Background Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional histopathology to computational approaches, arousing a hype of deep learning methods for histopathological analysis. We aimed at exploring the potential of deep learning models in the identification of lung cancer subtypes and cancer mimics from WSIs. Methods We initially obtained 741 WSIs from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) for the deep learning model development, optimization, and verification. Additional 318 WSIs from SYSUFH, 212 from Shenzhen People’s Hospital, and 422 from The Cancer Genome Atlas were further collected for multi-centre verification. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. Four pathologists of different levels from SYSUFH reviewed all the testing slides blindly, and the diagnosing results were used for quantitative comparisons with the best performing deep learning model. Results We developed the first deep learning-based six-type classifier for histopathological WSI classification of lung adenocarcinoma, lung squamous cell carcinoma, small cell lung carcinoma, pulmonary tuberculosis, organizing pneumonia, and normal lung. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873. Conclusions Multi-cohort testing demonstrated our six-type classifier achieved consistent and comparable performance to experienced pathologists and gained advantages over other existing computational methods. The visualization of prediction heatmap improved the model interpretability intuitively. The classifier with the threshold-based tumour-first label inferencing method exhibited excellent accuracy and feasibility in classifying lung cancers and confused nonneoplastic tissues, indicating that deep learning can resolve complex multi-class tissue classification that conforms to real-world histopathological scenarios.

Arteriovenous fistula (AVF) is prone to early dysfunction and relates to poor outcome. However, little is known about the role of early AVF dysfunction as an independent risk factor for death in hemodialysis patients. A retrospective cohort study was performed using data of patients who underwent initial AVF surgery at a single institution. Demographic, clinical, biochemistry and AVF parameters were extracted from the electronic records, and the association between these variables and mortality was analyzed by Cox proportional hazards model. A total of 501 patients on hemodialysis (63.4 ± 12.7 years, 57.3% male) were included, and the median observation period was 3.66 years. In multivariate analysis, early failure of AVF (hazard ratio (95% confidence interval): 1.54 (1.06–2.24); p = 0.023) was associated with overall mortality but not cardiovascular mortality. Other identified predictors of overall mortality included older age, peripheral artery disease (PAD), cardiomegaly, higher white blood cell (WBC) count and corrected calcium level, and lower total cholesterol level, while predictors of cardiovascular mortality included older age, coronary artery disease (CAD), PAD and lower hemoglobin level. In conclusion, patients with early AVF failure were associated with increased risk of overall mortality.

Aortic arch calcification (AAC) is recognized as an important cardiovascular risk factor in patients with end-stage renal disease (ESRD). The aim of the study was to evaluate the impact of AAC grade on patency rates of arteriovenous fistula (AVF) in this specific population. The data of 286 ESRD patients who had an initial AVF placed were reviewed. The extent of AAC identified on chest radiography was divided into four grades (0–3). The association between AAC grade, other clinical factors, and primary patency of AVF was then analyzed by Cox proportional hazard analysis. The multivariate analysis demonstrated that the presence of AAC grade 2 (hazard ratio (95% confidence interval): 1.80 (1.15–2.84); p = 0.011) and grade 3 (3.03 (1.88–4.91); p < 0.001), and higher level of intact-parathyroid hormone (p = 0.047) were associated with primary patency loss of AVF. In subgroup analysis, which included AVF created by a surgeon assisted with preoperative vascular mapping, only AAC grade 3 (2.41 (1.45–4.00); p = 0.001), and higher intact-parathyroid hormone (p = 0.025) level were correlated with AVF patency loss. In conclusion, higher AAC grade and intact-parathyroid hormone level predicted primary patency loss of AVF in an ESRD population.

Background Immune checkpoint blockade (ICB) therapy has revolutionized the treatment of lung squamous cell carcinoma (LUSC). However, a significant proportion of patients with high tumour PD-L1 expression remain resistant to immune checkpoint inhibitors. To understand the underlying resistance mechanisms, characterization of the immunosuppressive tumour microenvironment and identification of biomarkers to predict resistance in patients are urgently needed. Methods Our study retrospectively analysed RNA sequencing data of 624 LUSC samples. We analysed gene expression patterns from tumour microenvironment by unsupervised clustering. We correlated the expression patterns with a set of T cell exhaustion signatures, immunosuppressive cells, clinical characteristics, and immunotherapeutic responses. Internal and external testing datasets were used to validate the presence of exhausted immune status. Results Approximately 28 to 36% of LUSC patients were found to exhibit significant enrichments of T cell exhaustion signatures, high fraction of immunosuppressive cells (M2 macrophage and CD4 Treg), co-upregulation of 9 inhibitory checkpoints (CTLA4, PDCD1, LAG3, BTLA, TIGIT, HAVCR2, IDO1, SIGLEC7, and VISTA), and enhanced expression of anti-inflammatory cytokines (e.g. TGFβ and CCL18). We defined this immunosuppressive group of patients as exhausted immune class (EIC). Although EIC showed a high density of tumour-infiltrating lymphocytes, these were associated with poor prognosis. EIC had relatively elevated PD-L1 expression, but showed potential resistance to ICB therapy. The signature of 167 genes for EIC prediction was significantly enriched in melanoma patients with ICB therapy resistance. EIC was characterized by a lower chromosomal alteration burden and a unique methylation pattern. We developed a web application (http://lilab2.sysu.edu.cn/tex & http://liwzlab.cn/tex) for researchers to further investigate potential association of ICB resistance based on our multi-omics analysis data. Conclusions We introduced a novel LUSC immunosuppressive class which expressed high PD-L1 but showed potential resistance to ICB therapy. This comprehensive characterization of immunosuppressive tumour microenvironment in LUSC provided new insights for further exploration of resistance mechanisms and optimization of immunotherapy strategies.

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