Long-Term Prognosis of Patients With Resected Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma of the Lung

Establishing a threshold for maximum standardized uptake value on 18 F-fluorodeoxyglucose PET/CT to predict high-grade lung adenocarcinoma and its prognostic significance

OBJECTIVE

The objective of this study is to determine an optimal threshold for the maximum standardized uptake value (SUVmax) of 18 F-fluorodeoxyglucose PET/CT to predict the newly proposed high-grade tumor classification and assess its prognostic significance in invasive lung adenocarcinoma (LUAD).

METHODS

Surgical specimens from 185 patients with pathological stage I invasive LUAD in the training group, along with 90 patients in the validation group, were analyzed using the novel IASLC grading system. The receiver operating characteristic curve was used to determine the optimal SUVmax threshold and assess its predictive accuracy. Disease-free survival (DFS) and overall survival (OS) were analyzed using Kaplan-Meier survival curves and Cox regression analysis.

RESULTS

Linear correlation analysis demonstrated a significant positive association between SUVmax and the proportion of high-grade histological patterns ( R ² = 0.346, P  < 0.001). The optimal SUVmax cutoff for predicting grade 3 tumors was 3.8, with an area under the curve of 0.866 in the training dataset and 0.899 in the validation dataset. Multivariate logistic regression analysis identified an SUVmax >3.8 as an independent predictor of grade 3 tumors ( P  < 0.001). In Cox regression analysis, SUVmax >3.8 was independently associated with reduced DFS (HR = 4.009, 95% CI: 1.568-10.250, P  = 0.004) and OS (HR = 5.536, 95% CI: 1.175-26.075, P  = 0.030).

CONCLUSION

As a noninvasive preoperative parameter, SUVmax >3.8 is a significant indicator of high-grade tumors as classified by the IASLC grading system and is strongly associated with worse DFS and OS.

Low-Malignant-Potential Adenocarcinoma: A Histological Category with a Significantly Better Prognosis than Other Solid Adenocarcinomas at IA Stage

INTRODUCTION

Low-malignant-potential adenocarcinoma has been defined as a type of non-mucinous tumor, which has a total tumor size measuring ≤ 3 cm, exhibits ≥ 15% lepidic growth, lacks non-predominant high-grade patterns (≥10% cribriform, ≥5% micropapillary, ≥5% solid), has an absence of angiolymphatic or visceral pleural invasion, spread through air spaces (STAS), necrosis and >1 mitosis per 2 mm2. The aim of this study is to validate, with regard to cancer-specific survival (CSS) and disease-free survival (DFS), the proposed definition of LMP adenocarcinoma in an independent external cohort of lung adenocarcinoma patients having undergone surgical resection, and having presented with a long follow-up period.

METHODS

Clinicopathological characteristics of patients who underwent lung resection for adenocarcinoma from 1 January 2005 to 31 December 2014 were retrospectively analyzed. Patients with ground-glass opacity (GGO) and part-solid tumors, minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), tumors ≥5 cm in size, nodal involvement and/or distant metastases, patients who underwent neoadjuvant treatment, and those who had an incomplete follow-up or a follow-up shorter than 60 months were excluded. The proposed criteria for low-malignant-potential adenocarcinoma (LMPA) were tumor size ≤ 3 cm, invasive size ≥ 0,5 cm, lepidic growth ≥ 15%, and absence of the following: mitosis (>1 per 2 mm2), mucinous subtype, angiolymphatic invasion, visceral pleural invasion, spread through air spaces (STAS) and tumor necrosis. End points were disease-free survival (DFS) and cancer-specific survival (CSS). The log-rank test was used to assess differences between subgroups.

RESULTS

Out of 80 patients meeting the proposed criteria, 14 (17.5%) had the LMPA characteristics defined. The mean follow-up time was 67 ± 39 months. A total of 19 patients died, all in the non-LMPA category, and 33 patients experienced recurrence: 4 (28.5%) with LMPA and 29 (43.9%) with non-LMPA. Log-rank analysis showed 100% 10-year CSS for patients with LMPA and 77.4% for patients without LMPA, with this difference being statistically significant (p-value = 0.047). Univariate analysis showed a significant association with the cStage (AJCC eighth edition), both for CSS (p value = 0.005) and DFS (p-value = 0.003). LMPA classification did not show a statistically significant impact on CSS and DFS, likely due to the limited number of events (CSS p-value = 0.232 and DFS p-value = 0.213). No statistical association was found for CSS and DFS with pT, the number of resected nodes (< or >10) or the number of resected N2 stations (< or >2).

CONCLUSIONS

Our study confirmed the prognostic role of LMPA features, with a low risk of recurrence and a good CSS and DFS. The criteria for diagnosis are replicable and feasible for application. The clinical implications of these findings, such as pre-operative prediction and surveillance scheduling, may be the topic of future prospective studies.

Retrospective analysis of inflammatory biomarkers and prognosis in non-small cell lung cancer without adenocarcinoma in situ

Background

Inflammatory biomarkers have shown prognostic value in Non-Small Cell Lung Cancer (NSCLC), but the inclusion of Adenocarcinoma In Situ (AIS) cases in previous studies may introduce bias. This study aims to evaluate the prognostic significance of inflammatory biomarkers in NSCLC while excluding AIS.

Methods

This study included patients who received surgery for lung carcinoma from August 2016 and August 2019. We collected demographic, clinical, laboratory, and outcome information. Inflammatory biomarkers were analyzed using receiver operating characteristic (ROC) curves, Kaplan-Meier survival analysis, and Cox regression to assess their prognostic value.

Results

Higher levels of inflammatory biomarkers correlated with poorer survival, with significant differences in overall survival (OS) between high- and low-expression groups. However, multivariate Cox regression identified age, tumor stage, and differentiation as independent prognostic factors, while biomarkers were not independently predictive.

Conclusion

Inflammatory biomarkers have short-term prognostic value in invasive NSCLC, but traditional clinical and pathological factors remain key for long-term outcomes.

CENPF (+) cancer cells promote malignant progression of early-stage TP53 mutant lung adenocarcinoma

The prevention and precise treatment of early-stage lung adenocarcinoma (LUAD) characterized by small nodules (stage IA) remains a significant challenge for clinicians, which is due largely to the limited understanding of the oncogenic mechanisms spanning from preneoplasia to invasive adenocarcinoma. Our study highlights the pivotal role of cancer cells exhibiting high expression of centromere protein F (CENPF), driven by TP53 mutations, which become increasingly prevalent during the transition from preneoplasia to invasive LUAD. Biologically, cancer cells (CENPF+) exhibited robust proliferative and stem-like capabilities, thereby propelling the malignant progression of early-stage LUAD. Clinically, autoantibodies against CENPF in the serum and elevated cancer cells (CENPF+) in tissue correlated positively with the progression of early-stage LUAD, especially those in stage IA. Our findings suggest that cancer cells (CENPF+) play a central role in orchestrating the malignant evolution of LUAD and hold potential as a novel biomarker for early-stage detection and management of the disease.

Molecular subtyping of stage I lung adenocarcinoma via molecular alterations in pre-invasive lesion progression

BACKGROUND

Patients with adenocarcinoma in situ (AIS) and minimally invasive (MIA) lung adenocarcinoma (LUAD) are curable by surgery, whereas 20% stage I patients die within five years after surgery. We hypothesize that poor-prognosis stage I patients may exhibit key molecular characteristics deviating from AIS/MIA. Therefore, we tried to reveal molecularly and prognostically distinct subtypes of stage I LUAD by applying key molecular alterations from AIS/MIA to invasive LUAD progression.

METHODS

The RNA and whole-exome sequencing data of 197 tumor-normal matched samples from patients with AIS, MIA, and invasive LUAD were analyzed. ddPCR quantified 202 samples from 182 patients at the absolute expression level. Immunohistochemical quantified the protein expression levels of ACTA2. RNA-seq data from 954 LUAD patients, including 541 stage I patients, along with 12 published datasets comprising 1,331 stage I LUAD patients, were used to validate our findings.

RESULTS

Focal adhesion (FA) was identified as the only pathway significantly perturbed at both genomic and transcriptomic levels by comparing 98 AIS/MIA and 99 LUAD. Then, two FA genes (COL11A1 and THBS2) were found strongly upregulated from AIS/MIA to stage I while steadily expressed from normal to AIS/MIA. Furthermore, unsupervised clustering separated stage I patients into two molecularly and prognostically distinct subtypes (S1 and S2) based on COL11A1 and THBS2 expressions (FA2). Subtype S1 resembled AIS/MIA, whereas S2 exhibited more somatic alterations and activated cancer-associated fibroblast. Immunohistochemistry on 73 samples also observed that CAF was more active in S2 compared to S1 and AIS/MIA. The prognostic value of these two genes identified from our knowledge-driven process was confirmed by 541 stage I patients in a prospective dataset, ddPCR and 12 published datasets.

CONCLUSIONS

We successfully revealed two molecularly and prognostically distinct subtypes of stage I LUAD by applying key molecular alterations from AIS/MIA to invasive LUAD progression. Our model may help reliably identify high-risk stage I patients for more intensive post-surgery treatment.

Epigenetic modifications in early stage lung cancer: pathogenesis, biomarkers, and early diagnosis

The integration of liquid biopsy with epigenetic markers offers significant potential for early lung cancer detection and personalized treatment. Epigenetic alterations, including DNA methylation, histone modifications, and noncoding RNA changes, often precede genetic mutations and are critical in cancer progression. In this study, we explore how liquid biopsy, combined with epigenetic markers, can provide early detection of lung cancer, potentially predicting onset up to 4 years before clinical diagnosis. We discuss the challenges of targeting epigenetic regulators, which could disrupt cellular balance if overexploited, and the need for maintaining key gene expressions in therapeutic applications. This review highlights the promise and challenges of using liquid biopsy and epigenetic markers for early-stage lung cancer diagnosis, with a focus on optimizing treatment strategies for personalized and precision medicine.

The evolution of the treatment of non-small cell lung cancer: A shift in surgical paradigm to a more individualized approach

Surgical treatment is an integral part of the comprehensive therapeutic methods for lung cancer, especially for early-stage non-small cell lung cancer (NSCLC). With a deeper understanding of the disease, we found that lung cancer is more commonly detected in young females. For regions of Asia, more lung cancer has been detected in early-stage GGO-dominant non-smokers. Therefore, surgical strategies have also been reformed commensurate with the shift of the disease spectrum. However, the pursuit of lung-sparing individualized approaches has raised worldwide attention. Suitable surgical treatment within the curative time window is recommended to maximize the long-term benefit. This article summarizes the shift in surgical treatment for small NSCLCs and hopes to enlighten further innovations to fill in the gaps between the unmet needs and a more individualized approach.

The clinical value of spectral CT combined with targeted scanning technology in the diagnosis of invasive ground glass nodules in pulmonary adenocarcinoma

BackgroundPulmonary adenocarcinoma, a predominant form of lung cancer, is characterized by diverse histopathological subtypes, including ground-glass nodules (GGNs), which may represent different degrees of malignancy. The accurate differentiation between invasive and non-invasive GGNs is paramount, as it significantly influences clinical management and treatment strategies.ObjectiveTo evaluate the clinical efficacy of spectral CT combined with targeted scanning in diagnosing invasive GGNs in pulmonary adenocarcinoma.MethodsA retrospective analysis of 120 patients with GGNs, who underwent spectral CT and targeted scanning at the Second Affiliated Hospital of Qiqihar Medical College (Nov 2021 - Dec 2022), was conducted. Patients were categorized based on postoperative pathology into non-invasive (66) and invasive (54) groups. Imaging features and values of various indicators including water concentration (WC), spectral curve slope (k value), and iodine concentration across different phases were analyzed and compared. Key independent factors for invasive GGN and the predictive value of the combined imaging technique were explored.ResultsThe invasive lesion group showed a higher incidence of fissure sign, spiculation sign, and bronchial inflation sign, along with increased values of WC, WCAP, and WCVP across phases (p < 0.05). These factors were identified as main influencers of invasive GGN, with OR values >1. The combined imaging parameters achieved an AUC of 0.914, sensitivity of 0.925, and specificity of 0.880, significantly outperforming individual indicators.ConclusionFissure sign, spiculation sign, bronchial inflation sign, WC, WCAP, and WCVP effectively predict invasive GGNs in pulmonary adenocarcinoma. Their combined application enhances predictive accuracy.

Deciphering the intratumoral histologic heterogeneity of lung adenocarcinoma using radiomics

OBJECTIVE

To discern highly aggressive intratumoral areas among lung adenocarcinoma (LUAD) and its impact on occult nodal metastases and the recurrence rate with radiomic analysis.

METHODS

This prospective dual-institution study analyzed clinical information and high-resolution preoperative CT of 528 patients from institution A and 249 patients from institution B. We extracted radiomic features and performed pathologic evaluations for resected tumors, based on the 2020 International Association for the Study of Lung Cancer (IASLC) classification. Prediction models were developed to discern micropapillary and solid patterns within LUAD using clinical and radiomic features from institution A through logistic analysis.

RESULTS

Six selected CT radiomic features, sex, CTR (consolidation-to-tumor ratio), and solid diameter were selected to develop the prediction models. A composite model of radiomic and clinical characteristics outperformed radiomics-only and clinical-only models (AUC, 95% CI; the composite model: 0.84 [0.81-0.87]; the radiomics model: 0.82 [0.78-0.87]; the clinical model: 0.80 [0.76-0.83]) in institution A. External validation was performed with institution B cohort, showing even better results (AUC, 95% CI; the composite model: 0.91 [0.87-0.94]; the radiomics model: 0.89 [0.84-0.94]; the clinical model: 0.88 [0.84-0.92]).

CONCLUSIONS

Our study underscores the potential of radiomics to preoperatively predict aggressive histologic patterns in LUAD, enabling precise treatment planning and prognosis estimation.

KEY POINTS

Question Can any adjuvant methods address the limitations of core needle biopsies, which are invasive and may not capture the full heterogeneity of lung adenocarcinoma? Findings In a prospective study of 528 patients with cT1N0M0 lung adenocarcinoma, a composite model of clinical characteristics, conventional CT findings, and radiomics features predicted high-grade cancers. Clinical relevance Preoperative non-invasive diagnosis of histologically high-grade tumors using radiomics analysis offers crucial information for the treatment of lung adenocarcinoma with respect to occult lymph node metastasis and recurrence rate.

Cell-free epigenomes enhanced fragmentomics-based model for early detection of lung cancer

BACKGROUND

Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.

METHODS

To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.

RESULTS

Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.

CONCLUSIONS

With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.

KEYPOINTS

Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

Multisite DNA methylation alterations of peripheral blood mononuclear cells serve as novel biomarkers for the diagnosis of AIS/stage I lung adenocarcinoma: a multicenter cohort study

BACKGROUND

Early diagnosis remains an obstacle for improving the outcome of lung adenocarcinoma (LUAD). DNA methylation changes in peripheral blood mononuclear cells (PBMCs) could reflect an immune response to tumorigenesis, providing the theoretical basis for early cancer diagnosis based on immune cell profiling.

METHODS

This multi-center study evaluated the DNA methylation patterns based on PBMCs samples from 1115 individuals at nine medical centers. Genome-wide DNA methylation profiling of PBMCs in a discovery cohort (35 LUAD patients and 50 healthy controls) was performed using Illumina 850K microarray. Candidate differentially methylated CpG positions (DMPs) were selected and validated in a two-step DMPs screening cohort (65 LUAD patients and 80 healthy controls) by pyrosequencing and multiple target region methylation enrichment sequencing (MTRMES). Then, an early LUAD Diagnostic Panel (LDP score) based on multisite methylation-specific chip-based digital PCR was constructed in a training set and then confirmed in a validation set from the LDP score development cohort (389 AIS/stage I LUAD patients and 293 healthy controls). Besides, we included 157 other cancer patients, including 52 gastric cancer (GC) patients, 50 breast cancer (BC) patients, and 55 colorectal cancer (CRC) patients to assess the specificity of the LDP score. In addition, we also evaluated the early warning ability of LDP score for LUAD in a prospective cohort (46 people who were at high-risk of developing LC).

RESULTS

A total of 1415 LUAD-specific DMPs were identified. Then, six DMPs were selected for validation and three DMPs were finally verified. The LDP score was constructed by combining the three DMPs, age, and sex, and showed an AUC of 0.916, sensitivity of 88.17%, and specificity of 80.20% in a combined set, outperforming traditional methods, such as CEA and CT (detection rate: 87.79% vs. 4.69%; 87.79% vs. 35.21%). This diagnostic performance was confirmed in sub-types of LUAD with clinical challenges, such as 6-20 mm LUAD (AUC: 0.914, 95% CI: 0.889-0.934) and ground-glass nodules (AUC: 0.916, 95% CI: 0.889-0.938). Importantly, our LDP score had significant improvement in terms of selecting high-risk individuals who should receive low-dose computed tomography (87.80% vs. 9.28%). Remarkably, the LDP score could predict LUAD around 2 years before clinical diagnosis in our prospective cohort.

CONCLUSIONS

The novel developed LDP score represented a convenient and effective assay for the detection of AIS/stage I LUAD with high sensitivity and specificity, and had demonstrated unique advantages over traditional detection methods.

Propensity matching analysis of left upper tri-segmentectomy versus lobectomy for stage I non-small cell lung cancer

BACKGROUND

The equivalence between left upper lobectomy (LUL) and left upper tri-segmentectomy (LUTS) for stage I left upper non-small cell lung cancer (NSCLC) remains unclear. This study compares the perioperative and oncological outcomes of LUL and LUTS in this patient population.

METHODS

This study included patients who underwent LUL or LUTS at West China Hospital of Sichuan University and Sichuan ShangJin Hospital between August 2018 and November 2023. Patients with tumors located at least 2 cm from the lingular segment were included. Propensity score matching (PSM) addressed baseline imbalances between groups. Perioperative outcomes, overall survival (OS), recurrence-free survival (RFS), lung cancer-specific survival (LCSS), and subgroup analyses were assessed.

RESULTS

A total of 1019 patients were included (LUL: 524; LUTS: 495) with a median follow-up of 4.8 years (IQR: 2.5-8.1). Compared to LUL, LUTS was associated with significantly shorter operative times (103 vs. 120 min, p = 0.001), reduced postoperative drainage volume at 3 days (335 vs. 485 ml, p = 0.001) and total (360 vs. 530 ml, p = 0.001), lower conversion to thoracotomy rates (1.0% vs. 3.4%, p = 0.009), and fewer postoperative complications (9.9% vs. 14.9%, p = 0.016). No significant differences were observed in 5-year OS (86.7% vs. 85.4%, HR: 0.96; 95% CI: 0.66-1.39; p = 0.821), 5-year RFS (78.4% vs. 75.3%, HR: 0.85; 95% CI: 0.63-1.13; p = 0.258), or 5-year LCSS (90.2% vs. 91.3%, HR: 0.99; 95% CI: 0.62-1.57; p = 0.956) between the two groups.

CONCLUSION

For stage I left upper NSCLC, LUTS, while preserving adequate surgical margins, achieves superior perioperative and comparable oncological outcomes to LUL.

Prediction and analysis of the tumor invasiveness of pulmonary ground-glass nodules based on metabolomics

In recent years, the incidence of ground-glass nodular lung adenocarcinoma has gradually increased. Preoperative evaluation of the tumor invasiveness is very important, but there is a lack of effective methods. Plasma samples of ground-glass nodular lung adenocarcinoma and healthy volunteers were collected. Pulmonary nodules with different densities were compared by metabolomics. Different invasive degrees of lung adenocarcinoma were contrasted as well. Multivariate statistical methods were applied to search for significant metabolites from comparisons between two groups. The common metabolites among the different comparisons were selected and then assessed by various indices. Five metabolites were discovered for lung adenocarcinoma with different invasive degrees. Significant metabolites were selected for pulmonary nodules with different densities as well. When these metabolites were cross-compared, only the level of lysoPC(18:3) was significantly lower in ground-glass nodular lung adenocarcinoma than healthy population, as opposed to other metabolites. After identifying the invasive degree of pulmonary ground-glass nodules, lysoPC(18:3) showed a satisfactory sensitivity and specificity, both greater than 0.85. Metabolomics analysis has favorable advantages in the study of ground-glass nodular lung adenocarcinoma. LysoPC(18:3) may have the potential to differentiate precancerous lesions from invasive lung cancer, which could help clinicians to make proper judgment before surgery.

Pulmonary Alveolar Proteinosis-Like Pathological Changes Mimicking Lung Adenocarcinoma in Situ

An enlarging ground-glass nodule (GGN) in the lungs closely resembles the characteristic appearance of a well differentiated lung adenocarcinoma or adenocarcinoma in situ (AIS). Herein, we present an unusual case characterized by clinical features suggestive of AIS but pathologically confirmed as exhibiting pulmonary alveolar proteinosis (PAP)-like changes. Patients with enlarging pure GGNs warrant consideration for diagnostic and curative surgery. While a considerable proportion of such cases receives a pathological diagnosis of lung malignancy, it is imperative to consider alternative benign conditions in the differential diagnosis, such as PAP-like changes.

The value of interlobar fissure semilunar sign based on multifactor joint analysis in predicting the invasiveness of ground glass nodules with interlobar fissure attachment in the lungs

BACKGROUND

This study explores the value of interlobar fissure semilunar sign(IFSS) based on multifactor joint analysis in predicting the invasiveness of ground glass nodules(GGNs) with interlobar fissure attachment in the lungs.

METHODS

This was a retrospective analysis of clinical data and CT images of 203 GGNs attached to the interlobar fissures confirmed by surgery and pathology. According to pathological results, those GGNs were divided into three groups: glandular precursor lesion (atypical adenomatous hyperplasia/adenocarcinoma in situ), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). Various quantitative and qualitative parameters were analyzed.

RESULTS

Patient age, maximum diameter, mean size, maximum CT value, and mean CT value differed significantly among the three groups and between with the other group (P < 0.05). The types of GGNs, IFSS, lobulation, spiculation, cavity sign, air bronchogram sign, bronchial changes, and vascular changes had varying degrees of significance in the comparison of each group of lesions. Logistic regression analysis showed that IFSS is one of the important factors in predicting whether GGN is invasive. The regression model I was Logit (P) 1 = -3.578 + 0.272 × 2 + 2.253 × 5, with the area under curve (AUC) for diagnosis of MIA = 0.762. Model III was Logit (P) 3 = -4.494 + 0.376 × 2 + 2.363 × 5, with the AUC for diagnosis of MIA/IAC = 0.881. The sensitivity and specificity of IFSS in model III were 0.961 and 0.458, respectively.

CONCLUSIONS

The absence of IFSS in GGNs attached to the interlobar fissure suggests noninvasive lesions. The logistic regression model based on multi factor joint analysis IFSS and maximum diameter can better predict whether the GGN attached to the interlobar fissure pleura is invasive.

Sub-lobar resection versus lobectomy for challenging intraoperative frozen sections in lung adenocarcinoma within 3 cm

OBJECTIVES

Intraoperative frozen section (FS) analysis is pivotal in guiding surgical interventions for early-stage lung adenocarcinoma. However, the challenge arises when distinguishing between Minimally Invasive Adenocarcinoma (MIA) and Invasive Adenocarcinoma (IAC) poses diagnostic difficulties. This study investigates the prognosis and clinicopathological characteristics of patients encountering this diagnostic challenge.

METHODS

We conducted a retrospective analysis of 7082 intraoperative FSs from early-stage lung adenocarcinoma cases. The cases with pulmonary nodules within 3 cm and diagnosed as indeterminate FSs were included. We analyzed baseline data, computed tomography (CT) findings, and pathological characteristics. Prognostic data were obtained from patients with confirmed IAC diagnoses through final pathological examination.

RESULTS

Out of 7082 FSs, 551 cases presented challenges in distinguishing between MIA and IAC. Upon final pathological examination, 233 cases were identified as IAC, while 314 were classified as MIA. The median invasive pathological size in the IAC group was larger than that in the MIA group (0.6 cm vs 0.3 cm). 131 cases (56.2 %) with IAC underwent lobectomy, while 102 cases (43.8 %) underwent sub-lobar resection. Among the MIA cases, 220 cases (69.8 %) underwent sub-lobar resection, while 95 cases (30.2 %) underwent lobectomy. No recurrence and disease specific death was observed during the follow-up period, regardless of surgical strategy.

CONCLUSIONS

Indeterminate intraoperative FSs, posing diagnostic challenges in distinguishing between MIA and IAC. Sub-lobar resection presented the same long term survival benefit compared with the lobectomy for indeterminate lung adenocarcinoma within 3 cm during intraoperative FSs.

Comparison between community-acquired pneumonia and post-obstructive pneumonia associated with endobronchial tumors

BACKGROUND

Endobronchial tumors can infiltrate the bronchial wall or protrude into the bronchial lumen, causing post-obstructive pneumonia (POP). Differentiating between POP and community-acquired pneumonia (CAP) is challenging due to similar clinical, laboratory, and imaging findings, which can delay the diagnosis and treatment of endobronchial tumors.

METHODS

We compared general demographic information, laboratory test results, lung CT images, bronchoscopic observations, pathological findings between the POP group and the CAP group.

RESULTS

(1) The POP group consisted mainly of older individuals (mean age 69 vs. 56 years; P < 0.05), males (93.4% vs. 47.1%; P < 0.05), and smokers (67.2% vs. 14.7%; P < 0.05). Clinical symptoms varied, with chest pain (23.0% vs. 11.8%; P < 0.05) and hemoptysis (26.2% vs. 10.8%; P < 0.05) more prevalent in the POP group. MSCT showed that bronchial wall thickening, bronchial stenosis, occlusion, obstructive emphysema, mucoid impaction, and endobronchial shadows occurred more frequently in POP, while consolidation and exudation shadows were predominant in CAP (P < 0.05). (2) In the POP group, neoplasms were the most frequent bronchoscopic findings (57 cases, 93.44%), especially in the upper lungs. Squamous cell carcinoma was the primary pathological type (52 cases, 85.25%). The average delay in diagnosing endobronchial tumors was 214.8 days. In the POP group, 34 cases (55.74%) had abnormal CT images in the past and did not undergo bronchoscopy, resulting in delayed diagnosis. (3) Factors such as gender, age, bronchial occlusion, stenosis, mucus embolism, and intraluminal shadow were determined to be independent risk factors for endobronchial tumors (P < 0.05 and OR > 1).

CONCLUSIONS

Endobronchial tumors combined with POP are easily misdiagnosed as CAP in the early stage. Factors like bronchial occlusion, stenosis, mucus embolism, and intraluminal shadows on MSCT are significant independent risk factors for these tumors, indicating the need for early bronchoscopy.

The Correlation Between the Natural Course, Pathologic Properties With Ki-67 Expression in Lung Adenocarcinoma Presenting as Ground-Glass Nodules

BACKGROUND

With the increasing use of lung cancer screening, the detection of ground glass nodules (GGNs) has risen. However, the natural course of GGNs and their relationship to pathologic features remains unclear. Differentiating between invasive and pre-invasive lesions based on GGN growth may improve clinical intervention timing. Ki-67, a proliferation marker, holds value in assessing tumor malignancy. This study analyzes the association between GGN growth, pathology, and Ki-67 expression to provide new insights into early-stage lung cancer management.

METHODS

We retrospectively evaluated 183 GGNs with at least two preoperative CT scans. Nodule location, type, natural course, and volume doubling time (VDT) were compared between invasive adenocarcinoma (IAC) and pre-IAC groups. We also assessed differences in Ki-67 expression and correlated VDT with Ki-67 levels.

RESULTS

A total of 183 nodules were finally included; gender, nodule location, smoking history, and duration of follow-up did not differ between the IAC group and the pre-IAC group, whereas age was statistically different between the two groups. Of the 183 nodules, 52 showed growth and the predominant pathologic type was IAC, these IACs showed more PSN in nodule type, while the IAC group showed more significant differences in nodule type, nodules growth, and VDT than the pre-IAC group. There were also differences in pathologic type and VDT between different Ki-67 expression groups, and Ki-67 expression gradually increased as VDT decreased.

CONCLUSION

Lung adenocarcinoma (LUAD) presenting as GGNs exhibit distinct natural courses among pathologic subtypes. VDT effectively distinguishes these growth characteristics, with IACs showing shorter VDT. The significant correlation between VDT and Ki-67 expression suggests that combining these parameters may provide valuable insights into the biological behavior and invasiveness of LUAD.

Stereologic consequences of iatrogenic collapse: The morphology of adenocarcinoma in situ overlaps with invasive patterns. Proposal for a necessary modified classification of pulmonary adenocarcinomas

Recognizing non-invasive growth patterns is necessary for correct diagnosis, invasive size determination and pT-stage in resected non-small cell lung carcinoma. Due to iatrogenic collapse after resection, the distinction between adenocarcinoma in-situ (AIS) and invasive adenocarcinoma may be difficult. The aim of this study is to investigate the complex morphology of non-mucinous non-invasive patterns of AIS in resection specimen with iatrogenic collapse, and to relate this to follow-up. The effects of iatrogenic collapse on the morphology of collapsed AIS were simulated in a mathematical model. Three dimensional related criteria applied in a modified classification, using also cytokeratin 7 and elastin as additional stains, in two independent retrospective cohorts of primary pulmonary adenocarcinomas ≤3 cm resection specimen with available follow-up information. The model demonstrated that infolding of alveolar walls occurs during iatrogenic collapse and lead to a significant increase in tumor cell heights in maximal collapse areas, compared to less collapsed areas. The morphology of infolded AIS overlaps with patterns described as papillary and acinar adenocarcinoma according to the WHO classification, necessitating an adaptation. The modified classification incorporates recognition of iatrogenic and biologic collapse, tangential cutting effect true invasion and surrogate markers of invasion i.e. grey zone, covering a multilayering falling short of micropapillary, cribriform and solid alveolar filling growth. The use of elastin and CK7 staining aids in the morphologic recognition of iatrogenic collapsed AIS and the distinction from invasive adenocarcinoma. Out of a total of 70 resection specimens 1 case was originally classified as AIS and 9 were reclassified as iatrogenic collapsed AIS. Patients with collapsed AIS showed a 100 % recurrence-free survival after a mean follow-up time of 69.5 months. With the current WHO classification, AIS is overdiagnosed as invasive adenocarcinoma due to infolding. The modified classification facilitates the diagnosis of AIS.

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BACKGROUND

This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.

METHODS

Non-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong's test was used to compare the CPIs group with the VMIs group.

RESULTS

When distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong's test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).

CONCLUSION

The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

A clinically effective model based on cell-free DNA methylation and low-dose CT for risk stratification of pulmonary nodules

Accurate, non-invasive, and cost-effective tools are needed to assist pulmonary nodule diagnosis and management due to increasing detection by low-dose computed tomography (LDCT). We perform genome-wide methylation sequencing on malignant and non-malignant lung tissues and designed a panel of 263 differential DNA methylation regions, which is used for targeted methylation sequencing on blood cell-free DNA (cfDNA) in two prospectively collected and retrospectively analyzed multicenter cohorts. We develop and optimize an integrative model for risk stratification of pulmonary nodules based on 40 cfDNA methylation biomarkers, age, and five simple computed tomography (CT) imaging features using machine learning approaches and validate its good performance in two cohorts. Using the two-threshold strategy can effectively reduce unnecessary invasive surgeries, overtreatment costs, and injury for patients with benign nodules while advising immediate treatment for patients with lung cancer, which can potentially improve the overall diagnosis of lung cancer following LDCT/CT screening.

Habitat Imaging With Tumoral and Peritumoral Radiomics for Prediction of Lung Adenocarcinoma Invasiveness on Preoperative Chest CT: A Multicenter Study

BACKGROUND. Tumor growth processes result in spatial heterogeneity, with the development of tumor subregions (i.e., habitats) having unique biologic characteristics. OBJECTIVE. The purpose of our study was to develop and validate a habitat model combining tumor and peritumoral radiomic features on chest CT for predicting invasiveness of lung adenocarcinoma. METHODS. This retrospective study included 1156 patients (mean age, 57.5 years; 464 men, 692 women), from three centers and a public dataset, who underwent chest CT before lung adenocarcinoma resection (variable date ranges across datasets). Patients from one center formed training (n = 500) and validation (n = 215) sets; patients from the other sources formed three external test sets (n = 249, 113, 79). For each patient, a single nodule was manually segmented on chest CT. The nodule segmentation was combined with an automatically generated 4-mm peritumoral region into a whole-volume volume of interest (VOI). A gaussian mixture model (GMM) identified voxel clusters with similar first-order energy across patients. GMM results were used to divide each patient's whole-volume VOI into multiple habitats, which were defined consistently across patients. Radiomic features were extracted from each habitat. After feature selection, a habitat model was developed for predicting invasiveness, with the use of pathologic assessment as a reference. An integrated model was constructed, combining features extracted from habitats and whole-volume VOIs. Model performance was evaluated, including in subgroups based on nodule density (pure ground-glass, part-solid, and solid). The code for habitat imaging and model construction is publicly available (https://github.com/Shangyoulan/Habitat/). RESULTS. Invasive cancer was diagnosed in 626 of 1156 patients. GMM identified four as the optimal number of voxel clusters and thus of per-patient tumor habitats. The habitat model had an AUC of 0.932 in the validation set and 0.881, 0.880, and 0.764 in the three external test sets. The integrated model had an AUC of 0.947 in the validation set and 0.936, 0.908, and 0.800 in the three external test sets. In the three external test sets combined, across nodule densities, AUCs for the habitat model were 0.836-0.869 and for the integrated model were 0.846-0.917. CONCLUSION. Habitat imaging combining tumoral and peritumoral radiomic features could help predict lung adenocarcinoma invasiveness. Prediction is improved when combining information on tumor subregions and the tumor overall. CLINICAL IMPACT. The findings may aid personalized preoperative assessments to guide clinical decision-making in lung adenocarcinoma.

Precision patient selection for improved detection of circulating genetically abnormal cells in pulmonary nodules

Circulating genetically abnormal cells (CACs) have emerged as a promising biomarker for the early diagnosis of lung cancer, particularly in patients with pulmonary nodules. However, their performance may be suboptimal in certain patient populations. This study aimed to refine patient selection to improve the detection of CACs in pulmonary nodules. A retrospective analysis was conducted on 241 patients with pulmonary nodules who had undergone pathological diagnosis through surgical tissue specimens. Utilizing consensus clustering analysis, the patients were categorized into three distinct clusters. Cluster 1 was characterized by older age, larger nodule size, and a higher prevalence of hypertension and diabetes. Notably, the diagnostic efficacy of CACs in Cluster 1 surpassed that of the overall patient population (AUC: 0.855 vs. 0.689, P = 0.044). Moreover, for Cluster 1, an integrated diagnostic model was developed, incorporating CACs, sex, maximum nodule type, and maximum nodule size, resulting in a further improved AUC of 0.925 (95% CI 0.846-1.000). In conclusion, our study demonstrates that CACs detection shows better diagnostic performance in aiding the differentiation between benign and malignant nodules in older patients with larger pulmonary nodules and comorbidities such as diabetes and hypertension. Further research and validation are needed to explore how to better integrate CACs detection into clinical practice.

Tenascin-C in the early lung cancer tumor microenvironment promotes progression through integrin αvβ1 and FAK

Pre-cancerous lung lesions are commonly initiated by activating mutations in the RAS pathway, but do not transition to lung adenocarcinomas (LUAD) without additional oncogenic signals. Here, we show that expression of the extracellular matrix protein Tenascin-C (TNC) is increased in and promotes the earliest stages of LUAD development in oncogenic KRAS-driven lung cancer mouse models and in human LUAD. TNC is initially expressed by fibroblasts and its expression extends to tumor cells as the tumor becomes invasive. Genetic deletion of TNC in the mouse models reduces early tumor burden and high-grade pathology and diminishes tumor cell proliferation, invasion, and focal adhesion kinase (FAK) activity. TNC stimulates cultured LUAD tumor cell proliferation and migration through engagement of αv-containing integrins and subsequent FAK activation. Intringuingly, lung injury causes sustained TNC accumulation in mouse lungs, suggesting injury can induce additional TNC signaling for early tumor cell transition to invasive LUAD. Biospecimens from patients with stage I/II LUAD show TNC in regions of FAK activation and an association of TNC with tumor recurrence after primary tumor resection. These results suggest that exogenous insults that elevate TNC in the lung parenchyma interact with tumor-initiating mutations to drive early LUAD progression and local recurrence.

The 2023 American Association for Thoracic Surgery (AATS) Expert Consensus Document: Management of subsolid lung nodules

OBJECTIVE

Lung cancers that present as radiographic subsolid nodules represent a subtype with distinct biological behavior and outcomes. The objective of this document is to review the existing literature and report consensus among a group of multidisciplinary experts, providing specific recommendations for the clinical management of subsolid nodules.

METHODS

The American Association for Thoracic Surgery Clinical Practice Standards Committee assembled an international, multidisciplinary expert panel composed of radiologists, pulmonologists, and thoracic surgeons with established expertise in the management of subsolid nodules. A focused literature review was performed with the assistance of a medical librarian. Expert consensus statements were developed with class of recommendation and level of evidence for each of 4 main topics: (1) definitions of subsolid nodules (radiology and pathology), (2) surveillance and diagnosis, (3) surgical interventions, and (4) management of multiple subsolid nodules. Using a modified Delphi method, the statements were evaluated and refined by the entire panel.

RESULTS

Consensus was reached on 17 recommendations. These consensus statements reflect updated insights on subsolid nodule management based on the latest literature and current clinical experience, focusing on the correlation between radiologic findings and pathological classifications, individualized subsolid nodule surveillance and surgical strategies, and multimodality therapies for multiple subsolid lung nodules.

CONCLUSIONS

Despite the complex nature of the decision-making process in the management of subsolid nodules, consensus on several key recommendations was achieved by this American Association for Thoracic Surgery expert panel. These recommendations, based on evidence and a modified Delphi method, provide guidance for thoracic surgeons and other medical professionals who care for patients with subsolid nodules.

Ten-Year Follow-Up of Lung Cancer Patients with Resected Stage IA Invasive Non-Small Cell Lung Cancer

OBJECTIVE

The purpose of this study was to assess 10-year follow-up outcomes after surgical resection in patients with stage IA invasive non-small cell lung cancer (NSCLC) based on postoperative pathological diagnosis.

METHODS

Patients with stage IA invasive NSCLC who underwent resection between December 2008 and December 2013 were reviewed. Patients were categorized into the pure-ground glass opacity (pGGO), mixed-ground glass opacity (mGGO), and solid groups based on consolidation to tumor ratio (CTR). Postoperative survival and risk of recurrence and developing secondary primary lung cancer were analyzed in each group.

RESULTS

Among the 645 stage IA invasive NSCLC, the 10-year overall survival and recurrence-free survival rate was 79.38% and 77.44%, respectively. The 10-year overall survival for pGGO, mGGO, and solid group of patients was 95.08%, 86.21%, and 72.39%, respectively. The respective recurrence-free survival rate was 100%, 89.82%, and 65.83%. Multivariable Cox regression analysis associated tumor size and GGO components with recurrence and younger age, and tumors with GGO components were associated with longer overall survival. The cumulative incidence curve indicated no recurrence of GGO lung cancer ≥ 5 years postoperatively. Our cohort indicated that the number and stations of dissected lymph node did not influence long-term prognosis of IA invasive NSCLC.

CONCLUSIONS

Recurrence of invasive stage IA NSCLC with GGO was more prevalent in patients with tumor size >1 cm and CTR > 0.5, occurring within 5 years after surgery. This will provide important evidence for follow-up strategies in these patients.

Estimation of pathological subtypes in subsolid lung nodules using artificial intelligence

Objective

This study aimed to investigate the value of artificial intelligence (AI) for distinguishing pathological subtypes of invasive pulmonary adenocarcinomas in patients with subsolid nodules (SSNs).

Materials and methods

This retrospective study included 110 consecutive patients with 120 SSNs. The qualitative and quantitative imaging characteristics of SSNs were extracted automatically using an artificially intelligent assessment system. Then, radiologists had to verify these characteristics again. We split all cases into two groups: non-IA including 11 Atypical adenomatous hyperplasia (AAH) and 25 adenocarcinoma in situ (AIS) or IA including 7 minimally invasive adenocarcinoma (MIA) and 77 invasive adenocarcinoma (IAC). Variables that exhibited statistically significant differences between the non-IA and IA in the univariate analysis were included in the multivariate logistic regression analysis. Receiver operating characteristic (ROC) analyses were conducted to determine the cut-off values and their diagnostic performances.

Results

Multivariate logistic regression analysis showed that the major diameter (odds ratio [OR] = 1.38; 95 % confidence interval [CI], 1.02-1.87; P = 0.036) and entropy of three-dimensional(3D) CT value (OR = 3.73, 95 % CI, 1.13-2.33, P = 0.031) were independent risk factors for adenocarcinomas. The cut-off values of the major diameter and the entropy of 3D CT value for the diagnosis of invasive adenocarcinoma were 15.5 mm and 5.17, respectively. To improve the classification performance, we fused the major diameter and the entropy of 3D CT value as a combined model, and the (AUC） of the model was 0.868 (sensitivity = 0.845, specificity = 0.806).

Conclusion

The major diameter and entropy of 3D CT value can distinguish non-IA from IA. AI can improve performance in distinguishing pathological subtypes of invasive pulmonary adenocarcinomas in patients with SSNs.

Postoperative chronic operation-related symptoms after minimally invasive lung surgery: a prospective observational protocol

INTRODUCTION

Significant numbers of patients undergoing minimally invasive lung surgery develop chronic symptoms such as chronic pain and chronic cough after surgery, which may lead to a reduced quality of life (QoL). Despite this, there remains a dearth of high-quality prospective studies on this topic. Therefore, our study aims to systematically investigate the incidence and progression of long-term chronic symptoms following minimally invasive lung surgery, as well as changes in patient's psychological status and long-term QoL.

METHODS

This is a single-centre, observational, prospective study that included patients with stage I non-small cell lung cancer or benign lesions. Prior to surgery, patients' baseline levels of chronic pain, chronic cough and sleep will be documented. Anxiety, depression and QoL assessments will be conducted using the Hospital Anxiety and Depression Scale (HADS) and the European Organisation for Research and Treatment of Cancer (EORTC) 30-item QoL Questionnaire (QLQ-C30). Following surgery, pain and cough will be evaluated during the initial 3 days using the Numeric Pain Rating Scale and Visual Analogue Scale score, with assessments performed thrice daily. Additionally, sleep status will be recorded daily during this period. Subsequently, postoperative chronic symptoms and QoL will be assessed at weeks 1, 2, 4, 12, 26 and 52. Chronic cough will be evaluated using the Leicester Cough Questionnaire, chronic pain will be assessed via the Brief Pain Inventory and McGill Pain Questionnaire while the EORTC QLQ-C30 questionnaire and HADS will provide continuous monitoring of QoL, anxiety and depression statuses. Data will also include the timing of chronic symptom onset, predisposing factors, as well as aggravating and relieving factors.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the Ethics Committees of Fujian Medical University Union Hospital. The findings will be disseminated in peer-reviewed publications.

TRIAL REGISTRATION NUMBER

NCT06016881.

The Overlooked Cornerstone in Precise Medicine: Personalized Postoperative Surveillance Plan for NSCLC

Non-small cell lung cancer recurrence after curative-intent surgery remains a challenge despite advancements in treatment. We review postoperative surveillance strategies and their impact on overall survival, highlighting recommendations from clinical guidelines and controversies. Studies suggest no clear benefit from more intensive imaging, whereas computed tomography scans reveal promise in detecting recurrence. For early-stage disease, including ground-glass opacities and adenocarcinoma in situ or minimally invasive adenocarcinoma, less frequent surveillance may suffice owing to favorable prognosis. Liquid biopsy, especially circulating tumor deoxyribonucleic acid, holds potential for detecting minimal residual disease. Clinicopathologic factors and genomic profiles can also provide information about site-specific metastases. Machine learning may enable personalized surveillance plans on the basis of multi-omics data. Although precision medicine transforms non-small cell lung cancer treatment, optimizing surveillance strategies remains essential. Tailored surveillance strategies and emerging technologies may enhance early detection and improve patients' survival, necessitating further research for evidence-based protocols.

Diagnostic accuracy of low-dose dual-input computed tomography perfusion in the differential diagnosis of pulmonary benign and malignant ground-glass nodules

This study aimed to evaluate the value of low-dose dual-input computed tomography perfusion (CTP) imaging in the differential diagnosis of benign and malignant pulmonary ground-glass opacity nodules (GGO). A retrospective study was conducted in patients with GGO who underwent CTP in our hospital from January 2021 to October 2023. All nodules were confirmed via pathological analysis or disappeared during follow-up. Postprocessing analysis was conducted using the dual-input perfusion mode (pulmonary artery and bronchial artery) of the body perfusion software to measure the perfusion parameters of the pulmonary GGOs. A total of 101 patients with pulmonary GGOs were enrolled in this study, including 43 benign and 58 malignant nodules. The dose length product of the CTP (348 mGy.cm) was < 75% of the diagnostic reference level of the unenhanced chest CT (470 mGy.cm). The effective radiation dose was 4.872 mSV. The blood flow (BF), blood volume (BV), mean transit time (MTT), and flow extraction product (FEP) of malignant nodules were higher than those of the benign nodules (p < 0.05). The FEP had the highest accuracy for the diagnosis of malignant nodules (area under the curve [AUC] = 0.821, 95% confidence interval [CI]: 0.735-0.908) followed by BV (AUV = 0.713, 95% CI 0.608-0.819), BF (AUC = 0.688, 95% CI 0.587-0.797), and MTT (AUC = 0.616, 95% CI 0.506-0.726). When the FEP was ≥ 19.12 mL/100 mL/min, the sensitivity was 91.5% and the specificity was 62.8%. To distinguish between benign nodules and malignant nodules, the AUC of the combination of BV and FEP was 0.816 (95% CI 0.728-0.903), whereas the AUC of the combination of BF, BV, MTT, and FEP was 0.814 (95% CI 0.729-0.900). Low-dose dual-input perfusion CT was extremely effective in distinguishing between benign from malignant pulmonary GGOs, with FEP exhibiting the highest diagnostic capability.

Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma

Objective

Invasive lung adenocarcinoma（ILA） with micropapillary (MPP)/solid (SOL) components has a poor prognosis. Preoperative identification is essential for decision-making for subsequent treatment. This study aims to construct and evaluate a super-resolution（SR） enhanced radiomics model designed to predict the presence of MPP/SOL components preoperatively to provide more accurate and individualized treatment planning.

Methods

Between March 2018 and November 2023, patients who underwent curative intent ILA resection were included in the study. We implemented a deep transfer learning network on CT images to improve their resolution, resulting in the acquisition of preoperative super-resolution CT (SR-CT) images. Models were developed using radiomic features extracted from CT and SR-CT images. These models employed a range of classifiers, including Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). The diagnostic performance of the models was assessed by measuring the area under the curve (AUC).

Result

A total of 245 patients were recruited, of which 109 (44.5 %) were diagnosed with ILA with MPP/SOL components. In the analysis of CT images, the SVM model exhibited outstanding effectiveness, recording AUC scores of 0.864 in the training group and 0.761 in the testing group. When this SVM approach was used to develop a radiomics model with SR-CT images, it recorded AUCs of 0.904 in the training and 0.819 in the test cohorts. The calibration curves indicated a high goodness of fit, while decision curve analysis (DCA) highlighted the model's clinical utility.

Conclusion

The study successfully constructed and evaluated a deep learning（DL）-enhanced SR-CT radiomics model. This model outperformed conventional CT radiomics models in predicting MPP/SOL patterns in ILA. Continued research and broader validation are necessary to fully harness and refine the clinical potential of radiomics when combined with SR reconstruction technology.

Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs).

MATERIALS AND METHODS

Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.

RESULTS

Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05).

CONCLUSION

A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.

Development and validation of a risk prediction model for invasiveness of pure ground-glass nodules based on a systematic review and meta-analysis

BACKGROUND

Assessing the aggressiveness of pure ground glass nodules early on significantly aids in making informed clinical decisions.

OBJECTIVE

Developing a predictive model to assess the aggressiveness of pure ground glass nodules in lung adenocarcinoma is the study's goal.

METHODS

A comprehensive search for studies on the relationship between computed tomography(CT) characteristics and the aggressiveness of pure ground glass nodules was conducted using databases such as PubMed, Embase, Web of Science, Cochrane Library, Scopus, Wanfang, CNKI, VIP, and CBM, up to December 20, 2023. Two independent researchers were responsible for screening literature, extracting data, and assessing the quality of the studies. Meta-analysis was performed using Stata 16.0, with the training data derived from this analysis. To identify publication bias, Funnel plots and Egger tests and Begg test were employed. This meta-analysis facilitated the creation of a risk prediction model for invasive adenocarcinoma in pure ground glass nodules. Data on clinical presentation and CT imaging features of patients treated surgically for these nodules at the Third Affiliated Hospital of Kunming Medical University, from September 2020 to September 2023, were compiled and scrutinized using specific inclusion and exclusion criteria. The model's effectiveness for predicting invasive adenocarcinoma risk in pure ground glass nodules was validated using ROC curves, calibration curves, and decision analysis curves.

RESULTS

In this analysis, 17 studies were incorporated. Key variables included in the model were the largest diameter of the lesion, average CT value, presence of pleural traction, and spiculation. The derived formula from the meta-analysis was: 1.16×the largest lesion diameter + 0.01 × the average CT value + 0.66 × pleural traction + 0.44 × spiculation. This model underwent validation using an external set of 512 pure ground glass nodules, demonstrating good diagnostic performance with an ROC curve area of 0.880 (95% CI: 0.852-0.909). The calibration curve indicated accurate predictions, and the decision analysis curve suggested high clinical applicability of the model.

CONCLUSION

We established a predictive model for determining the invasiveness of pure ground-glass nodules, incorporating four key radiological indicators. This model is both straightforward and effective for identifying patients with a high likelihood of invasive adenocarcinoma.

The diagnostic potential role of thioredoxin reductase and TXNRD1 in early lung adenocarcinoma: A cohort study

BACKGROUND

Lung adenocarcinoma (LUAD) is the primary form of lung cancer, yet the reliable biomarkers for early diagnosis remain insufficient. Thioredoxin reductase (TrxR) is strongly linked to the occurrence, development, and drug resistance of lung cancer, making it a potential biomarker. However, further research is required to assess its diagnostic value in LUAD.

METHODS

A retrospective analysis was performed on patients who underwent pulmonary nodule resection at our center from 2018 to 2022. Clinical data, including preoperative TrxR levels, imaging, and laboratory characteristics, were identified as study variables. Two prediction models were constructed using multiple logistic regression, and their prediction performance was evaluated comprehensively. Besides, bioinformatics analyses of TrxR coding genes including differential expression, functional enrichment, immune infiltration, drug sensitivity, and single-cell landscape were performed based on TCGA database, which were subsequently validated by Human Protein Atlas.

RESULTS

A total of 506 eligible patients (72 benign lesions, 77 AISs, 185 MIAs and 172 IACs) were identified in the clinical cohort. Two TrxR-based models were developed, which were able to distinguish between benign and malignant pulmonary nodules, as well as pathological subtypes of LUAD, respectively. The models exhibited good predictive ability with all AUC values ranging from 0.7 to 0.9. Based on calibration curves and clinical decision analysis, the nomogram models showed high reliability. Functional analysis indicated that TXNRD1 primarily participated in cell cycle and lipid metabolism. Immune infiltration analysis showed that TXNRD1 has a strong association with immune cells and could impact immunotherapy. Then, we identified small molecular compounds that inhibit TXNRD1 and confirmed TXNRD1 expression by single-cell landscape and immunohistochemistry.

CONCLUSION

This study validated the diagnostic value of TrxR and TXNRD1 in clinical cohorts and transcriptional data, respectively. TrxR and TXNRD1 could be used in the risk diagnosis of early LUAD and facilitate personalized treatment strategies.

Invasion and Grading of Pulmonary Non-Mucinous Adenocarcinoma

Lung adenocarcinoma staging and grading were recently updated to reflect the link between histologic growth patterns and outcomes. The lepidic growth pattern is regarded as "in-situ," whereas all other patterns are regarded as invasive, though with stratification. Solid, micropapillary, and complex glandular patterns are associated with worse prognosis than papillary and acinar patterns. These recent changes have improved prognostic stratification. However, multiple pitfalls exist in measuring invasive size and in classifying lung adenocarcinoma growth patterns. Awareness of these limitations and recommended practices will help the pathology community achieve consistent prognostic performance and potentially contribute to improved patient management.

The Prognostic Impact of Epidermal Growth Factor Receptor Mutation in Clinical Stage I Lung Adenocarcinoma

BACKGROUND

This study investigated the prognostic impact of epidermal growth factor receptor (EGFR) mutation in clinical stage I lung adenocarcinoma patients.

METHODS

Data for 952 patients who received surgical resection and underwent detection of oncogenic driver mutations were retrospectively collected. Recurrence-free survival (RFS) and overall survival (OS) were estimated by the Kaplan-Meier method and compared using the log-rank test. The adjusted hazard ratio (aHR) with 95% CI of the prognosticator was calculated by Cox proportional hazards model, and cumulative incidence function was measured by competing risk regression model.

RESULTS

EGFR mutation was detected in 581 patients (61.0%) and was more frequent in women (63.9%), nonsmokers (85.5%), and those with ground-glass nodules (GGNs; 56.6%). EGFR mutation was not associated with recurrence and death in the entire cohort or GGN cohort. However, for patients with radiologic pure-solid appearance, EGFR mutation was an independent risk factor for RFS (aHR, 1.623; 95% CI, 1.192-2.210) and distant recurrence (aHR, 1.863; 95% CI, 1.311-2.650), but not OS. Subsequently, subgroup analysis based on EGFR mutation subtypes, including exon 19 deletions (19-Del), exon 21 L858R substitution (L858R), and rare mutations in patients with radiologic pure-solid appearance, revealed that all 3 subtypes have poorer RFS (19-Del: aHR, 1.424; 95% CI, 0.991-2.047; L858R: aHR, 1.708; 95% CI, 1.172-2.490; rare mutations: aHR, 2.500; 95% CI, 1.400-4.465) and higher prevalent distant recurrence (19-Del: aHR, 1.595; 95% CI, 1.061-2.400; L858R: aHR, 2.073; 95% CI, 1.371-3.140; rare mutations: aHR, 2.657; 95% CI, 1.397-5.050) compared with wild-type.

CONCLUSIONS

In clinical stage I lung adenocarcinoma, EGFR mutation was associated with worse RFS and higher prevalent distant recurrence in patients with radiologic pure-solid appearance but not in patients with GGN.

Genomic landscape and tumor mutational features of resected preinvasive to invasive lung adenocarcinoma

Introduction

Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are considered pre-invasive forms of lung adenocarcinoma (LUAD) with a 5-year recurrence-free survival of 100%. We investigated genomic profiles in early tumorigenesis and distinguished mutational features of preinvasive to invasive adenocarcinoma (IAC) for early diagnosis.

Methods

Molecular information was obtained from a 689-gene panel in the 90 early-stage LUAD Chinese patients using next-generation sequencing. Gene signatures were identified between pathology subtypes, including AIS/MIA (n=31) and IAC (n=59) in this cohort. Mutational and clinicopathological information was also obtained from the Cancer Genome Atlas (TCGA) as a comparison cohort.

Results

A higher mutation frequency of TP53, RBM10, MUC1, CSMD, MED1, LRP1B, GLI1, MAP3K, and RYR2 was observed in the IAC than in the AIS/MIA group. The AIS/MIA group showed higher mutation frequencies of ERBB2, BRAF, GRIN2A, and RB1. Comparable mutation rates for mutually exclusive genes (EGFR and KRAS) across cohorts highlight the critical transition to invasive LUAD. Compared with the TCGA cohort, EGFR, KRAS, TP53, and RBM10 were frequently mutated in both cohorts. Despite limited gene mutation overlap between cohorts, we observed variant mutation types in invasive LUAD. Additionally, the tumor mutation burden (TMB) values were significantly lower in the AIS/MIA group than in the IAC group in both the Chinese cohort (P=0.0053) and TCGA cohort (P<0.01).

Conclusion

These findings highlight the importance of distinguishing preinvasive from invasive LUAD in the early stages of LUAD and both pathology and molecular features in clinical practice, revealing genomic tumor heterogeneity and population differences.

Precise and automated lung cancer cell classification using deep neural network with multiscale features and model distillation

Lung diseases globally impose a significant pathological burden and mortality rate, particularly the differential diagnosis between adenocarcinoma, squamous cell carcinoma, and small cell lung carcinoma, which is paramount in determining optimal treatment strategies and improving clinical prognoses. Faced with the challenge of improving diagnostic precision and stability, this study has developed an innovative deep learning-based model. This model employs a Feature Pyramid Network (FPN) and Squeeze-and-Excitation (SE) modules combined with a Residual Network (ResNet18), to enhance the processing capabilities for complex images and conduct multi-scale analysis of each channel's importance in classifying lung cancer. Moreover, the performance of the model is further enhanced by employing knowledge distillation from larger teacher models to more compact student models. Subjected to rigorous five-fold cross-validation, our model outperforms existing models on all performance metrics, exhibiting exceptional diagnostic accuracy. Ablation studies on various model components have verified that each addition effectively improves model performance, achieving an average accuracy of 98.84% and a Matthews Correlation Coefficient (MCC) of 98.83%. Collectively, the results indicate that our model significantly improves the accuracy of disease diagnosis, providing physicians with more precise clinical decision-making support.

Primary lung cancer in children and adolescents

PURPOSE

Primary lung cancer is extremely rare in children and adolescents. The aim of this study is to clarify clinical features and outcomes of primary lung cancer in children and adolescents.

METHODS

Young patients (aged ≤ 20 years) diagnosed as primary lung cancer between 2012 and 2023 were retrospective reviewed. According to radiological appearance of the nodules, they were divided into solid nodule (SN) group and ground glass opacity (GGO) group.

RESULTS

A total of 74 patients were identified, with a median age at diagnosis of 18 years old (range: 11-20), including 7 patients in SN group and 67 patients in GGO group. In the GGO group, none of the nodules enlarged or changed during an average surveillance period of 10.8 months before surgery, except one. Wedge resection was the most common procedure (82.1%), followed by segmentectomy (16.4%) and lobectomy (1.5%). Histopathological analysis revealed that 64.2% of GGO nodules were adenocarcinoma in situ and minimally invasive adenocarcinomas, while the remaining 35.8% were invasive adenocarcinomas. Mutational analysis was performed in nine patients, with mutations identified in all cases. After a mean follow-up period of 1.73 ± 1.62 years, two patients in the SN group died due to multiple distant metastases, while all patients in the GGO group survived without recurrence. The overall survival (100%) of the GGO group was significantly higher than SN group (66.7%).

CONCLUSIONS

Primary lung cancer in children and adolescents are rare and histopathological heterogeneous. Persistent GGO nodules may indicate early-stage lung adenocarcinoma in children and adolescents.

Sublobectomy and lymph node sampling are adequate for patients with invasive lung adenocarcinoma presenting as pure ground glass nodules

PURPOSE

In this study, we aimed to investigate the prognosis of invasive lung adenocarcinoma that manifests as pure ground glass nodules (pGGNs) and confirm the effectiveness of sublobectomy and lymph node sampling in patients with pGGN-featured invasive adenocarcinoma (IAC).

MATERIALS AND METHODS

We retrospectively enrolled 139 patients with pGGN-featured IAC, who underwent complete resection in two medical institutions between January 2011 and May 2022. Stratification analysis was conducted to ensure balanced baseline characteristics among the patients. The 5-year overall survival (OS) and disease-free survival (DFS) rates were compared between the groups using Kaplan-Meier survival curves and log-rank test.

RESULTS

The 5-year OS and DFS rates for patients with IAC presenting as pGGNs after surgery were 96.5% and 100%, respectively. No lymph node metastasis or recurrence was observed in any of the enrolled patients. There was no statistically significant difference in the 5-year OS between patients who underwent lobectomy or sublobectomy, along with lymph node resection or sampling.

CONCLUSION

IAC presented as pGGNs exhibited low-grade malignancy and had a relatively good prognosis. Therefore, these patients may be treated with sublobectomy and lymph node sampling.

A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma

PURPOSE

Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients.

MATERIALS AND METHODS

Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models.

RESULTS

In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability.

CONCLUSION

Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.

Ten-year follow-up outcomes of limited resection trial for radiologically less-invasive lung cancer

BACKGROUND

The JCOG0804/WJOG4507L single-arm confirmatory trial indicated a satisfactory 10-year prognosis for patients who underwent limited resection for radiologically less-invasive lung cancer. However, only one prospective trial has reported a 10-year prognosis.

METHODS

We conducted a multicenter prospective study coordinated by the National Cancer Center Hospital East and Kanagawa Cancer Center. We analyzed the long-term prognosis of 100 patients who underwent limited resection of a radiologically less-invasive lung cancer in the peripheral lung field. We defined radiologically less-invasive lung cancer as lung adenocarcinoma with a maximum tumor diameter of ≤2 cm, tumor disappearance ratio of ≥0.5 and cN0. The primary endpoint was the 10-year local recurrence-free survival.

RESULTS

Our patients, with a median age of 62 years, included 39 males. A total of 58 patients were non-smokers; 87 had undergone wide wedge resection and 9 underwent segmentectomy. A total of four cases were converted to lobectomy because of the presence of poorly differentiated components in the frozen specimen or insufficient margin with segmentectomy. The median follow-up duration was 120.9 months. The 10-year recurrence-free survival and overall survival rates of patients with lung cancer were both 96.0%. Following the 10-year long-term follow-up, two patients experienced recurrences at resection ends after wedge resection.

CONCLUSIONS

Limited resection imparted a satisfactory prognosis for patients with radiologically less-invasive lung cancer, except two cases of local recurrence >5 years after surgery. These findings suggest that patients with this condition who underwent limited resection may require continued follow-up >5 years after surgery.

A metabolomics study on carcinogenesis of ground-glass nodules

Objective

This study aimed to identify differential metabolites and key metabolic pathways between lung adenocarcinoma (LUAD) tissues and normal lung (NL) tissues using metabolomics techniques, to discover potential biomarkers for the early diagnosis of lung cancer.

Material and Methods

Forty-five patients with primary ground-glass nodules (GGN) identified on computed tomography imaging and who were willing to undergo surgery at Shanghai General Hospital from December 2021 to December 2022 were recruited to the study. All participants underwent video thoracoscopy surgery with segmental or wedge resection of the lung. Tissue samples for pathological examination were collected from the site of ground-glass nodules (GGN) lesion and 3 cm away from the lesion (NL). The pathology results were 35 lung adenocarcinoma (LUAD) cases (13 invasive adenocarcinoma, 14 minimally invasive adenocarcinoma, and eight adenocarcinoma in situ), 10 benign samples, and 45 NL tissues. For the untargeted metabolomics technique, 25 LUAD samples were assigned as the case group and 30 NL tissues as the control group. For the targeted metabolomics technique, ten LUAD samples were assigned as the case group and 15 NL tissues as the control group. Samples were analyzed by untargeted and targeted metabolomics, with liquid chromatography-tandem mass spectrometry detection used as part of the experimental procedure.

Results

Untargeted metabolomics revealed 164 differential metabolites between the case and control groups, comprising 110 up regulations and 54 down regulations. The main metabolic differences found by the untargeted method were organic acids and their derivatives. Targeted metabolomics revealed 77 differential metabolites between the case and control groups, comprising 69 up regulations and eight down regulations. The main metabolic changes found by the targeted method were fatty acids, amino acids, and organic acids. The levels of organic acids such as lactic acid, fumaric acid, and malic acid were significantly increased in LUAD tissue compared to NL. Specifically, an increased level of L-lactic acid was found by both untargeted (variable importance in projection [VIP] = 1.332, fold-change [FC] = 1.678, q = 0.000) and targeted metabolomics (VIP = 1.240, FC = 1.451, q = 0.043). Targeted metabolomics also revealed increased levels of fumaric acid (VIP = 1.481, FC = 1.764, q = 0.106) and L-malic acid (VIP = 1.376, FC = 1.562, q = 0.012). Most of the 20 differential fatty acids identified were downregulated, including dodecanoic acid (VIP = 1.416, FC = 0.378, q = 0.043) and tridecane acid (VIP = 0.880, FC = 0.780, q = 0.106). Furthermore, increased levels of differential amino acids were found in LUAD samples.

Conclusion

Lung cancer is a complex and heterogeneous disease with diverse genetic alterations. The study of metabolic profiles is a promising research field in this cancer type. Targeted and untargeted metabolomics revealed significant differences in metabolites between LUAD and NL tissues, including elevated levels of organic acids, decreased levels of fatty acids, and increased levels of amino acids. These metabolic features provide valuable insights into LUAD pathogenesis and can potentially serve as biomarkers for prognosis and therapy response.

Synchrotron Radiation Refraction-Contrast Computed Tomography Based on X-ray Dark-Field Imaging Optics of Pulmonary Malignancy: Comparison with Pathologic Examination

Refraction-contrast computed tomography based on X-ray dark-field imaging (XDFI) using synchrotron radiation (SR) has shown superior resolution compared to conventional absorption-based methods and is often comparable to pathologic examination under light microscopy. This study aimed to investigate the potential of the XDFI technique for clinical application in lung cancer diagnosis. Two types of lung specimens, primary and secondary malignancies, were investigated using an XDFI optic system at beamline BL14B of the High-Energy Accelerator Research Organization Photon Factory, Tsukuba, Japan. Three-dimensional reconstruction and segmentation were performed on each specimen. Refraction-contrast computed tomographic images were compared with those obtained from pathological examinations. Pulmonary microstructures including arterioles, venules, bronchioles, alveolar sacs, and interalveolar septa were identified in SR images. Malignant lesions could be distinguished from the borders of normal structures. The lepidic pattern was defined as the invasive component of the same primary lung adenocarcinoma. The SR images of secondary lung adenocarcinomas of colorectal origin were distinct from those of primary lung adenocarcinomas. Refraction-contrast images based on XDFI optics of lung tissues correlated well with those of pathological examinations under light microscopy. This imaging method may have the potential for use in lung cancer diagnosis without tissue damage. Considerable equipment modifications are crucial before implementing them from the lab to the hospital in the near future.

Low-dose CT screening among never-smokers with or without a family history of lung cancer in Taiwan: a prospective cohort study

BACKGROUND

In Taiwan, lung cancers occur predominantly in never-smokers, of whom nearly 60% have stage IV disease at diagnosis. We aimed to assess the efficacy of low-dose CT (LDCT) screening among never-smokers, who had other risk factors for lung cancer.

METHODS

The Taiwan Lung Cancer Screening in Never-Smoker Trial (TALENT) was a nationwide, multicentre, prospective cohort study done at 17 tertiary medical centres in Taiwan. Eligible individuals had negative chest radiography, were aged 55-75 years, had never smoked or had smoked fewer than 10 pack-years and stopped smoking for more than 15 years (self-report), and had one of the following risk factors: a family history of lung cancer; passive smoke exposure; a history of pulmonary tuberculosis or chronic obstructive pulmonary disorders; a cooking index of 110 or higher; or cooking without using ventilation. Eligible participants underwent LDCT at baseline, then annually for 2 years, and then every 2 years up to 6 years thereafter, with follow-up assessments at each LDCT scan (ie, total follow-up of 8 years). A positive scan was defined as a solid or part-solid nodule larger than 6 mm in mean diameter or a pure ground-glass nodule larger than 5 mm in mean diameter. Lung cancer was diagnosed through invasive procedures, such as image-guided aspiration or biopsy or surgery. Here, we report the results of 1-year follow-up after LDCT screening at baseline. The primary outcome was lung cancer detection rate. The p value for detection rates was estimated by the χ2 test. Univariate and multivariable logistic regression analyses were used to assess the association between lung cancer incidence and each risk factor. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of LDCT screening were also assessed. This study is registered with ClinicalTrials.gov, NCT02611570, and is ongoing.

FINDINGS

Between Dec 1, 2015, and July 31, 2019, 12 011 participants (8868 females) were enrolled, of whom 6009 had a family history of lung cancer. Among 12 011 LDCT scans done at baseline, 2094 (17·4%) were positive. Lung cancer was diagnosed in 318 (2·6%) of 12 011 participants (257 [2·1%] participants had invasive lung cancer and 61 [0·5%] had adenocarcinomas in situ). 317 of 318 participants had adenocarcinoma and 246 (77·4%) of 318 had stage I disease. The prevalence of invasive lung cancer was higher among participants with a family history of lung cancer (161 [2·7%] of 6009 participants) than in those without (96 [1·6%] of 6002 participants). In participants with a family history of lung cancer, the detection rate of invasive lung cancer increased significantly with age, whereas the detection rate of adenocarcinoma in situ remained stable. In multivariable analysis, female sex, a family history of lung cancer, and age older than 60 years were associated with an increased risk of lung cancer and invasive lung cancer; passive smoke exposure, cumulative exposure to cooking, cooking without ventilation, and a previous history of chronic lung diseases were not associated with lung cancer, even after stratification by family history of lung cancer. In participants with a family history of lung cancer, the higher the number of first-degree relatives affected, the higher the risk of lung cancer; participants whose mother or sibling had lung cancer were also at an increased risk. A positive LDCT scan had 92·1% sensitivity, 84·6% specificity, a PPV of 14·0%, and a NPV of 99·7% for lung cancer diagnosis.

INTERPRETATION

TALENT had a high invasive lung cancer detection rate at 1 year after baseline LDCT scan. Overdiagnosis could have occurred, especially in participants diagnosed with adenocarcinoma in situ. In individuals who do not smoke, our findings suggest that a family history of lung cancer among first-degree relatives significantly increases the risk of lung cancer as well as the rate of invasive lung cancer with increasing age. Further research on risk factors for lung cancer in this population is needed, particularly for those without a family history of lung cancer.

FUNDING

Ministry of Health and Welfare of Taiwan.

Evolutionary proteogenomic landscape from pre-invasive to invasive lung adenocarcinoma

Lung adenocarcinoma follows a stepwise progression from pre-invasive to invasive. However, there remains a knowledge gap regarding molecular events from pre-invasive to invasive. Here, we conduct a comprehensive proteogenomic analysis comprising whole-exon sequencing, RNA sequencing, and proteomic and phosphoproteomic profiling on 98 pre-invasive and 99 invasive lung adenocarcinomas. The deletion of chr4q12 contributes to the progression from pre-invasive to invasive adenocarcinoma by downregulating SPATA18, thus suppressing mitophagy and promoting cell invasion. Proteomics reveals diverse enriched pathways in normal lung tissues and pre-invasive and invasive adenocarcinoma. Proteomic analyses identify three proteomic subtypes, which represent different stages of tumor progression. We also illustrate the molecular characterization of four immune clusters, including endothelial cells, B cells, DCs, and immune depression subtype. In conclusion, this comprehensive proteogenomic study characterizes the molecular architecture and hallmarks from pre-invasive to invasive lung adenocarcinoma, guiding the way to a deeper understanding of the tumorigenesis and progression of this disease.

Predicting the Invasiveness of Pulmonary Adenocarcinomas in Pure Ground-Glass Nodules Using the Nodule Diameter: A Systematic Review, Meta-Analysis, and Validation in an Independent Cohort

The nodule diameter was commonly used to predict the invasiveness of pulmonary adenocarcinomas in pure ground-glass nodules (pGGNs). However, the diagnostic performance and optimal cut-off values were inconsistent. We conducted a meta-analysis to evaluate the diagnostic performance of the nodule diameter for predicting the invasiveness of pulmonary adenocarcinomas in pGGNs and validated the cut-off value of the diameter in an independent cohort. Relevant studies were searched through PubMed, MEDLINE, Embase, and the Cochrane Library, from inception until December 2022. The inclusion criteria comprised studies that evaluated the diagnostic accuracy of the nodule diameter to differentiate invasive adenocarcinomas (IAs) from non-invasive adenocarcinomas (non-IAs) in pGGNs. A bivariate mixed-effects regression model was used to obtain the diagnostic performance. Meta-regression analysis was performed to explore the heterogeneity. An independent sample of 220 pGGNs (82 IAs and 128 non-IAs) was enrolled as the validation cohort to evaluate the performance of the cut-off values. This meta-analysis finally included 16 studies and 2564 pGGNs (761 IAs and 1803 non-IAs). The pooled area under the curve, the sensitivity, and the specificity were 0.85 (95% confidence interval (CI), 0.82-0.88), 0.82 (95% CI, 0.78-0.86), and 0.73 (95% CI, 0.67-0.78). The diagnostic performance was affected by the measure of the diameter, the reconstruction matrix, and patient selection bias. Using the prespecified cut-off value of 10.4 mm for the mean diameter and 13.2 mm for the maximal diameter, the mean diameter showed higher sensitivity than the maximal diameter in the validation cohort (0.85 vs. 0.72, p < 0.01), while there was no significant difference in specificity (0.83 vs. 0.86, p = 0.13). The nodule diameter had adequate diagnostic performance in differentiating IAs from non-IAs in pGGNs and could be replicated in a validation cohort. The mean diameter with a cut-off value of 10.4 mm was recommended.

SORC: an integrated spatial omics resource in cancer

The interactions between tumor cells and the microenvironment play pivotal roles in the initiation, progression and metastasis of cancer. The advent of spatial transcriptomics data offers an opportunity to unravel the intricate dynamics of cellular states and cell-cell interactions in cancer. Herein, we have developed an integrated spatial omics resource in cancer (SORC, http://bio-bigdata.hrbmu.edu.cn/SORC), which interactively visualizes and analyzes the spatial transcriptomics data in cancer. We manually curated currently available spatial transcriptomics datasets for 17 types of cancer, comprising 722 899 spots across 269 slices. Furthermore, we matched reference single-cell RNA sequencing data in the majority of spatial transcriptomics datasets, involving 334 379 cells and 46 distinct cell types. SORC offers five major analytical modules that address the primary requirements of spatial transcriptomics analysis, including slice annotation, identification of spatially variable genes, co-occurrence of immune cells and tumor cells, functional analysis and cell-cell communications. All these spatial transcriptomics data and in-depth analyses have been integrated into easy-to-browse and explore pages, visualized through intuitive tables and various image formats. In summary, SORC serves as a valuable resource for providing an unprecedented spatially resolved cellular map of cancer and identifying specific genes and functional pathways to enhance our understanding of the tumor microenvironment.

Updates on lung adenocarcinoma: invasive size, grading and STAS

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.