The novel histologic International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification system of lung adenocarcinoma is a stage-independent predictor of survival

The presence of a cribriform pattern is related to poor prognosis in lung adenocarcinoma after surgical resection: A meta-analysis

OBJECTIVE

Previous studies reported that the cribriform pattern (CP) was associated with poor prognosis in lung adenocarcinoma (ADC) patients; therefore, a meta-analysis was performed to thoroughly evaluate the prognostic impact of cribriform pattern in postoperative ADC patients.

METHODS

Eligible studies were retrieved from PubMed, Embase databases, and Web of Science until April 2023. Studies evaluating the effect of the cribriform pattern on the prognosis of postoperative ADC patients were included. Subsequently, subgroup analysis was conducted according to the proportion of the cribriform pattern, with disease-free survival (DFS) and/or overall survival (OS) as outcomes. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used as effect estimates in the meta-analyses, which were performed with a random-effects model despite the heterogeneity.

RESULTS

Nine studies published between 2015 and 2022 were included, with 4,289 ADC patients in total. The pooled results revealed a significantly poorer DFS (HR1.56, 95%CI 1.18-2.06, P = 0.11, I2 = 45%) and OS (HR2.11, 95%CI 1.63-2.72, P = 0.01, I2 = 56%) in patients with the cribriform pattern. Furthermore, the subgroup analysis showed that patients with a cribriform pattern (DFS: HR1.32, 95% CI 1.04-1.68 OS:HR2.30, 95% CI 1.55-3.39) and patients with a predominantly cribriform pattern (DFS:HR2.04, 95% CI 1.32--3.15 OS: HR1.92, 95% CI 1.41-2.61) were associated with poor prognosis.

CONCLUSIONS

The presence of a cribriform pattern is related to poor prognosis in postoperative ADC patients, despite not being a main tumor component. However, the results should be confirmed by large-scale and prospective studies owing to the small sample and potential heterogeneity.

Peri- and postoperative morbidity and mortality in older patients with non-small cell lung cancer: a matched-pair study

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Prognostic implications, genomic and immune characteristics of lung adenocarcinoma with lepidic growth pattern

AIMS

Conflicting data were provided regarding the prognostic impact and genomic features of lung adenocarcinoma (LUAD) with lepidic growth pattern (LP+A). Delineation of the genomic and immune characteristics of LP+A could provide deeper insights into its prognostic implications and treatment determination.

METHODS

We conducted a search of articles in PubMed, EMBASE and the Cochrane Library from inception to January 2024. A domestic cohort consisting of 52 LUAD samples was subjected to whole-exome sequencing as internal validation. Data from The Cancer Genomic Atlas and the Gene Expression Omnibus datasets were obtained to characterise the genomic and immune profiles of LP+A. Pooled HRs and rates were calculated.

RESULTS

The pooled results indicated that lepidic growth pattern was either predominant (0.35, 95% CI 0.22 to 0.56, p<0.01) or minor (HR 0.50, 95% CI 0.36 to 0.70, p<0.01) histological subtype was associated with favourable disease-free survival. Pooled gene mutation rates suggested higher EGFR mutation (0.55, 95% CI 0.46 to 0.64, p<0.01) and lower KRAS mutation (0.14, 95% CI 0.02 to 0.25, p=0.02) in lepidic-predominant LUAD. Lepidic-predominant LUAD had lower tumour mutation burden and pooled positive rate of PD-L1 expression compared with other subtypes. LP+A was characterised by abundance in resting CD4+memory T cells, monocytes and γδ T cells, as well as scarcity of cancer-associated fibroblasts.

CONCLUSIONS

LP+A was a unique histological subtype with a higher EGFR mutation rate, lower tumour mutation burden and immune checkpoint expression levels. Our findings suggested potential benefits from targeted therapy over immunotherapy in LP+A.

Novel Insights Into the International Association for the Study of Lung Cancer Grading System for Lung Adenocarcinoma

The new grading system for lung adenocarcinoma proposed by the International Association for the Study of Lung Cancer (IASLC) defines prognostic subgroups on the basis of histologic patterns observed on surgical specimens. This study sought to provide novel insights into the IASLC grading system, with particular focus on recurrence-specific survival (RSS) and lung cancer-specific survival among patients with stage I adenocarcinoma. Under the IASLC grading system, tumors were classified as grade 1 (lepidic predominant with <20% high-grade patterns [micropapillary, solid, and complex glandular]), grade 2 (acinar or papillary predominant with <20% high-grade patterns), or grade 3 (≥20% high-grade patterns). Kaplan-Meier survival estimates, pathologic features, and genomic profiles were investigated for patients whose disease was reclassified into a higher grade under the IASLC grading system on the basis of the hypothesis that they would strongly resemble patients with predominant high-grade tumors. Overall, 423 (29%) of 1443 patients with grade 1 or 2 tumors classified based on the predominant pattern-based grading system had their tumors upgraded to grade 3 based on the IASLC grading system. The RSS curves for patients with upgraded tumors were significantly different from those for patients with grade 1 or 2 tumors (log-rank P < .001) but not from those for patients with predominant high-grade patterns (P = .3). Patients with upgraded tumors had a similar incidence of visceral pleural invasion and spread of tumor through air spaces as patients with predominant high-grade patterns. In multivariable models, the IASLC grading system remained significantly associated with RSS and lung cancer-specific survival after adjustment for aggressive pathologic features such as visceral pleural invasion and spread of tumor through air spaces. The IASLC grading system outperforms the predominant pattern-based grading system and appropriately reclassifies tumors into higher grades with worse prognosis, even after other pathologic features of aggressiveness are considered.

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.

Clinicopathological differences between EGFR mutated and EGFR wild-type lung adenocarcinoma with papillary predominant pattern

OBJECTIVES

We aimed to reveal the clinicopathological differences between epidermal growth factor receptor (EGFR)-mutated and wild-type (WT) lung adenocarcinoma (LUAD) focusing on the predominant subtype.

METHODS

This study included 352 with EGFR mutation and 370 with WT patients in consecutive stage I LUAD classified by the predominant subtype, and their clinicopathological characteristics and prognosis were analyzed. Using the Cancer Genome Atlas Program (TCGA) cohort, we analyzed differences in gene expression between EGFR mutation and WT groups. Furthermore, we performed immunohistochemical evaluations for 46 with EGFR mutation and 47 with WT patients in consecutive stage I papillary predominant adenocarcinoma (PPA).

RESULTS

Compared to the PPA with WT [n = 115], those with EGFR mutation [n = 99] exhibited smaller invasive size (p = 0.03) and less frequent vessel invasion (p < 0.01). However, PPA with EGFR mutation showed significantly worse 5-ys recurrence-free survival (RFS) rates compared to those with WT (70.6 % versus 83.3 %, p = 0.03). Contrarily, no significant differences were observed in other predominant subtypes. In the TCGA cohort, PPA with EGFR mutation tended to show higher expression of galectin-3, which is associated with tumor metastasis and resistance to anoikis, compared to those with WT (p = 0.06). Immunohistochemical evaluation revealed that galectin-3 expression was significantly higher in PPA with EGFR mutation than in those with WT (p < 0.01).

CONCLUSIONS

The prognosis of PPA with EGFR mutation proved to be less favorable compared to that with WT, and galectin-3 is highly expressed in EGFR-mutated PPA.

LncRNA CARD8-AS1 suppresses lung adenocarcinoma progression by enhancing TRIM25-mediated ubiquitination of TXNRD1

Long non-coding RNAs (lncRNAs) play crucial roles in the tumorigenesis and progression of lung adenocarcinoma (LUAD). However, little was known about the role of lncRNAs in high-risk LUAD subtypes: micropapillary-predominant adenocarcinoma (MPA) and solid-predominant adenocarcinoma (SPA). In this study, we conducted a systematic screening of differentially expressed lncRNAs using RNA sequencing in 10 paired MPA/SPA tumor tissues and adjacent normal tissues. Consequently, 110 significantly up-regulated lncRNAs and 288 aberrantly down-regulated lncRNAs were identified (|Log2 Foldchange| ≥ 1 and corrected P < 0.05). The top 10 lncRNAs were further analyzed in 89 MPA/SPA tumor tissues and 59 normal tissues from The Cancer Genome Atlas database. Among them, CARD8-AS1 showed the most significant differential expression, and decreased expression of CARD8-AS1 was significantly associated with a poorer prognosis. Functionally, CARD8-AS1 overexpression remarkably suppressed the proliferation, migration and invasion of LUAD cells both in vitro and in vivo. Conversely, inhibition of CARD8-AS1 yielded opposite effects. Mechanistically, CARD8-AS1 acted as a scaffold to facilitate the interaction between TXNRD1 and E3 ubiquitin ligase TRIM25, thereby promoting the degradation of TXNRD1 through the ubiquitin-proteasome pathway. Additionally, TXNRD1 was found to promote LUAD cell proliferation, migration and invasion in vitro. Furthermore, the suppressed progression of LUAD cells resulting from CARD8-AS1 overexpression could be significantly reversed by simultaneous overexpression of TXNRD1. In conclusion, this study revealed that the lncRNA CARD8-AS1 played a suppressive role in the progression of LUAD by enhancing TRIM25-mediated ubiquitination of TXNRD1. The CARD8-AS1-TRIM25-TXNRD1 axis may represent a promising therapeutic target for LUAD.

Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis

BACKGROUND

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.

METHODS

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.

RESULT

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).

CONCLUSION

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.

Use of CT-derived radiomic features to preoperatively identify invasive mucinous adenocarcinoma in solitary pulmonary nodules ≤3 cm

Objective

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.

Methods

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).

Results

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).

Conclusion

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

Real-world efficacy of adjuvant therapy for totally resected stage I lung adenocarcinoma patients with pathological high-risk factors: propensity score analysis

BACKGROUND

We investigated the real-world efficacy of adjuvant therapy for stage I lung adenocarcinoma patients with pathological high-risk factors.

METHODS

Study participants were enrolled from November 1, 2016 and December 31, 2020. Clinical bias was balanced by propensity score matching. Disease-free survival (DFS) outcomes were compared by Kaplan-Meier analysis. The Cox proportional hazards regression was used to identify survival-associated factors. p ≤ 0.05 was the threshold for statistical significance.

RESULTS

A total of 454 patients, among whom 134 (29.5%) underwent adjuvant therapy, were enrolled in this study. One hundred and eighteen of the patients who underwent adjuvant therapy were well matched with non-treatment patients. Prognostic outcomes of the treatment group were significantly better than those of the non-treatment group, as revealed by Kaplan-Meier analysis after PSM. Differences in prevention of recurrence or metastasis between the targeted therapy and chemotherapy groups were insignificant. Adjuvant therapy was found to be positive prognostic factors, tumor size and solid growth patterns were negative.

CONCLUSIONS

Adjuvant therapy significantly improved the DFS for stage I lung adenocarcinoma patients with high-risk factors. Larger prospective clinical trials should be performed to verify our findings.

Feasibility of UTE-MRI-based radiomics model for prediction of histopathologic subtype of lung adenocarcinoma: in comparison with CT-based radiomics model

OBJECTIVES

To assess the feasibility of the UTE-MRI radiomic model in predicting the micropapillary and/or solid (MP/S) patterns of surgically resected lung adenocarcinoma.

MATERIALS AND METHODS

We prospectively enrolled 74 lesions from 71 patients who underwent UTE-MRI and CT before curative surgery for early lung adenocarcinoma. For conventional radiologic analysis, we analyzed the longest lesion diameter and lesion characteristics at both UTE-MRI and CT. Radiomic features were extracted from the volume of interest of the lesions and Rad-scores were generated using the least absolute shrinkage and selection operator with fivefold cross-validation. Six models were constructed by combining the conventional radiologic model, UTE-MRI Rad-score, and CT Rad-score. The areas under the curves (AUCs) of each model were compared using the DeLong method. Early recurrence after curative surgery was analyzed, and Kaplan-Meier survival analysis was performed.

RESULTS

Twenty-four lesions were MP/S-positive, and 50 were MP/S-negative. The longitudinal size showed a small systematic difference between UTE-MRI and CT, with fair intermodality agreement of lesion characteristic (kappa = 0.535). The Rad-scores of the UTE-MRI and CT demonstrated AUCs of 0.84 and 0.841, respectively (p = 0.98). Among the six models, mixed conventional, UTE-MRI, and CT Rad-score model showed the highest diagnostic performance (AUC = 0.879). In the survival analysis, the high- and low-risk groups were successfully divided by the Rad-score in UTE-MRI (p = 0.01) and CT (p < 0.01).

CONCLUSION

UTE-MRI radiomic model predicting MP/S positivity is feasible compared with the CT radiomic model. Also, it was associated with early recurrence in the survival analysis.

CLINICAL RELEVANCE STATEMENT

A radiomic model utilizing UTE-MRI, which does not present a radiation hazard, was able to successfully predict the histopathologic subtype of lung adenocarcinoma, and it was associated with the patient's recurrence-free survival.

KEY POINTS

• No studies have reported the ultrashort echo time (UTE)-MRI-based radiomic model for lung adenocarcinoma. • The UTE-MRI Rad-score showed comparable diagnostic performance with CT Rad-score for predicting micropapillary and/or solid histopathologic pattern. • UTE-MRI is feasible not only for conventional radiologic analysis, but also for radiomics analysis.

High core 1β1,3-galactosyltransferase 1 expression is associated with poor prognosis and promotes cellular radioresistance in lung adenocarcinoma

PURPOSE

Core 1β1,3-galactosyltransferase 1 (C1GALT1) exhibits elevated expression in multiple cancers. The present study aimed to elucidate the clinical significance of C1GALT1 aberrant expression and its impact on radiosensitivity in lung adenocarcinoma (LUAD).

METHODS

The C1GALT1 expression and its clinical relevance were investigated through public databases and LUAD tissue microarray analyses. A549 and H1299 cells with either C1GALT1 knockdown or overexpression were further assessed through colony formation, gamma-H2A histone family member X immunofluorescence, 5-ethynyl-2'-deoxyuridine incorporation, and flow cytometry assays. Bioinformatics analysis was used to explore single cell sequencing data, revealing the influence of C1GALT1 on cancer-associated cellular states. Vimentin, N-cadherin, and E-cadherin protein levels were measured through western blotting.

RESULTS

The expression of C1GALT1 was significantly higher in LUAD tissues than in adjacent non-tumor tissues both at mRNA and protein level. High expression of C1GALT1 was correlated with lymph node metastasis, advanced T stage, and poor survival, and was an independent risk factor for overall survival. Radiation notably upregulated C1GALT1 expression in A549 and H1299 cells, while radiosensitivity was increased following C1GALT1 knockdown and decreased following overexpression. Experiment results showed that overexpression of C1GALT1 conferred radioresistance, promoting DNA repair, cell proliferation, and G2/M phase arrest, while inhibiting apoptosis and decreasing E-cadherin expression, alongside upregulating vimentin and N-cadherin in A549 and H1299 cells. Conversely, C1GALT1 knockdown had opposing effects.

CONCLUSION

Elevated C1GALT1 expression in LUAD is associated with an unfavorable prognosis and contributes to increased radioresistance potentially by affecting DNA repair, cell proliferation, cell cycle regulation, and epithelial-mesenchymal transition (EMT).

Prognostic value of large amino acid transporter type 1 (LAT1) expression in pulmonary adenocarcinoma: A tissue microarray study

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

Comparative study of imaging and pathology of primary mucinous adenocarcinoma with different imaging manifestations

BACKGROUND

Pulmonary mucinous adenocarcinoma is a special type of lung cancer. Its imaging manifestations are diverse, which brings challenges to clinical diagnosis. However, its formation mechanism is unclear.

OBJECTIVE

The objective of this study is to analyse the relevant mechanisms of the formation of pulmonary mucinous adenocarcinoma by observing its different imaging and pathological manifestations.

DATA AND METHODS

Retrospective analysis was conducted on imaging manifestations and pathological data of 103 patients with pulmonary mucinous adenocarcinoma confirmed intraoperatively or pathologically.

RESULTS

Forty-three patients had pulmonary mucinous adenocarcinoma with a solitary nodule/mass, 41 patients with localized pneumonia and 19 patients with diffuse pneumonia. Their CT manifestations included 'falling snowflake sign', ground-glass opacity close to the heart, vacuous signs/honeycombing and withered tree branches. Under the microscope, all the three types of pulmonary mucinous adenocarcinoma had visibly formed mucus lakes but were made of tumour cells with totally different shapes, which included the goblet-like shape (tall column-like shape) and quasi-circular shape. Tall column-shaped tumour cells were negative or weakly positive for thyroid transcription factor-1 (TTF-1) and strongly positive for ALK mutation, whereas quasi-circular tumour cells were positive for TTF-1 and less positive for ALK mutation.

CONCLUSION

The different imaging manifestations of mucinous adenocarcinoma are possibly due to the different amounts or viscosity of mucus produced, and the mechanisms of its formation may include (1) tumour cells in different shapes have different abilities to produce mucus; (2) tumours in different stages produce different amounts or viscosity of mucus; and (3) the TTF-1 and ALK genes affect the production of mucus.

Integrating AI-Powered Digital Pathology and Imaging Mass Cytometry Identifies Key Classifiers of Tumor Cells, Stroma, and Immune Cells in Non-Small Cell Lung Cancer

Artificial intelligence (AI)-powered approaches are becoming increasingly used as histopathologic tools to extract subvisual features and improve diagnostic workflows. On the other hand, hi-plex approaches are widely adopted to analyze the immune ecosystem in tumor specimens. Here, we aimed at combining AI-aided histopathology and imaging mass cytometry (IMC) to analyze the ecosystem of non-small cell lung cancer (NSCLC). An AI-based approach was used on hematoxylin and eosin (H&E) sections from 158 NSCLC specimens to accurately identify tumor cells, both adenocarcinoma and squamous carcinoma cells, and to generate a classifier of tumor cell spatial clustering. Consecutive tissue sections were stained with metal-labeled antibodies and processed through the IMC workflow, allowing quantitative detection of 24 markers related to tumor cells, tissue architecture, CD45+ myeloid and lymphoid cells, and immune activation. IMC identified 11 macrophage clusters that mainly localized in the stroma, except for S100A8+ cells, which infiltrated tumor nests. T cells were preferentially localized in peritumor areas or in tumor nests, the latter being associated with better prognosis, and they were more abundant in highly clustered tumors. Integrated tumor and immune classifiers were validated as prognostic on whole slides. In conclusion, integration of AI-powered H&E and multiparametric IMC allows investigation of spatial patterns and reveals tissue relevant features with clinical relevance.

SIGNIFICANCE

Leveraging artificial intelligence-powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.

Beneficial implications of adjuvant chemotherapy for stage IB lung adenocarcinoma exhibiting elevated SUVmax in FDG-PET/CT: a retrospective study from a single center

Background

Controversy surrounds the efficacy of adjuvant chemotherapy (ACT) in the treatment of stage I lung adenocarcinoma (LUAD). The objective of this study was to examine the impact of the maximum standardized uptake value (SUVmax) as measured by 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) on the efficacy of ACT in patients diagnosed with stage I LUAD.

Methods

We scrutinized the medical records of 928 consecutive patients who underwent complete surgical resection for pathological stage I LUAD at our institution. The ideal cut-off value for primary tumor SUVmax in terms of disease-free survival (DFS) and overall survival (OS) was determined using the X-tile software. The Kaplan-Meier method and Cox regression analysis were used for survival analysis.

Results

Based on the SUVmax algorithm, the ideal cutoff values were determined to be 4.9 for DFS and 5.0 for OS. We selected 5.0 as the threshold because OS is the more widely accepted predictive endpoint. In a multivariate Cox regression analysis, SUVmax ≥ 5.0, problematic IB stage, and sublobectomy were identified as independent risk factors for poor DFS and OS. It is noteworthy that patients who were administered ACT had significantly longer DFS and OS than what was observed in the subgroup of patients with pathological stage IB LUAD and SUVmax ≥ 5.0 (p < 0.035 and p ≤ 0.046, respectively). However, there was no observed survival advantage for patients in stages IA or IB who had an SUVmax < 5.0.

Conclusion

The preoperative SUVmax of tumors served as an indicator of the impact of ACT in the context of completely resected pathological stage I LUAD. Notably, patients within the Stage IB category exhibiting elevated SUVmax levels emerged as a subgroup experiencing substantial benefits from postoperative ACT.

Lung adenocarcinoma: selection of surgical approaches in solid adenocarcinoma from the viewpoint of clinicopathologic features and tumor microenvironmental heterogeneity

Introduction

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.

Methods

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.

Results

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.

Conclusion

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.

Endobronchial spread of adenocarcinoma is a distinct pattern of invasion and associated with inferior clinical outcomes in lung adenocarcinoma

AIM

The spread of lung adenocarcinoma cells into the bronchi and bronchioles is not well documented. We termed this histological finding "endobronchial spreading of adenocarcinoma" (EBSA) and investigated its prevalence and clinical significance.

METHODS AND RESULTS

We reviewed 320 resected specimens from patients diagnosed with invasive adenocarcinoma, and EBSA was observed in 144 patients (45%). EBSA was significantly associated with advanced pathological stage, higher histological grade, larger tumour invasion, lymphovascular infiltration, and spread through air spaces. Patients with EBSA had significantly shorter relapse-free survival (RFS) and cancer-specific survival (CSS) in univariate analysis (P < 0.001). In a subgroup analysis of patient with small-sized (invasion size ≤30 mm) adenocarcinoma in the localized stage, EBSA was an independent inferior prognostic indicator in multivariate analysis. In a subgroup analysis of patients with small-sized Grade 1 nonmucinous adenocarcinoma (n = 61), EBSA was observed in 11 patients, and the presence of EBSA was associated with significantly shorter RFS and CSS (P = 0.026 and P = 0.001, respectively).

CONCLUSION

Our results demonstrated that EBSA is a significant risk factor for disease recurrence and cancer-related deaths. EBSA can be regarded as a distinctive pattern of invasion and its recognition can be beneficial in the diagnosis of lung adenocarcinoma.

Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas

PURPOSE

To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets.

MATERIALS AND METHODS

This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance.

CONCLUSION

A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.

Incorporation of the lepidic component as an additional pathological T descriptor for non-small cell lung cancer: Data from 3335 cases of lung adenocarcinoma

OBJECTIVES

The Lepidic Component (LP) identifies a subgroup with an excellent prognosis for lung adenocarcinoma (LUAD). Our research aimed to propose an improved pathological T (pT) stage for LUAD based on LP.

MATERIALS AND METHODS

Totally, 3335 surgical patients with pathological stage I LUAD were incorporated. Factors affecting survival were investigated by analyzing recurrence-free survival (RFS) and overall survival (OS) using the Kaplan-Meier method and Cox regression analyses. Subgroup analysis based on Lepidic Ratio (LR) was further evaluated. The net benefit from the modified pT category (pTm) was assessed using the Area Under the time-dependent Receiver Operating Curve (AUC), Harrell's Concordance Index (C-index), Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI).

RESULTS

The presence of LP (LP+) was identified in 1425 (42.7 %) patients, indicating a significantly better RFS (P < 0.001) and OS (P < 0.001) than those without LP, and similar results were reproduced in pT1a-pT2a subcategory (P < 0.050 for all). Multivariable Cox analysis revealed LP+ as an independent prognostic factor for both RFS (HR, 0.622; P < 0.001) and OS (HR, 0.710; P = 0.019). However, lepidic ratio (LR) was not independently associated with both RFS and OS for LP+ patients. The 5-year RFS and OS rates between T1a (LP-) and T1b (LP+), T1b (LP-) and T1c (LP+), and T1b (LP-) and T2a (LP+) were comparable (P > 0.050 for all). After modification, compared with current 8th edition pT stage system (pT8), pTm independently predicted RFS and OS, and AUCs, c-index, NRI, and IDI analysis all demonstrated pTm holds better discrimination performances than pT8 for LUAD prognosis.

CONCLUSION

LP can be an additional down-staged T descriptor for pathological stage I LUAD and improve the survival predictive performance of reclassification.

Molecular heterogeneity in histomorphologic subtypes of lung adeno carcinoma represents a challenge for treatment decision

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.

Systemic immune-inflammation index predicts survival in patients with resected lung invasive mucinous adenocarcinoma

BACKGROUND

The prognostic-related factors of lung invasive mucinous adenocarcinoma(IMA) are unclear because of its rarity. Various inflammation-based biomarkers were reported to predict the survival of malignant diseases. This study aims to explore the prognostic significance of the systemic immune-inflammation index(SII), which is calculated using absolute platelet, neutrophil, and lymphocyte counts, among patients with invasive mucinous adenocarcinoma.

METHODS

From January 2015 to December 2019, 106 patients were identified as having IMA accepted radical resection and enrolled in the retrospective study. We analyzed the overall survival and disease-free survival using the Kaplan-Meier method and log-rank test. Receiver operating characteristic curve was used to find the optimal SII cut-off values for survival. A Cox regression model was carried out for multivariable analyses.

RESULTS

The study cohort included 53 men and 53 women, with a mean age of 60 years (range 29 to 78 years, median 61 years). The median SII measured before surgery was 378.47 (range: 79.87-1701.97). ROC analyses revealed that the optimal cut-off values of SII was 379.43 for predicting both OS and DFS. An elevated SII (≥379.43) was observed in 52 patients (49.1 %), and was associated with younger age (P = 0.02), advanced T staging (P = 0.042), lymph node metastasis (P = 0.018) and pneumonic-type IMA (P = 0.018). Multivariable analysis showed that SII and pneumonic-type IMA were independent prognostic predictors of OS and DFS in radically resected IMA patients (P < 0.05).

CONCLUSION

High SII is correlated with worse outcome and can be a novel prognostic biomarker for IMA patients accepted radical surgery.

Survival benefit of adjuvant chemotherapy after resection of Stage I lung adenocarcinoma containing micropapillary components

BACKGROUND

The usefulness of postoperative adjuvant chemotherapy (ACT) for patients with stage I lung adenocarcinoma with micropapillary (MIP) components remains unclear. We analyzed whether postoperative ACT could reduce recurrence in patients with stage I lung adenocarcinoma with MIP components, thereby improving their overall survival (OS) and disease-free survival (DFS).

METHODS

Data for patients with pathologically confirmed stage I lung adenocarcinoma with MIP components from January 2012 to December 2018 were retrospectively analyzed. OS and DFS were analyzed in groups and subgroups.

RESULTS

Overall, 259 patients were enrolled. Patients who received ACT in stage IA showed significantly better survival than did those with no-adjuvant chemotherapy (NACT); (5-year OS 89.4% vs. 73.6%, p < 0.001; 5-year DFS 87.2% vs. 66.0%, p = 0.008). A difference was also observed for in-stage IB patients (5-year OS 82.0% vs. 51.8%, p = 0.001; 5-year DFS 76.0% vs. 41.11 %, p = 0.004). In subgroup analysis based on the proportion of MIP components, patients with 1%-5% MIP components had a significantly better prognosis in the ACT group than in the NACT group (5-year OS 82.4% vs. 66.0%, p = 0.005; 5-year DFS 76.5% vs. 49.1%, p = 0.032). A similar difference was observed for patients with MIP ≥5% (5-year OS 80.7% vs. 47.8%, p = 0.009; 5-year DFS 73.11% vs. 43.5%, p = 0.007).

CONCLUSION

Among patients with stage I lung adenocarcinoma with MIP components, those who received ACT showed significant survival benefits compared to those without ACT. Patients with lung adenocarcinoma with MIP components could benefit from ACT when the MIP was ≥1%.

Efficacy of Touch Imprint Cytology in Intraoperative Diagnosis of Invasive Mucinous Adenocarcinoma of the Lung: A Case Report and Literature Review

A preoperative diagnosis of the peripheral small lung nodule is often difficult, and an intraoperative frozen section diagnosis (FSD) is performed to guide treatment strategy. However, invasive mucinous adenocarcinoma (IMA) is prone to be overlooked because of the low sample quality and weak atypia. We herein report a case of IMA, in which touch imprint cytology (TIC) revealed diagnostic efficacy. A 74-year-old male with a small, subsolid nodule in the right upper lobe underwent a thoracoscopic wedge resection. A grayish brown, 10 × 7 mm-sized nodule was observed on the cut surface. Intraoperative FSD revealed lung tissue with mild alveolar septal thickening and stromal fibrosis but without overt atypia. Meanwhile, TIC revealed mucus and a few epithelial cells with intranuclear inclusions, which pathologists evaluated as reactive. Finally, focal organizing pneumonia was tentatively diagnosed, and surgery was finished without any additional resection. However, permanent section diagnosis revealed a microinvasive mucinous adenocarcinoma. Nuclear inclusions were confirmed in tumor cells. In the intraoperative setting, TIC may be more advantageous than FSD in observing nuclear inclusions and mucus. Mucinous background and nuclear inclusion on TIC may suggest IMA even if FSD does not suggest malignancy in an intraoperative diagnosis of the peripheral small lung nodule.

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study

Background

The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability.

Methods

We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance.

Results

The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05).

Conclusion

The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Proteomic analysis reveals LRPAP1 as a key player in the micropapillary pattern metastasis of lung adenocarcinoma

Objectives

Lung adenocarcinomas have different prognoses depending on their histological growth patterns. Micropapillary growth within lung adenocarcinoma, particularly metastasis, is related to dismal prognostic outcome. Metastasis accounts for a major factor leading to mortality among lung cancer patients. Understanding the mechanisms underlying early stage metastasis can help develop novel treatments for improving patient survival.

Methods

Here, quantitative mass spectrometry was conducted for comparing protein expression profiles among various histological subtypes, including adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma (including acinar and micropapillary [MIP] types). To determine the mechanism of MIP-associated metastasis, we identified a protein that was highly expressed in MIP. The expression of the selected highly expressed MIP protein was verified via immunohistochemical (IHC) analysis and its function was validated by an in vitro migration assay.

Results

Proteomic data revealed that low-density lipoprotein receptor-related protein-associated protein 1 (LRPAP1) was highly expressed in MIP group, which was confirmed by IHC. The co-expressed proteins in this study, PSMD1 and HSP90AB1, have been reported to be highly expressed in different cancers and play an essential role in metastasis. We observed that LRPAP1 promoted lung cancer progression, including metastasis, invasion and proliferation in vitro and in vivo.

Conclusion

LRPAP1 is necessary for MIP-associated metastasis and is the candidate novel anti-metastasis therapeutic target.

Imaging phenotyping using 18F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma

OBJECTIVES

To develop and validate a machine learning model using 18F-FDG PET/CT radiomics signature and clinical features to predict the presence of micropapillary and solid (MP/S) components in lung adenocarcinoma.

METHODS

Eight hundred and forty-six patients who underwent preoperative PET/CT with pathologically confirmed adenocarcinoma were enrolled. After segmentation, 1688 radiomics features were extracted from PET/CT and selected to construct predictive models. Then, we developed a nomogram based on PET/CT radiomics integrated with clinical features. Receiver operating curves, calibration curves, and decision curve analysis (DCA) were performed for diagnostics assessment and test of the developed models for distinguishing patients with MP/S components from the patients without.

RESULTS

PET/CT radiomics-clinical combined model could well distinguish patients with MP/S components from those without MP/S components (AUC = 0.87), which performed better than PET (AUC = 0.829, p < 0.05) or CT (AUC = 0.827, p < 0.05) radiomics models in the training cohort. In test cohorts, radiomics-clinical combined model outperformed the PET radiomics model in test cohort 1 (AUC = 0.859 vs 0.799, p < 0.05) and the CT radiomics model in test cohort 2 (AUC = 0.880 vs 0.829, p < 0.05). Calibration curve indicated good coherence between all model prediction and the actual observation in training and test cohorts. DCA revealed PET/CT radiomics-clinical model exerted the highest clinical benefit.

CONCLUSION

18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of MP/S components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies. The PET/CT radiomics-clinical combined model performed best. CRITICAL RELEVANCE STATEMENT: 18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of micropapillary and solid components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies.

Prognostic biomarker discovery based on proteome landscape of Chinese lung adenocarcinoma

Despite recent innovations in imaging and genomic screening promotes advance in diagnosis and treatment of lung adenocarcinoma (LUAD), there remains high mortality of LUAD and insufficient understanding of LUAD biology. Our previous study performed an integrative multi-omic analysis of LUAD, filling the gap between genomic alterations and their biological proteome effects. However, more detailed molecular characterization and biomarker resources at proteome level still need to be uncovered. In this study, a quantitative proteomic experiment of patient-derived benign lung disease samples was carried out. After that, we integrated the proteomic data with previous dataset of 103 paired LUAD samples. We depicted the proteomic differences between non-cancerous and tumor samples and among diverse pathological subtypes. We also found that up-regulated mitophagy was a significant characteristic of early-stage LUAD. Additionally, our integrative analysis filtered out 75 potential prognostic biomarkers and validated two of them in an independent LUAD serum cohort. This study provided insights for improved understanding proteome abnormalities of LUAD and the novel prognostic biomarker discovery offered an opportunity for LUAD precise management.

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.

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study

PURPOSE

No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction.

METHODS

A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups.

RESULTS

The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS).

CONCLUSION

The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.

Clinicopathologic Features and Survival Outcomes of Primary Lung Mucinous Adenocarcinoma Based on Different Radiologic Subtypes

BACKGROUND

Primary lung mucinous adenocarcinomas (LMAs) could be subclassified as the pure-solid, part-solid, and pneumonic types according to the findings of high-resolution computed tomography. This study aimed to expound on the clinicopathologic, radiologic, and prognostic characteristics of LMAs based on radiologic classification within a large set of patients.

METHODS

From November 2009 to December 2016, this study enrolled 294 resected LMAs, which were divided into the pure-solid (n = 169), part-solid (n = 87), and pneumonic (n = 38) types. The clinicopathologic and radiologic characteristics of the tumors were evaluated, and patient prognosis was determined through follow-up evaluation. Survival outcomes were calculated by Kaplan-Meier curves and compared using log-rank tests. The prognostic impact of clinicopathologic variables, including radiologic presentations, were evaluated by establishing a Cox proportional hazards model.

RESULTS

The LMAs were infrequently associated with lymph node metastasis (5.4 %), lymphatic/vascular invasion (4.4 %), or visceral pleural invasion (5.1 %). During the median 71-month follow-up period, recurrence was observed in 62 patients and death in 44 patients. The patients with pneumonic-type LMAs had a poorer prognosis (5-year recurrence-free survival [RFS], 23.7 %; 5-year overall survival [OS], 44.7 %) than those with the pure-solid type (RFS, 83.2 %; OS, 100 %) or part-solid type (RFS, 93.7 %; OS, 100 %). Besides, lymph node metastasis, emphysema, and clinical T stage were independent predictors of RFS and OS.

CONCLUSION

Solitary-type LMA patients had excellent prognoses, whereas the survival outcomes for pneumonic-type LMA patients were dismal. Furthermore, pneumonic-type LMA patients were prone to intrapulmonary metastasis by means of aerogenous dissemination rather than distant metastasis.

TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading

OBJECTIVES

Thyroid transcription factor 1 (TTF-1) is a well-established independent prognostic factor in lung adenocarcinoma (LUAD), irrespective of stage. This study aims to determine if TTF-1's prognostic impact is solely based on histomorphological differentiation (tumor grading) or if it independently relates to a biologically more aggressive phenotype. We analyzed a large bi-centric LUAD cohort to accurately assess TTF-1's prognostic value in relation to tumor grade.

PATIENTS AND METHODS

We studied 447 patients with resected LUAD from major German lung cancer centers (Berlin and Cologne), correlating TTF-1 status and grading with clinical, pathologic, and molecular data, alongside patient outcomes. TTF-1's impact was evaluated through univariate and multivariate Cox regression. Causal graph analysis was used to identify and account for potential confounders, improving the statistical estimation of TTF-1's predictive power for clinical outcomes.

RESULTS

Univariate analysis revealed TTF-1 positivity associated with significantly longer disease-free survival (DFS) (median log HR -0.83; p = 0.018). Higher tumor grade showed a non-significant association with shorter DFS (median log HR 0.30; p = 0,62 for G1 to G2 and 0.68; p = 0,34 for G2 to G3). In multivariate analysis, TTF-1 positivity resulted in a significantly longer DFS (median log HR -0.65; p = 0.05) independent of all other parameters, including grading. Adjusting for potential confounders as indicated by the causal graph confirmed the superiority of TTF-1 over tumor grading in prognostics power.

CONCLUSIONS

TTF-1 status predicts relapse and survival in LUAD independently of tumor grading. The prognostic power of tumor grading is limited to TTF-1-positive patients, and the effect size of TTF-1 surpasses that of tumor grading. We recommend including TTF1 status as a prognostic factor in the diagnostic guidelines of LUAD.

Distinct Recurrence Pattern and Survival Outcomes of Invasive Mucinous Adenocarcinoma of the Lung: The Potential Role of Local Therapy in Intrapulmonary Spread

BACKGROUND

Invasive mucinous adenocarcinoma (IMA) is distinct from non-mucinous adenocarcinoma, but studies on recurrent IMA are scarce. Thus, this study aimed to evaluate the recurrence patterns of IMA and the role of pulmonary local therapy (LT) in resectable pulmonary recurrence of IMA.

METHODS

The study reviewed 403 patients with surgically resected IMA between 1998 and 2018. The recurrence patterns were categorized as solitary pulmonary recurrence (SPR), multiple pulmonary recurrence (MPR), and extra-pulmonary recurrence (EPR). The clinicopathologic characteristics, overall survival (OS), and post-recurrence survival (PRS) were analyzed according to the recurrence pattern and LT administration.

RESULTS

Recurrences were found in 91 (22.6%) patients, including 18 patients with SPR, 37 patients with MPR, and 36 patients with EPR. Compared with the MPR and EPR groups, the SPR group had a longer disease-free interval (32.5 vs. 9.6 vs. 10.1 months, respectively; p < 0.01) and a better OS (5-year OS: 88.5%, 41.5%, and 22.9%, respectively; p < 0.01). In case of resectable pulmonary recurrence, pulmonary LT was administered to 15 patients with SPR and 3 patients with MPR. These patients showed a better 5-year PRS than the other patients with pulmonary recurrence (86.3% vs. 30.4%; p < 0.01). Notably, long-term survival was observed for one patient with MPR undergoing LT and two patients with SPR undergoing a second LT for a second pulmonary recurrence.

CONCLUSIONS

In this series, the patients with recurrent IMA showed different prognoses according to the recurrence pattern. The patients with pulmonary recurrence of IMA undergoing LT showed a favorable prognosis, suggesting the potential role of LT for resectable pulmonary recurrence of IMA.

Air bronchogram on chest CT in radiological pure-solid appearance lung cancer: Correlation analysis with genetic pathological features and survival outcomes

PURPOSE

To investigate the correlation of air bronchogram sign with clinicopathological characteristics and prognosis in patients with clinical stage (c-stage) I non-small cell lung cancer (NSCLC) with radiological pure-solid appearance.

METHOD

We retrospectively evaluated 276 patients with pure-solid c-stage I NSCLC and assessed the correlation between the air bronchogram and clinicopathological characteristics. A Cox proportional hazards model was performed to identify the effect of air bronchogram and clinicopathological variables on oncological outcomes. Recurrence-free survival (RFS) and overall survival (OS) were calculated by Kaplan-Meier curves and were compared using log-rank tests.

RESULTS

Presence of air bronchogram was associated with a well differentiated degree (P =.026), higher incidence of EGFR mutation (P <.001) and lower recurrence(P =.021). Kaplan-Meier survival curves showed that air bronchogram group was associated with favorable RFS(67.0% vs. 50.2%; P =.015). A multivariable analysis revealed that air bronchogram and EGFR mutation were independent significant prognostic factors associated with RFS (hazard ratio [HR] = 0.495, 95% confidence interval [CI]: 0.322-0.761, P =.001; HR = 1.625, 95% CI: 1.074-2.457, P =.021; respectively), but not with OS. Additionally, we found that pathological lymph node metastasis was identified as an independent prognostic factor associated with poor RFS and OS(HR = 2.808, 95% CI: 1.913-4.123, P <.001 for RFS; HR = 1.983, 95% CI: 1.185-3.318, P =.009 for OS).

CONCLUSIONS

Presence of air bronchogram was associated with well differentiated degree, higher incidence of EGFR mutation and had additional positive prognostic value for RFS in c-stage I NSCLC with a radiological pure-solid appearance.

Artificial intelligence-based recurrence prediction outperforms classical histopathological methods in pulmonary adenocarcinoma biopsies

INTRODUCTION

Between 10 and 50% of early-stage lung adenocarcinoma patients experience local or distant recurrence. Histological parameters such as a solid or micropapillary growth pattern are well-described risk factors for recurrence. However, not every patient presenting with such a pattern will develop recurrence. Designing a model which can more accurately predict recurrence on small biopsy samples can aid the stratification of patients for surgery, (neo-)adjuvant therapy, and follow-up.

MATERIAL AND METHODS

In this study, a statistical model on biopsies fed with histological data from early and advanced-stage lung adenocarcinomas was developed to predict recurrence after surgical resection. Additionally, a convolutional neural network (CNN)-based artificial intelligence (AI) classification model, named AI-based Lung Adenocarcinoma Recurrence Predictor (AILARP), was trained to predict recurrence, with an ImageNet pre-trained EfficientNet that was fine-tuned on lung adenocarcinoma biopsies using transfer learning. Both models were validated using the same biopsy dataset to ensure that an accurate comparison was demonstrated.

RESULTS

The statistical model had an accuracy of 0.49 for all patients when using histology data only. The AI classification model yielded a test accuracy of 0.70 and 0.82 and an area under the curve (AUC) of 0.74 and 0.87 on patch-wise and patient-wise hematoxylin and eosin (H&E) stained whole slide images (WSIs), respectively.

CONCLUSION

AI classification outperformed the traditional clinical approach for recurrence prediction on biopsies by a fair margin. The AI classifier may stratify patients according to their recurrence risk, based only on small biopsies. This model warrants validation in a larger lung biopsy cohort.

Explainable survival analysis with uncertainty using convolution-involved vision transformer

Image-based precision medicine research is able to help doctors make better decisions on treatments. Among all kinds of medical images, a special form is called Whole Slide Image (WSI), which is used for diagnosing patients with cancer, aiming to enable more accurate survival prediction with its high resolution. However, One unique challenge of the WSI-based prediction models is processing the gigabyte-size or even terabyte-size WSIs, which would make most models computationally infeasible. Although existing models mostly use a pre-selected subset of key patches or patch clusters as input, they might discard some important morphology information, making the prediction inferior. Another challenge is improving the prediction models' explainability, which is crucial to help doctors understand the predictions given by the models and make faithful decisions with high confidence. To address the above two challenges, in this work, we propose a novel explainable survival prediction model based on Vision Transformer. Specifically, we adopt dual-channel convolutional layers to utilize the complete WSIs for more accurate predictions. We also introduce the aleatoric uncertainty into our model to understand its limitation and avoid overconfidence in using the prediction results. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features as supporting evidence for predictions. Evaluations of two large cancer datasets show that our proposed model is able to make survival predictions more effectively and has better explainability for cancer diagnosis.

Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma

Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative 18F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10 ×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUVmax (accuracy: 83.5% vs. 74.7%, p = 9e-9) and identified aggressive subtypes by evaluating FDG uptake in the tumor and tumor shape. It also demonstrated a high negative predictive value of 95.6% compared to SUVmax (88.2%, p = 2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients.

Rapid label-free detection of early-stage lung adenocarcinoma and tumor boundary via multiphoton microscopy

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related deaths in China. Rapid and precise evaluation of tumor tissue during lung cancer surgery can reduce operative time and improve negative-margin assessment, thus increasing disease-free and overall survival rates. This study aimed to explore the potential of label-free multiphoton microscopy (MPM) for imaging adenocarcinoma tissues, detecting histopathological features, and distinguishing between normal and cancerous lung tissues. We showed that second harmonic generation (SHG) signals exhibit significant specificity for collagen fibers, enabling the quantification of collagen features in lung adenocarcinomas. In addition, we developed a collagen score that could be used to distinguish between normal and tumor areas at the tumor boundary, showing good classification performance. Our findings demonstrate that MPM imaging technology combined with an image-based collagen feature extraction method can rapidly and accurately detect early-stage lung adenocarcinoma tissues.

Development and validation of a nomogram for predicting overall survival of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy

INTRODUCTION

Currently, the prognosis of resected N2 non-small cell lung cancer patients undergoing neoadjuvant radiotherapy is poor. The goal of this research was to develop and validate a novel nomogram for exactly predicting the overall survival (OS) of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy.

METHODS

The data applied in our research were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. We divided selected data into a training cohort and a validation cohort using R software, with a ratio of 7:3. Univariate Cox regression and multivariate Cox regression were utilized to select significant variables to build the nomogram. To validate our nomogram, calibration curves, receiver operating characteristic curves (ROC), decision curve analysis (DCA), and Kaplan-Meier survival curves were employed. The nomogram model was also compared with the tumor-node-metastasis (TNM) staging system by utilizing net reclassification index (NRI) and integrated discrimination improvement (IDI).

RESULTS

Eight variables-age, sex, operative type, LN removed number, chemotherapy, AJCC stage, M stage, histology-were statistically significant in the multivariate Cox regression analysis and were selected to develop our nomogram. Based on ROC curves, calibration curves, and DCA analysis, our novel nomogram demonstrated good predictive accuracy and clinical utility. Using Kaplan-Meier (KM) survival curves and log-rank tests, the risk stratification system was able to stratify patients based on their estimated mortality risk. The nomogram performed better than the TNM staging system based on the NRI and IDI indexes.

CONCLUSIONS

We developed and validated a nomogram to predict prognosis of resected N2 NSCLC patients undergoing neoadjuvant radiotherapy. Using this nomogram, clinicians may find this nomogram useful in predicting OS of targeted patients and making more appropriate treatment decisions.

Clinical implication of the 2020 International Association for the Study of Lung Cancer histologic grading in surgically resected pathologic stage 1 lung adenocarcinomas: Prognostic value and association with computed tomography characteristics

OBJECTIVES

To investigate the incremental prognostic value of the 2020 International Association for the Study of Lung Cancer (IASLC) histologic grading system over traditional prognosticators in surgically resected pathologic stage 1 lung adenocarcinomas and to identify the clinical and radiologic characteristics of lung adenocarcinomas reclassified by the 2020 histologic grading system.

MATERIALS AND METHODS

We retrospectively enrolled 356 patients who underwent surgery for pathologic stage 1 adenocarcinoma between January 2016 and December 2017. The histologic grading was classified according to the predominant histologic subtype (conventional system) and the updated 2020 IASLC grading system. The clinical and computed tomography (CT) characteristics were compared according to the reclassification of the updated system. The performance of prognostic models for recurrence-free survival based on the combination of pathologic tumor size, histologic grade, and CT-based information was compared using the c-index.

RESULTS

Postoperative recurrence occurred in 6.7% of patients during the follow-up period (mean, 1589.2 ± 406.7 days). Fifty-nine of 244 (24.2%) tumors with intermediate grades in the conventional system were reclassified as grade 3 with the updated grading system. They showed significantly larger solid proportions and higher percentages of pure solid nodules on CT compared to tumors without reclassification (n = 185) (P < 0.05). Prognostic prediction models based on pathology tumor size and histologic grades had significantly higher c-indices (0.754-0.803) compared to the model based on pathologic tumor size only (c-index:0.723, P < 0.05).

CONCLUSION

The 2020 IASLC histologic grading system has significant incremental prognostic value over the pathologic stage in surgically resected pathologic stage 1 lung adenocarcinoma. Reclassified lung adenocarcinomas using the updated grading system have a larger solid proportion and a higher percentage of pure solid nodules on CT.

Lymph node metastasis and its risk factors in T1 lung adenocarcinoma

BACKGROUND

In this study, the focus was primarily on examining the occurrence of lymph node metastasis in T1 lung adenocarcinoma, while also analyzing the relationship between clinical variables such as imaging characteristics, pathological classifications, and lymph node metastasis.

METHODS

We retrospectively analyzed data from patients with T1 lung adenocarcinoma who underwent lobectomy and lymph node dissection between January 2016 and December 2019. Utilizing univariate and multivariate analyses, we assessed the associations between lymph node metastasis and various clinical factors, including imaging characteristics, lesion location and depth, and pathological subtypes.

RESULTS

Of the 433 patients with T1 lung adenocarcinoma, 139 had lymph node metastasis. Moreover, the incidence of node 1 (N1) lymph node, sequential, and node 2 (N2) skip metastases were 12.2%, 12.7%, and 7.2%, respectively. Univariate analysis revealed that tumor diameter, depth ratio, sex, invasive imaging features, and pathological subtype were significantly associated with lymph node metastasis. Multivariate analysis revealed that the tumor depth ratio, tumor diameter, pleural indentation or traction sign, nonvascular penetration sign, solid component, nonadherence, and micropapillary pathological subtype were risk factors for lymph node metastasis. In the multivariate analysis, the micropapillary pathological subtype was an independent risk factor for N2 skip metastasis.

CONCLUSIONS

In patients with clinical stage T1 lung adenocarcinoma, the risk of lymph node metastasis is higher for tumors located deep within the lung tissue with solid components, invasive preoperative imaging features, and larger diameters. For N2 skip lymph node metastasis, the micropapillary pathological subtype represents a significant high-risk factor.

Stepwise progression of invasive mucinous adenocarcinoma based on radiological and biological characteristics

INTRODUCTION

Invasive mucinous lung adenocarcinoma (IMA) has unique radiological findings and pathological characteristics. IMA is classified into solitary and pneumonic types; however, it is unclear whether these are biologically identical.

METHODS

A single-center retrospective analysis was performed for 70 IMA patients (solitary type [n = 38] and pneumonic type [n = 32]) who underwent pulmonary resection between January 2010 and December 2018. We compared clinical and biological characteristics between the two types.

RESULTS

The frequencies of genetic alternations such as EGFR, KRAS, BRAF, GNAS, ERBB2, TP53, NRG1, and MET were not different. Immunohistochemically, expression of MUC1 was significantly more common in the pneumonic type (5.0% versus 20.0%, p = 0.01) and diffuse MUC6 positive in the solitary type (39.0% versus 13.0%, p = 0.02). We further classified solitary types into those with or without ground-glass opacity (GGO) and pneumonic types into those with or without crazy-paving appearance (CPA), and evaluated their surgical outcomes. Five-year overall survival and relapse free survival rates were 95.8%/86.6%, 64.3%/70.7%, 74.6%/68.9%, and 50.0%/28.6% in patients with solitary type with GGO, solitary type without GGO, pneumonic type without CPA, and pneumonic type with CPA, respectively.

CONCLUSIONS

There were no differences in genetic alternations; however, mucin expression pattern was different. Surgical outcomes were different according to the presence of GGO in the solitary type and the presence of CPA in the pneumonic type. These findings suggested a stepwise progression from solitary to pneumonic IMA.

Tumoral Densities of T-Cells and Mast Cells Are Associated With Recurrence in Early-Stage Lung Adenocarcinoma

Introduction

Lung cancer is the deadliest cancer in the United States and worldwide, and lung adenocarcinoma (LUAD) is the most prevalent histologic subtype in the United States. LUAD exhibits a wide range of aggressiveness and risk of recurrence, but the biological underpinnings of this behavior are poorly understood. Past studies have focused on the biological characteristics of the tumor itself, but the ability of the immune response to contain tumor growth represents an alternative or complementary hypothesis. Emerging technologies enable us to investigate the spatial distribution of specific cell types within the tumor nest and characterize this immune response. This study aimed to investigate the association between immune cell density within the primary tumor and recurrence-free survival (RFS) in stage I and II LUAD.

Methods

This study is a prospective collection with retrospective evaluation. A total of 100 patients with surgically resected LUAD and at least 5-year follow-ups, including 69 stage I and 31 stages II tumors, were enrolled. Multiplexed immunohistochemistry panels for immune markers were used for measurement.

Results

Cox regression models adjusted for sex and EGFR mutation status revealed that the risk of recurrence was reduced by 50% for the unit of one interquartile range (IQR) change in the tumoral T-cell (adjusted hazard ratio per IQR increase = 0.50, 95% confidence interval: 0.27-0.93) and decreased by 64% in mast cell density (adjusted hazard ratio per IQR increase = 0.36, confidence interval: 0.15-0.84). The analyses were reported without the type I error correction for the multiple types of immune cell testing.

Conclusions

Analysis of the density of immune cells within the tumor and surrounding stroma reveals an association between the density of T-cells and RFS and between mast cells and RFS in early-stage LUAD. This preliminary result is a limited study with a small sample size and a lack of an independent validation set.

Immune Infiltration in Tumor and Adjacent Non-Neoplastic Regions Codetermines Patient Clinical Outcomes in Early-Stage Lung Cancer

INTRODUCTION

In recent years, the proportion of patients with NSCLC diagnosed at an early stage has increased continuously.

METHODS

In this study, we analyzed samples and data collected from 119 samples from 67 early stage patients with NSCLC, including 52 pairs of tumor and adjacent non-neoplastic samples, and performed RNA-sequencing analysis with high sequencing depth.

RESULTS

We found that immune-related genes were highly enriched among the differentially expressed genes and observed significantly higher inferred immune infiltration levels in adjacent non-neoplastic samples than in tumor samples. In survival analysis, the infiltration of certain immune cell types in tumor, but not adjacent non-neoplastic, samples were associated with overall patient survival, and excitingly, the differential infiltration between paired samples (tumor minus non-neoplastic) was more prognostic than expression in either non-neoplastic or tumor tissues. We also performed B cell receptor (BCR) and T cell receptor (TCR) repertoire analysis and observed more BCR/TCR clonotypes and increased BCR clonality in tumor than in non-neoplastic samples. Finally, we carefully quantified the fraction of the five histologic subtypes in our adenocarcinoma samples and found that higher histologic pattern complexity was associated with higher immune infiltration and low TCR clonality in the tumor-proximal regions.

CONCLUSIONS

Our results indicated significantly differential immune characteristics between tumor and adjacent non-neoplastic samples and suggested that the two regions provided complementary prognostic values in early-stage NSCLCs.

Differences of molecular events driving pathological and radiological progression of lung adenocarcinoma

BACKGROUND

Ground-glass opacity (GGO)-like lung adenocarcinoma (LUAD) has been detected increasingly in the clinic and its inert property and superior survival indicate unique biological characteristics. However, we do not know much about them, which hampers identification of key reasons for the inert property of GGO-like LUAD.

METHODS

Using whole-exome sequencing and RNA sequencing, taking into account both radiological and pathological classifications of the same 197 patients concomitantly, we systematically interrogate genes driving the progression from GGO to solid nodule and potential reasons for the inertia of GGO. Using flow cytometry and IHC, we validated the abundance of immune cells and activity of cell proliferation.

FINDINGS

Identifying the differences between GGO and solid nodule, we found adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) and GGO-like LUAD exhibited lower TP53 mutation frequency and less active cell proliferation-related pathways than solid nodule in LUAD. Identifying the differences in GGO between AIS/MIA and LUAD, we noticed that EGFR mutation frequency and CNV load were significantly higher in LUAD than in AIS/MIA. Regulatory T cell was also higher in LUAD, while CD8+ T cell decreased from AIS/MIA to LUAD. Finally, we constructed a transcriptomic signature to quantify the development from GGO to solid nodule, which was an independent predictor of patients' prognosis in 11 external LUAD datasets.

INTERPRETATION

Our results provide deeper insights into the indolent nature of GGO and provide a molecular basis for the treatment of GGO-like LUAD.

FUNDING

This study was supported in part by the National Natural Science Foundation of China (32170657), the National Natural Science Foundation of China (82203037), and Shanghai Sailing Program (22YF1408900).

Significance of Tumor-Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors

Background and Objectives: The present study aimed to elucidate the distribution and the prognostic implications of tumor-stroma ratio (TSR) in various malignant tumors through a meta-analysis. Materials and Methods: This meta-analysis included 51 eligible studies with information for overall survival (OS) or disease-free survival (DFS), according to TSR. In addition, subgroup analysis was performed based on criteria for high TSR. Results: The estimated rate of high TSR was 0.605 (95% confidence interval (CI) 0.565-0.644) in overall malignant tumors. The rates of high TSR ranged from 0.276 to 0.865. The highest rate of high TSR was found in endometrial cancer (0.865, 95% CI 0.827-0.895). The estimated high TSR rates of colorectal, esophageal, and stomach cancers were 0.622, 0.529, and 0.448, respectively. In overall cases, patients with high TSR had better OS and DFS than those with low TSR (hazard ratio (HR) 0.631, 95% CI 0.542-0.734, and HR 0.564, 95% CI 0.0.476-0.669, respectively). Significant correlations with OS were found in the breast, cervical, colorectal, esophagus, head and neck, ovary, stomach, and urinary tract cancers. In addition, there were significant correlations of DFS in breast, cervical, colorectal, esophageal, larynx, lung, and stomach cancers. In endometrial cancers, high TSR was significantly correlated with worse OS and DFS. Conclusions: The rate of high TSR was different in various malignant tumors. TSR can be useful for predicting prognosis through a routine microscopic examination of malignant tumors.

Current and future role of double-lung transplantation for bilateral lung cancer

Technological advances have progressively enhanced the survival rate of lung transplant recipients and expanded its indications for various diseases, including the recent coronavirus disease 2019 (COVID-19). However, according to the International Society for Heart and Lung Transplantation, lung cancer constituted a mere 0.1% of the indications for lung transplantation over the past two decades. This statistic has remained stagnant, and numerous lung cancer patients continue to be excluded from lung transplantation candidacy. Contrary to the general exclusion of lung cancer patients from transplantation, the post-transplant survival rate for these patients is not inferior to that of patients with non-cancerous diseases. Furthermore, lung transplantation may offer curative treatment for patients with bilateral lung cancer whose respiratory insufficiency has advanced independently of cancer progression. This review aims to elucidate and examine the role of double lung transplantation (DLT) in bilateral lung cancer. We summarize the established indications for lung transplantation, appropriate histologic or molecular subtypes of lung cancer for transplantation, technical advances to minimize recurrence, post-DLT survival outcomes for lung cancer patients, and related translational research. We suggest that although DLT for bilateral lung cancer presents challenges, it may be considered a potential treatment option in select circumstances.

Development of the semi-dry dot-blot method for intraoperative detecting micropapillary component in lung adenocarcinoma based on proteomics analysis

BACKGROUND

Micropapillary (MIP) component was a major concern in determining surgical strategy in lung adenocarcinoma (LUAD). We sought to develop a novel method for detecting MIP component during surgery.

METHODS

Differentially expressed proteins between MIP-positive and MIP-negative LUAD were identified through proteomics analysis. The semi-dry dot-blot (SDB) method which visualises the targeted protein was developed to detect MIP component.

RESULTS

Cellular retinoic acid-binding protein 2 (CRABP2) was significantly upregulated in MIP-positive LUAD (P < 0.001), and the high CRABP2 expression zone showed spatial consistency with MIP component. CRABP2 expression was also associated with decreased recurrence-free survival (P < 0.001). In the prospective cohort, the accuracy and sensitivity of detecting MIP component using SDB method by visualising CRABP2 were 82.2% and 72.7%, which were comparable to these of pathologist. Pathologist with the aid of SDB method would improve greatly in diagnostic accuracy (86.4%) and sensitivity (78.2%). In patients with minor MIP component (≤5%), the sensitivity of SDB method (63.6%) was significantly higher than pathologist (45.4%).

CONCLUSIONS

Intraoperative examination of CRABP2 using SDB method to detect MIP component reached comparable performance to pathologist, and SDB method had notable superiority than pathologist in detecting minor MIP component.

Predictive scoring of high-grade histology among early-stage lung cancer patients: The MOSS score

BACKGROUND

Poor prognosis associated with adenocarcinoma of International Association for the Study of Lung Cancer (IASLC) grade 3 has been recognized. In this study we aimed to develop a scoring system for predicting IASLC grade 3 based before surgery.

METHODS

Two retrospective datasets with significant heterogeneity were used to develop and evaluate a scoring system. The development set was comprised of patients with pathological stage I nonmucinous adenocarcinoma and they were randomly divided into training (n = 375) and validation (n = 125) datasets. Using multivariate logistic regression, a scoring system was developed and internally validated. Later, this new score was further tested in the testing set which was comprised of patients with clinical stage 0-I non-small cell lung cancer (NSCLC) (n = 281).

RESULTS

Four factors that were related to IASLC grade 3 were used to develop the new scoring system the MOSS score; male (M, point 1), overweight (O, point 1), size>10 mm (S, point 1), and solid lesions (S, point 3). Predictability of IASLC grade 3 increased from 0.4% to 75.2% with scores from 0 to 6. The area under the curve (AUC) of the MOSS was 0.889 and 0.765 for the training and validation datasets, respectively. The MOSS score exhibited similar predictability in the testing set (AUC: 0.820).

CONCLUSION

The MOSS score, which combines preoperative variables, can be used to identify high-risk early-stage NSCLC patients with aggressive histological features. It can support clinicians in determining a treatment plan and surgical extent. Further refinement of this scoring system with prospective validation is needed.

Micropapillary and Solid Histologic Patterns in N1 and N2 Lymph Node Metastases Are Independent Factors of Poor Prognosis in Patients With Stages II to III Lung Adenocarcinoma

INTRODUCTION

High-grade histologic patterns are associated with poor prognosis in patients with primary nonmucinous lung adenocarcinoma (ADC). We investigated whether the presence of micropapillary (MIP), solid (SOL), or both patterns in lymph node (LN) metastases has prognostic value.

METHODS

Patients who underwent lobectomy for pathologic stages II to III lung ADC with N1 or N2 LN metastases (N = 360; 2000-2012) were analyzed. We assessed overall survival (OS), lung cancer-specific cumulative incidence of death (LC-CID), and cumulative incidence of recurrence (CIR) between patients with and without MIP/SOL patterns in LN metastases. Multivariable Cox regression analysis was used to quantify the association between MIP/SOL patterns and outcomes.

RESULTS

MIP and SOL in LN metastases were associated with a higher incidence of smoking history (p = 0.004), tumor necrosis (p = 0.013), and spread of tumor through air spaces (p < 0.0001), a higher prevalence of MIP or SOL in the primary tumor (p < 0.0001), shorter OS (5-y OS, 40% [95% confidence interval or CI: 29%-56%] versus 63% [48%-83%] for no MIP/SOL in LNs, p = 0.03), higher LC-CID (5-y, 43% [29%-56%] versus 14% [4%-29%], p = 0.013), and higher CIR (5-y, 65% [50%-77%] versus 43% [25%-60%], p = 0.057). MIP and SOL in LN metastases were independently associated with poor outcomes: OS (hazard ratio [HR] = 1.81 [95% CI: 1.00-3.29], p = 0.05), LC-CID (HR = 3.10 [1.30-7.37], p = 0.01), and CIR (HR = 2.06 [1.09-3.90], p = 0.026).

CONCLUSIONS

MIP/SOL histologic patterns in N1 or N2 LN metastases are associated with worse outcomes in patients with stages II to III lung ADC. MIP/SOL histologic patterns in LN metastases can stratify patients with high-risk stages II to III lung ADC.