An ensemble learning with active sampling to predict the prognosis of postoperative non-small cell lung cancer patients.

Background: Lung cancer is the leading cause of cancer death worldwide. Prognostic prediction plays a vital role in the decision-making process for postoperative non-small cell lung cancer (NSCLC) patients. However, the high imbalance ratio of prognostic data limits the development of effective prognostic prediction models.

Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non-Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study.

Background: Lymph node metastasis (LNM) is critical for treatment decision making of patients with resectable non-small cell lung cancer, but it is difficult to precisely diagnose preoperatively. Electronic medical records (EMRs) contain a large volume of valuable information about LNM, but some key information is recorded in free text, which hinders its secondary use.

Objective: This study aims to develop LNM prediction models based on EMRs using natural language processing (NLP) and machine learning algorithms.

A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging.

Background: Computed tomography (CT) reports record a large volume of valuable information about patients' conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic study. However, the free-text nature of clinical reports is a critical barrier to use this data more effectively. In this study, we investigate a novel deep learning method to extract entities from Chinese CT reports for lung cancer screening and TNM staging.

Automatic Extraction of Lung Cancer Staging Information From Computed Tomography Reports: Deep Learning Approach.

Background: Lung cancer is the leading cause of cancer deaths worldwide. Clinical staging of lung cancer plays a crucial role in making treatment decisions and evaluating prognosis. However, in clinical practice, approximately one-half of the clinical stages of lung cancer patients are inconsistent with their pathological stages.

LncRNA SNHG8 promotes proliferation and invasion of gastric cancer cells by targeting the miR-491/PDGFRA axis.

This study aimed to investigate the effects of long non-coding small nucleolar RNA host gene 8 (SNHG8) on the proliferation and invasion of gastric cancer (GC). The GC tissues and adjacent normal tissues from 30 patients were collected. Human GC cell lines, including AGS, SGC-7901, MKN-1, and BGC-803 and normal human gastric epithelial cell line GES-1 were purchased and cultured.