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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.

Abstract Objectives Define if the presence of morphologic features of adenocarcinoma (ACA) in non–small cell lung carcinoma (NSCLC) on cytology specimens correlates with clinical and biologic features. Methods A total of 209 cases of NSCLC diagnosed on fine-needle aspiration in a 3-year period were included. Results After morphologic review, the cases were classified as ACA (n = 115), NSCLC favor ACA (n = 43), and NSCLC–not otherwise specified (NOS) (n = 18). Squamous cell (SCC) (n = 14) and NSCLC favor SCC (n = 19) were excluded from further analysis. Patients with EGFR-mutated tumors had longer overall survival than those with EGFR wild-type tumors (P = .01). When comparing cases with morphologic features of ACA, NSCLC favor ACA, and NSCLC-NOS, there were no differences in the presence or absence of tested mutations, clinical stage, or survival. Conclusion Patients diagnosed with pulmonary ACA, NSCLC favor ACA, or NSCLC-NOS in cytology specimens have similar clinical stage, survival, and molecular alterations.

Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them—STK11, EGFR, FAT1, SETBP1, KRAS and TP53—can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH . A convolutional neural network model using feature extraction and machine-learning techniques provides a tool for classification of lung cancer histopathology images and predicting mutational status of driver oncogenes

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and they play fundamental roles in immune regulation. Here we introduce an integrated algorithm, ImmLnc, for identifying lncRNA regulators of immune-related pathways. We comprehensively chart the landscape of lncRNA regulation in the immunome across 33 cancer types and show that cancers with similar tissue origin are likely to share lncRNA immune regulators. Moreover, the immune-related lncRNAs are likely to show expression perturbation in cancer and are significantly correlated with immune cell infiltration. ImmLnc can help prioritize cancer-related lncRNAs and further identify three molecular subtypes (proliferative, intermediate, and immunological) of non-small cell lung cancer. These subtypes are characterized by differences in mutation burden, immune cell infiltration, expression of immunomodulatory genes, response to chemotherapy, and prognosis. In summary, the ImmLnc pipeline and the resulting data serve as a valuable resource for understanding lncRNA function and to advance identification of immunotherapy targets. In cancer, long noncoding RNAs (lncRNAs) can regulate immune-related pathways. Here, the authors present ImmLnc, an algorithm that can help prioritise immune-related lncRNAs in cancer immunotherapy research

Small cell lung cancer (SCLC) is an aggressive malignancy characterized by early metastasis, rapid development of resistance to chemotherapy and genetic instability. This study profiles DNA methylation in SCLC, patient-derived xenografts (PDX) and cell lines at single-nucleotide resolution. DNA methylation patterns of primary samples are distinct from those of cell lines, whereas PDX maintain a pattern closely consistent with primary samples. Clustering of DNA methylation and gene expression of primary SCLC revealed distinct disease subtypes among histologically indistinguishable primary patient samples with similar genetic alterations. SCLC is notable for dense clustering of high-level methylation in discrete promoter CpG islands, in a pattern clearly distinct from other lung cancers and strongly correlated with high expression of the E2F target and histone methyltransferase gene EZH2 . Pharmacologic inhibition of EZH2 in a SCLC PDX markedly inhibited tumor growth.

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

Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid progression, early metastasis, and high recurrence rates. Historically considered a homogeneous disease, recent multi-omic studies have revealed distinct molecular subtypes driven by lineage-defining transcription factors, including ASCL1, NEUROD1, POU2F3, and YAP1, as well as an inflamed subtype (SCLC-I). These subtypes exhibit unique therapeutic vulnerabilities, thereby paving the way for precision medicine and targeted therapies. Despite recent advances in molecular classification, tumor heterogeneity, plasticity, and therapy resistance continue to hinder clinical success in treating SCLC patients. To this end, novel therapeutic strategies are being explored, including BCL2 inhibitors, DLL3-targeting agents, Aurora kinase inhibitors, PARP inhibitors, and epigenetic modulators. Additionally, immune checkpoint inhibitors (ICIs) show promise, particularly in immune-enriched subtypes of SCLC patients. Hence, a deeper understanding of SCLC subtype characteristics, evolution, and the regulatory mechanisms of subtype-specific transcription factors is crucial for rationally optimizing precision therapy. This knowledge not only facilitates the identification of subtype-specific therapeutic targets, but also provides a foundation for overcoming resistance and developing personalized combination treatment strategies. In the future, the integration of multi-omic data, dynamic molecular monitoring, and precision medicine approaches are expected to further advance the clinical translation of SCLC subtype-specific therapies, ultimately improving patient survival and outcomes.

Background Accurate subtyping and risk stratification are imperative for prognostication and clinical decision-making in small cell lung cancer (SCLC). However, traditional molecular subtyping is resource-intensive and challenging to translate into clinical practice. Methods A total of 517 SCLC patients and their corresponding hematoxylin and eosin (H&E)-stained whole slide images (WSIs) from three independent medical institutions were analyzed. A hybrid clustering-based unsupervised deep representation learning model was developed to identify histomorphological phenotypes (HIPO) and characterize tumor ecosystem diversity. Consensus clustering and a deep learning-based stratification system were used to define histomorphological subtypes (HIPOS) based on patient-level HIPO features. Survival analysis and Cox proportional hazards regression models were used to assess the clinical significance of HIPOS. An integrated analysis of pathomics, proteomics, and immunohistochemistry was conducted to explore the biological and microenvironmental correlates of HIPOS. Results We performed histomorphological phenotyping of SCLC using unsupervised deep representation learning from WSIs and identified 15 HIPOs. Unsupervised clustering of HIPO profiles stratified SCLCs into two reproducible image-based subtypes: HIPOS-I and HIPOS-II. Patients in the HIPOS-I group had better overall survival and disease-free survival compared to those in HIPOS-II, independent of clinical features and molecular subtypes. Multimodal analyses revealed that HIPOS-I tumors were characterized by enriched immune infiltration and immune activation, whereas HIPOS-II tumors displayed increased fibrosis, cellular pleomorphism, and dysregulated oxidative metabolism. Additionally, we developed a simplified deep-learning model to predict HIPOS subtypes to enhance clinical applications and validated the prognostic value of these subtypes in independent cohorts. Conclusions This study demonstrates the potential of a deep learning-based histomorphological subtyping system to improve patient stratification and prognosis prediction in SCLC. The HIPOS offers a promising and clinically applicable tool for personalized management using routine H&E-stained WSIs.

Many studies demonstrated unique microRNA profiles in lung cancer. Nonetheless, the role and related signal pathways of miR-375 in lung cancer are largely unknown. Our study investigated relationships between carcinogenesis and miR-375 in adenocarcinoma, squamous cell carcinoma and small cell lung carcinoma to identify new molecular targets for treatment. We evaluated 723 microRNAs in microdissected cancerous cells and adjacent normal cells from 126 snap-frozen lung specimens using microarrays. We validated the expression profiles of miR-375 and its 22 putative target mRNAs in an independent cohort of 78 snap-frozen lung cancer tissues using quantitative reverse-transcriptase PCR. Moreover, we performed dual luciferase reporter assay and Western blot on 6 targeted genes (FZD8, ITGA10, ITPKB, LRP5, PIAS1 andRUNX1) in small cell lung carcinoma cell line NCI-H82. We also detected the effect of miR-375 on cell proliferation in NCI-H82. We found that miR-375 expression was significantly up-regulated in adenocarcinoma and small cell lung carcinoma but down-regulated in squamous cell carcinoma. Among the 22 putative target genes, 11 showed significantly different expression levels in at least 2 of 3 pair-wise comparisons (adenocarcinoma vs. normal, squamous cell carcinoma vs. normal or small cell lung carcinoma vs. normal). Six targeted genes had strong negative correlation with the expression level of miR-375 in small cell lung carcinoma. Further investigation revealed that miR-375 directly targeted the 3'UTR of ITPKB mRNA and over-expression of miR-375 led to significantly decreased ITPKB protein level and promoted cell growth. Thus, our study demonstrates the differential expression profiles of miR-375 in 3 subtypes of lung carcinomas and finds thatmiR-375 directly targets ITPKB and promoted cell growth in SCLC cell line.