Diagnosis Of Pulmonary Nodules Research Articles

Elevated 68Ga-FAPI Activity in Pulmonary Inflammatory Pseudotumor.

We present a case of pulmonary inflammatory pseudotumor with elevated 68Ga-FAPI activity. Our case suggested that pulmonary inflammatory pseudotumor should be considered in the differential diagnosis of cancer-like solitary pulmonary nodules with increased 68Ga-FAPI uptake.

Value of peripheral blood rare cell EGFR gene amplification detection in the evaluation of benign and malignant pulmonary nodules

Objective: To explore the value of detection of epidermal growth factor receptor (EGFR) gene amplification in peripheral blood rare cells in the assessment of benign and malignant pulmonary nodules. Methods: A total of 262 patients with pulmonary nodules were selected as the retrospectively study subjects from the Second Affiliated Hospital of Army Military Medical University and Peking Union Medical College Hospital from July 2022 to August 2023. There were 98 males and 164 females, with the age range from 16 to 79 (52.1±12.1) years. The EGFR gene amplification testing was performed on the rare cells enriched from patients' peripheral blood, and the clinical manifestations, CT imaging features, histopathological and/or pathological cytological confirmed results of patients were collected. The receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of the method of detection of EGFR gene amplification in peripheral blood rare cells, and its diagnostic efficacy was evaluated. Results: Among the 262 patients, 143 were malignant pulmonary nodules and 119 were benign pulmonary nodules. The differences between malignant pulmonary nodules and benign pulmonary nodules in nodule diameter and nodule density were statistically significant (both P<0.001), while the differences in age, gender and nodule number were not statistically significant (all P>0.05). The number [M (Q1, Q3)] of EGFR gene amplification positive rare cells in patients with malignant pulmonary nodule was 8 (6, 11), which was higher than that in patients with benign pulmonary nodule [2 (1, 4), P<0.001]. The ROC curve results showed that when the optimal cut-off value was 5 (that was, the number of EGFR gene amplification positive rare cells was>5), the area under the curve (AUC) of the detection of EGFR gene amplification in peripheral blood rare cells for discrimination of benign and malignant pulmonary lesions was 0.816 (95%CI: 0.761-0.870), with a sensitivity of 83.2%, a specificity of 80.7%, and an accuracy of 82.1%. Based on the analysis of the diameter of the nodules, the AUC for distinguishing between benign and malignant pulmonary nodules with diameter 5-9 mm and 10-30 mm was 0.797 (95%CI: 0.707-0.887) and 0.809 (95%CI: 0.669-0.949), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule density, the AUC for distinguishing between benign and malignant solid nodule and subsolid nodule was 0.845 (95%CI: 0.751-0.939) and 0.790 (95%CI: 0.701-0.880), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule number, the AUC for distinguishing between benign and malignant solitary pulmonary nodule and multiple pulmonary nodule was 0.830 (95%CI: 0.696-0.965) and 0.817 (95%CI: 0.758-0.877), respectively, with sensitivity, specificity and accuracy reached 80% or above. Conclusion: The detection of EGFR gene amplification in peripheral blood rare cells contributes to the evaluation of benign and malignant pulmonary nodules, and can be used in the auxiliary diagnosis of benign and malignant pulmonary nodules.

Experts consensus on transbronchial diagnosis, localization and treatment of peripheral pulmonary nodules guided by the augmented reality optical lung navigation

Lung cancer is the second most common malignancy with the highest mortality rate worldwide. In recent years, the rapid development of various bronchoscopic navigation techniques has provided conditions for the minimally invasive diagnosis and treatment of peripheral pulmonary nodules through the airway.Augmented reality optical lung navigation is a new technology that combined virtual bronchoscopy navigation (VBN) with augmented reality (AR) and optical navigation technology, which could assist bronchoscopist and has been widely applied in clinics. The clinical evidence certified that the navigation, has the advantages of safety and efficacy in guiding transbronchial diagnosis, localization, and treatment of pulmonary nodules. In order to standardize the clinical operation of augmented reality optical lung navigation technology and guide its application in clinical practice, Interventional Group, Society of Respiratory Diseases, Chinese Medical Association/Interventional Pulmonology Group of the Zhejiang Medical Association organized multidisciplinary experts to take the lead in formulating the Consensus of experts on transbronchial diagnosis, localization and treatment of peripheral pulmonary nodules guided by the augmented reality optical lung navigation after multiple rounds of discussion, and provided recommendation opinions and clinical guidance for the indications and contraindications, equipment and devices, perioperative treatment, operating process and complication management of peripheral pulmonary nodules applicable to augmented reality optical lung diagnosis navigation technology.

Shape-Sensing Robotic-Assisted Bronchoscopy in the Multiple Pulmonary Nodules Diagnosis During a Single Anesthetic Event.

The widespread use of Computed tomography (CT) as a screening tool for early lung cancer has increased detection of pulmonary lesions. It is common to encounter patients with more than one peripheral pulmonary nodule (PPN) of uncertain etiology. Shape-sensing robotic-assisted bronchoscopy (ssRAB) emerges as a potential alternative to biopsy multiple PPN, in addition to mediastinal staging in single anesthetic procedure. Retrospective review of 22 patients who underwent ssRAB for evaluation of two or more PPN, between November 2021 and April 2023 at Mayo Clinic Florida, US. A total of 46 PPN were biopsied in 22 patients. All lesions were ≤ 2 cm with a maximum and minimum median lesion size of 1.40cm and 1.05cm, respectively. Diagnostic yield was 84.8% (n=39) and target reach was 91.3% (n=42). Most lesions were in the upper lobes, a solid pattern was found in 78.3% (n=36), bronchus sign was present in 82.6% of cases (n=38), 54.4% (n=25) were malignant nodules, and 30.4% (n=14) were benign. Fourteen patients had one malignant lesion out of two or more nodules sampled and ten patients had a malignant diagnosis for all sampled lesions. The complication rate was 9% (n=2) and pneumothorax rate was 4.5% (n=1). This study is, to our knowledge, the first to assess the use and safety of ssRAB for diagnosis of multiple PPN in a single anesthetic event. This procedure will mainly impact management decisions and subsequently shorten the time from diagnosis to treatment.

Saliva‑microbiome‑derived signatures: expected to become a potential biomarker for pulmonary nodules (MCEPN-1).

Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown. We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses. The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P < 0.001), but not with personal history of cancer (P > 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators. Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs. Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

Advancing Pulmonary Nodule Diagnosis by Integrating Engineered and Deep Features Extracted from CT Scans

Enhancing lung cancer diagnosis requires precise early detection methods. This study introduces an automated diagnostic system leveraging computed tomography (CT) scans for early lung cancer identification. The main approach is the integration of three distinct feature analyses: the novel 3D-Local Octal Pattern (LOP) descriptor for texture analysis, the 3D-Convolutional Neural Network (CNN) for extracting deep features, and geometric feature analysis to characterize pulmonary nodules. The 3D-LOP method innovatively captures nodule texture by analyzing the orientation and magnitude of voxel relationships, enabling the distinction of discriminative features. Simultaneously, the 3D-CNN extracts deep features from raw CT scans, providing comprehensive insights into nodule characteristics. Geometric features and assessing nodule shape further augment this analysis, offering a holistic view of potential malignancies. By amalgamating these analyses, our system employs a probability-based linear classifier to deliver a final diagnostic output. Validated on 822 Lung Image Database Consortium (LIDC) cases, the system’s performance was exceptional, with measures of 97.84%, 98.11%, 94.73%, and 0.9912 for accuracy, sensitivity, specificity, and Area Under the ROC Curve (AUC), respectively. These results highlight the system’s potential as a significant advancement in clinical diagnostics, offering a reliable, non-invasive tool for lung cancer detection that promises to improve patient outcomes through early diagnosis.

Clinical value of deep learning image reconstruction on the diagnosis of pulmonary nodule for ultra-low-dose chest CT imaging

Clinical value of deep learning image reconstruction on the diagnosis of pulmonary nodule for ultra-low-dose chest CT imaging

Abstract 877: Molecular and radiographic profiling of lung cancer in never-smokers

Abstract Gene expression changes in nasal and bronchial epithelium, and quantitative features from chest CT reflect the presence of lung cancer in current and former smokers and can serve as biomarkers. Between 10-20% of lung cancer cases are diagnosed in patients who have never smoked (&amp;lt;100 lifetime cigarettes) and biomarkers could assist the diagnosis of pulmonary nodules discovered incidentally in this population. We compared lung cancer-associated gene expression and radiomic features in never-smokers and ever-smokers. Nasal epithelial RNA was isolated and sequenced from brushings collected at UCLA from 73 patients presenting to interventional radiology for biopsy for suspected lung cancer. Limma was used to identify genes with lung cancer-associated expression in never-smokers by comparing 13 patients with benign nodules with 14 patients diagnosed with lung cancer. Similarities between cancer-associated genes in never-smokers and those previously identified in ever-smokers were assessed by gene set enrichment analysis (GSEA). Next, using all 73 samples, genes whose lung cancer-associated expression is modified by smoking status were identified via an interaction term. Radiomic features for 46 patients (31 ever-smokers, 15 never-smokers) were extracted using PyRadiomics, including features from the interior of the nodule and perinodular features generated 10, 15, and 20 mm away from the boundary of the nodule. A binomial model was used to identify features with a significant interaction effect between cancer and smoking status. We identified 74 genes decreased and 84 genes increased in cancer in never-smokers (p &amp;lt; 0.005), controlling for age and median TIN. Genes changed in ever-smoker lung cancer patients were significantly enriched among the genes most associated with lung cancer in never smokers (GSEA; p = 0.0059 and p = 4.38e-15). Next, there were 51 genes with a p-value &amp;lt; 0.01 for the cancer*smoking interaction term. Lastly, four radiomic features were associated with the perinodular term for cancer and smoking status (FDR&amp;lt; 0.2), including two nodular and two boundary features. These results suggest that lung cancer-associated gene expression differences in ever-smokers are preserved in never-smokers, suggesting the potential relevance of previously derived biomarkers for this population. There may also be never-smoker-specific gene expression patterns associated with lung cancer. Our radiomic findings suggest that both nodular and perinodular characteristics may be important predictors of malignancy. Citation Format: Minyi Lee, Anil Yadav, Gang Liu, Steven Dubinett, Jennifer Beane, William Hsu, Ashley Prosper, Denise Aberle, Marc Lenburg. Molecular and radiographic profiling of lung cancer in never-smokers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 877.

Virtual bronchoscopic navigation with intraoperative cone-beam CT for the diagnosis of peripheral pulmonary nodules

ObjectiveTransbronchial biopsy is a safe manner with fewer complications than percutaneous transthoracic needle biopsy; however, the current diagnostic yield is still necessitating further improvement. We aimed to evaluate the diagnostic yield of using virtual bronchoscopic navigation (VBN) and cone-beam CT (CBCT) for transbronchial biopsy and to investigate the factors that affected the diagnostic sensitivity.MethodsWe retrospectively investigated 255 patients who underwent VBN-CBCT-guided transbronchial biopsy at our two centers from May 2021 to April 2022. A total of 228 patients with final diagnoses were studied. Patient characteristics including lesion size, lesion location, presence of bronchus sign, lesion type and imaging tool used were collected and analyzed. Diagnostic yield was reported overall and in groups using different imaging tools.ResultsThe median size of lesion was 21 mm (range of 15.5–29 mm) with 46.1% less than 2 cm in diameter. Bronchus sign was present in 87.7% of the patients. The overall diagnostic yield was 82.1%, and sensitivity for malignancy was 66.3%. Patients with lesion > 2 cm or with bronchus sign were shown to have a significantly higher diagnostic yield. Four patients had bleeding and no pneumothorax occurred.ConclusionGuided bronchoscopy with VBN and CBCT was an effective diagnostic method and was associated with a high diagnostic yield in a safe manner. In addition, the multivariant analysis suggested that lesion size and presence of bronchus sign could be a predictive factor for successful bronchoscopic diagnosis.

Robotic Bronchoscopy in Lung Cancer Diagnosis.

The widespread use of chest CT has increased the number of detected pulmonary nodules. Nodules with intermediate risk of malignancy warrant further evaluation with PET-CT or sampling. Although sampling with conventional bronchoscopy presents lower complication rates compared to transthoracic needle biopsy (TTNB), it is limited by the inability to reach distal airways. To overcome this shortcoming, a new bronchoscopic technique named robotic bronchoscopy (RB) has emerged. A literature review was used to clarify the rationale behind RB emergence, describe RB procedure, and summarize data regarding its efficacy and safety. The FDA has approved three RB platforms for clinical use. RB is safe, presenting a mortality and complication rate of 0% and 0-8.1%, respectively. Common complications include pneumothorax (0-5.7%) and minor bleeding (0-3.2%). However, its diagnostic yield remains lower than that of TTNB. RB is a promising bronchoscopic technique that aims to overcome the limitations of conventional bronchoscopy and improve upon the current techniques of guided bronchoscopy for the investigation of pulmonary nodules. Despite the lower complication rate, current evidence suggests a lower diagnostic yield compared to TTNB. Additional studies are required to adequately evaluate the role of RB in the diagnosis of pulmonary nodules.

A case of lung FNA with atypical squamous cells.

Fine-needle aspiration (FNA) is a commonly employed method for initial diagnosis and work-up of pulmonary nodules. Utilization of rapid on-site evaluation (ROSE) has the added benefit of allowing for triaging of material as appropriate for ancillary studies including microbiology testing. While many pulmonary lesions are easily categorized by cytologic evaluation, more difficult cases exist. In particular, pulmonary lesions demonstrating atypical squamoid cells can cause diagnostic challenge given the morphologic overlap between benign and malignant pulmonary lesions showing atypical squamoid cells. We herein review these common and uncommon squamoid lesions, which may enter within the differential when encountering atypical squamoid cells in pulmonary FNA specimens with emphasis on morphologic pitfalls and approaches to appropriate categorization.

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules.

The benign and malignant status of solitary pulmonary nodules (SPNs) is a key determinant of treatment decisions. The main objective of this study was to validate the efficacy of machine learning (ML) models featured with dual-layer detector spectral computed tomography (DLCT) parameters in identifying the benign and malignant status of SPNs. 250 patients with pathologically confirmed SPN were included in this study. 8 quantitative and 16 derived parameters were obtained based on the regions of interest of the lesions on the patients' DLCT chest enhancement images. 6 ML models were constructed from 10 parameters selected after combining the patients' clinical parameters, including gender, age, and smoking history. The logistic regression model showed the best diagnostic performance with an area under the receiver operating characteristic curve (AUC) of 0.812, accuracy of 0.813, sensitivity of 0.750 and specificity of 0.791 on the test set. The results suggest that the ML models based on DLCT parameters are superior to the traditional CT parameter models in identifying the benign and malignant nature of SPNs, and have greater potential for application.

Chinese expert consensus on cone-beam CT-guided diagnosis, localization and treatment for pulmonary nodules.

Cone-beam computed tomography (CBCT) system can provide real-time 3D images and fluoroscopy images of the region of interest during the operation. Some systems can even offer augmented fluoroscopy and puncture guidance. The use of CBCT for interventional pulmonary procedures has grown significantly in recent years, and numerous clinical studies have confirmed the technology's efficacy and safety in the diagnosis, localization, and treatment of pulmonary nodules. In order to optimize and standardize the technical specifications of CBCT and guide its application in clinical practice, the consensus statement has been organized and written in a collaborative effort by the Professional Committee on Interventional Pulmonology of China Association for Promotion of Health Science and Technology.

Diagnostic yield using electromagnetic navigation bronchoscopy for peripheral pulmonary nodules

Although electromagnetic navigation bronchoscopy (ENB) is highly sensitive in the diagnosis of peripheral pulmonary nodules (PPNs), its diagnostic yield for subgroups of smaller PPNs is under evaluation. Diagnostic yield evaluation of biopsy using ENB for PPNs <2 cm. The diagnostic yield, sensitivity, specificity, positive predictive value, and negative predictive value of the ENB-mediated biopsy for PPNs were evaluated. Patients who had PPNs with diameters <2 cm and underwent ENB-mediated biopsy between May 2015 and February 2020 were consecutively enrolled. The final diagnosis was made via pathological examination after surgery. A total of 82 lesions from 65 patients were analyzed. The median tumor size was 11 mm. All lesions were subjected to ENB-mediated biopsy, of which 29 and 53 were classified as malignant and benign, respectively. Subsequent segmentectomy, lobectomy, or wedge resection, following pathological examinations were performed on 64 nodules from 57 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value for nodules <2 cm were 53.3%, 91.7%, 92.3%, and 51.2%, respectively. The receiver operating curve showed an area under the curve of 0.721 (p < 0.001). Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value were 62.5%, 100%, 100%, and 42.9%, respectively, for nodules with diameters equal to or larger than 1 cm; and 30.8%, 86.7%, 66.7%, and 59.1%, respectively, for nodules less than 1 cm. In the subgroup analysis, neither the lobar location nor the distance of the PPNs to the pleura affected the accuracy of the ENB diagnosis. However, the spiculated sign had a negative impact on the accuracy of the ENB biopsy (p = 0.010). ENB has good specificity and positive predictive value for diagnosing PPNs <2 cm; however, the spiculated sign may negatively affect ENB diagnostic accuracy. In addition, the diagnostic reliability may only be limited to PPNs equal to or larger than 1 cm.

ADGAN: Attribute-Driven Generative Adversarial Network for Synthesis and Multiclass Classification of Pulmonary Nodules.

Lung cancer is the leading cause of cancer-related deaths worldwide. According to the American Cancer Society, early diagnosis of pulmonary nodules in computed tomography (CT) scans can improve the five-year survival rate up to 70% with proper treatment planning. In this article, we propose an attribute-driven Generative Adversarial Network (ADGAN) for synthesis and multiclass classification of Pulmonary Nodules. A self-attention U-Net (SaUN) architecture is proposed to improve the generation mechanism of the network. The generator is designed with two modules, namely, self-attention attribute module (SaAM) and a self-attention spatial module (SaSM). SaAM generates a nodule image based on given attributes whereas SaSM specifies the nodule region of the input image to be altered. A reconstruction loss along with an attention localization loss (AL) is used to produce an attention map prioritizing the nodule regions. To avoid resemblance between a generated image and a real image, we further introduce an adversarial loss containing a regularization term based on KL divergence. The discriminator part of the proposed model is designed to achieve the multiclass nodule classification task. Our proposed approach is validated over two challenging publicly available datasets, namely LIDC-IDRI and LUNGX. Exhaustive experimentation on these two datasets clearly indicate that we have achieved promising classification accuracy as compared to other state-of-the-art methods.

Robust explanation supervision for false positive reduction in pulmonary nodule detection.

Lung cancer is the deadliest and second most common cancer in the United States due to the lack of symptoms for early diagnosis. Pulmonary nodules are small abnormal regions that can be potentially correlated to the occurrence of lung cancer. Early detection of these nodules is critical because it can significantly improve the patient's survival rates. Thoracic thin-sliced computed tomography (CT) scanning has emerged as a widely used method for diagnosing and prognosis lung abnormalities. The standard clinical workflow of detecting pulmonary nodules relies on radiologists to analyze CT images to assess the risk factors of cancerous nodules. However, this approach can be error-prone due to the various nodule formation causes, such as pollutants and infections. Deep learning (DL) algorithms have recently demonstrated remarkable success in medical image classification and segmentation. As an ever more important assistant to radiologists in nodule detection, it is imperative ensure the DL algorithm and radiologist to better understand the decisions from each other. This study aims to develop a framework integrating explainable AI methods to achieve accurate pulmonary nodule detection. A robust and explainable detection (RXD) framework is proposed, focusing on reducing false positives in pulmonary nodule detection. Its implementation is based on an explanation supervision method, which uses nodule contours of radiologists as supervision signals to force the model to learn nodule morphologies, enabling improved learning ability on small dataset, and enable small dataset learning ability. In addition, two imputation methods are applied to the nodule region annotations to reduce the noise within human annotations and allow the model to have robust attributions that meet human expectations. The 480, 265, and 265 CT image sets from the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset are used for training, validation, and testing. Using only 10, 30, 50, and 100 training samples sequentially, our method constantly improves the classification performance and explanation quality of baseline in terms of Area Under the Curve (AUC) and Intersection over Union (IoU). In particular, our framework with a learnable imputation kernel improves IoU from baseline by 24.0% to 80.0%. A pre-defined Gaussian imputation kernel achieves an even greater improvement, from 38.4% to 118.8% from baseline. Compared to the baseline trained on 100 samples, our method shows less drop in AUC when trained on fewer samples. A comprehensive comparison of interpretability shows that our method aligns better with expert opinions. A pulmonary nodule detection framework was demonstrated using public thoracic CT image datasets. The framework integrates the robust explanation supervision (RES) technique to ensure the performance of nodule classification and morphology. The method can reduce the workload of radiologists and enable them to focus on the diagnosis and prognosis of the potential cancerous pulmonary nodules at the early stage to improve the outcomes for lung cancer patients.

Differential Diagnosis of Benign and Malignant Pulmonary Nodules in CT Images Based on Multitask Learning.

Artificial intelligence-based aided diagnostic systems for pulmonary nodules can be divided into subtasks such as nodule detection, segmentation, and benign and malignant differentiation. Most current studies are limited to single-target tasks. However, aided diagnosis aims to distinguish benign from malignant pulmonary nodules, which requires the fusion of multiple-scale features and comprehensive discrimination based on the results of multiple learning tasks. This study focuses on the aspects of model design, network structure, and constraints and proposes a novel model that integrates the learning tasks of pulmonary nodule detection, segmentation, and classification under weakly supervised conditions. The main innovations include the following three aspects: (1) a two-dimensional sequence detection model based on a ConvLSTM (Convolutional Long Short-Term Memory) network and U-shaped structure network is proposed to obtain the context space features of image slices fully; (2) a differential diagnosis of benign and malignant pulmonary nodules based on multitask learning is proposed, which uses the annotated data of different types of tasks to mine the potential common features among tasks; and (3) an optimization strategy incorporating prior knowledge of computed tomography images and dynamic weight adjustment of multiple tasks is proposed to ensure that each task can efficiently complete training and learning. Experiments on the LIDC-IDRI and LUNA16 datasets showed that our proposed method achieved a final competition performance metric score of 87.80% for nodule detection and a Dice similarity coefficient score of 83.95% for pulmonary nodule segmentation. The cross-validation results of the LIDC-IDRI and LUNA16 datasets show that our model achieved 87.80% of the final competition performance metric score for nodule detection and 83.95% of the DSC score for pulmonary nodule segmentation, representing the optimal result for that dataset.

The Diagnostic Yield of Cone Beam Computed Tomography Combined With Radial-Endobronchial Ultrasound for the Diagnosis of Peripheral Pulmonary Nodules: Systematic Review and Meta-analysis

BackgroundIdentification of peripheral pulmonary nodules (PPNs) are becoming increasingly common with modern imaging and lung cancer screening programs. Navigational bronchoscopy has been developed to augment the diagnostic yield of sampling these nodules. Cone beam computed tomography (CBCT) is one navigational tool which can be used alongside the historical gold standard of fluoroscopy and radial endobronchial ultrasound (r-EBUS). Research questionsTo assess the diagnostic yield and safety profile of combining CBCT with r-EBUS for the diagnosis of PPNs. Study design and methodsEmbase, PubMed and Cochrane Central were searched in March 2023. Eligible studies used CBCT with r-EBUS as the primary navigation technique. The primary outcome; diagnostic yield, was analysed using random effects meta-analysis. Additional subgroup analysis was based on the use of additional navigational technologies. Risk of bias was assessed using the Critical Appraisal Skills programme (CASP) tool for diagnostic studies. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool was used to assess the quality of outcomes. ResultsFourteen studies (n=865 patients, n=882 lesions) were included. The risk of bias was significant as assessed using the CASP tool, which identified multiple confounders. The pooled diagnostic yield of combined CBCT and r-EBUS guided biopsy for the diagnosis of PPNs was 80% (95%CI 76-84%). Subgroup analysis of diagnostic yield for CBCT and r-EBUS alone was 80% (95%CI 76-83%). The diagnostic yield of CBCT, r-EBUS combined with additional navigational technology (electromagnetic navigational bronchoscopy, virtual bronchoscopic navigation and robotic assisted bronchoscopy) was 80% (95%CI 73-87%). The quality of outcomes was assessed as low to very low using the GRADE tool. There was a 2.01% pneumothorax rate and 1.08% bleeding rate. Although heterogeneously reported, total radiation dose was between 19.59Gycm2 and 85.9Gycm2 resulting in an approximate effective dose range of 3.1 to 13.8mSv InterpretationCBCT and r-EBUS for the diagnosis of PPN’s has a high diagnostic yield and acceptable safety profile. Studies showed moderate heterogeneity with significant bias and hence generalizability of the study is limited and further prospective trials are required. Trial RegistrationProspero CRD42023410221

Combining Low-Dose Computer-Tomography-Based Radiomics and Serum Metabolomics for Diagnosis of Malignant Nodules in Participants of Lung Cancer Screening Studies.

Radiomics is an emerging approach to support the diagnosis of pulmonary nodules detected via low-dose computed tomography lung cancer screening. Serum metabolome is a promising source of auxiliary biomarkers that could help enhance the precision of lung cancer diagnosis in CT-based screening. Thus, we aimed to verify whether the combination of these two techniques, which provides local/morphological and systemic/molecular features of disease at the same time, increases the performance of lung cancer classification models. The collected cohort consists of 1086 patients with radiomic and 246 patients with serum metabolomic evaluations. Different machine learning techniques, i.e., random forest and logistic regression were applied for each omics. Next, model predictions were combined with various integration methods to create a final model. The best single omics models were characterized by an AUC of 83% in radiomics and 60% in serum metabolomics. The model integration only slightly increased the performance of the combined model (AUC equal to 85%), which was not statistically significant. We concluded that radiomics itself has a good ability to discriminate lung cancer from benign lesions. However, additional research is needed to test whether its combination with other molecular assessments would further improve the diagnosis of screening-detected lung nodules.

Diagnosis of Malignant Pulmonary Nodules Using a Combination of Tumor-associated Autoantibodies and Computed Tomography.

Diagnosis of malignant pulmonary nodules can greatly reduce the occurrence of lung cancer death, and computed tomography (CT) is commonly used in diagnosis. In addition, tumor-associated autoantibodies (TAAbs) show high specificity and stability. We aim to establish a computable risk model of pulmonary nodules by combining CT with TAAb detection. The concentrations of 7 TAAbs (p53, PGP9.5, SOX2, GAGE7, GBU4-5, CAGE, MAGEA1, and CAGE) were assayed using the enzyme-linked immunosorbent assay in 136 patients with pulmonary nodules (84 with newly diagnosed lung adenocarcinoma, 21 with squamous cell carcinoma, and 31 with benign nodules) and 42 control subjects without pulmonary nodules. We then drew receiver operating characteristic curves and conducted logistic regression to analyze the diagnostic efficiency of our method in the detection of lung cancer. The positivity rate of the 7 TAAbs was 49.5%, and the specificity was 83.6%. Our regression results indicated 65% overall accuracy, 44.76% sensitivity, and 76.71% specificity. Notably, when combined with CT imaging and the demographic characteristics, diagnostic accuracy increased to 73.4%, sensitivity to 61.5%, and specificity to 87.1%. The positive predictive value and negative predictive value were 93% and 41%, respectively. Our study provides a method that combines 7 serum TAAbs with imaging and demographic characteristics to diagnose malignant pulmonary nodules more accurately than existing methods.