due to the uneven presence, fuzzy borders, diverse densities, shapes and sizes of lesions segmentation of liver & its tumour is a difficult task. At this point we mainly focused on deep learning algorithms for segmenting liver and its tumour from abdominal CT scan images thereafter minimising the time & energy used for a liver diseases diagnosis. The algorithm is used here is based on the modified ResUNet architecture. Here we present, an automatic method based on semantic segmentation convolutional neural networks (CNNs) to segment Liver from CT scans and lesions from segmented liver part. The proposed system attains a Dice Similarity Coefficients (DSCs) of 96.35% and 89.28% and accuracy of 99.71% and 99.72% for liver and tumour segmentations, respectively. Comparison with the linked methods confirms the promise of the proposed system for liver and tumour segmentations.

Reversibility of clinical and computed tomographic lesions mimicking pulmonary fibrosis in a young cat

BackgroundIn humans with idiopathic pulmonary fibrosis (IPF), specific thoracic computed tomographic (CT) features in the correct clinical context may be used in lieu of histologic examination. Cats develop an IPF-like condition with similar features to humans. As few cats have invasive lung biopsies, CT has appeal as a surrogate diagnostic, showing features consistent with architectural remodeling supporting “end-stage lung”.Case presentationA 1-year-old female spayed Domestic Shorthair cat presenting with progressive respiratory clinical signs and thoracic CT changes (reticular pattern, parenchymal bands, subpleural interstitial thickening, pleural fissure thickening, subpleural lines and regions of increased attenuation with traction bronchiectasis and architectural distortion) consistent with reports of IPF was given a grave prognosis for long-term survival. The cat was treated with prednisolone, fenbendazole, pradofloxacin and clindamycin. Five months later, while still receiving an anti-inflammatory dose of prednisolone, the cat was re-evaluated with owner-reported absent respiratory clinical signs. Thoracic CT demonstrated resolution of lung patterns consistent with fibrosis.ConclusionsFibrotic lung disease is irreversible. Despite this cat having compatible progressive respiratory signs and associated lung patterns on thoracic CT scan, these abnormalities resolved with non-specific therapy and time, negating the possibility of IPF. While the cause of the distinct CT lesions that ultimately resolved was not determined, infection was suspected. Experimental Toxocara cati infection shows overlapping CT features as this cat and is considered a treatable disease. Improvement of CT lesions months after experimental heartworm-associated respiratory disease in cats has been documented. Reversibility of lesions suggests inflammation rather than fibrosis was the cause of the thoracic CT lesions. This cat serves as a lesson that although thoracic CT has been advocated as a surrogate for histopathology in people with IPF, additional studies in cats are needed to integrate CT findings with signalment, other clinicopathologic features and therapeutic response before providing a diagnosis or prognosis of fibrotic lung disease.

Automated quantification of COVID-19 pneumonia severity in chest CT using histogram-based multi-level thresholding segmentation

BackgroundChest computed tomography (CT) has proven its critical importance in detection, grading, and follow-up of lung affection in COVID-19 pneumonia. There is a close relationship between clinical severity and the extent of lung CT findings in this potentially fatal disease. The extent of lung lesions in CT is an important indicator of risk stratification in COVID-19 pneumonia patients. This study aims to explore automated histogram-based quantification of lung affection in COVID-19 pneumonia in volumetric computed tomography (CT) images in comparison to conventional semi-quantitative severity scoring. This retrospective study enrolled 153 patients with proven COVID-19 pneumonia. Based on the severity of clinical presentation, the patients were divided into three groups: mild, moderate and severe. Based upon the need for oxygenation support, two groups were identified as follows: common group that incorporated mild and moderate severity patients who did not need intubation, and severe illness group that included patients who were intubated. An automated multi-level thresholding histogram-based quantitative analysis technique was used for evaluation of lung affection in CT scans together with the conventional semi-quantitative severity scoring performed by two expert radiologists. The quantitative assessment included volumes, percentages and densities of ground-glass opacities (GGOs) and consolidation in both lungs. The results of the two evaluation methods were compared, and the quantification metrics were correlated.ResultsThe Spearman’s correlation coefficient between the semi-quantitative severity scoring and automated quantification methods was 0.934 (p < 0.0001).ConclusionsThe automated histogram-based quantification of COVID-19 pneumonia shows good correlation with conventional severity scoring. The quantitative imaging metrics show high correlation with the clinical severity of the disease.

Noninvasive Detection of Arytenoid Cartilage Calcification Using Computed Tomography and Prediction of Prognosis in Laryngeal Contact Granuloma

Laryngeal contact granuloma (LCG) is a relatively uncommon disease with chronic inflammatory stimulation, and long-term reflux irritation is a vital factor for arytenoid cartilage calcification. Our investigation compared the severity of ipsilateral arytenoid cartilage calcification with the frequency of recurrence of LCG after surgical treatment. A retrospective chart review of prospectively gathered data over five years from 327 patients, including 153 subjects without laryngeal lesions, were age- and sex-matched normal controls, 103 patients with various other vocal cord lesions were in the laryngeal lesion group and 71 LCG patients met the diagnostic criteria pathologically. All subjects underwent laryngeal high-resolution computed tomography (HRCT) prior to therapeutic interventions. The computed tomography (CT) value and arytenoid cartilage calcification were obtained using image data before surgery, and their clinical significance was further analyzed. Seventy-one patients with LCG, including sixty-two males, were enrolled in the study. Among these cases, there were 67 patients with unilateral vocal cord lesions. Of the 103 eligible patients in the laryngeal lesion group, 87 had unilateral lesions, which including eighty-seven men. Of the 153 average subjects, 105 were male. The rate of arytenoid cartilage calcification in the LCG group was dramatically higher in the lesion side than in the laryngeal lesions and normal group (P < 0.01). Furthermore, the CT value (P < 0.01) and range of calcification (P < 0.01) were significantly higher in patients with LCG than in those with laryngeal lesions. Importantly, patients with high CT values and the calcification range of lesions in the arytenoid cartilage displayed a greater lesion size and recurrence rate than patients with low CT values and lesion areas (P < 0.01). Our results suggest that most patients with LCG present with calcification of the arytenoid cartilage. The more severe the calcification in the arytenoid cartilage, the greater the risk of granuloma size and recurrence in LCG after surgical treatment. CT and bone density testing of the arytenoid cartilage may be an essential method to evaluate the prognosis of LCG.