Management of incidental pulmonary nodules in CT examination.

Lung cancer is the leading cause of cancer-related death in Denmark and the world. The increase in CT examinations has led to an increase in detection of pulmonary nodules divided into solid and subsolid (including ground glass and part solid). Risk factors for malignancy include age, smoking, female gender, and specific ethnicities. Nodule traits like size, spiculation, upper-lobe location, and emphysema correlate with higher malignancy risk. Managing these potentially malignant nodules relies on evidence-based guidelines and risk stratification. These risk stratification models can standardize the approach for the management of incidental pulmonary findings, as argued in this review.

Decreased levels of sex hormones in females with solitary pulmonary nodules were risk factors for malignancy.

OBJECTIVE: The purpose of this research was to detect the relationship between the levels of sex hormones in females with solitary pulmonary nodules (SPNs) and their potential malignancies. METHODS: A total of 187 consecutive patients with pathologically confirmed SPNs by chest CT were enrolled in our study. They were divided into two groups based on the pathologic findings of SPNs after surgery: benign and malignant SPNs. Progesterone (P), estradiol (E2), and testosterone (T) levels in the two groups were measured. Meanwhile, we used binary logistic regression analysis to analyze the risk factors for SPNs. RESULTS: Of these 187 patients, 73 had benign SPNs, while 114 had malignant SPNs. We found that the levels of progesterone (P), estradiol (E2), and testosterone (T) were decreased significantly in patients with malignant SPNs compared to patients with benign SPNs (all P < 0.05). Multivariate logistic regression analysis revealed that second-hand smoke, burr sign, lobulation sign, pleural traction sign, vascular convergence sign, vacuole sign, and ≥ 1 cm nodules were independent risk factors for malignant pulmonary nodules in females. CONCLUSIONS: Decreased levels of sex hormones in females were associated with malignant pulmonary nodules, suggesting that they can contribute to the diagnosis of lung cancer.

Intelligent diagnosis of lung nodule images based on machine learning in the context of lung teaching.

The vast majority of intelligent diagnosis models have widespread problems, which seriously affect the medical staff judgment of patients' injuries. So depending on the situation, you need to use different algorithms, The study suggests a model for intelligent diagnosis of lung nodule images based on machine learning, and a support vector machine-based machine learning algorithm is selected. In order to improve the diagnostic accuracy of intelligent diagnosis of lung nodule images as well as the diagnostic model of lung nodule images. The objectives are broken down into algorithm determination and model construction, and the proposed optimized model is solved using machine learning techniques in order to achieve the original algorithm selected for intelligent diagnosis of lung nodule photos. The validation findings demonstrated that dimensionality reduction of the features produced 17 × 1120 and 17 × 2980 non-node matrices with 1216 nodes and 3407 non-nodes in 17 features. The support vector machine classification method has more benefits in terms of accuracy, sensitivity, and specificity when compared to other classification methods. Since there were some anomalies among both benign and malignant tumors and no discernible difference between them, the distribution of median values revealed that the data was symmetrical in terms of texture and gray scale. Non-small nodules can be identified from benign nodules, but more training is needed to separate them from the other 2 types. Pulmonary nodules are a common disease. MN are distinct from the other 2 types, non-small nodules and benign small nodules, which require further training to differentiate. This has great practical value in teaching practice. Therefore, building a machine learning-based intelligent diagnostic model for pulmonary nodules is of significant importance in helping to solve medical imaging diagnostic problems.

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.

Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans.

BACKGROUND: Chest Computed tomography (CT) scans detect lung nodules and assess pulmonary fibrosis. While pulmonary fibrosis indicates increased lung cancer risk, current clinical practice characterizes nodule risk of malignancy based on nodule size and smoking history; little consideration is given to the fibrotic microenvironment. PURPOSE: To evaluate the effect of incorporating fibrotic microenvironment into classifying malignancy of lung nodules in chest CT images using deep learning techniques. MATERIALS AND METHODS: We developed a visualizable 3D classification model trained with in-house CT dataset for the nodule malignancy classification task. Three slightly-modified datasets were created: (1) nodule alone (microenvironment removed); (2) nodule with surrounding lung microenvironment; and (3) nodule in microenvironment with semantic fibrosis metadata. For each of the models, tenfold cross-validation was performed. Results were evaluated using quantitative measures, such as accuracy, sensitivity, specificity, and area-under-curve (AUC), as well as qualitative assessments, such as attention maps and class activation maps (CAM). RESULTS: The classification model trained with nodule alone achieved 75.61% accuracy, 50.00% sensitivity, 88.46% specificity, and 0.78 AUC; the model trained with nodule and microenvironment achieved 79.03% accuracy, 65.46% sensitivity, 85.86% specificity, and 0.84 AUC. The model trained with additional semantic fibrosis metadata achieved 80.84% accuracy, 74.67% sensitivity, 84.95% specificity, and 0.89 AUC. Our visual evaluation of attention maps and CAM suggested that both the nodules and the microenvironment contributed to the task. CONCLUSION: The nodule malignancy classification performance was found to be improving with microenvironment data. Further improvement was found when incorporating semantic fibrosis information.

Comparison of the safety and effectiveness of the four-hook needle and hook wire for the preoperative positioning of localization ground glass nodules.

BACKGROUND: With the implementation of lung cancer screening programs, an increasing number of pulmonary nodules have been detected.Video-assisted thoracoscopic surgery (VATS) could provide adequate tissue specimens for pathological analysis, and has few postoperative complications.However, locating the nodules intraoperatively by palpation can be difficult for thoracic surgeons. The preoperative pulmonary nodule localization technique is a very effective method.We compared the safety and effectiveness of two methods for the preoperative localization of pulmonary ground glass nodules. METHODS: From October 2020 to April 2021, 133 patients who underwent CT-guided single pulmonary nodule localization were retrospectively reviewed. All patients underwent video-assisted thoracoscopic surgery (VATS) after successful localization. Statistical analysis was used to evaluate the localization accuracy, safety, information related to surgery and postoperative pathology information. The aim of this study was to evaluate the clinical effects of the two localization needles. RESULTS: The mean maximal transverse nodule diameters in the four-hook needle and hook wire groups were 8.97 ± 3.85 mm and 9.00 ± 3.19 mm, respectively (P = 0.967). The localization times in the four-hook needle and hook wire groups were 20.58 ± 2.65 min and 21.43 ± 3.06 min, respectively (P = 0.09). The dislodgement rate was significantly higher in the hook wire group than in the four-hook needle group (7.46% vs. 0, P = 0.024). The mean patient pain scores based on the visual analog scale in the four-hook needle and hook wire groups were 2.87 ± 0.67 and 6.10 ± 2.39, respectively (P = 0.000). All ground glass nodules (GGNs) were successfully resected by VATS. CONCLUSIONS: Preoperative pulmonary nodule localization with both a four-hook needle and hook wire is safe, convenient and effective.

Two-stage minimally invasive pulmonary resections with intraoperative localization technique for bilateral multiple primary lung cancers: A case report.

Multiple primary lung cancers (MPLCs) are becoming more and more common and these patients can benefit from minimally invasive surgery. Here, we report a case of a patient diagnosed with synchronous MPLCs who underwent bilateral thoracoscopic pulmonary resections in a two-stage strategy, and achieved a good surgical outcome and high quality of life. A 66-year-old female was found to have one major ground-glass nodule (GGN) in the right upper lobe and eight minor GGNs in the left upper and lower lobes. The patient underwent right upper lobe resection and systematic mediastinal lymph node dissection via single-utility port thoracoscopic surgery in September 2018. Pathology was lepidic predominant adenocarcinoma pT1bN0M0, IA2. Regular high-resolution computed tomography examination during 36 months after right upper lobectomy showed gradually increasing diameter and solid component of multiple GGNs in left lung. The patient underwent thoracoscopic multiple pulmonary resections using an intraoperative localization technique in a hybrid operating room in October 2021 and all eight nodules in the left lung were resected. Two segmentectomies and four wedge resections were performed, and the pathological results of the eight nodules included four adenocarcinomas, three adenocarcinomas in situ, and one alveolar epithelial hyperplasia. The two operations were successful with no intra- or postoperative 90-day complications. During more than 20 months of follow-up after the second operation, the patient had well recovered pulmonary function and physical status with a Karnofsky performance status score of 90 and no local recurrence or metastasis. A two-stage surgical strategy for synchronous MPLCs is therefore feasible. The surgical strategy, timing of intervention, and extent of pulmonary resection should be individually designed according to the location and characteristics of each nodule. Intraoperative localization of small GGNs is very important to ensure that all nodules are completely and accurately resected during the operation.

Intravascular Large B-cell Lymphoma Presenting as Pulmonary Ground-glass Nodules That Progressed Slowly over Several Months with No Overt Symptoms.

A 74-year-old man with no overt symptoms was referred for a chest computed tomography (CT) that revealed multiple bilaterally pulmonary ground-glass nodules (GGNs) with subtle changes in size over eight months. Surgical lung biopsies were performed in the left upper lobe. A pathologic study confirmed the intravascular large B-cell lymphoma (IVLBCL). This lesion was a nodule-like cluster of atypical cells, meaning that it had been localized for several months. Pulmonary IVLBCL may form focal lesions presenting as GGN on chest CT and progress slowly without apparent symptoms.

Randomized comparison of the four-hook anchor device and hook-wire use for the preoperative localization of pulmonary nodules.

OBJECTIVE: To compare the efficacy and safety of preoperative localization of small pulmonary nodules (SPNs) with 4-hook anchor device and hook-wire before video-assisted thoracoscopic surgery. METHODS: Patients with SPNs scheduled for computed tomography-guided nodule localization before video-assisted thoracoscopic surgery between May 2021 and June 2021 at our center were randomized to either 4-hook anchor group or hook-wire group. The primary end point was intraoperative localization success. RESULTS: After randomization, 28 patients with 34 SPNs were assigned to the 4-hook anchor group and 28 patients with 34 SPNs to the hook-wire group. The operative localization success rate was significantly greater in the 4-hook anchor group than in the hook-wire group (94.1% [32/34] vs 64.7% [22/34]; P = .007). All lesions in the 2 groups were successfully resected under thoracoscopy, but 4 patients in the hook-wire group who required transition from wedge resection to segmentectomy or lobectomy because of unsuccessful localization. Total localization-related complication rate was significantly lower in the 4-hook anchor group than in the hook-wire group (10.3% [3/28] vs 50.0% [14/28]; P = .004). The rate of chest pain requiring analgesia after the localization procedure was significantly lower in the 4-hook anchor group than in the hook-wire group (0 vs 5/28, 17.9%; P = .026). There were no significant differences in localization technical success rate, operative blood loss, hospital stay length and hospital cost between the 2 groups (all P > .05). CONCLUSIONS: The use of the 4-hook anchor device for SPN localization offers advantages over the traditional hook-wire technique.

A clinical-radiological predictive model for solitary pulmonary nodules and the relationship between radiological features and pathological subtype.

AIM: To develop a clinical-radiological model to predict the malignancy of solitary pulmonary nodules (SPNs) and to evaluate the accuracy of chest computed tomography imaging characteristics of SPN in diagnosing pathological type. MATERIALS AND METHODS: The predictive model was developed using a retrospective cohort of 601 SPN patients (Group A) between July 2015 and July 2020. The established model was tested using a second retrospective cohort of 124 patients between August 2020 and August 2021 (Group B). The radiological characteristics of all adenocarcinomas in two groups were analysed to determine the correlation between radiological and pathological characteristics. RESULTS: Malignant nodules were found in 78.87% of cases and benign in 21.13%. Two clinical characteristics (age and gender) and four radiological characteristics (calcification, vascular convergence, pleural retraction sign, and density) were identified as independent predictors of malignancy in patients with SPN using logistic regression analysis. The area under the receiver operating characteristic curve (0.748) of the present model was greater than the other two reported models. Diameter, spiculation, lobulation, vascular convergence, and pleural retraction signs differed significantly among pre-invasive lesions, minimally invasive adenocarcinoma, and invasive adenocarcinoma. Only diameter and density were significantly different among invasive adenocarcinoma subtypes. CONCLUSIONS: Older age, male gender, no calcification, vascular convergence, pleural contraction sign, and lower density were independent malignancy predictors of SPNs. Furthermore, the pathological classification can be clarified based on the radiological characteristics of SPN, providing a new option for the prevention and treatment of early lung cancer.

Pulmonary sclerosing pneumocytoma - approaching a solitary pulmonary nodule and the limitations of risk prediction models.

Our case is an asymptomatic, non-smoking, East Asian woman in her 40s presenting with a solitary pulmonary nodule (SPN). On imaging, the 1.7 cm solid SPN located in the left upper lobe, was rounded in morphology and moderately fluorodeoxyglucose avid. The clinical pretest probability of malignancy assessed by risk prediction models such as Brock (19.1%), Mayo Clinic (56.2%) and Herder (51.4%) was discordant. She underwent a percutaneous CT-guided needle biopsy, establishing a diagnosis of pulmonary sclerosing pneumocytoma (PSP). PSP is a rare benign lung neoplasm with indolent growth characteristics that has been described predominantly in non-smoking women. Our case illustrates the limitations of applying existing risk prediction models in Asia where the epidemiology and biology of lung cancer differ significantly from the Caucasian derivation cohorts. Additionally, the risk models do not account for tuberculosis, which is endemic in Asia and can mimic malignancy. Non-surgical lung biopsy remains useful in minimising unnecessary thoracotomy.

A novel fusion algorithm for benign-malignant lung nodule classification on CT images.

The accurate recognition of malignant lung nodules on CT images is critical in lung cancer screening, which can offer patients the best chance of cure and significant reductions in mortality from lung cancer. Convolutional Neural Network (CNN) has been proven as a powerful method in medical image analysis. Radiomics which is believed to be of interest based on expert opinion can describe high-throughput extraction from CT images. Graph Convolutional Network explores the global context and makes the inference on both graph node features and relational structures. In this paper, we propose a novel fusion algorithm, RGD, for benign-malignant lung nodule classification by incorporating Radiomics study and Graph learning into the multiple Deep CNNs to form a more complete and distinctive feature representation, and ensemble the predictions for robust decision-making. The proposed method was conducted on the publicly available LIDC-IDRI dataset in a 10-fold cross-validation experiment and it obtained an average accuracy of 93.25%, a sensitivity of 89.22%, a specificity of 95.82%, precision of 92.46%, F1 Score of 0.9114 and AUC of 0.9629. Experimental results illustrate that the RGD model achieves superior performance compared with the state-of-the-art methods. Moreover, the effectiveness of the fusion strategy has been confirmed by extensive ablation studies. In the future, the proposed model which performs well on the pulmonary nodule classification on CT images will be applied to increase confidence in the clinical diagnosis of lung cancer.

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules.

For patients with early multiple pulmonary nodules, it is essential, from a diagnostic perspective, to determine the spatial distribution, size, location, and relationship with surrounding lung tissue of these nodules throughout the entire lung. This is crucial for identifying the primary lesion and developing more scientifically grounded treatment plans for doctors. However, pattern recognition methods based on machine vision are susceptible to false positives and false negatives and, therefore, cannot fully meet clinical demands in this regard. Visualization methods based on maximum intensity projection (MIP) can better illustrate local and individual pulmonary nodules but lack a macroscopic and holistic description of the distribution and spatial features of multiple pulmonary nodules. Therefore, this study proposes a whole-lung 3D reconstruction method. It extracts the 3D contour of the lung using medical image processing technology against the background of the entire lung and performs 3D reconstruction of the lung, pulmonary artery, and multiple pulmonary nodules in 3D space. This method can comprehensively depict the spatial distribution and radiological features of multiple nodules throughout the entire lung, providing a simple and convenient means of evaluating the diagnosis and prognosis of multiple pulmonary nodules.

Diagnostic Accuracy of CT-Guided Percutaneous Pulmonary Biopsy for Distinguishing Benign and Malignant Solitary Pulmonary Nodules.

Background: Isolated pulmonary nodules (SPNs) are small, circular lesions within lung tissue, often challenging to diagnose due to their size and lack of typical imaging features. Timely diagnosis is crucial for treatment decisions. However, the difficulty in qualitative diagnosis necessitates clinical biopsies. Objective: This study aimed to assess the diagnostic accuracy of CT-guided percutaneous lung biopsy for SPNs and identify potential risk factors for malignancy. Methods: We conducted a retrospective analysis of 112 patients with SPNs who underwent CT-guided core needle biopsy (CT-CNB) between June 2020 and June 2022. Histological and cytological results were obtained for all patients, and clinical data and imaging characteristics were compared between benign and malignant SPN groups. Binary logistic regression was used to analyze risk factors for malignancy, and complications were observed. Results: Cytological and histological specimens were successfully obtained for all patients. The cohort consisted of 43 patients with benign SPNs and 69 with malignant SPNs. Among the malignant SPN group, 67 cases were confirmed via CT-CNB and 2 through surgery, resulting in a sensitivity of 97.10% and specificity of 100.00%. The malignant nodules comprised 45 adenocarcinomas, 14 squamous cell carcinomas, 8 metastatic tumors, and 2 small cell carcinomas. Notably, 2 initially diagnosed as malignant cases were found to have chronic inflammation on preoperative biopsy but revealed adenocarcinoma and squamous cell carcinoma post-surgery. The benign nodules encompassed 20 granulomatous inflammation cases, 15 chronic inflammation, 3 fungal granulomas, 2 hamartomas, and 1 fibrous tissue. Cytological smears exhibited a sensitivity of 81.3% and a specificity of 100.0% for malignancy. Significantly, age ≥60, elevated tumor markers, and specific imaging signs (burr, foliation, pleural pull) were identified as risk factors for malignant SPNs using Binary Logistic regression (all P < .05). Conclusions: CT-guided percutaneous lung biopsy demonstrates excellent diagnostic efficacy and safety for distinguishing benign and malignant SPNs.

Novel electromagnetic navigation bronchoscopy system for the diagnosis of peripheral pulmonary nodules: a prospective, multicentre study.

BACKGROUND: Traditional electromagnetic navigation bronchoscopy (ENB) is a real-time image-guided system and used with thick bronchoscopes for the diagnosis of peripheral pulmonary nodules (PPNs). A novel ENB that could be used with thin bronchoscopes was developed. This study aimed to evaluate the diagnostic yield and the experience of using this ENB system in a real clinical scenario. METHODS: This multicentre study enrolled consecutive patients with PPNs adopting ENB from March 2019 to August 2021. ENB was performed with different bronchoscopes, ancillary techniques and sampling instruments according to the characteristics of the nodule and the judgement of the operator. The primary endpoint was the diagnostic yield. The secondary endpoints included the diagnostic yield of subgroups, procedural details and complication rate. RESULTS: In total, 479 patients with 479 nodules were enrolled in this study. The median lesion size was 20.9 (IQR, 15.9-25.9) mm. The overall diagnostic yield was 74.9% (359/479). A thin bronchoscope was used in 96.2% (461/479) nodules. ENB in combination with radial endobronchial ultrasound (rEBUS), a guide sheath (GS) and a thin bronchoscope was the most widely used guided method, producing a diagnostic yield of 74.1% (254/343). The median total procedural time was 1325.0 (IQR, 1014.0-1676.0) s. No severe complications occurred. CONCLUSION: This novel ENB system can be used in combination with different bronchoscopes, ancillary techniques and sampling instruments with a high diagnostic yield and safety profile for the diagnosis of PPNs, of which the combination of thin bronchoscope, rEBUS and GS was the most common method in clinical practice. TRIAL REGISTRATION NUMBER: NCT03716284.

Diagnostic Yield of Shape-Sensing Robotic-Assisted Bronchoscopy for Ground-Glass Nodules and Subsolid Nodules with a Solid Component Less than 6 mm.

BACKGROUND: Ground-glass pulmonary nodules (GGNs) are most commonly sampled by percutaneous transthoracic biopsy. Diagnostic yield for ground-glass nodules using robotic-assisted bronchoscopy has been scarcely described, with a reported yield of 70.6%. OBJECTIVES: The aim of this study is to assess diagnostic yield for GGNs using shape-sensing robotic-assisted bronchoscopy (ssRAB). METHOD: A retrospective study of patients who underwent ssRAB for evaluation of GGNs, from September 2021 to April 2023. Primary outcome was diagnostic yield of ssRAB for GGNs, secondary outcomes were sensitivity for malignancy, and complications that required admission or intervention. RESULTS: A total of 23 nodules were biopsied from 22 patients. Median age was 71 years (IQR 66-81), 63.6% were female, and 40.9% had a previous history of cancer. Forty-three percent of nodules were in the right upper lobes, and the median lesion size was 1.8 × 1.21. Twelve were subsolid nodules (SSNs), and 11 were pure GGNs. Overall diagnostic yield was 87%, with a sensitivity for malignancy of 88.9%. Adenocarcinoma was the most common malignancy diagnosed (70%). No procedure-related complications were reported. CONCLUSION: The use of ssRAB shows a high diagnostic yield for diagnosing GGN and SSN with less than 6 mm solid component with a low risk for complications.

A decision tree model to distinguish between benign and malignant pulmonary nodules on CT scans.

OBJECTIVE: Chest computed tomography (CT) is increasingly being used to screen for lung cancer. Machine learning models could facilitate the distinction between benign and malignant pulmonary nodules. This study aimed to develop and validate a simple clinical prediction model to distinguish between benign and malignant lung nodules. PATIENTS AND METHODS: Patients who underwent a video thoracic-assisted lobectomy between January 2013 and December 2020 at a Chinese hospital were enrolled in the study. The clinical characteristics of the patients were extracted from their medical records. Univariate and multivariate analyses were used to identify the risk factors for malignancy. A decision tree model with 10-fold cross-validation was constructed to predict the malignancy of the nodules. The sensitivity, specificity, and area under the curve (AUC) of a receiver operatic characteristics curve were used to evaluate the model's prediction accuracy in relation to the pathological gold standard. RESULTS: Out of the 1,199 patients with pulmonary nodules enrolled in the study, 890 were pathologically confirmed to have malignant lesions. The multivariate analysis identified satellite lesions as an independent predictor for benign pulmonary nodules. Conversely, the lobulated sign, burr sign, density, vascular convergence sign, and pleural indentation sign were identified as independent predictors for malignant pulmonary nodules. The decision tree analysis identified the density of the lesion, the burr sign, the vascular convergence sign, and the drinking history as predictors of malignancy. The area under the curve of the decision tree model was 0.746 (95% CI 0.705-0.778), while the sensitivity and specificity were 0.762 and 0.799, respectively. CONCLUSIONS: The decision tree model accurately characterized the pulmonary nodule and could be used to guide clinical decision-making.

The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules.

PURPOSE: Computed tomography is the standard method by which pulmonary nodules are detected. Greater than 40% of pulmonary biopsies are not lung cancer and therefore not necessary, suggesting that improved diagnostic tools are needed. The LungLB™ blood test was developed to aid the clinical assessment of indeterminate nodules suspicious for lung cancer. LungLB™ identifies circulating genetically abnormal cells (CGACs) that are present early in lung cancer pathogenesis. METHODS: LungLB™ is a 4-color fluorescence in-situ hybridization assay for detecting CGACs from peripheral blood. A prospective correlational study was performed on 151 participants scheduled for a pulmonary nodule biopsy. Mann-Whitney, Fisher's Exact and Chi-Square tests were used to assess participant demographics and correlation of LungLB™ with biopsy results, and sensitivity and specificity were also evaluated. RESULTS: Participants from Mount Sinai Hospital (n = 83) and MD Anderson (n = 68), scheduled for a pulmonary biopsy were enrolled to have a LungLB™ test. Additional clinical variables including smoking history, previous cancer, lesion size, and nodule appearance were also collected. LungLB™ achieved 77% sensitivity and 72% specificity with an AUC of 0.78 for predicting lung cancer in the associated needle biopsy. Multivariate analysis found that clinical and radiological factors commonly used in malignancy prediction models did not impact the test performance. High test performance was observed across all participant characteristics, including clinical categories where other tests perform poorly (Mayo Clinic Model, AUC = 0.52). CONCLUSION: Early clinical performance of the LungLB™ test supports a role in the discrimination of benign from malignant pulmonary nodules. Extended studies are underway.

Circulating proteome for pulmonary nodule malignancy.

BACKGROUND: Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant from benign screen-detected pulmonary nodules. METHODS: Based on 4 international low-dose computed tomography screening studies, we assayed 1078 protein markers using prediagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays, and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores (PBSs) for overall nodule malignancy and imminent tumors were estimated. RESULTS: We identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within 1 year. Increases in PBSs for overall nodule malignancy and imminent tumors by 1 standard deviation were associated with odds ratios of 2.29 (95% confidence interval: 1.95 to 2.72) and 2.81 (95% confidence interval: 2.27 to 3.54) for nodule malignancy overall and within 1 year of diagnosis, respectively. Both PBSs for overall nodule malignancy and for imminent tumors were substantially higher for those with malignant nodules than for those with benign nodules, even when limited to Lung Computed Tomography Screening Reporting and Data System (LungRADS) category 4 (P < .001). CONCLUSIONS: Circulating protein markers can help differentiate malignant from benign pulmonary nodules. Validation with an independent computed tomographic screening study will be required before clinical implementation.