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.

A dual data stream hybrid neural network for classifying pathological images of lung adenocarcinoma.

Lung cancer has seriously threatened human health due to its high lethality and morbidity. Lung adenocarcinoma, in particular, is one of the most common subtypes of lung cancer. Pathological diagnosis is regarded as the gold standard for cancer diagnosis. However, the traditional manual screening of lung cancer pathology images is time consuming and error prone. Computer-aided diagnostic systems have emerged to solve this problem. Current research methods are unable to fully exploit the beneficial features inherent within patches, and they are characterized by high model complexity and significant computational effort. In this study, a deep learning framework called Multi-Scale Network (MSNet) is proposed for the automatic detection of lung adenocarcinoma pathology images. MSNet is designed to efficiently harness the valuable features within data patches, while simultaneously reducing model complexity, computational demands, and storage space requirements. The MSNet framework employs a dual data stream input method. In this input method, MSNet combines Swin Transformer and MLP-Mixer models to address global information between patches and the local information within each patch. Subsequently, MSNet uses the Multilayer Perceptron (MLP) module to fuse local and global features and perform classification to output the final detection results. In addition, a dataset of lung adenocarcinoma pathology images containing three categories is created for training and testing the MSNet framework. Experimental results show that the diagnostic accuracy of MSNet for lung adenocarcinoma pathology images is 96.55 %. In summary, MSNet has high classification performance and shows effectiveness and potential in the classification of lung adenocarcinoma pathology images.

Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma; ADC, LCNEC, SCC, SCLC, and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We first evaluated the diagnostic performance of each model in the test sets. The Xception model and the CNN built from scratch both achieved the highest performance with a macro average AUC of 0.90. The CNN built from scratch model obtained a macro average AUC of 0.97 on the dataset of four classes excluding LCNEC, and 0.95 on the dataset of three subtypes of lung carcinomas; NSCLC, SCLC, and non-tumor, respectively. Of particular note is that the relatively simple CNN built from scratch may be an approach for pathological image analysis.

Stiffness reduction and collagenase resistance of aging lungs measured using scanning acoustic microscopy.

Lung tissue stiffness is altered with aging. Quantitatively evaluating lung function is difficult using a light microscope (LM) alone. Scanning acoustic microscope (SAM) calculates the speed-of-sound (SOS) using sections to obtain histological images by plotting SOS values on the screen. As SOS is positively correlated with stiffness, SAM has a superior characteristic of simultaneously evaluating tissue stiffness and structure. SOS images of healthy bronchioles, arterioles, and alveoli were compared among young, middle-aged, and old lung sections. Formalin-fixed, paraffin-embedded (FFPE) sections consistently exhibited relatively higher SOS values than fresh-frozen sections, indicating that FFPE became stiffer but retained the relative stiffness reflecting fresh samples. All lung components exhibited gradually declining SOS values with aging and were associated with structural alterations such as loss of smooth muscles, collagen, and elastic fibers. Moreover, reaction to collagenase digestion resulted in decreased SOS values. SOS values of all components were significantly reduced in young and middle-aged groups, whereas no significant reduction was observed in the old group. Protease damage in the absence of regeneration or loss of elastic components was present in old lungs, which exbited dilated bronchioles and alveoli. Aging lungs gradually lose stiffness with decreasing structural components without exposure to specific insults such as inflammation.

Elastin in pulmonary pathology: relevance in tumours with a lepidic or papillary appearance. A comprehensive understanding from a morphological viewpoint.

Elastin and collagen are the main components of the lung connective tissue network, and together provide the lung with elasticity and tensile strength. In pulmonary pathology, elastin staining is used to variable extents in different countries. These uses include evaluation of the pleura in staging, and the distinction of invasion from collapse of alveoli after surgery (iatrogenic collapse). In the latter, elastin staining is used to highlight distorted but pre-existing alveolar architecture from true invasion. In addition to variable levels of use and experience, the interpretation of elastin staining in some adenocarcinomas leads to interpretative differences between collapsed lepidic patterns and true papillary patterns. This review aims to summarise the existing data on the use of elastin staining in pulmonary pathology, on the basis of literature data and morphological characteristics. The effect of iatrogenic collapse and the interpretation of elastin staining in pulmonary adenocarcinomas is discussed in detail, especially for the distinction between lepidic patterns and papillary carcinoma.

High KIF11 expression is associated with poor outcome of NSCLC.

PURPOSE: To clarify the correlation between KIF11 (kinesin family member 11) and clinicopathologic characteristics of non-small cell lung cancer (NSCLC) and identify the prognostic value of KIF11 in patients with NSCLC. METHODS: For investigating the expression of KIF11 in NSCLC, two tissue microarrays (TMAs: one contained 60 paired NSCLC tissues and paratumor tissues, the other contained 140 NSCLC tissues and 10 normal lung tissues) were constructed, stained, and scored. The Cancer Genome Atlas (TCGA) datasets were used to explore the differential expression level of KIF11 between NSCLC and paratumor. Kaplan-Meier survival curves were plotted and multivariate analysis were carried out. RESULTS: The staining of KIF11 mainly distributed throughout the cytoplasm of tumor cells. Its expression was higher in NSCLC than paratumor cells, and similar results were obtained from TCGA datasets. We found that high expression of KIF11 had a significant correlation with lymph node metastases (p = 0.024) and pathologic stage (p = 0.018); that significant difference was not found in any other clinicopathologic characteristic. As univariate and multivariate analysis showed, KIF11 expression was significantly correlated with overall survival time of NSCLC (p = 0.002, p = 0.025, respectively). High KIF11 expression was found to significantly associate with overall survival of stage II-III (p = 0.001) and lung adenocarcinoma (p = 0.036). CONCLUSION: High KIF11 expression predicts poor outcome in NSCLC. KIF11 is expected to be a viable prognostic biomarker for NSCLC.

Identification of A-to-I RNA editing profiles and their clinical relevance in lung adenocarcinoma.

Adenosine-to-inosine (A-to-I) RNA editing is a widespread posttranscriptional modification that has been shown to play an important role in tumorigenesis. Here, we evaluated a total of 19,316 RNA editing sites in the tissues of 80 lung adenocarcinoma (LUAD) patients from our Nanjing Lung Cancer Cohort (NJLCC) and 486 LUAD patients from the TCGA database. The global RNA editing level was significantly increased in tumor tissues and was highly heterogeneous across patients. The high RNA editing level in tumors was attributed to both RNA (ADAR1 expression) and DNA alterations (mutation load). Consensus clustering on RNA editing sites revealed a new molecular subtype (EC3) that was associated with the poorest prognosis of LUAD patients. Importantly, the new classification was independent of classic molecular subtypes based on gene expression or DNA methylation. We further proposed a simplified model including eight RNA editing sites to accurately distinguish the EC3 subtype in our patients. The model was further validated in the TCGA dataset and had an area under the curve (AUC) of the receiver operating characteristic curve of 0.93 (95%CI: 0.91-0.95). In addition, we found that LUAD cell lines with the EC3 subtype were sensitive to four chemotherapy drugs. These findings highlighted the importance of RNA editing events in the tumorigenesis of LUAD and provided insight into the application of RNA editing in the molecular subtyping and clinical treatment of cancer.

Characteristic of molecular subtypes in lung adenocarcinoma based on m6A RNA methylation modification and immune microenvironment.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major subtype of lung cancer and closely associated with poor prognosis. N6-methyladenosine (m6A), one of the most predominant modifications in mRNAs, is found to participate in tumorigenesis. However, the potential function of m6A RNA methylation in the tumor immune microenvironment is still murky. METHODS: The gene expression profile cohort and its corresponding clinical data of LUAD patients were downloaded from TCGA database and GEO database. Based on the expression of 21 m6A regulators, we identified two distinct subgroups by consensus clustering. The single-sample gene-set enrichment analysis (ssGSEA) algorithm was conducted to quantify the relative abundance of the fraction of 28 immune cell types. The prognostic model was constructed by Lasso Cox regression. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic model. RESULT: Consensus classification separated the patients into two clusters (clusters 1 and 2). Those patients in cluster 1 showed a better prognosis and were related to higher immune scores and more immune cell infiltration. Subsequently, 457 differentially expressed genes (DEGs) between the two clusters were identified, and then a seven-gene prognostic model was constricted. The survival analysis showed poor prognosis in patients with high-risk score. The ROC curve confirmed the predictive accuracy of this prognostic risk signature. Besides, further analysis indicated that there were significant differences between the high-risk and low-risk groups in stages, status, clustering subtypes, and immunoscore. Low-risk group was related to higher immune score, more immune cell infiltration, and lower clinical stages. Moreover, multivariate analysis revealed that this prognostic model might be a powerful prognostic predictor for LUAD. Ultimately, the efficacy of this prognostic model was successfully validated in several external cohorts (GSE30219, GSE50081 and GSE72094). CONCLUSION: Our study provides a robust signature for predicting patients' prognosis, which might be helpful for therapeutic strategies discovery of LUAD.

Association of Lung Adenocarcinoma Subtypes According to the IASLC/ATS/ERS Classification and Programmed Cell Death Ligand 1 (PD-L1) Expression in Tumor Cells.

Background: Programmed cell death-ligand 1 (PD-L1) protein expression is one of the most extensively studied biomarkers in patients with non-small cell lung cancer (NSCLC). However, there is scarce information regarding its association with distinct adenocarcinoma subtypes. This study evaluated the frequency of PD-L1 expression according to the IASLC/ATS/ERS classification and other relevant histological and clinical features. Patients and Methods: PD-L1 expression was assessed by immunohistochemistry (IHC). According to its positivity in tumor cells membrane, we stratified patients in three different tumor proportions score (TPS) cut-off points: a) <1% (negative), b) between 1 and 49%, and c) ≥50%; afterward, we analyzed the association among PD-L1 expression and lung adenocarcinoma (LADC) predominant subtypes, as well as other clinical features. As an exploratory outcome we evaluated if a PD-L1 TPS score ≥15% was useful as a biomarker for determining survival. Results: A total of 240 patients were included to our final analysis. Median age at diagnosis was 65 years (range 23-94 years). A PD-L1 TPS ≥1% was observed in 52.5% of the entire cohort; regarding specific predominant histological patterns, a PD-L1 TPS ≥1 was documented in 31.2% of patients with predominant-lepidic pattern, 46.2% of patients with predominant-acinar pattern, 42.8% of patients with a predominant-papillary pattern, and 68.7% of patients with predominant-solid pattern (p = 0.002). On the other hand, proportion of tumors with PD-L1 TPS ≥50% was not significantly different among adenocarcinoma subtypes. At the univariate survival analysis, a PD-L1 TPS cut-off value of ≥15% was associated with a worse PFS and OS. Conclusion: According to IASLC/ATS/ERS lung adenocarcinoma classification, the predominant-solid pattern is associated with a higher proportion of PD-L1 positive samples, no subtype was identified to be associated with a high (≥50%) TPS PD-L1.

Comparison of Two Proposed Changes to the Current Nodal Classification for Non--small Cell Lung Cancer Based on the Number and Ratio of Metastatic Lymph Nodes.

BACKGROUND: The current nodal classification is unsatisfactory in distinguishing the prognostically heterogeneous N1 or N2 non-small cell lung cancer (NSCLC). RESEARCH QUESTION: Is the combination of the current N category and the number of metastatic lymph nodes (N-#number) or the combination of the current N category and the ratio of the number of positive to resected lymph nodes (N-#ratio) better than the current N category alone? STUDY DESIGN AND METHODS: We identified 2,162 patients with N1 or N2 NSCLC from the Surveillance, Epidemiology, and End Results database (2004-2016). We classified these patients into three N-#number categories (N-#number-1, N-#number-2a, N-#number-2b) and three N-#ratio categories (N-#ratio-1, N-#ratio-2a, N-#ratio-2b). Lung cancer-specific survival (LCSS) were compared using the Kaplan-Meier method. The prognostic significance of the new nodal classifications was validated across each tumor size category (≤3 cm, 3-5 cm, 5-7cm, >7 cm). Cox proportional hazards regression was used to evaluate the association between each nodal classification and LCSS. RESULTS: The survival curves showed clear differences between each pair of N-#number and N-#ratio categories. A significant tendency toward the deterioration of LCSS from N-#number-1 to N-#number-2b was observed in all tumor size categories. However, the differences between each pair of N-#ratio categories were significant only in tumors from 3 to 7 cm. Although all three nodal classifications were independent prognostic indicators, the N-#number classification provided more accurate prognostic stratifications compared with the N-#ratio classification and the current nodal classification. INTERPRETATION: The N-#number classification followed by the N-#ratio classification might be better prognostic determinants than the current nodal classification in prognostically heterogeneous N1 or N2 NSCLC.

A systematic dissection of the epigenomic heterogeneity of lung adenocarcinoma reveals two different subclasses with distinct prognosis and core regulatory networks.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly malignant and heterogeneous tumor that involves various oncogenic genetic alterations. Epigenetic processes play important roles in lung cancer development. However, the variation in enhancer and super-enhancer landscapes of LUAD patients remains largely unknown. To provide an in-depth understanding of the epigenomic heterogeneity of LUAD, we investigate the H3K27ac histone modification profiles of tumors and adjacent normal lung tissues from 42 LUAD patients and explore the role of epigenetic alterations in LUAD progression. RESULTS: A high intertumoral epigenetic heterogeneity is observed across the LUAD H3K27ac profiles. We quantitatively model the intertumoral variability of H3K27ac levels at proximal gene promoters and distal enhancers and propose a new epigenetic classification of LUAD patients. Our classification defines two LUAD subgroups which are highly related to histological subtypes. Group II patients have significantly worse prognosis than group I, which is further confirmed in the public TCGA-LUAD cohort. Differential RNA-seq analysis between group I and group II groups reveals that those genes upregulated in group II group tend to promote cell proliferation and induce cell de-differentiation. We construct the gene co-expression networks and identify group-specific core regulators. Most of these core regulators are linked with group-specific regulatory elements, such as super-enhancers. We further show that CLU is regulated by 3 group I-specific core regulators and works as a novel tumor suppressor in LUAD. CONCLUSIONS: Our study systematically characterizes the epigenetic alterations during LUAD progression and provides a new classification model that is helpful for predicting patient prognosis.

Hepatoid adenocarcinoma of lung: A diagnostic challenge - Series of six cases with histopathological, predictive molecular and PD.L1 assessment.

Hepatoid adenocarcinoma of lung is a rare entity, accounting for 5% of all hepatoid adenocarcinoma. Distinguishing it from metastatic hepatocellular carcinoma is essential, but occasionally can be very challenging, especially with concurrent liver mass. A judicious immunohistochemical panel is warranted for accurate diagnosis and subsequent preservation of tissue for molecular testing. There is limited data on the mutational status, behavior and management strategies of this type of lung adenocarcinoma. We report largest series of six cases of hepatoid adenocarcinoma of lung citing the clinical, histopathological, immunohistochemical and molecular parameters including PD-L1 immunoexpression as a predictive biomarker for immunotherapy. None of the evaluated cases showed targetable mutation; however, four out of six cases showed significant PD-L1 expression. All the cases presented with advanced stage and received chemotherapy, however overall prognosis was dismal. In view of significant PD-L1 expression in these tumors and poor response to conventional chemotherapy, these cases might be considered for upfront immunotherapy.

Identification of potential serum biomarkers for simultaneously classifying lung adenocarcinoma, squamous cell carcinoma and small cell carcinoma.

BACKGROUND: Histological subtypes of lung cancer are crucial for making treatment decisions. However, multi-subtype classifications including adenocarcinoma (AC), squamous cell carcinoma (SqCC) and small cell carcinoma (SCLC) were rare in the previous studies. This study aimed at identifying and screening potential serum biomarkers for the simultaneous classification of AC, SqCC and SCLC. PATIENTS AND METHODS: A total of 143 serum samples of AC, SqCC and SCLC were analyzed by 1HNMR and UPLC-MS/MS. The stepwise discriminant analysis (DA) and multilayer perceptron (MLP) were employed to screen the most efficient combinations of markers for classification. RESULTS: The results of non-targeted metabolomics analysis showed that the changes of metabolites of choline, lipid or amino acid might contribute to the classification of lung cancer subtypes. 17 metabolites in those pathways were further quantified by UPLC-MS/MS. DA screened out that serum xanthine, S-adenosyl methionine (SAM), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE) and squamous cell carcinoma antigen (SCC) contributed significantly to the classification of AC, SqCC and SCLC. The average accuracy of 92.3% and the area under the receiver operating characteristic curve of 0.97 would be achieved by MLP model when a combination of those five variables as input parameters. CONCLUSION: Our findings suggested that metabolomics was helpful in screening potential serum markers for lung cancer classification. The MLP model established can be used for the simultaneous diagnosis of AC, SqCC and SCLC with high accuracy, which is worthy of further study.

Volume and Mass Doubling Time of Lung Adenocarcinoma according to WHO Histologic Classification.

OBJECTIVE: This study aimed to evaluate the tumor doubling time of invasive lung adenocarcinoma according to the International Association of the Study for Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) histologic classification. MATERIALS AND METHODS: Among the 2905 patients with surgically resected lung adenocarcinoma, we retrospectively included 172 patients (mean age, 65.6 ± 9.0 years) who had paired thin-section non-contrast chest computed tomography (CT) scans at least 84 days apart with the same CT parameters, along with 10 patients with squamous cell carcinoma (mean age, 70.9 ± 7.4 years) for comparison. Three-dimensional semiautomatic segmentation of nodules was performed to calculate the volume doubling time (VDT), mass doubling time (MDT), and specific growth rate (SGR) of volume and mass. Multivariate linear regression, one-way analysis of variance, and receiver operating characteristic curve analyses were performed. RESULTS: The median VDT and MDT of lung cancers were as follows: acinar, 603.2 and 639.5 days; lepidic, 1140.6 and 970.1 days; solid/micropapillary, 232.7 and 221.8 days; papillary, 599.0 and 624.3 days; invasive mucinous, 440.7 and 438.2 days; and squamous cell carcinoma, 149.1 and 146.1 days, respectively. The adjusted SGR of volume and mass of the solid-/micropapillary-predominant subtypes were significantly shorter than those of the acinar-, lepidic-, and papillary-predominant subtypes. The histologic subtype was independently associated with tumor doubling time. A VDT of 465.2 days and an MDT of 437.5 days yielded areas under the curve of 0.791 and 0.795, respectively, for distinguishing solid-/micropapillary-predominant subtypes from other subtypes of lung adenocarcinoma. CONCLUSION: The tumor doubling time of invasive lung adenocarcinoma differed according to the IASCL/ATS/ERS histologic classification.

Radiologic Features of Resected Lung Adenocarcinoma With Epithelial-Mesenchymal Transition.

BACKGROUND: Epithelial-mesenchymal transition plays a crucial role in cancer progression and is a significant prognosticator for postoperative survival in patients with lung cancer. Predicting epithelial-mesenchymal transition preoperatively using computed tomography may help to determine the optimal surgical strategy. METHODS: We performed an immunohistochemical analysis of E-cadherin and vimentin expressions using tumor specimens from resected primary lung adenocarcinoma and classified the results into 3 subgroups according to the expressions: epithelial, intermediate, and mesenchymal. The intermediate and mesenchymal groups were classified as the epithelial-mesenchymal transition conversion group. We analyzed the association between epithelial-mesenchymal transition and radiologic characteristics, especially computed tomographic features. RESULTS: The epithelial-mesenchymal transition conversion group comprised 162 patients (49.1%). Computed tomography revealed that tumors with epithelial-mesenchymal transition conversion showed a high consolidation/tumor ratio compared with those without conversion. Univariate analysis demonstrated that tumors with epithelial-mesenchymal transition were significantly associated with bronchial and/or vascular convergence (P < .001) and notching (P = .028). When the cutoff value for the consolidation/tumor ratio was set by the receiver operating characteristic curve, independent predictive factors for epithelial-mesenchymal transition by multivariate analysis were high ratio (>0.7946; P < .001) and the presence of convergence (P = .05). Tumors with a high consolidation/tumor ratio and convergence had a 4-fold higher odds ratio for epithelial-mesenchymal transition, and patients had significantly poorer survival. CONCLUSIONS: Convergence and a high consolidation/tumor ratio were independently associated with epithelial-mesenchymal transition conversion. These preoperative radiologic results will help to predict epithelial-mesenchymal transition conversion in lung adenocarcinoma.

Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data.

BACKGROUND: The small number of samples and the curse of dimensionality hamper the better application of deep learning techniques for disease classification. Additionally, the performance of clustering-based feature selection algorithms is still far from being satisfactory due to their limitation in using unsupervised learning methods. To enhance interpretability and overcome this problem, we developed a novel feature selection algorithm. In the meantime, complex genomic data brought great challenges for the identification of biomarkers and therapeutic targets. The current some feature selection methods have the problem of low sensitivity and specificity in this field. RESULTS: In this article, we designed a multi-scale clustering-based feature selection algorithm named MCBFS which simultaneously performs feature selection and model learning for genomic data analysis. The experimental results demonstrated that MCBFS is robust and effective by comparing it with seven benchmark and six state-of-the-art supervised methods on eight data sets. The visualization results and the statistical test showed that MCBFS can capture the informative genes and improve the interpretability and visualization of tumor gene expression and single-cell sequencing data. Additionally, we developed a general framework named McbfsNW using gene expression data and protein interaction data to identify robust biomarkers and therapeutic targets for diagnosis and therapy of diseases. The framework incorporates the MCBFS algorithm, network recognition ensemble algorithm and feature selection wrapper. McbfsNW has been applied to the lung adenocarcinoma (LUAD) data sets. The preliminary results demonstrated that higher prediction results can be attained by identified biomarkers on the independent LUAD data set, and we also structured a drug-target network which may be good for LUAD therapy. CONCLUSIONS: The proposed novel feature selection method is robust and effective for gene selection, classification, and visualization. The framework McbfsNW is practical and helpful for the identification of biomarkers and targets on genomic data. It is believed that the same methods and principles are extensible and applicable to other different kinds of data sets.

ROS1 positive non-small cell lung cancer with pulmonary embolism in a 22-year woman.

ROS1-rearrangement occurs in 1-2% of non-small cell lung cancer (NSCLC). This mutation is predominantly seen in relatively young, non-smoker, female with adenocarcinoma. Association of pulmonary embolism with ROS1-rearranged NSCLC has been suggested. We report a case of a 22-year-old woman with ROS1-positive NSCLC and pulmonary embolism. This case possibly represents the youngest patient in the literature.

A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma.

OBJECTIVES: To develop an imaging reporting system for the classification of 3 adenocarcinoma subtypes of computed tomography (CT)-detected subsolid pulmonary nodules (SSNs) in clinical patients. METHODS: Between November 2011 and October 2017, 437 pathologically confirmed SSNs were retrospectively identified. SSNs were randomly divided 2:1 into a training group (291 cases) and a testing group (146 cases). CT-imaging characteristics were analyzed using multinomial univariable and multivariable logistic regression analysis to identify discriminating factors for the 3 adenocarcinoma subtypes (pre-invasive lesions, minimally invasive adenocarcinoma, and invasive adenocarcinoma). These factors were used to develop a classification and regression tree model. Finally, an SSN Imaging Reporting System (SSN-IRS) was constructed based on the optimized classification model. For validation, the classification performance was evaluated in the testing group. RESULTS: Of the CT-derived characteristics of SSNs, qualitative density (nonsolid or part-solid), core (non-core or core), semantic features (pleural indentation, vacuole sign, vascular invasion), and diameter of solid component (≤6 mm or >6 mm), were the most important factors for the SSN-IRS. The total sensitivity, specificity, and diagnostic accuracy of the SSN-IRS was 89.0% (95% confidence interval [CI], 84.8%-92.4%), 74.6% (95% CI, 70.8%-78.1%), and 79.4% (95% CI, 76.5%-82.0%) in the training group and 84.9% (95% CI, 78.1%-90.3%), 68.5% (95% CI, 62.8%-73.8%), and 74.0% (95% CI, 69.6%-78.0%) in the testing group, respectively. CONCLUSIONS: The SSN-IRS can classify 3 adenocarcinoma subtypes using CT-based characteristics of subsolid pulmonary nodules. This classification tool can help clinicians to make follow-up recommendations or decisions for surgery in clinical patients with SSNs.

Reciprocal expression of trefoil factor-1 and thyroid transcription factor-1 in lung adenocarcinomas.

Molecular targeted therapies against EGFR and ALK have improved the quality of life of lung adenocarcinoma patients. However, targetable driver mutations are mainly found in thyroid transcription factor-1 (TTF-1)/NK2 homeobox 1 (NKX2-1)-positive terminal respiratory unit (TRU) types and rarely in non-TRU types. To elucidate the molecular characteristics of the major subtypes of non-TRU-type adenocarcinomas, we analyzed 19 lung adenocarcinoma cell lines (11 TRU types and 8 non-TRU types). A characteristic of non-TRU-type cell lines was the strong expression of TFF-1 (trefoil factor-1), a gastric mucosal protective factor. An immunohistochemical analysis of 238 primary lung adenocarcinomas resected at Jichi Medical University Hospital revealed that TFF-1 was positive in 31 cases (13%). Expression of TFF-1 was frequently detected in invasive mucinous (14/15, 93%), enteric (2/2, 100%), and colloid (1/1, 100%) adenocarcinomas, less frequent in acinar (5/24, 21%), papillary (7/120, 6%), and solid (2/43, 5%) adenocarcinomas, and negative in micropapillary (0/1, 0%), lepidic (0/23, 0%), and microinvasive adenocarcinomas or adenocarcinoma in situ (0/9, 0%). Expression of TFF-1 correlated with the expression of HNF4-α and MUC5AC (P < .0001, P < .0001, respectively) and inversely correlated with that of TTF-1/NKX2-1 (P < .0001). These results indicate that TFF-1 is characteristically expressed in non-TRU-type adenocarcinomas with gastrointestinal features. The TFF-1-positive cases harbored KRAS mutations at a high frequency, but no EGFR or ALK mutations. Expression of TFF-1 correlated with tumor spread through air spaces, and a poor prognosis in advanced stages. Moreover, the knockdown of TFF-1 inhibited cell proliferation and soft-agar colony formation and induced apoptosis in a TFF-1-high and KRAS-mutated lung adenocarcinoma cell line. These results indicate that TFF-1 is not only a biomarker, but also a potential molecular target for non-TRU-type lung adenocarcinomas.