Lung Nodules Research Articles

Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience.

Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolutional neural networks (CNN); the software of AI-Rad Companion™ (AIRC), capable of self-optimising feature recognition. This study aims to demonstrate the practical value of AI-based lung nodule detection in a clinical setting; a limited body of research. One hundred and eighty-three non-contrast CT chest studies from a single centre were assessed for AIRC software analysis. Prospectively collected data from AIRC detection and characterisation of lung nodules (size: ≥3 mm) were assessed against the reference standard; reported findings of a blinded consultant radiologist. One hundred and sixty-seven CT chest studies were included; 52% indicated for nodule or lung cancer surveillance. Of 289 lung nodules, 219 (75.8%) nodules (mean size: 10.1 mm) were detected by both modalities, 28 (9.7%) were detected by AIRC alone and 42 (14.5%) by radiologist alone. Solid nodules missed by AIRC were larger than those missed by radiologist (11.5 mm vs 4.7 mm, P < 0.001). AIRC software sensitivity was 87.3%, with significant false positive and negative rates demonstrating 12.5% specificity (PPV 0.6, NPV 0.4). In a population of high nodule prevalence, AIRC lung nodule detection software demonstrates sensitivity comparable to that of consultant radiologist. The clinical significance of larger sized nodules missed by AIRC software presents a barrier to current integration in practice. We consider this research highly relevant in providing focus for ongoing software development, potentiating the future success of AI-based tools within diagnostic radiology.

Impact of AI-assisted CXR analysis in detecting incidental lung nodules and lung cancers in non-respiratory outpatient clinics.

The use of artificial intelligence (AI) for chest X-ray (CXR) analysis is becoming increasingly prevalent in medical environments. This study aimed to determine whether AI in CXR can unexpectedly detect lung nodule detection and influence patient diagnosis and management in non-respiratory outpatient clinics. In this retrospective study, patients over 18 years of age, who underwent CXR at Yongin Severance Hospital outpatient clinics between March 2021 and January 2023 and were identified to have lung nodules through AI software, were included. Commercially available AI-based lesion detection software (Lunit INSIGHT CXR) was used to detect lung nodules. Out Of 56,802 radiographic procedures, 40,191 were from non-respiratory departments, with AI detecting lung nodules in 1,754 cases (4.4%). Excluding 139 patients with known lung lesions, 1,615 patients were included in the final analysis. Out of these, 30.7% (495/1,615) underwent respiratory consultation and 31.7% underwent chest CT scans (512/1,615). As a result of the CT scans, 71.5% (366 cases) were found to have true nodules. Among these, the final diagnoses included 36 lung cancers (7.0%, 36/512), 141 lung nodules requiring follow-up (27.5%, 141/512), 114 active pulmonary infections (22.3%, 114/512), and 75 old inflammatory sequelae (14.6%, 75/512). The mean AI nodule score for lung cancer was significantly higher than that for other nodules (56.72 vs. 33.44, p < 0.001). Additionally, active pulmonary infection had a higher consolidation score, and old inflammatory sequelae had the highest fibrosis score, demonstrating differences in the AI analysis among the final diagnosis groups. This study indicates that AI-detected incidental nodule abnormalities on CXR in non-respiratory outpatient clinics result in a substantial number of clinically significant diagnoses, emphasizing AI's role in detecting lung nodules and need for further evaluation and specialist consultation for proper diagnosis and management.

ALKU-Net: Adaptive Large Kernel Attention Convolution Network for Lung Nodule Segmentation

The accurate segmentation of lung nodules in computed tomography (CT) images is crucial for the early screening and diagnosis of lung cancer. However, the heterogeneity of lung nodules and their similarity to other lung tissue features make this task more challenging. By using large receptive fields from large convolutional kernels, convolutional neural networks (CNNs) can achieve higher segmentation accuracies with fewer parameters. However, due to the fixed size of the convolutional kernel, CNNs still struggle to extract multi-scale features for lung nodules of varying sizes. In this study, we propose a novel network to improve the segmentation accuracy of lung nodules. The network integrates adaptive large kernel attention (ALK) blocks, employing multiple convolutional layers with variously sized convolutional kernels and expansion rates to extract multi-scale features. A dynamic selection mechanism is also introduced to aggregate the multi-scale features obtained from variously sized convolutional kernels based on selection weights. Based on this, we propose a lightweight convolutional neural network with large convolutional kernels, called ALKU-Net, which integrates the ALKA module in a hierarchical encoder and adopts a U-shaped decoder to form a novel architecture. ALKU-Net efficiently utilizes the multi-scale large receptive field and enhances the model perception capability through spatial attention and channel attention. Extensive experiments demonstrate that our method outperforms other state-of-the-art models on the public dataset LUNA-16, exhibiting considerable accuracy in the lung nodule segmentation task.

Exploring synthetic datasets for computer-aided detection: a case study using phantom scan data for enhanced lung nodule false positive reduction.

Synthetic datasets hold the potential to offer cost-effective alternatives to clinical data, ensuring privacy protections and potentially addressing biases in clinical data. We present a method leveraging such datasets to train a machine learning algorithm applied as part of a computer-aided detection (CADe) system. Our proposed approach utilizes clinically acquired computed tomography (CT) scans of a physical anthropomorphic phantom into which manufactured lesions were inserted to train a machine learning algorithm. We treated the training database obtained from the anthropomorphic phantom as a simplified representation of clinical data and increased the variability in this dataset using a set of randomized and parameterized augmentations. Furthermore, to mitigate the inherent differences between phantom and clinical datasets, we investigated adding unlabeled clinical data into the training pipeline. We apply our proposed method to the false positive reduction stage of a lung nodule CADe system in CT scans, in which regions of interest containing potential lesions are classified as nodule or non-nodule regions. Experimental results demonstrate the effectiveness of the proposed method; the system trained on labeled data from physical phantom scans and unlabeled clinical data achieves a sensitivity of 90% at eight false positives per scan. Furthermore, the experimental results demonstrate the benefit of the physical phantom in which the performance in terms of competitive performance metric increased by 6% when a training set consisting of 50 clinical CT scans was enlarged by the scans obtained from the physical phantom. The scalability of synthetic datasets can lead to improved CADe performance, particularly in scenarios in which the size of the labeled clinical data is limited or subject to inherent bias. Our proposed approach demonstrates an effective utilization of synthetic datasets for training machine learning algorithms.

Retraction Note: An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques

Retraction Note: An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques

Performance of AI for preoperative CT assessment of lung metastases: Retrospective analysis of 167 patients

ObjectivesTo evaluate the performance of artificial intelligence (AI) in the preoperative detection of lung metastases on CT. Materials and methodsPatients who underwent lung metastasectomy in our institution between 2016 and 2020 were enrolled, their preoperative CT reports having been performed before an AI solution (Veye Lung Nodules, version 3.9.2, Aidence) became available as a second reader in our department.All CT scans were retrospectively processed by AI. The sensitivities of unassisted radiologists (original CT radiology reports), AI reports alone and both combined were compared. Ground truth was established by a consensus reading of two radiologists, who analyzed whether the nodules mentioned in the pathology report were retrospectively visible on CT. Multivariate analysis was performed to identify nodule characteristics associated with detectability. ResultsA total of 167 patients (men: 62.9 %; median age, 59 years [47–68]) with 475 resected nodules were included. AI detected an average of 4 nodules (0–17) per CT, of which 97 % were true nodules. The combination of radiologist plus AI (92.4 %) had significantly higher sensitivity than unassisted radiologists (80.4 %) (p < 0.001). In 27/57 (47.4 %) patients who had multiple preoperative CT scans, AI detected lung nodules earlier than the radiologist. Vascular contact was associated with non-detection by radiologists (OR:0.32[0.19, 0.54], p < 0.001), whilst the presence of cavitation (OR:0.26[0.13, 0.54], p < 0.001) or pleural contact (OR:0.10[0.04, 0.22], p < 0.001) was associated with non-detection by AI. ConclusionAI significantly increases the sensitivity of preoperative detection of lung metastases and enables earlier detection, with a significant potential benefit for patient management.

Clinical Presentation and Treatment Strategies for Laryngeal Neuroendocrine Carcinoma: Insights from a Rare Case

Intriduction -Neuroendocrine neoplasms (NEN) of the larynx are very rare, accounting for &lt;1% of all laryngeal carcinomas. Based on the 2022 IARCH/WHO classification, NENs are characterised as well-differentiated neuroendocrine tumours (NET), which can further be divided into grades 1-3 based on Ki-67 levels and mitotic count, or poorly differentiated neuroendocrine carcinomas (NEC) consisting of small cell carcinoma and large cell carcinoma. In this case report, we describe a rare presentation of NEC of the larynx encountered in a 54-year-old man and how diagnostic and therapeutic dilemmas unfolded. Case description: A 6-month history of breathing and swallowing difficulties led to a tracheostomy due to stridor, in the emergency department. Imaging revealed a soft tissue mass involving the supraglottic larynx but no significant associated cervical lymphadenopathy. The biopsy from the growth was suggestive of poorly differentiated NEC as an AJCC stage cT3N0M0. However, there was local progression post-initial treatment with vincristine, doxorubicin and cyclophosphamide (VAC). The chemotherapy regimen was switched to carboplatin and etoposide. The patient received definitive radiation therapy and completed the planned dose of radiation.After 10 months of disease-free interval, there was local recurrence with lung nodules that prompted rechallenge with platinum and etoposide. The patient is doing well and remains clinically and radiologically stable to maintenance therapy with carboplatin &amp; etoposide. Conclusion: NENs of the larynx and especially NECs, are difficult to diagnose as they are rare and have varied molecular profiles. Its management frequently calls for multimodal approaches with chemotherapy and radiation therapy. Due to the developments in diagnostic and therapeutic modalities, our knowledge of NENs has been enhanced which contributed to better practice standards. Nevertheless, standardized treatment strategies remain to be determined by large cohort studies and comprehensive molecular profiling that can determine improved clinical outcomes.

A Multi-Modal Machine Learning Methodology for Predicting Solitary Pulmonary Nodule Malignancy in Patients Undergoing PET/CT Examination

This study explores a multi-modal machine-learning-based approach to classify solitary pulmonary nodules (SPNs). Non-small cell lung cancer (NSCLC), presenting primarily as SPNs, is the leading cause of cancer-related deaths worldwide. Early detection and appropriate management of SPNs are critical to improving patient outcomes, necessitating efficient diagnostic methodologies. While CT and PET scans are pivotal in the diagnostic process, their interpretation remains prone to human error and delays in treatment implementation. This study proposes a machine-learning-based network to mitigate these concerns, integrating CT, PET, and manually extracted features in a multi-modal manner by integrating multiple image modalities and tabular features). CT and PET images are classified by a VGG19 network, while additional SPN features in combination with the outputs of VGG19 are processed by an XGBoost model to perform the ultimate diagnosis. The proposed methodology is evaluated using patient data from the Department of Nuclear Medicine of the University Hospital of Patras in Greece. We used 402 patient cases with human annotations to internally validate the model and 96 histopathological-confirmed cases for external evaluation. The model exhibited 97% agreement with the human readers and 85% diagnostic performance in the external set. It also identified the VGG19 predictions from CT and PET images, SUVmax, and diameter as key malignancy predictors. The study suggests that combining all available image modalities and SPN characteristics improves the agreement of the model with the human readers and the diagnostic efficiency.

Primary pulmonary meningioma presenting as a pulmonary ground glass nodule: a case report and review of the literature

BackgroundA primary pulmonary meningioma is an extremely rare entity. Primary pulmonary meningiomas manifested with a ground glass nodule are a very rare occurrence in clinical practice.Case presentationIn this study, we report a case of a primary pulmonary meningioma with atypical computed tomography features. A 59-year-old Han Chinese female came to our hospital for treatment and reported that her physical examination revealed a ground glass nodule in the right lung for over 3 months. The histologic result revealed a primary pulmonary meningioma. The patient underwent a thoracoscopic lung wedge resection of the right upper lobe for a ground glass nodule. After 1 year of follow-up, the patient is still alive without evidence of metastasis or recurrence.ConclusionsPrimary pulmonary meningiomas could have a variety of radiological findings. As there are no specific radiologic features for the diagnosis of primary pulmonary meningiomas, complete resection of the lesion is required for both diagnosis and treatment. It is necessary to note the imaging features of primary pulmonary meningiomas, presenting as a ground glass nodule; this rare tumor should be considered in differential diagnoses.

Distribution of Solid Lung Nodules Presence and Size by Age and Sex in a Northern European Nonsmoking Population.

Background Most of the data regarding prevalence and size distribution of solid lung nodules originates from lung cancer screening studies that target high-risk populations or from Asian general cohorts. In recent years, the identification of lung nodules in non-high-risk populations, scanned for clinical indications, has increased. However, little is known about the presence of solid lung nodules in the Northern European nonsmoking population. Purpose To study the prevalence and size distribution of solid lung nodules by age and sex in a nonsmoking population. Materials and Methods Participants included nonsmokers (never or former smokers) from the population-based Imaging in Lifelines study conducted in the Northern Netherlands. Participants (age ≥ 45 years) with completed lung function tests underwent chest low-dose CT scans. Seven trained readers registered the presence and size of solid lung nodules measuring 30 mm3 or greater using semiautomated software. The prevalence and size of lung nodules (≥30 mm3), clinically relevant lung nodules (≥100 mm3), and actionable nodules (≥300 mm3) are presented by 5-year categories and by sex. Results A total of 10 431 participants (median age, 60.4 years [IQR, 53.8-70.8 years]; 56.6% [n = 5908] female participants; 46.1% [n = 4812] never smokers and 53.9% [n = 5619] former smokers) were included. Of these, 42.0% (n = 4377) had at least one lung nodule (male participants, 47.5% [2149 of 4523]; female participants, 37.7% [2228 of 5908]). The prevalence of lung nodules increased from age 45-49.9 years (male participants, 39.4% [219 of 556]; female participants, 27.7% [236 of 851]) to age 80 years or older (male participants, 60.7% [246 of 405]; female participants, 50.9% [163 of 320]). Clinically relevant lung nodules were present in 11.1% (1155 of 10 431) of participants, with prevalence increasing with age (male participants, 8.5%-24.4%; female participants, 3.7%-15.6%), whereas actionable nodules were present in 1.1%-6.4% of male participants and 0.6%-4.9% of female participants. Conclusion Lung nodules were present in a substantial proportion of all age groups in the Northern European nonsmoking population, with slightly higher prevalence for male participants than female participants. © RSNA, 2024 Supplemental material is available for this article.

Independent evaluation of the accuracy of 5 artificial intelligence software for detecting lung nodules on chest X-rays.

The integration of artificial intelligence (AI) into medicine is growing, with some experts predicting its standalone use soon. However, skepticism remains due to limited positive outcomes from independent validations. This research evaluates AI software's effectiveness in analyzing chest X-rays (CXR) to identify lung nodules, a possible lung cancer indicator. This retrospective study analyzed 7,670,212 record pairs from radiological exams conducted between 2020 and 2022 during the Moscow Computer Vision Experiment, focusing on CXR and computed tomography (CT) scans. All images were acquired during clinical routine. The final dataset comprised 100 CXR images (50 with lung nodules, 50 without), selected consecutively and based on inclusion and exclusion criteria, to evaluate the performance of all five AI-based solutions, participating in the Moscow Computer Vision Experiment and analyzing CXR. The evaluation was performed in 3 stages. In the first stage, the probability of a nodule in the lung obtained from AI services was compared with the Ground Truth (1-there is a nodule, 0-there is no nodule). In the second stage, 3 radiologists evaluated the segmentation of nodules performed by the AI services (1-nodule correctly segmented, 0-nodule incorrectly segmented or not segmented at all). In the third stage, the same radiologists additionally evaluated the classification of the nodules (1-nodule correctly segmented and classified, 0-all other cases). The results obtained in stages 2 and 3 were compared with Ground Truth, which was common to all three stages. For each stage, diagnostic accuracy metrics were calculated for each AI service. Three software solutions (Celsus, Lunit INSIGHT CXR, and qXR) demonstrated diagnostic metrics that matched or surpassed the vendor specifications, and achieved the highest area under the receiver operating characteristic curve (AUC) of 0.956 [95% confidence interval (CI): 0.918 to 0.994]. However, when evaluated by three radiologists for accurate nodule segmentation and classification, all solutions performed below the vendor-declared metrics, with the highest AUC reaching 0.812 (95% CI: 0.744 to 0.879). Meanwhile, all AI services demonstrated 100% specificity at stages 2 and 3 of the study. To ensure the reliability and applicability of AI-based software, it is crucial to validate performance metrics using high-quality datasets and engage radiologists in the evaluation process. Developers are recommended to improve the accuracy of the underlying models before allowing the standalone use of the software for lung nodule detection. The dataset created during the study may be accessed at https://mosmed.ai/datasets/mosmeddatargogksnalichiemiotsutstviemlegochnihuzlovtipvii/.

Development of a genome atlas for discriminating benign, preinvasive, and invasive lung nodules.

To tackle misdiagnosis in lung cancer screening with low-dose computed tomography (LDCT), we aimed to compile a genome atlas for differentiating benign, preinvasive, and invasive lung nodules and characterize their molecular pathogenesis. We collected 432 lung nodule tissue samples from Chinese patients, spanning benign, atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA). We performed comprehensive sequencing, examining somatic variants, gene expressions, and methylation levels. Our findings uncovered EGFR and TP53 mutations as key drivers in - early lung cancer development, with EGFR mutation frequency increasing with disease progression. Both EGFR mutations and EGF/EGFR hypo-methylation activated the EGFR pathway, fueling cancer growth. Transcriptome analysis identified four lung nodule subtypes (G1-4) with distinct molecular features and immune cell infiltrations: EGFR-driven G1, EGFR/TP53 co-mutation G2, inflamed G3, stem-like G4. Estrogen/androgen response was associated with the EGFR pathway, proposing a new therapy combining tyrosine kinase inhibitors with antiestrogens. Preinvasive nodules exhibited stem cell pathway enrichment, potentially hindering invasion. Epigenetic regulation of various genes was essential for lung cancer initiation and development. This study provides insights into the molecular mechanism of neoplastic progression and identifies potential diagnostic biomarkers and therapeutic targets for lung cancer.

Non-palpable Pulmonary Nodules and Uniportal-VATS: Radio-guided Localization (ROLL) Experience of a Lung Multidisciplinary Team.

Surgical resection with a minimally invasive approach is the standard for diagnosing and treating solitary pulmonary nodules. A computed tomography (CT)-guided technetium99m-macroaggregated albumin (99mTc-MAA) injection-based procedure has been employed for small and non-palpable lung nodule radio-guided preoperative localization (ROLL). This procedure is usually followed by video-assisted thoracoscopic surgery (VATS). This study retrospectively evaluated the feasibility, clinicopathologic outcomes, and complications of this localization radio-guided procedure followed by uniportal VATS. This retrospective study included 63 patients with suspicious lung nodules who underwent 99mTc-MAA CT-guided localization before uniportal VATS. The analysis examined the imaging and procedure characteristics, procedural risks, successful intra-operative localization, wedge resection, conversion from VATS to open thoracotomy, the reason, and histological diagnosis for each nodule. Also, it was evaluated how nodule and procedure features affected successful intra-operative localization. All patients were diagnosed using a CT scan, and 90.4% had a PET scan at basal staging. A round-glass morphology was present in 9.6% of cases, whereas most had a solid appearance. The mean nodule size was 9.78 mm (maximal tumoral diameter) with a 1-23 mm range. The mean distance from the pleural surface was 15.6 mm (range=1-117 mm). The detection rate of the 99mTc-MAA CT-guided localization procedure was 100%. Surgical procedures were uniportal VATS and transpleural thoracoscopy in 52 (82.5%) and 11 (17.5%) patients, respectively. The intraoperative localization rate was 98.4%. Pneumothorax represented the most frequent complication (6.3%), with one case clinically significant and three only with minimal radiological evidence. Pathology confirmed radical excision in all cases. Lung nodule localization with CT-guided 99mTc-MAA followed by uniportal VATS is feasible with a high success rate and low complication rate.

Nodule Detection and Generation on Chest X-Rays: NODE21 Challenge.

Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.

Artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer: an evidence synthesis early value assessment.

Lung cancer is one of the most common types of cancer in the United Kingdom. It is often diagnosed late. The 5-year survival rate for lung cancer is below 10%. Early diagnosis may improve survival. Software that has an artificial intelligence-developed algorithm might be useful in assisting with the identification of suspected lung cancer. This review sought to identify evidence on adjunct artificial intelligence software for analysing chest X-rays for suspected lung cancer, and to develop a conceptual cost-effectiveness model to inform discussion of what would be required to develop a fully executable cost-effectiveness model for future economic evaluation. The data sources were MEDLINE All, EMBASE, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, Epistemonikos, ACM Digital Library, World Health Organization International Clinical Trials Registry Platform, clinical experts, Tufts Cost-Effectiveness Analysis Registry, company submissions and clinical experts. Searches were conducted from 25 November 2022 to 18 January 2023. Rapid evidence synthesis methods were employed. Data from companies were scrutinised. The eligibility criteria were (1) primary care populations referred for chest X-ray due to symptoms suggestive of lung cancer or reasons unrelated to lung cancer; (2) study designs that compared radiology specialist assessing chest X-ray with adjunct artificial intelligence software versus radiology specialists alone and (3) outcomes relating to test accuracy, practical implications of using artificial intelligence software and patient-related outcomes. A conceptual decision-analytic model was developed to inform a potential full cost-effectiveness evaluation of adjunct artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer. None of the studies identified in the searches or submitted by the companies met the inclusion criteria of the review. Contextual information from six studies that did not meet the inclusion criteria provided some evidence that sensitivity for lung cancer detection (but not nodule detection) might be higher when chest X-rays are interpreted by radiology specialists in combination with artificial intelligence software than when they are interpreted by radiology specialists alone. No significant differences were observed for specificity, positive predictive value or number of cancers detected. None of the six studies provided evidence on the clinical effectiveness of adjunct artificial intelligence software. The conceptual model highlighted a paucity of input data along the course of the diagnostic pathway and identified key assumptions required for evidence linkage. This review employed rapid evidence synthesis methods. This included only one reviewer conducting all elements of the review, and targeted searches that were conducted in English only. No eligible studies were identified. There is currently no evidence applicable to this review on the use of adjunct artificial intelligence software for the detection of suspected lung cancer on chest X-ray in either people referred from primary care with symptoms of lung cancer or people referred from primary care for other reasons. Future research is required to understand the accuracy of adjunct artificial intelligence software to detect lung nodules and cancers, as well as its impact on clinical decision-making and patient outcomes. Research generating key input parameters for the conceptual model will enable refinement of the model structure, and conversion to a full working model, to analyse the cost-effectiveness of artificial intelligence software for this indication. This study is registered as PROSPERO CRD42023384164. This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135755) and is published in full in Health Technology Assessment; Vol. 28, No. 50. See the NIHR Funding and Awards website for further award information.

Clinical and radiological features of a cluster of immunocompetent adolescents with varicella pneumonia: a descriptive study.

Varicella pneumonia is one of the most common and severe complications of chickenpox infection. This study aimed to describe the clinical and radiological features of varicella pneumonia in a cluster of immunocompetent adolescents. A retrospective analysis was conducted on a cohort of adolescent patients diagnosed with varicella pneumonia at Wuhan Jinyintan Hospital between February 2023 and May 2023. The clinical and imaging data were collected and analyzed. A total of 116 patients were divided into two groups by the absence (group 1, n=57) or presence (group 2, n=59) of lower respiratory symptoms for data comparison. Among 116 patients (median age, 16 years; 60 males), rash (100%) was the most prevalent clinical symptom. The most common respiratory symptom and sign were fever (42.2%) and coarse breath sounds (41.4%). Chest computed tomography (CT) performed within five days of symptoms onset revealed multiple (89.7%), peripheral (51.7%), and ill-defined (73.3%) lung nodules in most patients, which gradually improved 6-10 days after symptom onset. Group 2 had higher levels of interleukin-6 (P<0.001), C-reactive protein (P=0.02), serum amyloid-A protein (P=0.002), longer hospital stays (P=0.04), more involved lung lobes (P=0.02), and a higher incidence of multiple nodules (P=0.043) than those of group 1. In immunocompetent adolescents, clustered varicella pneumonia often presents as mild and more uniform in clinical and radiological presentations than sporadic cases. The most common CT findings were multiple pulmonary nodules. Patients with lower respiratory symptoms exhibited more severe clinical and radiological manifestations. Generally, it is not recommended that patients undergo frequent CT scans in a short period.

Implementation of Lung Nodule Detection Model Based on Incremental Meta-Learning

In response to the issue that traditional lung nodule detection models cannot dynamically optimize and update with the increase of new data, a new lung nodule detection model-task incremental meta-learning model (TIMLM) is proposed. This model comprises of two loops: the inner loop imposes incremental learning regularization update constraints, while the outer loop employs a meta-update strategy to sample old and new knowledge and learn a set of generalized parameters that adapt to old and new data. Under the condition that the main structure of the model is not changed as much as possible, it preserves the old knowledge that was learned previously. Experimental verification on the publicly available lung dataset showed that, compared with traditional deep network models and mainstream incremental models, TIMLM has greatly improved in terms of accuracy, sensitivity, and other indicators, demonstrating good continuous learning and anti-forgetting capabilities.

Impact on Image Quality and Diagnostic Performance of Dual-Layer Detector Spectral CT for Pulmonary Subsolid Nodules: Comparison With Hybrid and Model-Based Iterative Reconstruction.

To evaluate the image quality and diagnostic performance of pulmonary subsolid nodules on conventional iterative algorithms, virtual monoenergetic images (VMIs), and electron density mapping (EDM) using a dual-layer detector spectral CT (DLSCT). This retrospective study recruited 270 patients who underwent DLSCT scan for lung nodule screening or follow-up. All CT examinations with subsolid nodules (pure ground-glass nodules [GGNs] or part-solid nodules) were reconstructed with hybrid and model-based iterative reconstruction, VMI at 40, 70, 100, and 130 keV levels, and EDM. The CT number, objective image noise, signal-to-noise ratio, contrast-to-noise ratio, diameter, and volume of subsolid nodules were measured for quantitative analysis. The overall image quality, image noise, visualization of nodules, artifact, and sharpness were subjectively rated by 2 thoracic radiologists on a 5-point scale (1 = unacceptable, 5 = excellent) in consensus. The objective image quality measurements, diameter, and volume were compared among the 7 groups with a repeated 1-way analysis of variance. The subjective scores were compared with Kruskal-Wallis test. A total of 198 subsolid nodules, including 179 pure GGNs, and 19 part-solid nodules were identified. Based on the objective analysis, EDM had the highest signal-to-noise ratio (164.71 ± 133.60; P < 0.001) and contrast-to-noise ratio (227.97 ± 161.96; P < 0.001) among all image sets. Furthermore, EDM had a superior mean subjective rating score (4.80 ± 0.42) for visualization of GGNs compared to other reconstructed images (all P < 0.001), although the model-based iterative reconstruction had superior subjective scores of overall image quality. For pure GGNs, the measured diameter and volume did not significantly differ among different reconstructions (both P > 0.05). EDM derived from DLSCT enabled improved image quality and lesion conspicuity for the evaluation of lung subsolid nodules compared to conventional iterative reconstruction algorithms and VMIs.

The value of net influx constant based on FDG PET/CT dynamic imaging in the differential diagnosis of metastatic from non-metastatic lymph nodes in lung cancer.

This study aims to evaluate the value of the dynamic and static quantitative metabolic parameters derived from 18F-fluorodeoxyglucose (FDG)-positron emission tomography/CT (PET/CT) in the differential diagnosis of metastatic from non-metastatic lymph nodes (LNs) in lung cancer and to validate them based on the results of a previous study. One hundred and twenty-one patients with lung nodules or masses detected on chest CT scan underwent 18F-FDG PET/CT dynamic + static imaging with informed consent. A retrospective collection of 126 LNs in 37 patients with lung cancer was pathologically confirmed. Static image analysis parameters include LN-SUVmax and LN-SUVmax/primary tumor SUVmax (LN-SUVmax/PT-SUVmax). Dynamic metabolic parameters including the net influx rate (Ki) and the surrogate of perfusion (K1) and of each LN were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. Ki/K1 was then calculated as a separate marker. Based on the pathological findings, we divided into a metastatic group and a non-metastatic group. The χ2 test was used to evaluate the agreement of the individual and combined diagnosis of each metabolic parameter with the gold standard. The receiver-operating characteristic (ROC) analysis was performed for each parameter to determine the diagnostic efficacy in differentiating non-metastatic from metastatic LNs with high FDG-avid. P < 0.05 was considered statistically significant. Among the 126 FDG-avid LNs confirmed by pathology, 70 LNs were metastatic, and 56 LNs were non-metastatic. For ROC analysis, in separate assays, the dynamic metabolic parameter Ki [sensitivity (SEN) of 84.30%, specificity (SPE) of 94.60%, accuracy of 88.89%, and AUC of 0.895] had a better diagnostic value than the static metabolic parameter SUVmax (SEN of 82.90%, SPE of 62.50%, accuracy of 74.60%, and AUC of 0.727) in differentiating between metastatic from non-metastatic LNs groups, respectively. In the combined diagnosis group, the combined SUVmax + Ki diagnosis had a better diagnostic value in the differential diagnosis of metastatic from non-metastatic LNs, with SEN, SPE, accuracy, and AUC of 84.3%, 94.6%, 88.89%, and 0.907, respectively. When the cutoff value of Ki was 0.022ml/g/min, it had a high diagnostic value in the differential diagnosis between metastasis and non-metastasis in FDG-avid LNs of lung cancer, especially in improving the specificity. The combination of SUVmax and Ki is expected to be a reliable metabolic parameter for N-staging of lung cancer.

Diagnosis and treatment of a patient with pulmonary infection caused by Emergomyces Orientalis: a case report.

Emergomycosis is a dimorphic fungal disease that is typically disseminated and fatal among immunocompromised individuals. In the case report, we presented a patient with intermittent fever, night sweats, coughingand phlegm. Chest computed tomography revealed multiple soft-tissue nodules in both lungs. Routine pathological and microbiological tests did not confirm the diagnosis. Therefore, we conducted pathogen detection using metagenomic next-generation sequencing in bronchoalveolar lavage fluid and identified the pulmonary infection caused by Emergomyces orientalis (Es. orientalis). During the antifungal treatment, the patient experienced renal function damage, and we have attempted various antifungal drugs for treatment. Finally, the patient's condition was brought under control. Therefore, the metagenomic next-generation sequencing pathogen detection was essential.