Probability of cancer in pulmonary nodules detected on first screening CT

Incidental findings are frequent in shoulder CT and MRI scans and increase with age

Objectives

CT and MRI scans of the shoulder can lead to the identification of incidental findings (IF), which can have a major impact on the further treatment of the patient. The aim of this retrospective study was to record the prevalence of IF, incidentalomas (IT) and malignant IT for CT and MRI examinations of the shoulder and to investigate the effect of patient characteristics on the statistical occurrence of IF, IT and malignant IT.

Materials and methods

A total of 903 shoulder examinations (415 CT, 488 MRI) were retrospectively analyzed for the presence of IF, subsequently categorized (harmless IF, IT requiring clarification, malignant IT) and analyzed regarding patient characteristics. The statistical analysis was carried out using independent t- and chi-square tests. A significance level of p < 0.05 was set.

Results

Among the 903 patients evaluated (436 female, 467 male), 153 (16.9%) patients experienced IF (harmless IF: 101 (11.2%) patients, IT: 94 (10.4%), malignant IT: 4 (0.4%). The average age of the patients without IF and IT was significantly lower compared to the patients with IF and IT (p < 0.001). While IF occurred in 31.1% of the CT, IF was only detected in 4.9% of the MRI (p < 0.001).

Conclusion

IF have a high prevalence (16.9%), especially in CT examinations of the shoulder, which increases with age. The exact detection and initiation of appropriate therapy is of great clinical importance, as early detection of life-threatening diseases enables more effective treatment and a potential gain in health and lifespan.

Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique

OBJECTIVE

To intelligently evaluate the invasiveness of pure ground-glass nodules with multiple classifications using deep learning.

METHODS

pGGNs in 1136 patients were pathologically confirmed as lung precursor lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Four different models [EfficientNet-b0 2D, dual-head ResNet_3D, a 3D model combining three features (3D_3F), and a 3D model combining 19 features (3D_19F)] were constructed to evaluate the invasiveness of pGGNs using the EfficientNet and ResNet networks. The Obuchowski index was used to evaluate the differences in diagnostic efficiency among the four models.

RESULTS

The patients with pGGNs (360 men, 776 women; mean age, 54.63 ± 12.36 years) included 235 cases of AAH + AIS, 332 cases of MIA, and 569 cases of IAC. In the validation group, the areas under the curve in detecting the invasiveness of pGGNs as a three-category classification (AAH + AIS, MIA, IAC) were 0.8008, 0.8090, 0.8165, and 0.8158 for EfficientNet-b0 2D, dual-head ResNet_3D, 3D_3F, and 3D_19F, respectively, whereas the accuracies were 0.6422, 0.6158, 0.651, and 0.6364, respectively. The Obuchowski index revealed no significant differences in the diagnostic performance of the four models.

CONCLUSIONS

The dual-head ResNet_3D_3F model had the highest diagnostic efficiency for evaluating the invasiveness of pGGNs in the four models.

A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology

INTRODUCTION

Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer.

METHODS

This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models.

RESULTS

Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001).

CONCLUSIONS

In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.

Multiple primary lung cancer: Updates and perspectives

Management of multiple primary lung cancer (MPLC) remains challenging, partly due to its increasing incidence, especially with the significant rise in cases of multiple lung nodules caused by low-dose computed tomography screening. Moreover, the indefinite pathogenesis, diagnostic criteria, and treatment selection add to the complexity. In recent years, there have been continuous efforts to dissect the molecular characteristics of MPLC and explore new diagnostic approaches as well as treatment modalities, which will be reviewed here, with a focus on newly emerging evidence and future perspectives, hope to provide new insights into the management of MPLC and serve as inspiration for future research related to MPLC.

Risk and Time to Diagnosis of Lung Cancer in Incidental Pulmonary Nodules

PURPOSE

To determine the risk of lung cancer in incidental pulmonary nodules, as well as the time until cancer growth is detected.

PATIENTS AND METHODS

This retrospective study examined patients with incidental nodules detected on chest computed tomography (CT) in 2017. Characteristics of the dominant nodule were automatically extracted from CT reports, and cancer diagnoses were manually verified by a thoracic radiologist. Nodules were categorized per Fleischner Society guideline categories: solid <6 mm, solid 6 to 8 mm, solid >8 mm, subsolid <6 mm, ground glass nodules ≥6 mm, and part-solid nodules ≥6 mm. The time to nodule growth was determined by CT reports.

RESULTS

A total of 3180 patients (nodules) were included, of which 155 (5%) were diagnosed with lung cancer. By category, 7/1601 (0.4%) solid nodules <6 mm, 11/713 (1.5%) solid nodules 6 to 8 mm, 71/446 (15.9%) solid nodules >8 mm, 1/124 (0.8%) subsolid nodules <6 mm, 29/202 (14.4%) ground glass nodules ≥6 mm, and 36/94 (37.9%) part-solid nodules ≥6 mm were malignant. Of solid lung cancers <6 mm, growth was observed in 1/4 imaged by 1 year and 2/5 by 2 years; of solid lung cancers 6 to 8 mm, growth was observed in 3/10 imaged by 1 year and 6/10 by 2 years.

CONCLUSION

Solid nodules <6 mm have a very low risk of malignancy and may not require routine follow-up. However, when malignant, growth is often not observed until 2 or more years later; therefore, stability at 1 to 2 years does not imply benignity.

Non-invasive diagnosis of pulmonary nodules by circulating tumor DNA methylation: A prospective multicenter study

BACKGROUND

With the popularization of computed tomography, more and more pulmonary nodules (PNs) are being detected. Risk stratification of PNs is essential for detecting early-stage lung cancer while minimizing the overdiagnosis of benign nodules. This study aimed to develop a circulating tumor DNA (ctDNA) methylation-based, non-invasive model for the risk stratification of PNs.

METHODS

A blood-based assay ("LUNG-TRAC") was designed to include novel lung cancer ctDNA methylation markers identified from in-house reduced representative bisulfite sequencing data and known markers from the literature. A stratification model was trained based on 183 ctDNA samples derived from patients with benign or malignant PNs and validated in 62 patients. LUNG-TRAC was further single-blindly tested in a single- and multi-center cohort.

RESULTS

The LUNG-TRAC model achieved an area under the curve (AUC) of 0.810 (sensitivity = 74.4 % and specificity = 73.7 %) in the validation set. Two test sets were used to evaluate the performance of LUNG-TRAC, with an AUC of 0.815 in the single-center test (N = 61; sensitivity = 67.5 % and specificity = 76.2 %) and 0.761 in the multi-center test (N = 95; sensitivity = 50.7 % and specificity = 80.8 %). The clinical utility of LUNG-TRAC was further assessed by comparing it to two established risk stratification models: the Mayo Clinic and Veteran Administration models. It outperformed both in the validation and the single-center test sets.

CONCLUSION

The LUNG-TRAC model demonstrated accuracy and consistency in stratifying PNs for the risk of malignancy, suggesting its utility as a non-invasive diagnostic aid for early-stage peripheral lung cancer.

CLINICAL TRIAL REGISTRATION

www.

CLINICALTRIALS

gov (NCT03989219).

The application of indocyanine green fluorescence imaging to determine intersegmental plane during thoracoscopic segmentectomy: A meta-analysis and systematic review

To investigate whether the application of intravenous indocyanine green fluorescence imaging(ICG-FI) had advantage in intersegmental plane visualization and perioperative outcome than using traditional inflation-deflation method(control group) in thoracoscopic segmentectomy. We searched PubMed, Embase, Cochrane Library, EMBASE, Wanfang Database, VIP Database, and CNKI Database to include comparative studies focusing on the comparisons of ICG-FI and control, up to December 2022. We used standard mean differences (SMD, continuous variables) or risk ratios (RR, categorical variables) with their corresponding 95 % confidence interval (CI) were used to assess pooled effects. This analysis was conducted according to the PRISMA guideline. Total, seven published studies with 905 patients (ICG-FI group n = 428, control group n = 477) were included for further analysis. The ICG-FI group was significantly associated with less bleeding during the surgery (SMD = -0.23,95 % CI: -0.08∼-0.38, P < 0.05), shorter surgery time (SMD = -0.87, 95 % CI: -1.75∼-0.17, P < 0.05) and intersegmental boundary line (IBL) presentation time (SMD = -4.50, 95 % CI: -4.97∼-4.07, P < 0.01). The ICG-FI group had shorter postoperative hospitalization time (SMD = -0.18, 95 % CI: -0.34∼-0.03), P < 0.05), and the drainage duration (SMD = -0.18, 95 % CI: -0.34∼-0.03,P < 0.05) than that in the control group. The ICG-FI group also showed the less postoperative complications (RR = 0.75, 95 % CI: 0.64-0.88). There were no significant differences in the number of lymph node resection. No significant publication bias were found in this analysis. Compared with inflation-deflation method, application of ICG-FI in thoracoscopic segmentectomy could reduce operation time, IBL presentation time, length of hospital stay, intraoperative blood loss, and overall complication incidence.

Prediction and verification of benignancy and malignancy of pulmonary nodules based on inflammatory related biological markers

Objective

Inflammation plays an important role in the transformation of pulmonary nodules (PNs) from benign to malignant. Prediction of benignancy and malignancy of PNs is still lacking efficacy methods. Although Mayo or Brock model have been widely applied in clinical practices, their application conditions are limited. This study aims to construct a diagnostic model of PNs by machine learning using inflammation-related biological markers (IRBMs).

Methods

Inflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result.

Results

4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718.

Conclusion

SVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.

Precision therapy for cancer prevention by targeting carcinogenesis

Cancer represents a major global public health burden, with new cases estimated to increase from 14 million in 2012 to 24 million by 2035. Primary prevention is an effective strategy to reduce the costs associated with cancer burden. For example, measures to ban tobacco consumption have dramatically decreased lung cancer incidence and vaccination against human papillomavirus can prevent cervical cancer development. Unfortunately, the etiological factors of many cancer types are not completely clear or are difficult to actively control; therefore, the primary prevention of such cancers is not practical. In this review, we update the progress on precision therapy by targeting the whole carcinogenesis process, especially for three high-risk groups: (1) those with chronic inflammation, (2) those with inherited germline mutations, and (3) those with precancerous lesions like polyps, gastritis, actinic keratosis or dysplasia. We believe that attenuating chronic inflammation, treating precancerous lesions, and removing high-risk tissues harboring germline mutations are precision methods for cancer prevention.

Testosterone, β-estradiol, and hepatocellular carcinoma: stimulation or inhibition? A comparative effect analysis on cell cycle, apoptosis, and Wnt signaling of HepG2 cells

Unlike breast and prostate cancers, which are specifically affected by estrogens or androgens, hepatocellular carcinoma has been reported to be influenced by both sex hormones. Given the coincidental differences of hepatocellular carcinoma in men and women, we investigated the effects of β-estradiol and testosterone on the cell cycle, apoptosis, and Wnt signaling in a model of hepatocellular carcinoma to understand the sex hormone-related etiology. To determine the effective concentration of both hormones, an MTT assay was performed. The effects of β-estradiol and testosterone on cell proliferation and death were evaluated by specific staining and flow cytometry. In addition, gene expression levels of estimated factors involved in GPC3-Wnt survival signaling were analyzed using quantitative real-time polymerase chain reaction. Both hormones inhibited hepatic cell proliferation through arresting the cell cycle at S/G2 and increased the apoptosis rate in HepG2 cells. Both hormones dose-dependently decreased GPC3, Wnt, and DVL expression levels as activators of the Wnt-signaling pathway. In the case of Wnt-signaling inhibitors, the effects of both hormones on WIF were negligible, but they increased DKK1 levels in a dose-dependent manner. In each of the effects mentioned above, β-estradiol was notably more potent than testosterone. In contrast to the primary hypothesis of the project, in which testosterone was considered a stimulating carcinogenic factor in HCC pathogenesis, testosterone inhibited the occurrence of HCC similarly to β-estradiol. However, this inhibitory effect was weaker than that of β-estradiol and requires further study.

Solitary pulmonary capillary hemangioma - An underrecognized rare tumor. Report of 32 new cases with literature review

OBJECTIVE

To explore the clinical, imaging, pathologic characteristics and differential diagnosis of solitary pulmonary capillary hemangioma (SPCH).

METHODS

Thirty two cases of SPCH were collected and studied, with literature review.

RESULTS

This study included 13 males and 19 females, with a male-to-female ratio of 1:1.5. The age ranged from 26 to 70 years (median age of 43 years). All patients were asymptomatic at presentation. Lung nodules were incidentally discovered during chest computed tomography (CT). Imaging features included 21 cases with partial solid nodules (PSN), 7 cases with ground-glass nodules (GGN), and 4 cases with solid nodules (SN). Eleven cases were in the left lung lower basal segment, 11 cases in the right lung lower basal segment, 6 cases in the right lung upper anterior segment, and 4 cases in the right lung middle lateral segment. The lower basal segments of the lungs were involved in 22 (11 in each lung) cases (22/32, 68 %). The tumors ranged from 6 to 18 mm (average 10 mm). Macroscopically, 16 cases had clear boundaries, while 16 cases had unclear boundaries, and gray-red or dark brown on cut surfaces. Intraoperative frozen section was performed in 27 cases, with diagnosis of SPCH in 12 and pneumonia or inflammatory lesion in 15. Microscopically, the nodules were composed of densely proliferated and dilated capillaries. The capillary walls were lined with a single layer of flat endothelial cells, without atypical features. Collapsed alveolar septa were replaced by a large number of capillaries. All cases showed proliferating capillaries spreading into the walls of small veins/arteries and bronchi, with 3 cases showing dilated capillaries protruding into the bronchiolar lumens as polyp-like structures. Twenty-six cases (26/32, 81 %) showed proliferating capillaries passed over the interlobular septa. Twenty-six cases (26/32, 81 %) showed irregular intimal thickening of small muscular arteries in the peripheral areas of the lesions, with the thickened intima being cellular or fibrous. In twenty-seven cases (27/32, 84 %) the lesions were located in the subpleura, with 6 cases involving the pleura.

CONCLUSION

SPCH is a rare benign lung tumor that mostly occurs in the lung lower basal segments with predominance in females. It usually appears as a ground-glass nodule on CT and is very similar to early-stage lung cancer. Accurate diagnosis requires collaboration of radiologists, surgeons, and pathologists. SPCH should be regarded as an important differential diagnosis of small incidental lung nodules.

A Retrospective External Validation of the Cleveland Clinic Malignancy Probability Prediction Model for Indeterminate Pulmonary Nodules

Objective

To perform a retrospective, multicenter, external validation of the Cleveland Clinic malignancy probability prediction model for incidental pulmonary nodules.

Patients and Methods

From July 1, 2022, to May 31, 2023, we identified 296 patients who underwent tissue acquisition at Mayo Clinic (MC) (n=198) and Loyola University Medical Center (n=98) with histopathology indicating malignant (n=195) or benign (n=101). Data was collected at initial radiographic identification (point 1) and at the time of intervention (point 2). Point 3 represented the most recent data. The areas under the receiver operating characteristics were calculated for each model per time point. Calibration was evaluated by comparing the predicted and observed rates of malignancy.

Results

The areas under the receiver operating characteristics at time points 1, 2, and 3 for the MC model were 0.67 (95% CI, 0.61-0.74), 0.67 (95% CI, 0.58-0.77), and 0.70 (95% CI, 0.63-0.76), respectively. The Cleveland Clinic model (CCM) was 0.68 (95% CI, 0.61-0.74), 0.75 (95% CI, 0.65-0.84), and 0.72 (95% CI, 0.66-0.78), respectively. The mean ± SD estimated probability for malignant pulmonary nodules (PNs) at time points 1, 2, and 3 for the CCM was 64.2±25.9, 65.8±24.0, and 64.7±24.4, which resembled the overall proportion of malignant PNs (66%). The mean estimated probability of malignancy for the MC model at each time point was 38.3±27.4, 36.2±24.4, and 42.1±27.3, substantially lower than the observed proportion of malignancies.

Conclusion

The CCM found discrimination similar to its internal validation and good calibration. The CCM can be used to augment clinical and shared decision-making when evaluating high-risk PNs.

Early Detection and Interception of Lung Cancer

Recent advances in lung cancer treatment have led to dramatic improvements in 5-year survival rates. And yet, lung cancer remains the leading cause of cancer-related mortality, in large part, because it is often diagnosed at an advanced stage, when cure is no longer possible. Lung cancer screening (LCS) is essential for intercepting the disease at an earlier stage. Unfortunately, LCS has been poorly adopted in the United States, with less than 5% of eligible patients being screened nationally. This article will describe the data supporting LCS, the obstacles to LCS implementation, and the promising opportunities that lie ahead.

Expert consensus on the multidisciplinary diagnosis and treatment of multiple ground glass nodule-like lung cancer (2024 Edition)

ABSTRACT

This expert consensus reviews current literature and provides clinical practice guidelines for the diagnosis and treatment of multiple ground glass nodule-like lung cancer. The main contents of this review include the following: ① follow-up strategies, ② differential diagnosis, ③ diagnosis and staging, ④ treatment methods, and ⑤ post-treatment follow-up.

Imaging diagnostics of pulmonary ground-glass nodules: a narrative review with current status and future directions

Background and Objective

The incidence rate of lung cancer, which also has the highest mortality rates for both men and women worldwide, is increasing globally. Due to advancements in imaging technology and the growing inclination of individuals to undergo screening, the detection rate of ground-glass nodules (GGNs) has surged rapidly. Currently, artificial intelligence (AI) methods for data analysis and interpretation, image processing, illness diagnosis, and lesion prediction offer a novel perspective on the diagnosis of GGNs. This article aimed to examine how to detect malignant lesions as early as possible and improve clinical diagnostic and treatment decisions by identifying benign and malignant lesions using imaging data. It also aimed to describe the use of computed tomography (CT)-guided biopsies and highlight developments in AI techniques in this area.

Methods

We used PubMed, Elsevier ScienceDirect, Springer Database, and Google Scholar to search for information relevant to the article's topic. We gathered, examined, and interpreted relevant imaging resources from the Second Affiliated Hospital of Nanchang University's Imaging Center. Additionally, we used Adobe Illustrator 2020 to process all the figures.

Key Content and Findings

We examined the common signs of GGNs, elucidated the relationship between these signs and the identification of benign and malignant lesions, and then described the application of AI in image segmentation, automatic classification, and the invasiveness prediction of GGNs over the last three years, including its limitations and outlook. We also discussed the necessity of conducting biopsies of persistent pure GGNs.

Conclusions

A variety of imaging features can be combined to improve the diagnosis of benign and malignant GGNs. The use of CT-guided puncture biopsy to clarify the nature of lesions should be considered with caution. The development of new AI tools brings new possibilities and hope to improving the ability of imaging physicians to analyze GGN images and achieving accurate diagnosis.

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.

Multi-omics analysis unveils immunosuppressive microenvironment in the occurrence and development of multiple pulmonary lung cancers

Multiple pulmonary lung cancers (MPLCs) are frequently encountered on computed tomography (CT) scanning of chest, yet their intrinsic characteristics associated with genomic features and radiological or pathological textures that may lead to distinct clinical outcomes remain largely unexplored. A total of 27 pulmonary nodules covering different radiological or pathological textures as well as matched adjacent normal tissues and blood samples were collected from patients diagnosed with MPLCs. Whole-exome sequencing (WES) and whole-transcriptome sequencing were performed. The molecular and immune features of MPLCs associated with distinct radiological or pathological textures were comprehensively investigated. Genomics analysis unveiled the distinct branches of pulmonary nodules originating independently within the same individual. EGFR and KRAS mutations were found to be prevalent in MPLCs, exhibiting mutual exclusivity. The group with KRAS mutations exhibited stronger immune signatures compared to the group with EGFR mutations. Additionally, MPLCs exhibited a pronounced immunosuppressive microenvironment, which was particularly distinct when compared with normal tissues. The expression of the FDSCP gene was specifically observed in MPLCs. When categorizing MPLCs based on radiological or pathological characteristics, a progressive increase in mutation accumulation was observed, accompanied by heightened chromatin-level instability as ground-glass opacity component declined or invasive progression occurred. A close association with the immunosuppressive microenvironment was also observed during the progression of pulmonary nodules. Notably, the upregulation of B cell and regulatory T cell marker genes occurred progressively. Immune cell abundance analysis further demonstrated a marked increase in exhausted cells and regulatory T cells during the progression of pulmonary nodules. These results were further validated by independent datasets including nCounter RNA profiling, single-cell RNA sequencing, and spatial transcriptomic datasets. Our study provided a comprehensive representation of the diverse landscape of MPLCs originating within the same individual and emphasized the significant influence of the immunosuppressive microenvironment in the occurrence and development of pulmonary nodules. These findings hold great potential for enhancing the clinical diagnosis and treatment strategies for MPLCs.

Comparative analysis of three-dimensional and two-dimensional models for predicting the malignancy probability of subsolid nodules

AIM

To construct three-dimensional (3D) and two-dimensional (2D) models to predict the malignancy probability of subsolid nodules (SSNs) and compare their effectiveness.

MATERIALS AND METHODS

A total of 371 SSNs from 332 patients, collected between January 2020 and January 2024, were included in the study. The SSNs were divided into a training set for constructing the models and a test set for validating the models. Models were developed using binary logistic backward regression, based on factors that showed significant differences in univariate analyses. The performance of the models was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). The AUCs of different models were compared using the DeLong test.

RESULTS

The AUCs for the two 3D models, one 2D model, and the Brock model were 0.785 (0.733-0.836), 0.776 (0.723-0.829), 0.764 (0.710-0.818), and 0.738 (0.679-0.798) in the training set. In the test set, these AUCs were 0.817 (0.706-0.928), 0.796 (0.679-0.913), 0.771 (0.647-0.895), and 0.790 (0.678-0.903). The two 3D models demonstrated statistically significant differences from the Brock model in the training set (P=0.024 and P=0.046). None of the four models showed significant differences in the test set (all P>0.05).

CONCLUSION

The 3D models outperform both the 2D model and the Brock model in predicting the malignancy probability of SSNs, and the 3D model incorporating volume, mean CT attenuation value, and lobulation as factors performed the best.

Volumetric Analysis: Effect on Diagnosis and Management of Indeterminate Solid Pulmonary Nodules in Routine Clinical Practice

OBJECTIVE

To evaluate the effect of volumetric analysis on the diagnosis and management of indeterminate solid pulmonary nodules in routine clinical practice.

METHODS

This was a retrospective study with 107 computed tomography (CT) cases of solid pulmonary nodules (range, 6-15 mm), 57 pathology-proven malignancies (lung cancer, n = 34; metastasis, n = 23), and 50 benign nodules. Nodules were evaluated on a total of 309 CT scans (average number of CTs/nodule, 2.9 [range, 2-7]). CT scans were from multiple institutions with variable technique. Nine radiologists (attendings, n = 3; fellows, n = 3; residents, n = 3) were asked their level of suspicion for malignancy (low/moderate or high) and management recommendation (no follow-up, CT follow-up, or care escalation) for baseline and follow-up studies first without and then with volumetric analysis data. Effect of volumetry on diagnosis and management was assessed by generalized linear and logistic regression models.

RESULTS

Volumetric analysis improved sensitivity (P = 0.009) and allowed earlier recognition (P < 0.05) of malignant nodules. Attending radiologists showed higher sensitivity in recognition of malignant nodules (P = 0.03) and recommendation of care escalation (P < 0.001) compared with trainees. Volumetric analysis altered management of high suspicion nodules only in the fellow group (P = 0.008). κ Statistics for suspicion for malignancy and recommended management were fair to substantial (0.38-0.66) and fair to moderate (0.33-0.50). Volumetric analysis improved interobserver variability for identification of nodule malignancy from 0.52 to 0.66 (P = 0.004) only on the second follow-up study.

CONCLUSIONS

Volumetric analysis of indeterminate solid pulmonary nodules in routine clinical practice can result in improved sensitivity and earlier identification of malignant nodules. The effect of volumetric analysis on management recommendations is variable and influenced by reader experience.

Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images

To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.

Bronchoscopy with and without needle-based confocal laser endomicroscopy for peripheral lung nodule diagnosis: protocol for a multicentre randomised controlled trial (CLEVER trial)

INTRODUCTION

Despite many technological advances, the diagnostic yield of bronchoscopic peripheral lung nodule analysis remains limited due to frequent mispositioning. Needle-based confocal laser endomicroscopy (nCLE) enables real-time microscopic feedback on needle positioning, potentially improving the sampling location and diagnostic yield. Previous studies have defined and validated nCLE criteria for malignancy, airway and lung parenchyma. Larger studies demonstrating the effect of nCLE on diagnostic yield are lacking. We aim to investigate if nCLE-imaging integrated with conventional bronchoscopy results in a higher diagnostic yield compared with conventional bronchoscopy without nCLE.

METHODS AND ANALYSIS

This is a parallel-group randomised controlled trial. Recruitment is performed at pulmonology outpatient clinics in universities and general hospitals in six different European countries and one hospital in the USA. Consecutive patients with a for malignancy suspected peripheral lung nodule (10-30 mm) with an indication for diagnostic bronchoscopy will be screened, and 208 patients will be included. Web-based randomisation (1:1) between the two procedures will be performed. The primary outcome is diagnostic yield. Secondary outcomes include diagnostic sensitivity for malignancy, needle repositionings, procedure and fluoroscopy duration, and complications. Pathologists will be blinded to procedure type; patients and endoscopists will not.

ETHICS AND DISSEMINATION

Primary approval by the Ethics Committee of the Amsterdam University Medical Center. Dissemination involves publication in a peer-reviewed journal.

SUPPORT

Financial and material support from Mauna Kea Technologies.

TRIAL REGISTRATION NUMBER

NCT06079970.

Surveillance of subsolid nodules avoids unnecessary resections in lung cancer screening: long-term results of the prospective BioMILD trial

Background

The management of subsolid nodules (SSNs) in lung cancer screening (LCS) is still a topic of debate, with no current uniform strategy to deal with these lesions at risk of overdiagnosis and overtreatment. The BioMILD LCS trial has implemented a prospective conservative approach for SSNs, managing with annual low-dose computed tomography nonsolid nodules (NSNs) and part-solid nodules (PSNs) with a solid component <5 mm, regardless of the size of the nonsolid component. The present study aims to determine the lung cancer (LC) detection and survival in BioMILD volunteers with SSNs.

Materials and methods

Eligible participants were 758 out of 4071 (18.6%) BioMILD volunteers without baseline LC and at least one SSN detected at the baseline or further low-dose computed tomography rounds. The outcomes of the study were LC detection and long-term survival.

Results

A total of 844 NSNs and 241 PSNs were included. LC detection was 3.7% (31 out of 844) in NSNs and 7.1% (17 out of 241) in PSNs, being significantly greater in prevalent than incident nodules (8.4% versus 1.3% in NSNs; 14.1% versus 2.1% in PSNs; p-value for both nodule types p<0.01). Most LCs from SSNs were stage I (42/48, 87.5%), resectable (47/48, 97.9%), and caused no deaths. The 8-year cumulative survival of volunteers with LC derived from SSNs and not derived from SSNs was 93.8% and 74.9%, respectively.

Conclusion

Conservative management of SSNs in LCS enables timely diagnosis and treatment of LCs arising from SSNs while ensuring the resection of more aggressive LCs detected away from SSNs.

Screening for lung cancer using thin-slice low-dose computed tomography in southwestern China: a population-based real-world study

OBJECTIVES

Lung cancer is one of the most common malignant tumors threatening human life and health. At present, low-dose computed tomography (LDCT) screening for the high-risk population to achieve early diagnosis and treatment of lung cancer has become the first choice recommended by many authoritative international medical organizations. To further optimize the lung cancer screening method, we conducted a real-world study of LDCT lung cancer screening in a large sample of a healthy physical examination population, comparing differences in lung nodules and lung cancer detection between thin and thick-slice LDCT scanning.

METHODS

A total of 29 296 subjects who underwent low-dose thick-slice CT scanning (5 mm thickness) from January 2015 to December 2015 and 28 058 subjects who underwent low-dose thin-slice CT scanning (1 mm thickness) from January 2018 to December 2018 in West China Hospital were included. The positive detection rate, detection rate of lung cancer, pathological stage of lung cancer, and mortality rate of lung cancer were analyzed and compared between the two groups.

RESULTS

The positive rate of LDCT screening in the thin-slice scanning group was significantly higher than that in the thick-slice scanning group (20.1% vs. 14.4%, p < 0.001). In addition, the lung cancer detection rate in the thin-slice LDCT screening positive group was significantly higher than that in the thick-slice scanning group (78.0% vs. 52.9%, p < 0.001).

CONCLUSIONS

The screening positive rate of low-dose thin-slice CT scanning is higher and more early-stage lung cancer (IA1 stage) can be detected in the screen-positive group.

Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected in vivo lesions

Purpose

Medical imaging-based machine learning (ML) for computer-aided diagnosis of in vivo lesions consists of two basic components or modules of (i) feature extraction from non-invasively acquired medical images and (ii) feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps.

Approach

Three feature extraction methods were investigated. One uses the mathematical descriptor of gray-level co-occurrence image texture measure to extract the Haralick image texture features (HFs). One uses the convolutional neural network (CNN) architecture to extract deep learning (DL) image abstractive features (DFs). The third one uses the interactions between lesion tissues and X-ray energy of CT to extract tissue-energy specific characteristic features (TFs). All the above three categories of extracted features were classified by the random forest (RF) classifier with comparison to the DL-CNN method, which reads the images, extracts the DFs, and classifies the DFs in an end-to-end manner. The ML diagnosis of lesions or prediction of lesion malignancy was measured by the area under the receiver operating characteristic curve (AUC). Three lesion image datasets were used. The lesions' tissue pathological reports were used as the learning labels.

Results

Experiments on the three datasets produced AUC values of 0.724 to 0.878 for the HFs, 0.652 to 0.965 for the DFs, and 0.985 to 0.996 for the TFs, compared to the DL-CNN of 0.694 to 0.964. These experimental outcomes indicate that the RF classifier performed comparably to the DL-CNN classification module and the extraction of tissue-energy specific characteristic features dramatically improved AUC value.

Conclusions

The feature extraction module is more important than the feature classification module. Extraction of tissue-energy specific characteristic features is more important than extraction of image abstractive and characteristic features.

Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images

OBJECTIVES

To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings.

MATERIALS AND METHODS

Four deep learning models were trained using CT images of sub-centimeter pulmonary nodules from West China Hospital, internally tested, and externally validated on three cohorts. The four models respectively learned 3D deep features from the baseline whole lung region, baseline image patch where the nodule located, baseline nodule box, and baseline plus follow-up nodule boxes. All regions of interest were automatically segmented except that the nodule boxes were additionally manually checked. The performance of models was compared with each other and that of three respiratory clinicians.

RESULTS

There were 1822 nodules (981 malignant) in the training set, 806 (416 malignant) in the testing set, and 357 (253 malignant) totally in the external sets. The area under the curve (AUC) in the testing set was 0.754, 0.855, 0.928, and 0.942, respectively, for models derived from baseline whole lung, image patch, nodule box, and the baseline plus follow-up nodule boxes. When baseline models externally validated (follow-up images not available), the nodule-box model outperformed the other two with AUC being 0.808, 0.848, and 0.939 respectively in the three external datasets. The resident, junior, and senior clinicians achieved an accuracy of 67.0%, 82.5%, and 90.0%, respectively, in the testing set. The follow-up model performed comparably to the senior clinician.

CONCLUSION

The deep learning algorithms solely mining nodule information can efficiently predict malignancy of incidental sub-centimeter pulmonary nodules.

CLINICAL RELEVANCE STATEMENT

The established models may be valuable for supporting clinicians in routine clinical practice, potentially reducing the number of unnecessary examinations and also delays in diagnosis.

KEY POINTS

• According to different regions of interest, four deep learning models were developed and compared to evaluate the malignancy of sub-centimeter pulmonary nodules by CT images. • The models derived from baseline nodule box or baseline plus follow-up nodule boxes demonstrated sufficient diagnostic accuracy (86.4% and 90.4% in the testing set), outperforming the respiratory resident (67.0%) and junior clinician (82.5%). • The proposed deep learning methods may aid clinicians in optimizing follow-up recommendations for sub-centimeter pulmonary nodules and may lead to fewer unnecessary diagnostic interventions.

Risk of Lung Cancer in Peripheral Pulmonary Nodules

PURPOSE

To determine the risk of lung cancer and inter-observer agreement for small pulmonary nodules either touching or near the pleura.

METHODS

Nodules were derived from two cohorts: patients from the National Lung Screening Trial with a solid nodule measuring 6-9.5 mm; and patients with incidental pulmonary nodules in our healthcare system with a solid nodule measuring 1-8 mm. Only the dominant nodule was evaluated for each patient. All malignant nodules as well as a random sample of 200 benign nodules from each cohort were included. Two fellowship-trained thoracic radiologists independently reviewed each case to record nodule morphology (compatible with lymph node or not) and nodule location (pleural-based, septal connection to the pleura, or neither). One radiologist measured the distance to the pleura.

RESULTS

After exclusion criteria were applied, a total of 434 nodules were included, of which 45 were lung cancers. Considering all pleural-based nodules with lymph node morphology as benign, 0-7% of cancers were misclassified as benign, specificity 33%, and κ = 0.69. Considering subpleural nodules and those with septal connection to the pleura, 7-11% of cancers were misclassified (p = 0.16-0.25 versus pleural-based), specificity 40-52% (p < .0001), and κ = 0.60. Considering nodules with lymph node morphology ≤ 2 mm from the pleura, 2-7% of cancers were misclassified (p = 1 versus pleural-based), specificity 41-36% (p < .0001), and κ = 0.78.

CONCLUSION

Considering nodules with lymph node morphology with septal connection, or those ≤ 2 mm from the pleura, as benign does not lead to significant misclassification of lung cancers as benign.

Efficiency of pulmonary nodule risk scoring systems in Turkish population

Malignancy risk calculation models were developed using the clinical and radiological features. It was aimed to compare pulmonary nodule risk calculation models and evaluate their effectiveness and applicability for the Turkish population. Between 2014 and 2019, 351 patients who were operated on for pulmonary nodules were evaluated with the following data: age, gender, smoking history, family history of lung cancer, extrapulmonary malignancy and granulomatous disease, nodule diameter, attenuation character, side, localization, spiculation, nodule count, presence of pulmonary emphysema, FDG uptake in PET/CT of the nodule, and definitive pathology data. Malignancy risk scores were calculated using the equations of the Brock, Mayo, and Herder models. The results were evaluated statistically. The mean age of the 351 patients (236 men, 115 women) was 57.84 ± 10.87 (range 14-79) years, and 226 malignant and 125 benign nodules were observed. Significant correlations were found between malignancy and age (p < 0.001), nodule diameter (p < 0.001), gender (p < 0.009), speculation (p < 0.001), emphysema (p < 0.05), FDG uptake (p < 0.001). All three models were found effective in the differentiation (p < 0.001). The ideal threshold value was determined for the Brock (19.5%), Mayo (23.1%), and Herder (56%) models. All models were effective for nodules of > 10 mm, but none of them were for 0-10 mm. Brock was effective in ground-glass nodules (p = 0.02) and all models were effective for semi-solid and solid nodules. None of the groups could provide AUC values as high as those achieved in the original studies. This suggests the need to optimize models and malignancy risk thresholds for Turkish population.

A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules

BACKGROUND

Currently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules.

METHODS

The current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People's Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).

RESULTS

A cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804-0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application.

CONCLUSION

In summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.

Construction of a predictive model of 2-3 cm ground-glass nodules developing into invasive lung adenocarcinoma using high-resolution CT

Background

The purpose of this study was to analyze the imaging risk factors for the development of 2-3 cm ground-glass nodules (GGN) for invasive lung adenocarcinoma and to establish a nomogram prediction model to provide a reference for the pathological prediction of 2-3 cm GGN and the selection of surgical procedures.

Methods

We reviewed the demographic, imaging, and pathological information of 596 adult patients who underwent 2-3 cm GGN resection, between 2018 and 2022, in the Department of Thoracic Surgery, Second Affiliated Hospital of the Air Force Medical University. Based on single factor analysis, the regression method was used to analyze multiple factors, and a nomogram prediction model for 2-3 cm GGN was established.

Results

(1) The risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma were pleural depression sign (OR = 1.687, 95%CI: 1.010-2.820), vacuole (OR = 2.334, 95%CI: 1.222-4.460), burr sign (OR = 2.617, 95%CI: 1.008-6.795), lobulated sign (OR = 3.006, 95%CI: 1.098-8.227), bronchial sign (OR = 3.134, 95%CI: 1.556-6.310), diameter of GGN (OR = 3.118, 95%CI: 1.151-8.445), and CTR (OR = 172.517, 95%CI: 48.023-619.745). (2) The 2-3 cm GGN risk prediction model was developed based on the risk factors with an AUC of 0.839; the calibration curve Y was close to the X-line, and the decision curve was drawn in the range of 0.0-1.0.

Conclusion

We analyzed the risk factors for the development of 2-3  cm GGN during the invasion stage of the lung adenocarcinoma. The predictive model developed based on the above factors had some clinical significance.

Baseline Cell-Free DNA Can Predict Malignancy of Nodules Observed in the ITALUNG Screening Trial

The role of total plasma cell-free DNA (cfDNA) in lung cancer (LC) screening with low-dose computed tomography (LDCT) is uncertain. We hypothesized that cfDNA could support differentiation between malignant and benign nodules observed in LDCT. The baseline cfDNA was measured in 137 subjects of the ITALUNG trial, including 29 subjects with screen-detected LC (17 prevalent and 12 incident) and 108 subjects with benign nodules. The predictive capability of baseline cfDNA to differentiate malignant and benign nodules was compared to that of Lung-RADS classification and Brock score at initial LDCT (iLDCT). Subjects with prevalent LC showed both well-discriminating radiological characteristics of the malignant nodule (16 of 17 were classified as Lung-RADS 4) and markedly increased cfDNA (mean 18.8 ng/mL). The mean diameters and Brock scores of malignant nodules at iLDCT in subjects who were diagnosed with incident LC were not different from those of benign nodules. However, 75% (9/12) of subjects with incident LC showed a baseline cfDNA ≥ 3.15 ng/mL, compared to 34% (37/108) of subjects with benign nodules (p = 0.006). Moreover, baseline cfDNA was correlated (p = 0.001) with tumor growth, measured with volume doubling time. In conclusion, increased baseline cfDNA may help to differentiate subjects with malignant and benign nodules at LDCT.

Recommendations to Improve Management of Incidental Pulmonary Nodules in Canada: Expert Panel Consensus

Introduction: Incidental pulmonary nodules (IPN) are common radiologic findings, yet management of IPNs is inconsistent across Canada. This study aims to improve IPN management based on multidisciplinary expert consensus and provides recommendations to overcome patient and system-level barriers. Methods: A modified Delphi consensus technique was conducted. Multidisciplinary experts with extensive experience in lung nodule management in Canada were recruited to participate in the panel. A survey was administered in 3 rounds, using a 5-point Likert scale to determine the level of agreement (1 = extremely agree, 5 = extremely disagree). Results: Eleven experts agreed to participate in the panel; 10 completed all 3 rounds. Consensus was achieved for 183/217 (84.3%) statements. Panellists agreed that radiology reports should include a standardized summary of findings and follow-up recommendations for all nodule sizes (ie, <6, 6-8, and >8 mm). There was strong consensus regarding the importance of an automated system for patient follow-up and that leadership support for organizational change at the administrative level is of utmost importance in improving IPN management. There was no consensus on the need for standardized national referral pathways, development of new guidelines, or establishing a uniform picture archiving and communication system. Conclusion: Canadian IPN experts agree that improved IPN management should include standardized radiology reporting of IPNs, standardized and automated follow-up of patients with IPNs, guideline adherence and implementation, and leadership support for organizational change. Future research should focus on the implementation and long-term effectiveness of these recommendations in clinical practice.

Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors' evaluation in lung cancer screening

OBJECTIVE

To evaluate the consistency between doctors and artificial intelligence (AI) software in analysing and diagnosing pulmonary nodules, and assess whether the characteristics of pulmonary nodules derived from the two methods are consistent for the interpretation of carcinomatous nodules.

MATERIALS AND METHODS

This retrospective study analysed participants aged 40-74 in the local area from 2011 to 2013. Pulmonary nodules were examined radiologically using a low-dose chest CT scan, evaluated by an expert panel of doctors in radiology, oncology, and thoracic departments, as well as a computer-aided diagnostic(CAD) system based on the three-dimensional(3D) convolutional neural network (CNN) with DenseNet architecture(InferRead CT Lung, IRCL). Consistency tests were employed to assess the uniformity of the radiological characteristics of the pulmonary nodules. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy. Logistic regression analysis is utilized to determine whether the two methods yield the same predictive factors for cancerous nodules.

RESULTS

A total of 570 subjects were included in this retrospective study. The AI software demonstrated high consistency with the panel's evaluation in determining the position and diameter of the pulmonary nodules (kappa = 0.883, concordance correlation coefficient (CCC) = 0.809, p = 0.000). The comparison of the solid nodules' attenuation characteristics also showed acceptable consistency (kappa = 0.503). In patients diagnosed with lung cancer, the area under the curve (AUC) for the panel and AI were 0.873 (95%CI: 0.829-0.909) and 0.921 (95%CI: 0.884-0.949), respectively. However, there was no significant difference (p = 0.0950). The maximum diameter, solid nodules, subsolid nodules were the crucial factors for interpreting carcinomatous nodules in the analysis of expert panel and IRCL pulmonary nodule characteristics.

CONCLUSION

AI software can assist doctors in diagnosing nodules and is consistent with doctors' evaluations and diagnosis of pulmonary nodules.

Computed tomography characteristics of cN0 primary non-small cell lung cancer predict occult lymph node metastasis

RATIONALE

Occult lymph node metastasis (OLNM) is frequently found in patients with resectable non-small cell lung cancer (NSCLC), despite using diagnostic methods recommended by guidelines.

OBJECTIVES

To evaluate the risk of OLNM in NSCLC patients using the radiologic characteristics of the primary tumor on computed tomography (CT).

METHODS

We retrospectively reviewed clinicopathologic features of 2042 clinical T1-4N0 NSCLC patients undergoing curative intent pulmonary resection. Unique radiological features (i.e., air-bronchogram throughout the whole tumor, heterogeneous ground-glass opacity (GGO), mainly cystic appearance, endobronchial location), percentage of solid portion, and shape of tumor margin were analyzed via a stepwise approach. We used multivariable logistic regression to assess the relationship between OLNM and tumor characteristics.

RESULTS

Compared with the other unique features, endobronchial tumors were associated with the highest risk of OLNM (OR = 3.9, 95% confidence interval (CI) = 2.29-6.62), and heterogeneous GGO and mainly cystic tumors were associated with a low risk of OLNM. For tumors without unique features, the percentage of the solid portion was measured, and solid tumors were associated with OLNM (OR = 2.49, 95% CI = 1.86-3.35). Among part-solid tumors with solid proportion > 50%, spiculated margin, and peri-tumoral GGO were associated with OLNM.

CONCLUSIONS

The risk of OLNM could be assessed using radiologic characteristics on CT. This could allow us to adequately select optimal candidates for invasive nodal staging procedures (INSPs) and complete systematic lymph node dissection.

CLINICAL RELEVANCE STATEMENT

These data may be helpful for clinicians to select appropriate candidates for INSPs and complete surgical systematic lymph node dissection in NSCLC patients.

KEY POINTS

Lymph node metastasis status plays a key role in both prognostication and treatment planning. Solid tumors, particularly endobronchial tumors, were associated with occult lymph node metastasis (OLNM). The risk of OLNM can be assessed using radiologic characteristics acquired from CT images.

Risk factors of pneumothorax in computed tomography guided lung nodule marking using autologous blood: a retrospective study

BACKGROUND

To investigate the risk factors of pneumothorax of using computed tomography (CT) guidance to inject autologous blood to locate isolated lung nodules.

METHODS

In the First Hospital of Putian City, 92 cases of single small pulmonary nodules were retrospectively analyzed between November 2019 and March 2023. Before each surgery, autologous blood was injected, and the complications of each case, such as pneumothorax and pulmonary hemorrhage, were recorded. Patient sex, age, position at positioning, and nodule type, size, location, and distance from the visceral pleura were considered. Similarly, the thickness of the chest wall, the depth and duration of the needle-lung contact, the length of the positioning procedure, and complications connected to the patient's positioning were noted. Logistics single-factor and multi-factor variable analyses were used to identify the risk factors for pneumothorax. The multi-factor logistics analysis was incorporated into the final nomogram prediction model for modeling, and a nomogram was established.

RESULTS

Logistics analysis suggested that the nodule size and the contact depth between the needle and lung tissue were independent risk factors for pneumothorax.

CONCLUSION

The factors associated with pneumothorax after localization are smaller nodules and deeper contact between the needle and lung tissue.

The diagnosis and management of multiple ground-glass nodules in the lung

The prevalence of low-dose CT (LDCT) in lung cancer screening has gradually increased, and more and more lung ground glass nodules (GGNs) have been detected. So far, a consensus has been reached on the treatment of single pulmonary ground glass nodules, and there have been many guidelines that can be widely accepted. However, at present, more than half of the patients have more than one nodule when pulmonary ground glass nodules are found, which means that different treatment methods for nodules may have different effects on the prognosis or quality of life of patients. This article reviews the research progress in the diagnosis and treatment strategies of pulmonary multiple lesions manifested as GGNs.

Incidental pulmonary nodules - current guidelines and management

BACKGROUND

 Due to the greater use of high-resolution cross-sectional imaging, the number of incidental pulmonary nodules detected each year is increasing. Although the vast majority of incidental pulmonary nodules are benign, many early lung carcinomas could be diagnosed with consistent follow-up. However, for a variety of reasons, the existing recommendations are often not implemented correctly. Therefore, potential for improvement with respect to competence, communication, structure, and process is described.

METHODS

 This article presents the recommendations for incidental pulmonary nodules from the current S3 guideline for lung cancer (July 2023). The internationally established recommendations (BTS guidelines and Fleischner criteria) are compared and further studies on optimized management were included after a systematic literature search in PubMed.

RESULTS AND CONCLUSION

 In particular, AI-based software solutions are promising, as they can be used in a support capacity on several levels at once and can lead to simpler and more automated management. However, to be applicable in routine clinical practice, software must fit well into the radiology workflow and be integrated. In addition, "Lung Nodule Management" programs or clinics that follow a high-quality procedure for patients with incidental lung nodules or nodules detected by screening have been established in the USA. Similar structures might also be implemented in Germany in a future screening program in which patients with incidental pulmonary nodules could be included.

KEY POINTS

  · Incidental pulmonary nodules are common but are often not adequately managed. · The updated S3 guideline for lung cancer now includes recommendations for incidental pulmonary nodules. · Competence, communication, structure, and process levels offer significant potential for improvement.

CITATION FORMAT

· Glandorf J, Vogel-Claussen J, . Incidental pulmonary nodules - current guidelines and management. Fortschr Röntgenstr 2024; 196: 582 - 590.

Dual-branch feature fusion S3D V-Net network for lung nodules segmentation

BACKGROUND

Accurate segmentation of lung nodules can help doctors get more accurate results and protocols in early lung cancer diagnosis and treatment planning, so that patients can be better detected and treated at an early stage, and the mortality rate of lung cancer can be reduced.

PURPOSE

Currently, the improvement of lung nodule segmentation accuracy has been limited by his heterogeneous performance in the lungs, the imbalance between segmentation targets and background pixels, and other factors. We propose a new 2.5D lung nodule segmentation network model for lung nodule segmentation. This network model can well improve the extraction of edge information of lung nodules, and fuses intra-slice and inter-slice features, which makes good use of the three-dimensional structural information of lung nodules and can more effectively improve the accuracy of lung nodule segmentation.

METHODS

Our approach is based on a typical encoding-decoding network structure for improvement. The improved model captures the features of multiple nodules in both 3-D and 2-D CT images, complements the information of the segmentation target's features and enhances the texture features at the edges of the pulmonary nodules through the dual-branch feature fusion module (DFFM) and the reverse attention context module (RACM), and employs central pooling instead of the maximal pooling operation, which is used to preserve the features around the target and to eliminate the edge-irrelevant features, to further improve the performance of the segmentation of the pulmonary nodules.

RESULTS

We evaluated this method on a wide range of 1186 nodules from the LUNA16 dataset, and averaging the results of ten cross-validated, the proposed method achieved the mean dice similarity coefficient (mDSC) of 84.57%, the mean overlapping error (mOE) of 18.73% and average processing of a case is about 2.07 s. Moreover, our results were compared with inter-radiologist agreement on the LUNA16 dataset, and the average difference was 0.74%.

CONCLUSION

The experimental results show that our method improves the accuracy of pulmonary nodules segmentation and also takes less time than more 3-D segmentation methods in terms of time.

Approach to Pulmonary Nodules in Connective Tissue Disease

The assessment of pulmonary nodules is a common and often challenging clinical scenario. This evaluation becomes even more complex in patients with connective tissue diseases (CTDs), as a range of disease-related factors must also be taken into account. These diseases are characterized by immune-mediated chronic inflammation, leading to tissue damage, collagen deposition, and subsequent organ dysfunction. A thorough examination of nodule features in these patients is required, incorporating anatomic and functional information, along with patient demographics, clinical factors, and disease-specific knowledge. This integrated approach is vital for effective risk stratification and precise diagnosis. This review article addresses specific CTD-related factors that should be taken into account when evaluating pulmonary nodules in this patient group.

Interpreting Lung Cancer Screening CTs: Practical Approach to Lung Cancer Screening and Application of Lung-RADS

Lung cancer screening via low-dose computed tomography (CT) reduces mortality from lung cancer, and eligibility criteria have recently been expanded to include patients aged 50 to 80 with at least 20 pack-years of smoking history. Lung cancer screening CTs should be interepreted with use of Lung Imaging Reporting and Data System (Lung-RADS), a reporting guideline system that accounts for nodule size, density, and growth. The revised version of Lung-RADS includes several important changes, such as expansion of the definition of juxtapleural nodules, discussion of atypical pulmonary cysts, and stepped management for suspicious nodules. By using Lung-RADS, radiologists and clinicians can adopt a uniform approach to nodules detected during CT lung cancer screening and reduce false positives.

Imaging of Solid Pulmonary Nodules

Early detection with accurate classification of solid pulmonary nodules is critical in reducing lung cancer morbidity and mortality. Computed tomography (CT) remains the most widely used imaging examination for pulmonary nodule evaluation; however, other imaging modalities, such as PET/CT and MRI, are increasingly used for nodule characterization. Current advances in solid nodule imaging are largely due to developments in machine learning, including automated nodule segmentation and computer-aided detection. This review explores current multi-modality solid pulmonary nodule detection and characterization with discussion of radiomics and risk prediction models.

Radiomics and Clinical Data for the Diagnosis of Incidental Pulmonary Nodules and Lung Cancer Screening: Radiolung Integrative Predictive Model

INTRODUCTION

Early diagnosis of lung cancer (LC) is crucial to improve survival rates. Radiomics models hold promise for enhancing LC diagnosis. This study assesses the impact of integrating a clinical and a radiomic model based on deep learning to predict the malignancy of pulmonary nodules (PN).

METHODOLOGY

Prospective cross-sectional study of 97 PNs from 93 patients. Clinical data included epidemiological risk factors and pulmonary function tests. The region of interest of each chest CT containing the PN was analysed. The radiomic model employed a pre-trained convolutional network to extract visual features. From these features, 500 with a positive standard deviation were chosen as inputs for an optimised neural network. The clinical model was estimated by a logistic regression model using clinical data. The malignancy probability from the clinical model was used as the best estimate of the pre-test probability of disease to update the malignancy probability of the radiomic model using a nomogram for Bayes' theorem.

RESULTS

The radiomic model had a positive predictive value (PPV) of 86%, an accuracy of 79% and an AUC of 0.67. The clinical model identified DLCO, obstruction index and smoking status as the most consistent clinical predictors associated with outcome. Integrating the clinical features into the deep-learning radiomic model achieves a PPV of 94%, an accuracy of 76% and an AUC of 0.80.

CONCLUSIONS

Incorporating clinical data into a deep-learning radiomic model improved PN malignancy assessment, boosting predictive performance. This study supports the potential of combined image-based and clinical features to improve LC diagnosis.

Value of CT-Based Deep Learning Model in Differentiating Benign and Malignant Solid Pulmonary Nodules ≤ 8 mm

RATIONALE AND OBJECTIVES

We examined the effectiveness of computed tomography (CT)-based deep learning (DL) models in differentiating benign and malignant solid pulmonary nodules (SPNs) ≤ 8 mm.

MATERIALS AND METHODS

The study patients (n = 719) were divided into internal training, internal validation, and external validation cohorts; all had small SPNs and had undergone preoperative chest CTs and surgical resection. We developed five DL models incorporating features of the nodule and five different peri-nodular regions with the Multiscale Dual Attention Network (MDANet) to differentiate benign and malignant SPNs. We selected the best-performing model, which was then compared to four conventional algorithms (VGG19, ResNet50, ResNeXt50, and DenseNet121). Furthermore, another five DL models were constructed using MDANet to distinguish benign tumors from inflammatory nodules and the one performed best was selected out.

RESULTS

Model 4, which incorporated the nodule and 15 mm peri-nodular region, best differentiated benign and malignant SPNs. The model had an area under the curve (AUC), accuracy, recall, precision, and F1-score of 0.730, 0.724, 0.711, 0.705, and 0.707 in the external validation cohort. Model 4 also performed better than the other four conventional algorithms. Model 8, which incorporated the nodule and 10 mm peri-nodular region, was the best model for distinguishing benign tumors from inflammatory nodules. The model had an AUC, accuracy, recall, precision, and F1-score of 0.871, 0.938, 0.863, 0.904, and 0.882 in the external validation cohort.

CONCLUSION

The study concludes that CT-based DL models built with MDANet can accurately discriminate among small benign and malignant SPNs, benign tumors and inflammatory nodules.

Transcription Factor ETV4 Activates AURKA to Promote PD-L1 Expression and Mediate Immune Escape in Lung Adenocarcinoma

INTRODUCTION

The occurrence and progression of lung adenocarcinoma (LUAD) impair T-cell immune responses, causing immune escape and subsequently affecting the efficacy of immunotherapy in patients. Aurora kinase A (AURKA) is upregulated in varying cancers, but its role in LUAD immune escape is elusive. This work attempted to explore molecular mechanisms of AURKA regulation in LUAD immune escape.

METHODS

Through bioinformatics analysis, AURKA level in LUAD was evaluated, and potential upstream transcription factors of AURKA were predicted using hTFtarget. ETS variant transcription factor 4 (ETV4) expression in LUAD was analyzed through The Cancer Genome Atlas. Pearson's correlation analysis was then utilized to test the correlation between AURKA and ETV4. Interaction and binding between AURKA and ETV4 were validated through dual-luciferase assay and chromatin immunoprecipitation. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) tested relative mRNA expression of AURKA and ETV4 in LUAD cells, cell counting kit-8 assayed cell viability, and Western blot analysis was conducted to determine the protein level of programmed death-ligand 1 (PD-L1). Coculture of LUAD cells with activated CD8+ T cells was carried out, and an LDH assay was used to assess the cytotoxicity of CD8+ T cells against LUAD cells. Interferon-γ (IFN-γ), interleukin-2 (IL-2), and tumor necrosis factor-α (TNF-α) levels in the coculture system were assessed by enzyme-linked immunosorbent assay (ELISA). Western blot assessed protein levels of JAK2, p-JAK2, STAT3, and p-STAT3.

RESULTS

Compared to normal tissues, AURKA and ETV4 were upregulated in tumor tissues, and AURKA presented a negative association with CD8+ T-cell immune infiltration but a positive association with PD-L1. qRT-PCR unveiled significantly upregulated mRNA of AURKA and ETV4 in LUAD cells compared to normal lung epithelial cells. Knockdown of AURKA significantly decreased cell viability and PD-L1 protein level in LUAD cells, enhanced cytotoxicity of CD8+ T cells against LUAD cells and IFN-γ, IL-2, and TNF-α expression, while overexpression of AURKA yielded opposite results. Furthermore, the knockdown of ETV4 could reverse the oncogenic characteristics of cells caused by AURKA overexpression.

CONCLUSION

Our study illustrated that ETV4/AURKA axis promoted PD-L1 expression, suppressed CD8+ T-cell activity, and mediated immune escape in LUAD by regulating the JAK2/STAT3 signaling pathway.

Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review and Meta-analysis

BACKGROUND

There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (CADx) models, and assess their diagnostic performance for predicting the risk of malignancy in computed tomography (CT)-detected pulmonary nodules.

METHODS

An electronic search was performed in four databases (from inception to 10 August 2023). Studies were eligible if they were peer-reviewed experimental or observational articles comparing the diagnostic performance of externally validated DL-based CADx models with models widely used in clinical practice to predict the risk of malignancy. A bivariate random-effect approach for the meta-analysis on the included studies was used.

RESULTS

Seventeen studies were included, comprising 8553 participants and 9884 nodules. Pooled analyses showed DL-based CADx models were 11.6% more sensitive than physician judgement alone, and 14.5% more than clinical risk models alone. They had a similar pooled specificity to physician judgement alone [0.77 (95% CI 0.68-0.84) v 0.81 (95% CI 0.71-0.88)], and were 7.4% more specific than clinical risk models alone. They had superior pooled areas under the receiver operating curve (AUC), with relative pooled AUCs of 1.03 (95% CI 1.00-1.07) and 1.10 (95% CI 1.07-1.13) versus physician judgement and clinical risk models alone, respectively.

CONCLUSION

DL-based models are already used in clinical practice in certain settings for nodule management. Our results show their diagnostic performance potentially justifies wider, more routine deployment alongside experienced physician readers to help inform multidisciplinary team decision-making.

Development and validation of a model for predicting upstage in minimally invasive lung adenocarcinoma in Chinese people

BACKGROUND

Sublobar resection for ground-glass opacity became a recommend surgery choice supported by the JCOG0804/JCOG0802/JCOG1211 results. Sublobar resection includes segmentectomy and wedge resection, wedge resection is suitable for non-invasive lesions, but in clinical practice, when pathologists are uncertain about the intraoperative frozen diagnosis of invasive lesions, difficulty in choosing the appropriate operation occurs. The purpose of this study was to analyze how to select invasive lesions with clinic-pathological characters.

METHODS

A retrospective study was conducted on 134 cases of pulmonary nodules diagnosed with minimally invasive adenocarcinoma by intraoperative freezing examination. The patients were divided into two groups according to intraoperative frozen results: the minimally invasive adenocarcinoma group and the at least minimally invasive adenocarcinoma group. A variety of clinical features were collected. Chi-square tests and multiple regression logistic analysis were used to screen out independent risk factors related to pathological upstage, and then ROC curves were established. In addition, an independent validation set included 1164 cases was collected.

RESULTS

Independent risk factors related to pathological upstage were CT value, maximum tumor diameter, and frozen result of AL-MIA. The AUC of diagnostic mode was 71.1% [95%CI: 60.8-81.3%]. The independent validation included 1164 patients, 417 (35.8%) patients had paraffin-based pathology of invasive adenocarcinoma. The AUC of diagnostic mode was 75.7% [95%CI: 72.9-78.4%].

CONCLUSIONS

The intraoperative frozen diagnosis was AL-MIA, maximum tumor diameter larger than 15 mm and CT value is more than - 450Hu, highly suggesting that the lung GGO was invasive adenocarcinoma which represent a higher risk to recurrence. For these patients, sublobectomy would be insufficient, lobectomy or complementary treatment is encouraged.

A Self-supervised Learning-Based Fine-Grained Classification Model for Distinguishing Malignant From Benign Subcentimeter Solid Pulmonary Nodules

RATIONALE AND OBJECTIVES

Diagnosing subcentimeter solid pulmonary nodules (SSPNs) remains challenging in clinical practice. Deep learning may perform better than conventional methods in differentiating benign and malignant pulmonary nodules. This study aimed to develop and validate a model for differentiating malignant and benign SSPNs using CT images.

MATERIALS AND METHODS

This retrospective study included consecutive patients with SSPNs detected between January 2015 and October 2021 as an internal dataset. Malignancy was confirmed pathologically; benignity was confirmed pathologically or via follow-up evaluations. The SSPNs were segmented manually. A self-supervision pre-training-based fine-grained network was developed for predicting SSPN malignancy. The pre-trained model was established using data from the National Lung Screening Trial, Lung Nodule Analysis 2016, and a database of 5478 pulmonary nodules from the previous study, with subsequent fine-tuning using the internal dataset. The model's efficacy was investigated using an external cohort from another center, and its accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined.

RESULTS

Overall, 1276 patients (mean age, 56 ± 10 years; 497 males) with 1389 SSPNs (mean diameter, 7.5 ± 2.0 mm; 625 benign) were enrolled. The internal dataset was specifically enriched for malignancy. The model's performance in the internal testing set (316 SSPNs) was: AUC, 0.964 (95% confidence interval (95%CI): 0.942-0.986); accuracy, 0.934; sensitivity, 0.965; and specificity, 0.908. The model's performance in the external test set (202 SSPNs) was: AUC, 0.945 (95% CI: 0.910-0.979); accuracy, 0.911; sensitivity, 0.977; and specificity, 0.860.

CONCLUSION

This deep learning model was robust and exhibited good performance in predicting the malignancy of SSPNs, which could help optimize patient management.

The use of diagnostic complex robotic-assisted segmentectomy in the management of incidental and screen-detected pulmonary nodules

OBJECTIVES

Robotic-assisted thoracoscopic surgery (RATS) facilitates complex pulmonary segmentectomy which offers one-stage diagnostic and therapeutic management of small pulmonary nodules. We aimed to explore the potential advantages of a faster, simplified pathway and earlier diagnosis against the disadvantages of unnecessary morbidity in benign cases.

METHODS

In an observational study, patients with small, solitary pulmonary nodules deemed suspicious of malignancy by a multidisciplinary team were offered surgery without a pre or intraoperative biopsy. We report our initial experience with RATS complex segmentectomy (using >1 parenchymal staple line) to preserve as much functioning lung tissue as possible.

RESULTS

Over a 4-year period, 245 RATS complex segmentectomies were performed; 140 right: 105 left. A median of 2 (1-4) segments was removed. There was no in-hospital mortality and no requirement for postoperative ventilation. Complications were reported in 63 (25.7%) cases, of which 36 (57.1%) were hospital-acquired pneumonia. A malignant diagnosis was found in 198 (81%) patients and a benign diagnosis in 47 (19%). The malignant diagnoses included: adenocarcinoma in 136, squamous carcinoma in 31 and carcinoid tumour in 15. The most frequent benign diagnosis was granulomatous inflammation in 18 cases.

CONCLUSIONS

RATS complex segmentectomy offers a precise, safe and effective one-stop therapeutic biopsy in incidental and screen-detected pulmonary nodules.

Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning

BACKGROUND

Lung cancer is the leading cause of cancer-related death worldwide. This study aimed to establish novel multiclassification prediction models based on machine learning (ML) to predict the probability of malignancy in pulmonary nodules (PNs) and to compare with three published models.

METHODS

Nine hundred fourteen patients with PNs were collected from four medical institutions (A, B, C and D), which were organized into tables containing clinical features, radiologic features and laboratory test features. Patients were divided into benign lesion (BL), precursor lesion (PL) and malignant lesion (ML) groups according to pathological diagnosis. Approximately 80% of patients in A (total/male: 632/269, age: 57.73 ± 11.06) were randomly selected as a training set; the remaining 20% were used as an internal test set; and the patients in B (total/male: 94/53, age: 60.04 ± 11.22), C (total/male: 94/47, age: 59.30 ± 9.86) and D (total/male: 94/61, age: 62.0 ± 11.09) were used as an external validation set. Logical regression (LR), decision tree (DT), random forest (RF) and support vector machine (SVM) were used to establish prediction models. Finally, the Mayo model, Peking University People's Hospital (PKUPH) model and Brock model were externally validated in our patients.

RESULTS

The AUC values of RF model for MLs, PLs and BLs were 0.80 (95% CI: 0.73-0.88), 0.90 (95% CI: 0.82-0.99) and 0.75 (95% CI: 0.67-0.88), respectively. The weighted average AUC value of the RF model for the external validation set was 0.71 (95% CI: 0.67-0.73), and its AUC values for MLs, PLs and BLs were 0.71 (95% CI: 0.68-0.79), 0.98 (95% CI: 0.88-1.07) and 0.68 (95% CI: 0.61-0.74), respectively. The AUC values of the Mayo model, PKUPH model and Brock model were 0.68 (95% CI: 0.62-0.74), 0.64 (95% CI: 0.58-0.70) and 0.57 (95% CI: 0.49-0.65), respectively.

CONCLUSIONS

The RF model performed best, and its predictive performance was better than that of the three published models, which may provide a new noninvasive method for the risk assessment of PNs.

Clinical Outcomes of Resected Pure Ground-Glass, Heterogeneous Ground-Glass, and Part-Solid Pulmonary Nodules

BACKGROUND. Increased (but not definitively solid) attenuation within pure ground-glass nodules (pGGNs) may indicate invasive adenocarcinoma and the need for resection rather than surveillance. OBJECTIVE. The purpose of this study was to compare the clinical outcomes among resected pGGNs, heterogeneous ground-glass nodules (GGNs), and part-solid nodules (PSNs). METHODS. This retrospective study included 469 patients (335 female patients and 134 male patients; median age, 68 years [IQR, 62.5-73.5 years]) who, between January 2012 and December 2020, underwent resection of lung adenocarcinoma that appeared as a subsolid nodule on CT. Two radiologists, using lung windows, independently classified each nodule as a pGGN, a heterogeneous GGN, or a PSN, resolving discrepancies through discussion. A heterogeneous GGN was defined as a GGN with internal increased attenuation not quite as dense as that of pulmonary vessels, and a PSN was defined as having an internal solid component with the same attenuation as that of the pulmonary vessels. Outcomes included pathologic diagnosis of invasive adenocarcinoma, 5-year recurrence rates (locoregional or distant), and recurrence-free survival (RFS) and overall survival (OS) over 7 years, as analyzed by Kaplan-Meier and Cox proportional hazards regression analyses, with censoring of patients with incomplete follow-up. RESULTS. Interobserver agreement for nodule type, expressed as a kappa coefficient, was 0.69. Using consensus assessments, 59 nodules were pGGNs, 109 were heterogeneous GGNs, and 301 were PSNs. The frequency of invasive adenocarcinoma was 39.0% in pGGNs, 67.9% in heterogeneous GGNs, and 75.7% in PSNs (for pGGNs vs heterogeneous GGNs, p < .001; for pGGNs vs PSNs, p < .001; and for heterogeneous GGNs vs PSNs, p = .28). The 5-year recurrence rate was 0.0% in patients with pGGNs, 6.3% in those with heterogeneous GGNs, and 10.8% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .06; for pGGNs vs PSNs, p = .02; and for heterogeneous GGNs vs PSNs, p = .18). At 7 years, RFS was 97.7% in patients with pGGNs, 82.0% in those with heterogeneous GGNs, and 79.4% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .02; for pGGNs vs PSNs, p = .006; and for heterogeneous GGNs vs PSNs, p = .40); OS was 98.0% in patients with pGGNs, 84.6% in those with heterogeneous GGNs, and 82.9% in those with PSNs (for pGGNs vs heterogeneous GGNs, p = .04; for pGGNs vs PSNs, p = .01; and for heterogeneous GGNs vs PSNs, p = .50). CONCLUSION. Resected pGGNs had excellent clinical outcomes. Heterogeneous GGNs had relatively worse outcomes, more closely resembling outcomes for PSNs. CLINICAL IMPACT. The findings support surveillance for truly homogeneous pGGNs versus resection for GGNs showing internal increased attenuation even if not having a true solid component.