Identifying risk factors and biomarkers for severe CIP in lung cancer patients- a retrospective case series study.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, but they can induce immune-related adverse events, including immune checkpoint inhibitor-associated pneumonia (CIP), a severe lung complication. CIP, particularly Grades 3-4, is associated with poor prognosis, indicating a critical need for research on this issue. Our study aimed to investigate the risk factors and biomarkers associated with severe CIP in lung cancer patients treated with ICIs, where OS represents overall survival and PFS denotes progression-free survival.

The Gap in the Thickness: Estimating Effectiveness of Pulmonary Nodule Detection in Thick- and Thin-Section CT Images with 3D Deep Neural Networks.

Background And Objectives: There is a noticeable gap in diagnostic evidence strength between the thick and thin scans of Low-Dose CT (LDCT) for pulmonary nodule detection. When the thin scans are needed is unknown, especially when aided with an artificial intelligence nodule detection system.

Methods: A case study is conducted with a set of 1,000 pulmonary nodule screening LDCT scans with both thick (5.

Prognostic Value of Albumin-to-Alkaline Phosphatase Ratio for -Mutated Advanced Non-Small-Cell Lung Cancer Patients Treated with First-Line EGFR-TKIs: A Large Population-Based Study and Literature Review.

Background: Resistance inevitably develops in epidermal growth factor receptor (EGFR)-mutated advanced non-small-cell lung cancer (NSCLC) patients after treatment of EGFR tyrosine kinase inhibitors (EGFR-TKIs). The albumin-to-alkaline phosphatase ratio (AAPR), a novel index, has been reported to be associated with survival in various cancers. In this study, we explored the prognostic value of AAPR in -mutated advanced NSCLC patients treated with first-line EGFR-TKIs.

Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study.

Background: Epidermal growth factor receptor (EGFR) genotype is crucial for treatment decision making in lung cancer, but it can be affected by tumour heterogeneity and invasive biopsy during gene sequencing. Importantly, not all patients with an EGFR mutation have good prognosis with EGFR-tyrosine kinase inhibitors (TKIs), indicating the necessity of stratifying for EGFR-mutant genotype. In this study, we proposed a fully automated artificial intelligence system (FAIS) that mines whole-lung information from CT images to predict EGFR genotype and prognosis with EGFR-TKI treatment.

Risk factors for predicting mortality of COVID-19 patients: A systematic review and meta-analysis.

Background: Early and accurate prognosis prediction of the patients was urgently warranted due to the widespread popularity of COVID-19. We performed a meta-analysis aimed at comprehensively summarizing the clinical characteristics and laboratory abnormalities correlated with increased risk of mortality in COVID-19 patients.

Methods: PubMed, Scopus, Web of Science, and Embase were systematically searched for studies considering the relationship between COVID-19 and mortality up to 4 June 2020.

DeepLN: an artificial intelligence-based automated system for lung cancer screening.

Background: Lung cancer causes more deaths worldwide than any other cancer. For early-stage patients, low-dose computed tomography (LDCT) of the chest is considered to be an effective screening measure for reducing the risk of mortality. The accuracy and efficiency of cancer screening would be enhanced by an intelligent and automated system that meets or surpasses the diagnostic capabilities of human experts.

MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks.

The accurate identification of malignant lung nodules using computed tomography (CT) screening images is vital for the early detection of lung cancer. It also offers patients the best chance of cure, because non-invasive CT imaging has the ability to capture intra-tumoral heterogeneity. Deep learning methods have obtained promising results for the malignancy identification problem; however, two substantial challenges still remain.

Diminishing microbiome richness and distinction in the lower respiratory tract of lung cancer patients: A multiple comparative study design with independent validation.

Objectives: Current evidence suggests that microorganisms are associated with neoplastic diseases; however, the role of the airway microbiome in lung cancer remains unknown. To investigate the taxonomic profiles of the lower respiratory tract (LRT) microbiome in patients with lung cancer.

Materials And Methods: BALF samples were collected in a discovery set comprising 150 individuals, including 91 patients with lung cancer, 29 patients with nonmalignant pulmonary diseases and 30 healthy subjects, and an independent validation set including 85 participants.

A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy.

We established a CT-derived approach to achieve accurate progression-free survival (PFS) prediction to EGFR tyrosine kinase inhibitors (TKI) therapy in multicenter, stage IV -mutated non-small cell lung cancer (NSCLC) patients. A total of 1,032 CT-based phenotypic characteristics were extracted according to the intensity, shape, and texture of NSCLC pretherapy images. On the basis of these CT features extracted from 117 stage IV -mutant NSCLC patients, a CT-based phenotypic signature was proposed using a Cox regression model with LASSO penalty for the survival risk stratification of EGFR-TKI therapy.

Development and clinical application of radiomics in lung cancer.

Since the discovery of X-rays at the end of the 19 century, medical imageology has progressed for 100 years, and medical imaging has become an important auxiliary tool for clinical diagnosis. With the launch of the human genome project (HGP) and the development of various high-throughput detection techniques, disease exploration in the post-genome era has extended beyond investigations of structural changes to in-depth analyses of molecular abnormalities in tissues, organs and cells, on the basis of gene expression and epigenetics. These techniques have given rise to genomics, proteomics, metabolomics and other systems biology subspecialties, including radiogenomics.

Hyperphosphorylation of RPS6KB1, rather than overexpression, predicts worse prognosis in non-small cell lung cancer patients.

RPS6KB1 is the kinase of ribosomal protein S6 which is 70 kDa and is required for protein translation. Although the abnormal activation of RPS6KB1 has been found in types of diseases, its role and clinical significance in non-small cell lung cancer (NSCLC) has not been fully investigated. In this study, we identified that RPS6KB1 was over-phosphorylated (p-RPS6KB1) in NSCLC and it was an independent unfavorable prognostic marker for NSCLC patients.