Patients with part-solid adenocarcinomas treated by surgery generally have more favorable outcomes than those with pure-solid adenocarcinomas. We conducted this study to understand the effects of the lepidic components and preoperative characteristics on the postoperative survival of patients with part-solid adenocarcinomas.

The subjects of this retrospective study were 313 patients with stage 1 part-solid adenocarcinomas and 634 patients with pure-solid adenocarcinomas, treated at our institution between 2006 and 2020. Propensity score matching was performed to analyze survival in an unmatched cohort (PSM0, n = 313 vs. 634); a matched cohort based on the consolidation diameter (PSM1, n = 217 each); and a matched cohort based on 11 clinical characteristics (PSM2, n = 103 each). Multivariate analysis was also performed.  RESULTS: The 5-year overall/recurrence-free survival rates for part-solid and pure-solid adenocarcinomas were 90.2%/79.3% and 80.8%/66.0% in the PSM0 cohort (P < 0.0001), 87.4%/79.2% and 76.3%/68.6% in the PSM1 cohort (P < 0.05), and 91.6%/92.1% and 76.6%/79.0% in the PSM2 cohort (P > 0.05), respectively. Multivariate analysis revealed that male sex (P = 0.04) and the carcinoembryonic antigen value (P < 0.0001) were significant factors affecting overall survival, while the carcinoembryonic antigen value (P = 0.0002) and consolidation tumor size (P = 0.002) affected recurrence-free survival. The lepidic component was not related to overall (P = 0.45) or recurrence-free (P = 0.78) survival.

Preoperative factors are strongly associated with "consolidation size", which could be the "representative factor" indicating the malignant potential in adenocarcinomas being consistent with the current eighth edition of the TNM.

Lung adenocarcinoma (LUAD) is an aggressive cancer defined by oncogenic drivers and metabolic reprogramming. Here we leverage next-generation spatial screens to identify glycogen as a critical and previously underexplored oncogenic metabolite. High-throughput spatial analysis of human LUAD samples revealed that glycogen accumulation correlates with increased tumour grade and poor survival. Furthermore, we assessed the effect of increasing glycogen levels on LUAD via dietary intervention or via a genetic model. Approaches that increased glycogen levels provided compelling evidence that elevated glycogen substantially accelerates tumour progression, driving the formation of higher-grade tumours, while the genetic ablation of glycogen synthase effectively suppressed tumour growth. To further establish the connection between glycogen and cellular metabolism, we developed a multiplexed spatial technique to simultaneously assess glycogen and cellular metabolites, uncovering a direct relationship between glycogen levels and elevated central carbon metabolites essential for tumour growth. Our findings support the conclusion that glycogen accumulation drives LUAD cancer progression and provide a framework for integrating spatial metabolomics with translational models to uncover metabolic drivers of cancer.

The 2021 World Health Organisation classification of lung adenocarcinoma is based on the predominance and percentage of high-grade histological patterns, e.g. solid and micropapillary patterns, determined by semiquantitative estimation. Digital pathology can be used to evaluate the area of each pattern and calculate the exact percentage. To evaluate the prognostic predictive ability of a histological model for invasive non-mucinous adenocarcinoma using digital pathology. This retrospective cohort study included 76 patients with invasive non-mucinous lung adenocarcinoma who underwent lung resection at Songklanagarind Hospital between January 2010 and December 2016. The histological pattern area was measured on a digital slide using the QuPath Open software version 0.3.2. Clinical and pathological data, including the presence of tumour spread through airspaces, tumour necrosis, tumour-infiltrating lymphocytes, and lymphovascular invasion, were collected. The primary outcome was 5-year overall survival. The best model was provided by the Akaike information criterion, and the prognostic discrimination ability was compared with that of other models from previous studies by identifying the area under the curve (AUC) in the receiver operating characteristic analysis. The best model was validated using bootstrapping. The best model was a combination of stage and an 82 % cut-off high-grade pattern (AUC = 0.776). Tumours with ≥82 % high-grade pattern resulted in significantly worse prognoses (p = 0.001) than those with <82 % high-grade pattern. Our model had the highest AUC among all models from previous studies. This was validated using bootstrapping, with an AUC of 0.708. The best model for survival prediction was a combination of stage and an 82 % cut-off high-grade pattern.

Lung adenocarcinomas often contain multiple histological components. This study discusses the role of minor components in the progression of the disease. A retrospective evaluation was conducted on 108 patients with lung adenocarcinoma who underwent surgery at our center between 2013 and 2018, with tumor sizes less than 3 cm. The patients were categorized into four groups based on the presence of lepidic (L) and micropapillary/solid (MP/S) patterns at a minimum threshold of 5% ("L+, MP/S-", "L+, MP/S+", "L-, MP/S-", "L-, MP/S+"). The groups were compared in terms of standard uptake value, pleural invasion, lymphovascular invasion, perineural invasion, spread through air spaces, N1-N2 station lymph node metastasis, recurrence, and survival. No tumors of perineural invasion, spread through air spaces, or lymph node metastasis was observed in the "L+, MP/S-" group, and lymphovascular invasion was found to be significantly lower compared to other groups (p = 0.040). The standard uptake value levels in groups containing the lepidic pattern were significantly lower than in other groups (p = 0.006). The time to recurrence in the "L+, MP/S-" group was 121.5 ± 10.9 months, with a median survival time of 110.9 ± 10.6 months, which was longer compared to the other groups (86.2 ± 5.9 and 77 ± 13.4 months). In lung adenocarcinomas, prognosis estimation should be based not only on the dominant component but also on the presence of histological components such as lepidic and micropapillary/solid, even if they are minor.

This study aimed to investigate the relationship between imaging features of pure ground-glass opacities (pGGOs) and the pathological subtypes of minimally invasive adenocarcinoma (MIA) and precursor lesions. A retrospective analysis was conducted on data from 1521 patients diagnosed with GGOs as lung adenocarcinoma or precursor lesions between January 2015 and March 2021. The pGGOs were categorized into atypical adenomatous hyperplasia (AAH) / adenocarcinoma in situ (AIS) and MIA groups. Clinical information and CT imaging features were collected. Statistical analysis, logistic regression, and receiver operating characteristic (ROC) curve analysis were performed. A total of 127 patients with 139 pGGOs were included. Maximum radiodensity, minimum radiodensity, mean radiodensity, variance, and skewness showed significant differences between the two groups. Maximum radiodensity and maximum cross-sectional area were identified as risk factors for pathology. The logistic regression model yielded an area under the curve (AUC) of 0.747 (95% CI, 0.666-0.816) for predicting pathological subtypes. The intensity features of pGGOs were found to be significantly different between AAH/AIS and MIA groups. Maximum radiodensity and skewness were independent risk factors for pathology. However, these features did not exhibit satisfactory diagnostic efficiency.

Spatial omics is enabling unprecedented tissue characterization, but the ability to adequately compare spatial features across samples under different conditions is lacking. We propose a quantitative framework that catalogs significant, normalized, colocalizations between pairs of cell subpopulations, enabling comparisons among a variety of biological samples. We perform cell-pair colocalization analysis on multiplexed immunofluorescence images of assembloids constructed with lung adenocarcinoma (LUAD) organoids and cancer-associated fibroblasts derived from human tumors. Our data show that assembloids recapitulate human LUAD tumor-stroma spatial organization, justifying their use as a tool for investigating the spatial biology of human disease. Intriguingly, drug-perturbation studies identify drug-induced spatial rearrangements that also appear in treatment-naïve human tumor samples, suggesting potential directions for characterizing spatial (re)-organization related to drug resistance. Moreover, our work provides an opportunity to quantify spatial data across different samples, with the common goal of building catalogs of spatial features associated with disease processes and drug response.

This study investigated the prognostic impact of non-predominant lepidic components in invasive nonmucinous adenocarcinoma.

Patients who underwent lobectomy and were diagnosed with stage I nonmucinous, non-lepidic-predominant invasive adenocarcinoma based on pathologic findings were included. Tumors were staged according to the eighth edition of TNM classification and categorized on the basis of the presence of lepidic components in the final pathologic findings. Overall survival (OS) and recurrence-free survival (RFS) were analyzed before and after applying inverse probability of treatment weighting. Competing risk analyses for recurrence were also compared in the two groups.

Of the 1270 patients, 858 (67.6%) had lepidic components (+). The pathologic stage and histologic grade were higher in the lepidic (-) group (p < 0.001, respectively). The 5-year OS and RFS were significantly worse in the lepidic (-) group than in the lepidic (+) group (OS: 88.2% versus 94.9%, p < 0.001; RFS: 79.4% versus 91.9%, p < 0.001). These trends were consistent after weighted analysis (OS: 92.4% versus 96.4%, p = 0.029; RFS: 85.6% versus 92.3%, p = 0.007). The 5-year cumulative incidence of any recurrence was 14.0% in the lepidic (-) group and 4.1% in the lepidic (+) group (p < 0.001). Multivariable Fine-Gray regression analysis found that the lepidic (+) group exhibited a lower risk of recurrence than did the lepidic (-) group (hazard ratio = 0.52, 95% confidence interval: 0.29-0.93, p = 0.031).

In pathologic stage I invasive nonmucinous adenocarcinoma, the presence of histologically diagnosed non-predominant lepidic components might be associated with a better prognosis after curative surgery.

Primary lung adenocarcinoma can sometimes present atypically, mimicking interstitial lung disease (ILD), and posing significant diagnostic challenges. Such presentations often lead to misdiagnoses, delaying appropriate treatment.

A 35-year-old female non-smoker presented with a six-month history of progressive cough, mild hemoptysis, fatigue, and exertional dyspnea, with no associated weight loss. Imaging studies revealed diffuse ground-glass opacities and interstitial infiltrates, while pulmonary function tests were consistent with interstitial lung disease. Despite these findings, bronchoscopy results were normal. A definitive diagnosis was ultimately made through a biopsy, which identified a moderately to poorly differentiated adenocarcinoma with acinar and micropapillary features.

This case highlights the diagnostic complexity when lung adenocarcinoma presents atypically, mimicking ILD. Conventional diagnostic tools, such as imaging and pulmonary function tests, may overlap with ILD findings, leading to misdiagnoses. Early consideration of malignancy and the use of invasive diagnostic procedures, such as biopsy, are essential for distinguishing between ILD and malignancy in atypical cases.

This case underscores the importance of maintaining a high index of suspicion for malignancy in atypical ILD presentations. Early invasive diagnostic techniques are crucial for achieving a timely and accurate diagnosis, ultimately improving patient outcomes.

The benefit of treatment with tyrosine kinase inhibitors targeting the epidermal growth factor receptor (EGFR-TKI) for lung adenocarcinoma (ADC), stratified by ethnicity, has not yet been fully elucidated.

We searched PubMed, Embase, and Cochrane databases for studies that investigated EGFR-TKI for lung ADC. We computed hazard ratios (HRs) or risk ratios (RRs) for binary endpoints, with 95% confidence intervals (CIs). We used DerSimonian and Laird random-effect models for all endpoints. Heterogeneity was assessed using I2 statistics. R, version 4.2.3, was used for statistical analyses.

A total of 18 studies, comprising 4,497 patients with lung ADC randomized to TKIs or chemotherapy alone. TKIs significantly improved OS (HR 0.91; 95% CI 0.88-0.95), PFS (HR 0.60; 95% CI 0.38-0.97), and ORR (HR 0.34; 95% CI 0.25-0.48) in Asian patients, compared with the chemotherapy alone. In Caucasian patients, TKIs significantly improved PFS compared with chemotherapy alone (HR 0.34; 95% CI 0.25-0.48) and ORR(RR 2.35; 95% CI: 1.05-5.28). TKIs significantly reduced any adverse events of any grade in patients with mixed ethnicity (RR 0.86; 95% CI 0.76-0.98) and any adverse events of grade ≥ 3 in Caucasian patients (RR 0.67; 95% CI 0.51-0.89).

This is the first meta-analysis to reveal the ethnic influence on the outcomes of oncologic treatments for patients with lung ADC. In collaboration with in-depth molecular characterization, these data will allow the creation of a clinical-pathological predictive model to increase the magnitude of the expected benefit for patients from different ethnic groups.

EBUS-TBNA is the most common interventional pulmonology procedure performed globally and remains the cornerstone of the diagnosis and staging not only of lung cancer but also for other neoplastic, inflammatory, and infective pathologies of the mediastinum. Infective complications of EBUS-TBNA are underreported in the literature, but the constantly rising incidence of lung cancer is leading to an increasing number of EBUS-TBNA procedures and, therefore, to a significant number of infective complications, even 4 weeks following the procedure. In this review we attempt to summarize the risk factors related to these infective complications, along with useful biomarkers that can be used to identify patients that might develop infective complications, to facilitate the prediction or even prompt treatment of these.

Lung cancer is the leading cause of cancer-related deaths worldwide, with invasive non-mucinous adenocarcinoma (INMA) being the most common type and carrying a poor prognosis. In 2020, the International Association for the Study of Lung Cancer (IASLC) pathology committee proposed a new histological grading system, which offers more precise prognostic assessments by combining the proportions of major and high-grade histological patterns. Accurate identification of lung INMA grading is crucial for clinical diagnosis, treatment planning, and prognosis evaluation. Currently, non-invasive imaging methods (such as CT, PET/CT, and MRI) are increasingly being studied to predict the new grading of lung INMA, showing promising application prospects. This review outlines the establishment and prognostic efficiency of the new IASLC grading system, highlights the application and latest progress of non-invasive imaging techniques in predicting lung INMA grading, and discusses their role in personalized treatment of lung INMA and future research directions. CRITICAL RELEVANCE STATEMENT: The new IASLC grading system has important prognostic implications for patients with lung invasive non-mucinous adenocarcinoma (INMA), and non-invasive imaging methods can be used to predict it, thereby improving patient prognoses. KEY POINTS: The new IASLC grading system more accurately prognosticates for patients with lung INMA. Preoperative prediction of the new grading is challenging because of the complexity of INMA subtypes. It is feasible to apply non-invasive imaging methods to predict the new IASLC grading system.

To compare the long-term survival between robotic and video-assisted thoracic surgery (VATS) lobectomy for stage Ⅰ non-small-cell lung cancer (NSCLC) with radiologic solid tumors.

Clinical stage Ⅰ NSCLC patients with radiologic solid tumors who underwent robotic-assisted thoracic surgery (RATS) or VATS lobectomy between 2015 and 2017 were retrospectively reviewed. A propensity score matching analysis was performed to balance the baseline characteristics. The primary end points were overall survival (OS) and recurrence-free survival (RFS).

A total of 518 patients (225 RATS and 293 VATS) were included. After propensity score matching, there were 170 cases in each group. Patients undergoing RATS had shorter operative time than VATS (98.12 min vs. 112.26 min; P < 0.001). The RATS approach resulted in a higher number of resected lymph nodes (LNs) (11.75 vs. 9.77; P < 0.001). The postoperative complication rates were comparable (7.6% vs. 10.0%, P = .566). The rates of 5-year OS and RFS for the RATS and VATS were 92% versus 89% (P = .62) and 82% vs. 86% (P = .70), respectively. Multivariate analysis revealed that the number of resected LNs was significantly associated with overall survival (OR = 1.94 [95% confidence interval [CI]: 1.07-3.51], P = .029).

The long-term survival outcomes of RATS and VATS are similar for c-stage Ⅰ NSCLC with radiologic solid tumors. The use of robotics is associated with more lymph node dissection and shorter operative time. We suggested that the number of examined lymph nodes rather than surgical approaches was associated with overall survival.

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.

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.

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.

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

The purpose of this study was to investigate the effect of tumor size and differentiation grade on long term survival in patients with early-stage lung adenocarcinoma (LUAD) after lobectomy and segmentectomy.

Patients with stage T1-2N0M0 LUAD who underwent lobectomy and segmentectomy were identified from the Surveillance, Epidemiology, and End Results database. Patients were stratified as grade I (well differentiated), grade II (moderately differentiated), and grade III/IV (poorly differentiated/undifferentiated) carcinomas. The effect of tumor size on overall survival (OS) and lung cancer-specific survival (LCSS) was evaluated using the multivariate Cox regression model, including the interaction between tumor size, type of surgery, and tumor differentiation grade. The inverse probability of treatment weighting method was used to adjust for bias between the groups.

A total of 19,857 patients were identified, including 18,759 (94.4%) who underwent lobectomy and 1098 (5.5%) who underwent segmentectomy. A three-way interaction among tumor size, differentiation grade, and type of surgery was observed in the overall cohort. After stratifying by differentiation grade, plots of interaction revealed that lobectomy was associated with improved survival compared with segmentectomy when the tumor size exceeded 23 mm for grade I LUAD and 14 mm for grade II LUAD. No interaction was observed between the studied factors in grade III/IV carcinomas.

This study interpreted the interaction between tumor size and type of surgery on long-term survival in patients with early stage LUAD and established a tumor size threshold beyond which lobectomy provided survival benefits compared with segmentectomy.

The main difficulty in the diagnosis of atypical in situ adenocarcinoma lies in the distinction between true and false stromal invasion. Moreover, how to identify local alveolar wall collapse in situ lung adenocarcinoma and how to identify whether the trapped adenoid structure around scar is an invasion component have become the key points for accurate diagnosis of lung adenocarcinoma. In the present study, we detected 40 cases of lung adenocarcinoma in situ and 40 cases of invasive adenocarcinoma by using immunohistochemical techniques. We found FAP-α had not immunreactivity in the stroma of adenocarcinoma in situ. However, it stained in the stroma of invasive areas in lung adenocarcinoma. FAP-α staining pattern could represent hyperplastic myofibroblast and demonstrated the true invasion of stroma. This study provides strong evidence that FAP-α is an effective tool to evaluate the presence or absence of stroma invasion of lung adenocarcinoma. Our findings will contribute to the accurate diagnosis of lung invasive adenocarcinoma.

Invasive mucinous adenocarcinoma (IMA) of lung is a unique subset of adenocarcinomas characterized by an intrapulmonary aerogenous spread resulting in multicentric, multilobar, and bilateral lesions with a low frequency of distant metastasis. The treatment options for IMA are limited, and advanced IMA has a poor prognosis, with a median survival of less than a year. Lung transplantation performed in a handful of selected patients showed improved survival outcomes and clinical improvement. However, high postoperative recurrence rates have been observed and recurrence appeared to originate from the primary tumor in many cases. Techniques, such as non-sequential double lung transplantation utilizing cardiopulmonary bypass, have been performed to reduce recurrence. Here, we present the first case of bilateral lung transplantation employing cardiopulmonary bypass in a patient with stage ⅣA lung-limited IMA without lymph node or distant metastasis. At 15 months post-transplantation, the patient remains stable with no evidence of disease recurrence or organ rejection. Additionally, we describe the classification, clinical outcomes, protein expression, and genetic characteristics of IMA. IMA was previously classified as a subset of bronchioalveolar carcinoma (BAC), which is invasive and mucinous with goblet or columnar cells secreting mucin. We reviewed and summarized the lung transplantation cases reported to date for BAC. The 5-year overall survival and disease-free survival have been reported approximately 50% (range, 39-100) and 50% (range, 35-100), respectively. The literature shows these outcomes are comparable to bilateral lung transplantation performed for non-cancerous pulmonary disease.

The solid pattern is a highly malignant subtype of lung adenocarcinoma. In the current era of transitioning from lobectomy to sublobar resection for the surgical treatment of small lung cancers, preoperative identification of this subtype is highly important for patient surgical approach selection and long-term prognosis.

A total of 1489 patients with clinical stage IA1-2 primary lung adenocarcinoma were enrolled. Based on patient clinical characteristics and lung imaging features obtained via deep learning, highly correlated diagnostic factors were identified through LASSO regression and decision tree analysis. Subsequently, a logistic model and nomogram were constructed. A restricted cubic spline (RCS) was used to calculate the optimal inflection point of quantitative data and the differences between the groups.

The three-dimensional proportion of solid component (PSC), sex, and smoking status was identified as being highly correlated diagnostic factors for solid predominant adenocarcinoma. The logistic model had good prediction efficiency, and the area under the ROC curve was 0.85. Decision curve analysis demonstrated that the application of diagnostic factors can improve patient outcomes. RCS analysis indicated that the proportion of solid adenocarcinomas increased by 4.6 times when the PSC was ≥72%. A PSC of 72% is a good cutoff point.

The preoperative diagnosis of solid-pattern adenocarcinoma can be confirmed by typical imaging features and clinical characteristics, assisting the thoracic surgeon in developing a more precise surgical plan.

To differentiate invasive lepidic predominant adenocarcinoma (iLPA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) of lung utilizing visual semantic and computer-aided detection (CAD)-based texture features on subjects initially diagnosed as AIS or MIA with CT-guided biopsy.

From 2011 to 2017, all patients with CT-guided biopsy results of AIS or MIA who subsequently underwent resection were identified. CT scan before the biopsy was used to assess visual semantic and CAD texture features, totaling 23 semantic and 95 CAD-based quantitative texture variables. The least absolute shrinkage and selection operator (LASSO) method or forward selection was used to select the most predictive feature and combination of semantic and texture features for detection of invasive lung adenocarcinoma.

Among the 33 core needle-biopsied patients with AIS/MIA pathology, 24 (72.7%) had invasive LPA and 9 (27.3%) had AIS/MIA on resection. On CT, visual semantic features included 21 (63.6%) part-solid, 5 (15.2%) pure ground glass, and 7 (21.2%) solid nodules. LASSO selected seven variables for the model, but all were not statistically significant. "Volume" was found to be statistically significant when assessing the correlation between independent variables using the backward selection technique. The LASSO selected "tumor_Perc95", "nodule surround", "small cyst-like spaces", and "volume" when assessing the correlation between independent variables.

Lung biopsy results showing noninvasive LPA underestimate invasiveness. Although statistically non-significant, some semantic features showed potential for predicting invasiveness, with septal stretching absent in all noninvasive cases, and solid consistency present in a significant portion of invasive cases.

Mucinous neoplasm with extracellular mucin can be challenging to interpret on fine needle aspiration and core biopsies. Determining the biologic origin of the mucin/mucinous cells, that is, benign/incidental versus neoplasm, invasive versus in situ, and primary versus metastatic tumors, requires a thorough multidisciplinary evaluation. The work up of these lesions includes morphologic analysis with ancillary immunohistochemical and/or molecular studies and correlation with clinical and imaging studies. This review outlines a practical approach to the diagnosis of mucinous lesions in the lung with comprehensive review of literature.

Lepidic-type adenocarcinomas (LPAs) can be multifocal, and treatment is often deferred until growth is observed. This study investigated the potential downside of that strategy by evaluating the relationship of nodal involvement with tumor size and survival.

The impact of tumor size on lymph node involvement and survival was evaluated for National Cancer Database patients who underwent surgery without induction therapy as primary treatment for cT1-3 N0 M0 histologically confirmed LPA from 2006 to 2019 by using logistic regression, Kaplan-Meier, and Cox analyses.

Positive nodes occurred in 442 of 8286 patients (5.3%). The incidence of having positive nodes approximately doubled with each 1-cm increment increase in size. Patients with positive nodes were more likely to have larger tumors (27 mm vs 20 mm, P < .001) and clinical ≥T2 disease (40.7% vs 26.8%, P < .001) compared with node-negative patients. However, tumor size was the only significant independent predictor of having positive nodal disease in logistic regression analysis, and this association grew stronger with each incremental centimeter increase in size. Patients with positive nodes were more likely to undergo adjuvant radiotherapy (23.5% vs 1.1%, P < .001) and chemotherapy (72.9% vs 7.9%, P < .001), and expectedly, had worse survival compared with the node-negative group in univariate (5-year overall survival, 50.9% vs 81.1%, P < .001) and multivariable (hazard ratio, 2.56; 95% CI, 2.14-3.05; P < .001) analyses.

Nodal involvement is relatively uncommon in early-stage LPAs but steadily increases with tumor size and is associated with dramatically worse survival. These data can be used to inform treatment decisions when evaluating LPA patients.

The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide images to investigate the relationship between the distribution of histological subtypes and clinicopathological factors.

Using histological subtype images, which are typical for pathologists, we trained and validated an artificial intelligence model. Then, the model was applied to whole-slide images of resected lung adenocarcinoma specimens from 147 cases.

The model achieved an accuracy of 99.7% in training sets and 90.4% in validation sets consisting of typical tiles of histological subtyping for pathologists. When the model was applied to whole-slide images, the predominant subtype according to the artificial intelligence model classification matched that determined by pathologists in 75.5% of cases. The predominant subtype and tumor grade (using the WHO fourth and fifth classifications) determined by the artificial intelligence model resulted in similar recurrence-free survival curves to those determined by pathologists. Furthermore, we stratified the recurrence-free survival curves for patients with different proportions of high-grade components (solid, micropapillary and cribriform) according to the physical distribution of the high-grade component. The results suggested that tumors with centrally located high-grade components had a higher malignant potential (P < 0.001 for 5-20% high-grade component).

The new artificial intelligence model for histological subtyping of lung adenocarcinoma achieved high accuracy, and subtype quantification and subtype distribution analyses could be achieved. Artificial intelligence model therefore has potential for clinical application for both quantification and spatial analysis.

Lung cancer is a devastating disease, with brain metastasis being one of the most common distant metastases of lung adenocarcinoma. This study aimed to investigate the prognostic characteristics of individuals with brain metastases originating from invasive lung adenocarcinoma of distinct pathological subtypes, providing a reference for the management of these patients.

Clinical data from 156 patients with lung adenocarcinoma-derived brain metastases were collected, including age, sex, smoking status, Karnofsky Performance Status scores, pathological subtype, lymph node metastasis, tumor site, treatment mode, T stage, and N stage. Patients were classified into two groups (highly differentiated and poorly differentiated) based on their pathological subtypes. Propensity score matching was used to control for confounding factors. The prognostic value of pathological subtypes was assessed using Kaplan-Meier analysis and Cox proportional hazards regression modeling.

Kaplan-Meier analysis indicated that patients in the moderately to highly differentiated group had better prognoses. Multivariate analysis revealed that being in the poorly differentiated group was a risk factor for poorer prognosis. Thoracic tumor radiation therapy, chemotherapy, and surgery positively influenced the time interval between lung cancer diagnosis and brain metastasis.

The pathological subtypes of lung adenocarcinoma-derived brain metastases are associated with patient prognosis. Patients in the poorly differentiated group have worse prognoses compared to those in the moderately to highly differentiated group. Therefore, patients in the poorly differentiated group may require more frequent follow-ups and aggressive treatment.

Invasive mucinous adenocarcinoma exhibits distinct prognostic outcomes compared to non-mucinous adenocarcinoma (ADC). This study investigated and compared the clinical outcomes and prognostic factors of invasive mucinous and non-mucinous ADC patients.

This retrospective study included patients who underwent curative surgery for ADC between 2011 and 2021. Patient characteristics were balanced using propensity score matching. Cumulative incidence was analysed to evaluate cancer recurrence incidence, and the Kaplan-Meier method was used to calculate overall survival (OS) for each group.

A total of 6101 patients were included. After matching, the non-mucinous group and mucinous groups comprised 798 and 408 patients, respectively. The patients in the mucinous group had a lower recurrence incidence than those in the non-mucinous group (P = 0.014). The recurrence incidence in the mucinous group was between those of grades 1 (P = 0.011) and 2 (P = 0.012) and the OS rates were comparable to those of grades 2 (P = 0.6) and 3 (P = 0.2). Multivariable analysis revealed that the maximal standardized uptake value [hazard ratio (HR): 1.13, P = 0.11] and progressed pathological stages (pStage II, HR: 3.9, P = 0.028; pStage III, HR: 8.33, P = 0.038) served as adverse prognostic factors for the mucinous group.

Patients with mucinous ADC demonstrated lower recurrence incidence and similar OS rates compared to those with non-mucinous ADC. The recurrence incidence of mucinous ADC was between those of International Association for the Study of Lung Cancer grades 1 and 2, with the OS rates comparable to those of grades 2 and 3.

None.

To examine the accuracy of CT radiomics to predict histopathological features of aggressiveness in lung cancer using a systematic review of test accuracy studies.

Data sources searched included Medline, Embase, Web of Science, and Cochrane Library from up to 3 November 2023. Included studies reported test accuracy of CT radiomics models to detect the presence of: spread through air spaces (STAS), predominant adenocarcinoma pattern, adenocarcinoma grade, lymphovascular invasion (LVI), tumour infiltrating lymphocytes (TIL) and tumour necrosis, in patients with lung cancer. The primary outcome was test accuracy. Two reviewers independently assessed articles for inclusion and assessed methodological quality using the QUality Assessment of Diagnostic Accuracy Studies-2 tool. A single reviewer extracted data, which was checked by a second reviewer. Narrative data synthesis was performed.

Eleven studies were included in the final analysis. 10/11 studies were in East Asian populations. 4/11 studies investigated STAS, 6/11 investigated adenocarcinoma invasiveness or growth pattern, and 1/11 investigated LVI. No studies investigating TIL or tumour necrosis met inclusion criteria. Studies were of generally mixed to poor methodological quality. Reported accuracies for radiomic models ranged from 0.67 to 0.94.

Due to the high risk of bias and concerns regarding applicability, the evidence is inconclusive as to whether radiomic features can accurately predict prognostically important histopathological features of cancer aggressiveness. Many studies were excluded due to lack of external validation. Rigorously conducted prospective studies with sufficient external validity will be required for radiomic models to play a role in improving lung cancer outcomes.

Reports from case series suggest that operative outcomes are comparable amongst different age groups following surgery with curative intent for non-small cell lung cancer (NSCLC). The purpose of this study was to compare morbidity and mortality after NSCLC surgery in older patients (≥ 75 years) versus younger patients (< 75 years) and identify independent predictive risk factors.

We identified 2015 patients with postoperative stages IA to IIIA according to AJCC/UICC 7th edition who had undergone NSCLC surgery with curative intent at a single specialized lung cancer center from January 2010 to December 2015. A matched-pair analysis was performed on 227 older patients and corresponding 227 younger patients. Short-term surgical outcomes were postoperative morbidity, length of hospital stay, 30-day and 90-day mortality. Long-term operative outcomes were disease-free and overall survival.

454 patients were included in the matched-pair analysis. 36% of younger patients developed postoperative complications versus 42% in older patients (p = 0.163). Age was not significantly associated with the occurrence of postoperative complications. Median length of hospital stay was 14 days in older patients and 13 days in younger patients (p = 0.185). 90-day mortality was 2.2% in younger patients compared to 4% in older patients (p = 0.424). In patients aged 75 and older impaired performance status (ECOG ≥ 1) was associated with decreased overall survival (HR = 2.15, CI 1.34-3.46), as were preoperative serum C-reactive protein / albumin ratio ≥ 0.3 (HR = 1.95, CI 1.23-3.11) and elevated preoperative serum creatinine levels ≥ 1.1 mg/dl (HR = 1.84, CI 1.15-2.95). In the younger cohort male sex (HR = 2.26, CI 1.17-4.36), postoperative stage III disease (HR 4.61, CI 2.23-9.54) and preoperative anemia (hemoglobin < 12 g/dl) (HR 2.09, CI 1.10-3.96) were associated with decreased overall survival.

Lung resection for NSCLC in older patients is associated with postoperative morbidity and mortality comparable to those of younger patients. In older patients, physical activity, comorbidities and nutritional status are related to survival and should influence the indication for surgery rather than age alone.

In invasive lung adenocarcinoma (LUAD), patients with micropapillary (MIP) or solid (SOL) components had a significantly poorer prognosis than those with only lepidic (LEP), acinar (ACI) or papillary (PAP) components. It is interesting to explore the genetic features of different histologic subtypes, especially the highly aggressive components. Based on a cohort of 5,933 patients, this study observed that in different tumor size groups, LUAD with MIP/SOL components showed a different prevalence, and patients with ALK alteration or TP53 mutations had a higher probability of developing MIP/SOL components. To control individual differences, this research used spatial whole-exome sequencing (WES) via laser-capture microdissection of five patients harboring these five coexistent components and identified genetic features among different histologic components of the same tumor. In tracing the evolution of components, we found that titin (TTN) mutation might serve as a crucial intratumor potential driver for MIP/SOL components, which was validated by a cohort of 146 LUAD patients undergoing bulk WES. Functional analysis revealed that TTN mutations enriched the complement and coagulation cascades, which correlated with the pathway of cell adhesion, migration, and proliferation. Collectively, the histologic subtypes of invasive LUAD were genetically different, and certain trunk genotypes might synergize with branching TTN mutation to develop highly aggressive components.

The status of the lung adenocarcinoma (LUAD) grading system and the association between LUAD differentiation, driver genes, and clinicopathological features remain to be elucidated.

We included patients with invasive non-mucinous LUAD, evaluated their differentiation, and collected available clinicopathological information, gene mutations, and analyzed clinical outcomes.

Among the 907 patients with invasive non-mucinous LUAD, 321 (35.4 %) were poorly differentiated, 422 (46.5 %) were moderately differentiated, and 164 (18.1 %) were well differentiated. EGFR mutation was more common in the LUADs accompanied without CGP (complex glandular pattern) than LUADs with CGP (p < 0.001). Correlation analysis between mutations and clinical characteristics showed that EGFR gene mutation (p < 0.001), KRAS gene mutation (p < 0.05), and ALK gene rearrangement (p < 0.001) were significantly related to the degree of tumor differentiation, and the KRAS and ALK gene mutation frequencies were higher in the low-differentiation group than in the high and medium differentiation groups. The EGFR mutation frequency was higher in the well/moderately differentiated adenocarcinoma group.

Our study adds to the evidence regarding the role of the grading system in prognosis. EGFR, KRAS, and ALK are related to the degree of tumor differentiation.

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.

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).

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.

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.

Among mucus-producing lung cancers, invasive mucinous adenocarcinoma of the lung is a rare and unique subtype of pulmonary adenocarcinoma. Notably, mucus production may also be observed in the five subtypes of adenocarcinoma grouped under the higher-level diagnosis of Invasive Non-mucinous Adenocarcinomas (NMA). Overlapping pathologic features in mucus-producing tumors can cause diagnostic confusion with significant clinical consequences. In this study, we established lung tumoroids, PDT-LUAD#99, from a patient with NMA and mucus production. The tumoroids were derived from the malignant pleural effusion of a patient with lung cancer and have been successfully developed for long-term culture (> 11 months). Karyotyping by fluorescence in situ hybridization using an alpha-satellite probe showed that tumoroids harbored aneuploid karyotypes. Subcutaneous inoculation of PDT-LUAD#99 lung tumoroids into immunodeficient mice resulted in tumor formation, suggesting that the tumoroids were derived from cancer. Xenografts from PDT-LUAD#99 lung tumoroids reproduced the solid adenocarcinoma with mucin production that was observed in the patient's metastatic lymph nodes. Immunoblot analysis showed MUC5AC secretion into the culture supernatant of PDT-LUAD#99 lung tumoroids, which in contradistinction was barely detected in the culture supernatants of NCI-A549 and NCI-H2122 pulmonary adenocarcinoma cells known for their mucin-producing abilities. Here, we established a novel high-mucus-producing lung tumoroids from a solid adenocarcinoma. This preclinical model may be useful for elucidating the pathogenesis of mucus-producing lung cancer.

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.

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.

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.

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.

The role of mast cells in malignancies remains unclear, and there is no clear correlation between mast cells and tumor microvessels, tumor growth, or lung adenocarcinoma (LUAD) prognosis. This study aims to explore the association between mast cell density (MCD) and intratumoral microvessel density (MVD), clinicopathological parameters, and prognosis in LUAD, by evaluating mast cell infiltration characteristics and their prognostic significance.

This retrospective investigation involved 238 patients with LUAD undergoing complete resection. Tumor and normal lung tissue sections outside the tumor were immunohistochemically stained for MCD in the intratumoral and outside regions, respectively. CD34 polyclonal antibody was used to measure intratumoral MVD.

Intratumoral regions of LUAD had a higher MCD (P < 0.001) than normal lung tissue. In the intratumoral region, MCD and CD34-MVD were positively correlated (r = 0.411, P < 0.001). Intratumoral MCD correlated with sex, smoking history, tumor differentiation, pathological subtype, and tumor size. Female sex (P = 0.012), no smoking history (P = 0.002), acinar predominant type (P = 0.012), and tumor size ≤ 3 cm (P = 0.009) were associated with a higher MCD, whereas poorly differentiated (P = 0.039) and solid/micropapillary predominant types (P = 0.001) were associated with a lower MCD. Higher intratumoral MCD exhibited a marginally improved overall survival, and individuals with higher MCD infiltration ratios (intratumoral MCD/outside the MCD) had higher disease-free and overall survival rates (log-rank P < 0.001). A high MCD infiltration ratio was associated with decreased risk of tumor progression and death following complete resection.

The tumor microenvironment controls mast cell infiltration in LUAD, and patients with increased intratumoral mast cell infiltration have better prognosis.

Lung adenocarcinoma (LUAD), which accounts for a large proportion of lung cancers, is divided into five major subtypes based on histologic characteristics. The clinical characteristics, prognosis, and responses to treatments vary among subtypes. Here, we demonstrate that the variations of cell-cell contact energy result in the LUAD subtype-specific morphogenesis. We reproduced the morphologies of the papillary LUAD subtypes with the cellular Potts Model (CPM). Simulations and experimental validations revealed modifications of cell-cell contact energy changed the morphology from a papillary-like structure to micropapillary or solid subtype-like structures. Remarkably, differential gene expression analysis revealed subtype-specific expressions of genes relating to cell adhesion. Knockdown experiments of the micropapillary upregulated ITGA11 gene resulted in the morphological changes of the spheroids produced from an LUAD cell line PC9. This work shows the consequences of gene mutations and gene expressions on patient prognosis through differences in tissue composing physical forces among LUAD subtypes.

In this study, we aimed to utilize computed tomography (CT)-derived radiomics and various machine learning approaches to differentiate between invasive mucinous adenocarcinoma (IMA) and invasive non-mucinous adenocarcinoma (INMA) preoperatively in solitary pulmonary nodules (SPN) ≤3 cm.

A total of 538 patients with SPNs measuring ≤3 cm were enrolled, categorized into either the IMA group (n = 50) or INMA group (n = 488) based on postoperative pathology. Radiomic features were extracted from non-contrast-enhanced CT scans and identified using the least absolute shrinkage and selection operator (LASSO) algorithm. In constructing radiomics-based models, logistic regression, support vector machines, classification and regression trees, and k-nearest neighbors were employed. Additionally, a clinical model was developed, focusing on CT radiological features. Subsequently, this clinical model was integrated with the most effective radiomic model to create a combined model. Performance assessments of these models were conducted, utilizing metrics such as the area under the receiver operating characteristic curve (AUC), DeLong's test, net reclassification index (NRI), and integrated discrimination improvement (IDI).

The support vector machine approach showed superior predictive efficiency, with AUCs of 0.829 and 0.846 in the training and test cohorts, respectively. The clinical model had AUCs of 0.760 and 0.777 in the corresponding cohorts. The combined model had AUCs of 0.847 and 0.857 in the corresponding cohorts. Furthermore, compared to the radiomic model, the combined model significantly improved performance in both the training (DeLong test P = 0.045, NRI 0.206, IDI 0.024) and test cohorts (P = 0.029, NRI 0.125, IDI 0.032), as well as compared to the clinical model in both the training (P = 0.01, NRI 0.310, IDI 0.09) and test cohorts (P = 0.047, NRI 0.382, IDI 0.085).

the combined model exhibited excellent performance in distinguishing between IMA and INMA in SPNs ≤3 cm.

To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model.

DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis.

In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01-1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002-1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005-1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13).

The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas.

Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management.

• A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13).

Large amino acid transporter type 1 (LAT1) provides cancer cells with essential amino acids for both protein synthesis and cell growth and may predict patient prognosis. Additionally, LAT1 inhibition can be a therapeutic target. This study aimed to examine the prognostic significance of LAT1 expression in lung cancer, paying special attention to adenocarcinoma subtypes.

Tissue microarrays (TMA) of 1,560 total cores obtained from surgically resected lung cancer specimens between 1995 and 2008 at our hospital were used. Overall, 795 cases of adenocarcinoma were identified, and 717 underwent further evaluation. Immunohistochemical staining of whole slides and TMA cores were assessed to set H-score cutoff value.. Immunohistochemical expression of LAT1 was examined based on the subtypes of adenocarcinoma. Statistical analyses explored the prognostic significance of LAT1.

Adenocarcinoma accounted for 71.8% of all cases (n = 795), and 216 cases (27.1%) expressed LAT1. The 795 cases were categorized into five subtypes: lepidic (n = 29, 3.6%), papillary (n = 601, 75.6%), acinar (n = 58, 7.3%), and solid (n = 9, 1.1%); 717 of the 795 cases were further assessed according to the exclusion criteria. The LAT1-positive ratio increased as the architectural grade increased. Notably, in papillary adenocarcinoma, the LAT1-positive group had significantly lower overall survival compared to the negative group (10-year survival: 45.6% vs. 60.8%, p < 0.001).

LAT1 expression was higher in high-grade subtypes of pulmonary adenocarcinoma. Moreover, LAT1 expression is useful for predicting prognosis, particularly in papillary adenocarcinoma, facilitating prognostic stratification of papillary adenocarcinoma.

Solid adenocarcinoma represents a notably aggressive subtype of lung adenocarcinoma. Amidst the prevailing inclination towards conservative surgical interventions for diminutive lung cancer lesions, the critical evaluation of this subtype's malignancy and heterogeneity stands as imperative for the formulation of surgical approaches and the prognostication of long-term patient survival.

A retrospective dataset, encompassing 2406 instances of non-solid adenocarcinoma (comprising lepidic, acinar, and papillary adenocarcinoma) and 326 instances of solid adenocarcinoma, was analyzed to ascertain the risk factors concomitant with diverse histological variants of lung adenocarcinoma. Concurrently, RNA-sequencing data delineating explicit pathological subtypes were extracted from 261 cases in the TCGA database and 188 cases in the OncoSG database. This data served to illuminate the heterogeneity across lung adenocarcinoma (LUAD) specimens characterized by differential histological features.

Solid adenocarcinoma is associated with an elevated incidence of pleural invasion, microscopic vessel invasion, and lymph node metastasis, relative to other subtypes of lung adenocarcinoma. Furthermore, the tumor microenvironment (TME) in solid pattern adenocarcinoma displayed suboptimal oxygenation and acidic conditions, concomitant with augmented tumor cell proliferation and invasion capacities. Energy and metabolic activities were significantly upregulated in tumor cells of the solid pattern subtype. This subtype manifested robust immune tolerance and capabilities for immune evasion.

This present investigation identifies multiple potential metrics for evaluating the invasive propensity, metastatic likelihood, and immune resistance of solid pattern adenocarcinoma. These insights may prove instrumental in devising surgical interventions that are tailored to patients diagnosed with disparate histological subtypes of LUAD, thereby offering valuable directional guidance.

Lung cancer is the leading cause in cancer related death, with non-small cell lung cancer (NSCLC) being the most frequent subtype. The importance of NSCLC is reflected by the various targeted therapy options especially for NSCLC adenocarcinomas (lung adeno carcinoma (LUAD)) as well as a set of options for immune therapies. However, despite these therapy advances, the majority of patients do not show a long-term response to either targeted therapy or immune checkpoint inhibition. One reason for treatment failure appears to be the NSCLC tumor heterogeneity. NSCLC heterogeneity might lead to an insufficient molecular characterization of a given sample due to the limited tumor material used for pathological assessment as the majority of analyses is performed on small biopsies. To get a more detailed insight into the tumor heterogeneity of NSCLC LUAD, especially in the light of its different histomorphological growth patterns, we analysed isolated NSCLC growth pattern areas and the corresponding entire tumor samples of a cohort of 31 NSLCS LUAD patients and compared their mutational landscape and their expression profiles. While significant differences of complex biomarkers, like tumor mutational burden (TMB) or microsatellite instability (MSI), were not detected between the five growth patterns -lepidic, papillary, micropapillary, acinar, and solid- we observed various subclonal mutations and copy number variants. Moreover, RNASeq analysis revealed growth pattern specific expression profiles affecting cellular processes like apoptosis, metastasis and proliferation. Taken together, our data provide novel insights into the tumor heterogeneity of LUAD required to overcome tumor heterogeneity related therapy resistance.