Pulmonary Imaging Research Articles

Application of electrical impedance tomography imaging technology combined with generative adversarial network in pulmonary ventilation monitoring

Electrical impedance tomography (EIT) plays a crucial role in the monitoring of pulmonary ventilation and regional pulmonary function test. However, the inherent ill-posed nature of EIT algorithms results in significant deviations in the reconstructed conductivity obtained from voltage data contaminated with noise, making it challenging to obtain accurate distribution images of conductivity change as well as clear boundary contours. In order to enhance the image quality of EIT in lung ventilation monitoring, a novel approach integrating the EIT with deep learning algorithm was proposed. Firstly, an optimized operator was introduced to enhance the Kalman filter algorithm, and Tikhonov regularization was incorporated into the state-space expression of the algorithm to obtain the initial lung image reconstructed. Following that, the imaging outcomes were fed into a generative adversarial network model in order to reconstruct accurate lung contours. The simulation experiment results indicate that the proposed method produces pulmonary images with clear boundaries, demonstrating increased robustness against noise interference. This methodology effectively achieves a satisfactory level of visualization and holds potential significance as a reference for the diagnostic purposes of imaging modalities such as computed tomography.

Miliary Tuberculosis as Postmortem Diagnosis in Solid Organ Transplant Recipient: Case Report and Review of the Literature

The diagnosis of tuberculosis (TB) in solid organ transplant (SOT) recipients presents challenges that may lead to treatment delay. These include atypical clinical presentations, increased likelihood of negative tuberculin skin test or/and interferon-gamma release assays, and negative sputum smear results despite active disease. The treatment poses challenges due to pharmacokinetic interactions, allograft-related toxicity, and inadequate immune response. We report the case of a 70-year-old man after kidney transplantation in 2012. The patient was transferred from the urology unit with deteriorating renal function and presumed urosepsis. His pulmonary chest X-ray showed hilar pulmonary infiltrates. Computed tomography of the chest/abdomen/pelvis revealed mediastinal lymphadenopathy, pulmonary infiltrates, pulmonary effusion, and splenomegaly. His blood results showed pancytopenia and high inflammatory and renal markers. He was treated with broad-spectrum antibiotics covering bacterial, fungal, and viral infections. Despite initial clinical improvement, his kidney function deteriorated, and he required hemodialysis. His temperature continued to spike. On physical examination, he was confused and lethargic. He was scheduled to have a mediastinoscopy with lymph node biopsy, but he died the day before. The postmortem examination revealed miliary tuberculosis with tuberculosis of many organs: kidney transplant, native kidney, bone marrow, mediastinal lymph nodes, lungs, and spleen. The diagnosis of active TB in transplant recipients requires a high index of suspicion and invasive procedures. The majority of all cases of active TB after SOT are disseminated or occur at extrapulmonary sites. Only a small minority of patients have classic cavitary changes on pulmonary imaging.

The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases.

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Deployment of a human-centred clinical decision support system for pulmonary embolism: evaluation of impact on quality of diagnostic decisions

Pulmonary embolism (PE) is a serious condition that presents a diagnostic challenge for which diagnostic errors often happen. The literature suggests that a gap remains between PE diagnostic guidelines and...

Effectiveness of Filtering Methods in Enhancing Pulmonary Carcinoma Image Quality: A Comparative Analysis

<span lang="EN-IN">In recent years, information technology has vastly improved. The quality of the image has been degraded by noise, which defeats the purpose of the noisy images. The major purpose of this paper is to find out which filters provide a better outcome while preprocessing medical images using computer tomography scans. The purpose of this paper is to remove noise from any images, whether they are real-time datasets or online datasets. To enhance an image for preprocessing, I have compared various filters; these filters are already available, but the major purpose is to identify the best filter. I compared the different parameters to find the best and finally found that the modified bilateral filtering provided a better result. The noise has been removed by using a bilateral filter, and the image clarity has not changed when using this filter. We have discussed the advantages and drawbacks of each approach. The effectiveness of these filters is compared using the peak signal-to-noise ratio, structural similarity index, signal-to-noise ratio, and mean square error. An enhanced image processing analysis using different filtering techniques can improve the accuracy of diagnosis, facilitating timely treatment and ultimately improving patient outcomes.</span>

Deep learning in computed tomography pulmonary angiography imaging: A dual-pronged approach for pulmonary embolism detection

The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA) for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need for improved diagnostic solutions. The primary objective of this study is to leverage deep learning techniques to enhance the Computer Assisted Diagnosis (CAD) of PE. With this aim, we propose a classifier-guided detection approach that effectively leverages the classifier’s probabilistic inference to direct the detection predictions, marking a novel contribution in the domain of automated PE diagnosis. Our classification system includes an Attention-Guided Convolutional Neural Network (AG-CNN) that uses local context by employing an attention mechanism. This approach emulates a human expert's attention by looking at both global appearances and local lesion regions before making a decision. The classifier demonstrates robust performance on the FUMPE dataset, achieving an AUROC of 0.927, sensitivity of 0.862, specificity of 0.879, and an F1-score of 0.805 with the Inception-v3 backbone architecture. Moreover, AG-CNN outperforms the baseline DenseNet-121 model, achieving an 8.1% AUROC gain. While previous research has mostly focused on finding PE in the main arteries, our use of cutting-edge object detection models and ensembling techniques greatly improves the accuracy of detecting small embolisms in the peripheral arteries. Finally, our proposed classifier-guided detection approach further refines the detection metrics, contributing new state-of-the-art to the community: mAP50, sensitivity, and F1-score of 0.846, 0.901, and 0.779, respectively, outperforming the former benchmark with a significant 3.7% improvement in mAP50. Our research aims to elevate PE patient care by integrating AI solutions into clinical workflows, highlighting the potential of human-AI collaboration in medical diagnostics.

Study on high-precision three-dimensional reconstruction of pulmonary lesions and surrounding blood vessels based on CT images.

The adoption of computerized tomography (CT) technology has significantly elevated the role of pulmonary CT imaging in diagnosing and treating pulmonary diseases. However, challenges persist due to the complex relationship between lesions within pulmonary tissue and the surrounding blood vessels. These challenges involve achieving precise three-dimensional reconstruction while maintaining accurate relative positioning of these elements. To effectively address this issue, this study employs a semi-automatic precise labeling process for the target region. This procedure ensures a high level of consistency in the relative positions of lesions and the surrounding blood vessels. Additionally, a morphological gradient interpolation algorithm, combined with Gaussian filtering, is applied to facilitate high-precision three-dimensional reconstruction of both lesions and blood vessels. Furthermore, this technique enables post-reconstruction slicing at any layer, facilitating intuitive exploration of the correlation between blood vessels and lesion layers. Moreover, the study utilizes physiological knowledge to simulate real-world blood vessel intersections, determining the range of blood vessel branch angles and achieving seamless continuity at internal blood vessel branch points. The experimental results achieved a satisfactory reconstruction with an average Hausdorff distance of 1.5 mm and an average Dice coefficient of 92%, obtained by comparing the reconstructed shape with the original shape,the approach also achieves a high level of accuracy in three-dimensional reconstruction and visualization. In conclusion, this study is a valuable source of technical support for the diagnosis and treatment of pulmonary diseases and holds promising potential for widespread adoption in clinical practice.

Analysis of 9 cases of drug induced hypersensitivity syndrome related hemophagocytic lymphohistiocytosis

Objective: To analyze the clinical features,treatment and prognosis of drug induced hypersensitivity syndrome related hemophagocytic lymphohistiocytosis (DIHS-HLH). Methods: This was a retrospective case study. Clinical characteristics, laboratory results, treatment and prognosis of 9 patients diagnosed with DIHS-HLH in Beijing Children's hospital between January 2020 and December 2022 were summarized. Kaplan-Meier survival analysis was used to calculate the overall survival rate. Results: Among all 9 cases, there were 6 males and 3 females, with the age ranged from 0.8 to 3.1 years. All patients had fever, rash, hepatomegaly and multiple lymph node enlargement. Other manifestations included splenomegaly (4 cases), pulmonary imaging abnormalities (6 cases), central nervous system symptoms (3 cases), and watery diarrhea (3 cases). Most patients showed high levels of soluble-CD25 (8 cases), hepatic dysfunction (7 cases) and hyperferritinemia (7 cases). Other laboratory abnormalities included hemophagocytosis in bone marrow (5 cases), hypofibrinogenemia (3 cases) and hypertriglyceridemia (2 cases). Ascending levels of interleukin (IL) 5, IL-8 and interferon-γ (IFN-γ) were detected in more than 6 patients. All patients received high dose intravenous immunoglobulin, corticosteroid and ruxolitinib, among which 4 patients were also treated with high dose methylprednisolone, 2 patients with etoposide and 2 patients with cyclosporin A. After following up for 0.2-38.6 months, 7 patients survived, and the 1-year overall survival rate was (78±14)%. Two patients who had no response to high dose immunoglobulin, methylprednisolone 2 mg/(kg·d) and ruxolitinib died. Watery diarrhea, increased levels of IL-5 and IL-8 and decreased IgM were more frequently in patients who did not survive. Conclusions: For children with fever, rash and a suspicious medication history, when complicated with hepatomegaly, impaired liver function and high levels of IL-5 and IL-8, DIHS-HLH should be considered. Once diagnosed with DIHS-HLH, suspicious drugs should be stopped immediately, and high dose intravenous immunoglobulin, corticosteroid and ruxolitinib could be used to control disease.

Self-evolving persistent luminescence nanoprobes for autofluorescence-free ratiometric imaging and on-demand enhanced chemodynamic therapy of pulmonary metastatic tumors.

Precise imaging-guided therapy of a pulmonary metastasis tumor is of great significance for tumor management and prognosis. Persistent luminescence nanoparticles (PLNPs) are promising probes due to their in situ excitation-free and low-background imaging characteristics. However, most of the PLNP-based probes cannot intelligently distinguish between normal and tumor tissues or balance the needs of targeted accumulation and rapid metabolism, resulting in false positive signals and potential side effects. Besides, the luminescence intensity of single-emissive PLNPs is affected by external factors. Herein, we report a self-evolving double-emissive PLNP-based nanoprobe ZGMC@ZGC-TAT for pulmonary metastatic tumor imaging and therapy. Acid-degradable green-emitting PLNPs (ZGMC) with good afterglow performance and therapeutic potential are synthesized by systematic optimization of dopants. Ultra-small red-emitting PLNPs (ZGC) are then prepared as imaging and reference probes. The two PLNPs are finally covalently coupled and further modified with a cell-penetrating peptide (TAT) to obtain ZGMC@ZGC-TAT. Dual emission ensures a stable luminescence ratio (I700/I537) independent of probe concentration, test voltage and time gate. ZGMC degrades and phosphorescence disappears in a tumor microenvironment (TME), resulting in an increase in I700/I537, thus enabling tumor-specific ratiometric imaging. Cu2+ and Mn2+ released by ZGMC degradation achieve GSH depletion and enhance CDT, effectively inhibiting tumor cell proliferation. Meanwhile, the size of ZGMC@ZGC-TAT decreases sharply, and the resulting ZGC-TAT further causes nuclear pyknosis and quickly clear metabolism. The developed ZGMC@ZGC-TAT turns non-targeted lung aggregation of nanomaterials into a unique advantage, and integrates TME-triggered phosphorescence and size self-evolution, and on-demand therapeutic functions, showing outstanding prospects in precise imaging and efficient treatment of pulmonary metastatic tumors.

Bayesian Statistical Analysis for Bacterial Detection in Pulmonary Endomicroscopic Fluorescence Lifetime Imaging.

Pneumonia, a respiratory disease often caused by bacterial infection in the distal lung, requires rapid and accurate identification, especially in settings such as critical care. Initiating or de-escalating antimicrobials should ideally be guided by the quantification of pathogenic bacteria for effective treatment. Optical endomicroscopy is an emerging technology with the potential to expedite bacterial detection in the distal lung by enabling in vivo and in situ optical tissue characterisation. With advancements in detector technology, optical endomicroscopy can utilize fluorescence lifetime imaging (FLIM) to help detect events that were previously challenging or impossible to identify using fluorescence intensity imaging. In this paper, we propose an iterative Bayesian approach for bacterial detection in FLIM. We model the FLIM image as a linear combination of background intensity, Gaussian noise, and additive outliers (labelled bacteria). While previous bacteria detection methods model anomalous pixels as bacteria, here the FLIM outliers are modelled as circularly symmetric Gaussian-shaped objects, based on their discrete shape observed through visual analysis and the physical nature of the imaging modality. A Hierarchical Bayesian model is used to solve the bacterial detection problem where prior distributions are assigned to unknown parameters. A Metropolis-Hastings within Gibbs sampler draws samples from the posterior distribution. The proposed method's detection performance is initially measured using synthetic images, and shows significant improvement over existing approaches. Further analysis is conducted on real optical endomicroscopy FLIM images annotated by trained personnel. The experiments show the proposed approach outperforms existing methods by a margin of +16.85% ( F1 ) for detection accuracy.

Six and twelve-month respiratory outcomes in a cohort of severe and critical COVID-19 survivors: A prospective monocentric study in Latin America.

Severe COVID-19 can result in long-term sequelae known as "chronic COVID," characterized by a wide range of persistent physical and mental symptoms. Chest imaging and pulmonary function test alterations have been observed in recovered patients. Most studies focus on up to a 3-month follow-up after symptom onset or hospital discharge, with few reports on long-term follow-up and limited evidence regarding disease progression in Latin America. This study aims to describe the clinical characteristics and changes in pulmonary function, imaging, and quality of life in severe and critical COVID-19 patients requiring ICU admission in a high-complexity hospital in Latin America. A prospective cohort of survivors underwent clinical, radiological, pulmonary function, and quality of life assessments 6 and 12 months post-discharge. One hundred twelve patients were included, all of whom attended the 6-month follow-up, and 99 returned for the 12-month follow-up. Most subjects had no previous respiratory symptoms or significant medical history. At the end of the follow-up period, 74% of the patients showed interstitial infiltrates in chest tomography and a higher frequency of fibroatelectatic tracts and parenchymal bands. Pulmonary function tests returned to normal ranges, except for carbon monoxide diffusion, but no altered scores were reported in the questionnaires. Despite residual radiological findings, most parameters studied in severe and critical COVID-19 survivors improved over the 12-month follow-up period. Regardless of the imaging abnormalities, the improvement in variables such as symptomatic relief and normal pulmonary function suggests that these alterations are transient. Carbon monoxide diffusion did not normalize by the end of the follow-up, which is consistent with the abnormalities reported in multiple studies, indicating a potential disease-related pattern.

Deep Learning based Pulmonary Embolism Detection using Convolutional Feature Maps of CT Pulmonary Angiography Images

Pulmonary Embolism (PE) is a blood clot in the pulmonary arteries of the lungs. Currently, Computerized Tomography Pulmonary Angiography (CTPA) scans are used to diagnose this condition. However, to manually locate the presence of a PE in the scan is laborious. In this paper, the detection of PE and prediction of its features like location, Right Ventricle to Left Ventricle (RV/LV) ratio, and chronicity are automated. First, for the detection of the embolisms, image features from the RSNA Pulmonary Embolism CT (RSPECT) dataset - containing over 12,000 CTPA studies, are extracted by several pre-trained Convolutional Neural Networks (CNN) - AlexNet, Inception V3, ResNet-18, and ResNet-50. Then, various Machine Learning (ML) classifiers are trained on these features and their performances are compared. Second, for the prediction of labels, multi-label classification is performed by training these networks with Binary Cross Entropy Logits (BCE Logits) loss. The results showed that for the detection task, k-NN classifier trained on ResNet-50 features achieved the highest sensitivity of 0.98 with 1.7 false positives per scan and an AUROC score of 97.4%. This system also improved on the state-of-the-art sensitivity score by 0.0975 and AUROC score by 3.4%. For multi-label classification, ResNet-18 performed comparatively better with the best validation loss of 0.07 and weighted macro-average ROC AUC score of 0.61.

Corticosteroid treatment for persistent pulmonary infiltrates following COVID-19 infection: Clearing the fog?

Systemic corticosteroids have been shown to improve outcomes in severe coronavirus disease 2019 (COVID-19) pneumonia; however, their role in post-COVID-19 persistent lung abnormalities is not well defined. Here, we describe our experience with corticosteroids in patients with persistent lung infiltrates following COVID-19 infection. What is the efficacy of systemic corticosteroids in improving lung function and radiological abnormalities in patients following COVID-19 pneumonia? This is a single-center retrospective study evaluating patients with persistent respiratory symptoms and abnormal chest computed tomography findings. Patients were divided into two groups based on treatment with corticosteroids: "steroid group" and "nonsteroid group." Clinical data were collected from the electronic medical records. Between March 2020 and December 2021, 227 patients were seen in the post-COVID-19 pulmonary clinic, of which 75 were included in this study. The mean age was 56 years, 63% were female, and 75% were white. The main physiologic deficit was reduced Diffusing capacity of the Lungs for Carbon Monoxide (DLCO) at 72% (±22). On chest imaging, the most common findings were ground-glass opacities (91%) and consolidation (29%). Thirty patients received corticosteroid (steroid group) and 45 did not (nonsteroid group). Patients treated with corticosteroids had lower DLCO (DLCO [%]: steroid group 63 ± 17, nonsteroid group 78 ± 23; P = 0.005) and all had ground-glass opacities on imaging compared to 84% in the nonsteroid group (P = 0.04). At follow-up, patients in the steroid group (n = 16) had a significant improvement in spirometry and DLCO. In addition, there was a significant improvement with resolution of ground-glass opacities in both the groups (P < 0.05). The use of systemic corticosteroids in patients with persistent respiratory symptoms and radiological abnormalities post-COVID-19 was associated with significant improvement in pulmonary function testing and imaging. Prospective studies are needed to confirm whether these findings are the effect of corticosteroid therapy or disease evolution over time.

Introducing SPINE: A Holistic Approach to Synthetic Pulmonary Imaging Evaluation Through End-to-End Data and Model Management.

In the evolving field of medical imaging and machine learning (ML), this paper introduces a novel framework for evaluating synthetic pulmonary imaging aiming to assess synthetic data quality and applicability. Our study concentrates on synthetic X-ray chest images, crucial for diagnosing respiratory diseases. We employ SPINE (Synthetic Pulmonary Imaging Evaluation) framework, a threefold synthetic images evaluation method including expert domain assessment, statistical data analysis and adversarial evaluation. In order to replicate and validate our methodology, we followed an End-to-End data and model management process which begins with a dataset of Normal and Pneumonia chest X-rays, generating synthetic images using Generative Adversarial Networks (GANs) and training a baseline classifier, essential in the adversarial evaluation axis, testing synthetic images against real data assessing their predictive value. The critical outcome of our approach is the post-market analysis of synthetic images. This innovative method evaluates synthetic images using clinical, statistical, and scientific criteria independently from traditional generation performance metrics. This independent evaluation provides deep insights into the clinical and research effectiveness of the synthetic data. By ensuring these images mirror real data's statistical properties and maintain clinical accuracy, our framework establishes a new standard for the ethical and reliable use of synthetic data in medical imaging and research.

PPE59 antibodies in tuberculous patients and potential use for diagnosis when assayed with other rapid biomarkers.

PPE 59, which is absent from bacillus Calmette Guérin (BCG) strains, seems to induce a humoral immune response in patients with tuberculosis (TB). Additional studies are needed to better evaluate this protein in immune response to tuberculosis. To evaluate the response of antibodies to PPE59 in TB individuals, its combination with IgG response to other, previously tested mycobacterial antigens (Ag) and with sputum smear microbiology (SM) results. We have cloned and expressed the rv3429 gene that encodes PPE59, then IgG, IgM, and IgA against PPE59 antigens measured by enzyme-linked immunosorbent assay (ELISA) in 212 sera samples obtained from the following subject cohorts: TB residents from Italy (79) and in Brazil (52); and an all-Brazilian cohort of 55 patients with other respiratory disorders; 10 patients infected with non-tuberculous mycobacteria, and 16 asymptomatic subjects. Drawing on results from a previous study(17) of serum samples from Brazilian subjects tested for IgG by ELISA against mycobacterial antigens ESAT-6, 16kDa, MT10.3, MPT-64 and 38kDa, the results were analysed in combination with those of the PPE59 and SM tests. Keeping the specificity rate at 97%, the overall PPE59 IgA sensitivity was 42.7%, while IgG and IgM showed lower performance (p < 0.0001). Combining PPE59 IgA/16kDa IgG results increased sensitivity to 71%, and even higher rates when the results were combined with SM results (86.5%, p = 0.001), at 88.9% specificity. Positive IgA was associated with pulmonary image alterations of high TB probability (p < 0.05). Tests with TB patients found a moderate frequency of positivity for PPE59 IgA. However, the higher level of sensitivity attained in combination with PPE59 IgA/16kDa IgG/SM results unheard of before, although imperfect, suggests that this may be a potential additional tool for rapid detection of TB in low-resource areas.

Chronic thromboembolic pulmonary disease: Association with exercise-induced pulmonary hypertension and right ventricle adaptation over time

Background and AimChronic thromboembolic pulmonary disease (CTEPD) is a progressive condition caused by fibrotic thrombi and vascular remodeling in the pulmonary circulation despite prolonged anticoagulation. We evaluated clinical factors associated with CTEPD, as well as its impact on functional capacity, pulmonary haemodynamics at rest and after exercise, and right ventricle (RV) morphology and function. MethodsWe compared 33 consecutive patients with a history of acute pulmonary embolism and either normal pulmonary vascular imaging (negative Q-scan, group 1, n = 16) or persistent defects on lung perfusion scan (positive Q-scan) despite oral anticoagulation at 4 months (group 2, n = 17). Investigations included thrombotic load, the Pulmonary Embolism Severity Index (PESI) score, functional class, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), cardiopulmonary exercise test (CPET) and echocardiographic parameters at rest and after exercise (ESE), at 4 and at 24 months. ResultsCompared with group 1, group 2 featured a higher PESI score (p = 0.02) and a higher thrombotic load (p = 0.004) at hospital admission. At 4 months, group 2 developed exercise-induced pulmonary hypertension (Ex-PH) at CPET (p < 0.001) and ESE (p < 0.001). At 24 months group 2 showed higher NT-proBNP (p < 0.001), WHO-FC (p < 0.001), systolic (p<0.001) and diastolic (p = 0.037) RV dysfunction and worse RV-arterial coupling (p < 0.001) despite maintaining a low or intermediate echocardiographic probability of PH. ConclusionsThis is the first "proof of concept" study showing that patients with a positive Q-scan frequently develop Ex-PH and RV functional deterioration as well as reduced functional capacity, generating the hypothesis that Ex-PH could help detect the progression to CTEPD.

Is chest imaging needed as part of pT1a renal cell carcinoma surveillance after surgical resection?

IntroductionFollowing surgical excision of pT1a renal cell carcinoma (RCC), 2% to 5% will recur, with 50% to 60% being lung metastases. The ideal surveillance strategy to identify recurrences is unclear. Guidelines are mixed, with NCCN and AUA recommending surveillance via chest x-ray (CXR) at least annually for 5 years, while EAU guidelines do not specifically recommend the use of CXR. In an effort to clarify the utility of surveillance CXR, we retrospectively evaluated pT1a patients following surgical treatment at a single institution. MethodsWe performed retrospective analysis of unique patients who underwent surgical excision of pT1 RCC between January 2000 and January 2020. In addition to demographic information, we collected RCC pathology, recurrence details, and most recent chest imaging. We excluded non-RCC pathology, and patients with pulmonary nodules on baseline imaging. ResultsWe identified 463 unique patients (mean age 58.3 years, range 23–87) that underwent surgical excision of pT1a RCC with mean follow-up of 47.6 months (range 1–201). On the most recent pulmonary surveillance imaging, 72.4% (335/463) had CXR while 27.6% (128/463) had chest CT performed. Regardless of modality, pulmonary recurrence was not detected on any surveillance imaging (0/463). ConclusionIn patients without baseline preoperative lung pathology, we found that there is questionable clinical value in surveillance for pulmonary recurrence after resection of pT1a RCC.

Highly accelerated, Dixon-based non-contrast MR angiography versus high-pitch CT angiography

ObjectivesTo compare a novel, non-contrast, flow-independent, 3D isotropic magnetic resonance angiography (MRA) sequence that combines respiration compensation, electrocardiogram (ECG)-triggering, undersampling, and Dixon water-fat separation with an ECG-triggered aortic high-pitch computed tomography angiography (CTA) of the aorta.Materials and methodsTwenty-five patients with recent CTA were scheduled for non-contrast MRA on a 3 T MRI. Aortic diameters and cross-sectional areas were measured on MRA and CTA using semiautomatic measurement tools at 11 aortic levels. Image quality was assessed independently by two radiologists on predefined aortic levels, including myocardium, proximal aortic branches, pulmonary veins and arteries, and the inferior (IVC) and superior vena cava (SVC). Image quality was assessed on a 5-point Likert scale.ResultsAll datasets showed diagnostic image quality. Visual grading was similar for MRA and CTA regarding overall image quality (0.71), systemic arterial image quality (p = 0.07–0.91) and pulmonary artery image quality (p = 0.05). Both readers favored MRA for SVC and IVC, while CTA was preferred for pulmonary veins (all p < 0.05). No significant difference was observed in aortic diameters or cross-sectional areas between native MRA and contrast-enhanced CTA (p = 0.08–0.94).ConclusionThe proposed non-contrast MRA enables robust imaging of the aorta, its proximal branches and the pulmonary arteries and great veins with image quality and aortic diameters and cross-sectional areas comparable to that of CTA. Moreover, this technique represents a suitable free-breathing alternative, without the use of contrast agents or ionizing radiation. Therefore, it is especially suitable for patients requiring repetitive imaging.

Radiofrequency ablation induces tumor cell dissemination in a mouse model of hepatocellular carcinoma

BackgroundWe tested the hypothesis that radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC) promotes tumor cell release and explored a method for reducing these effects.MethodsA green fluorescent protein-transfected orthotopic HCC model was established in 99 nude mice. In vivo flow cytometry was used to monitor circulating tumor cell (CTC) dynamics. Pulmonary fluorescence imaging and pathology were performed to investigate lung metastases. First, the kinetics of CTCs during the periablation period and the survival rate of CTCs released during RFA were investigated. Next, mice were allocated to controls, sham ablation, or RFA with/without hepatic vessel blocking (ligation of the portal triads) for evaluating the postablation CTC level, lung metastases, and survival over time. Moreover, the kinetics of CTCs, lung metastases, and mice survival were evaluated for RFA with/without ethanol injection. Pathological changes in tumors and surrounding parenchyma after ethanol injection were noted. Statistical analysis included t-test, ANOVA, and Kaplan-Meier survival curves.ResultsCTC counts were 12.3-fold increased during RFA, and 73.7% of RFA-induced CTCs were viable. Pre-RFA hepatic vessel blocking prevented the increase of peripheral CTCs, reduced the number of lung metastases, and prolonged survival (all p ≤ 0.05). Similarly, pre-RFA ethanol injection remarkably decreased CTC release during RFA and further decreased lung metastases with extended survival (all p ≤ 0.05). Histopathology revealed thrombus formation in blood vessels after ethanol injection, which may clog tumor cell dissemination during RFA.ConclusionRFA induces viable tumor cell dissemination, and pre-RFA ethanol injection may provide a prophylactic strategy to reduce this underestimated effect.Relevance statementRFA for HCC promotes viable tumor cell release during ablation, while ethanol injection can prevent RFA induced tumor cell release.Key points• RFA induced the release of viable tumor cells during the ablation procedure in an animal model.• Hepatic vessel blocking can suppress tumor cells dissemination during RFA.• Ethanol injection can prevent RFA-induced tumor cell release, presumably because of the formation of thrombosis.Graphical

Research of correlation between personality traits and hormones with the nature of pulmonary nodules

BackgroundRising rates of lung cancer screening have contributed to an increase in pulmonary nodule diagnosis rates. Studies have shown that psychosocial factors and hormones have an impact on the development of the oncological diseases. Therefore, we conducted this study to explore the potential relationship between pulmonary nodules pathology and patient personality traits and hormone levels. MethodsThis study enrolled 245 individuals who had first been diagnosed with pulmonary nodules in Tangdu Hospital and admitted for surgery. The personality profile of these patients was analyzed on admission using the C-Type Behavioral Scale and hormone levels were measured in preoperative serum samples. Associations between nodule pathology, personality scores, and hormone levels， were then assessed through Statistical methods analysis. ResultsBehavioral scale analyses revealed significant differences four items, including depression, anger outward, optimism, and social support (P< 0.05). Specifically, patients with higher depression scores were more likely to harbor malignant pulmonary nodules, as were patients with lower levels of anger outward, social support, and optimism. Univariate analyses indicated that nodule pathology was associated with significant differences in nodule imaging density, CT value, testosterone levels, and T4 levels(P< 0.05), and logistic regression analyses revealed pulmonary nodule imaging density and T4 levels to be significant differences of nodule pathology. ConclusionThe results showed a significant association between nodules pathology and the personality characteristics of the patients (depression, anger outward, optimism, social support), the patients' T4 levels and the imaging density of the nodules.