Lung Cancer

A machine learning model integrating circulating Temra cell transcriptional profiles to predict immunotherapy efficacy.

BACKGROUND: While immunotherapy has transformed treatment paradigms for cancer, durable response rates remain below 30%. Currently available biomarkers are insufficient for precise patient stratification. Blood-based biomarkers, as minimally invasive and accessible tools, have attracted attention for their potential to dynamically monitor immune checkpoint inhibitor response. METHODS: Paired perip...

Subclassification of Small Cell Lung Cancer Based on Gene Expression Signatures and Machine Learning.

Small-cell lung cancer (SCLC) is frequently subdivided into four molecular subtypes according to the activity of key transcription factors (TFs) NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY). There is no consensus on the diagnostic procedures to determine these subtypes. Downstream transcriptional programs of the four TFs could play an important role in the development of an advanced SCLC subtyping appro...

Experiences of Medical Imaging and Radiation Sciences students regarding an assessment based on an artificial intelligence-generated research concept paper.

INTRODUCTION: Artificial intelligence (AI) can complete tasks that once required human cognitive effort. As a result, assessments that traditionally m...

Research on Lung Cancer Classification Based on Multidimensional Hematological Indicators and Machine Learning Models.

Accurate subtyping of lung cancer is crucial for developing personalized treatment plans and improving patient outcomes. This study established machin...

Metabolic factor-based machine learning model for mortality prediction in acute hepatitis E: Development and validation from a dual-center cohort.

BACKGROUND: Hepatitis E virus (HEV) infection remains a major cause of liver failure with high short-term mortality, yet predictive models incorporati...

Assessment of GFR Estimation Methods in Patients With Obesity.

CONTEXT: Obesity is an independent risk factor for chronic kidney disease, and accurate estimation of the glomerular filtration rate (GFR) is crucial....

Identifying a better-prognosis pancreatic cancer from its benign inflammatory mimic: a machine learning approach with contrast-enhanced ultrasound for early intervention.

PURPOSE: Hypervascular pancreatic ductal adenocarcinoma (PDAC) and mass-forming pancreatitis (MFP) represent a classic diagnostic mimicry on contrast-...

Topologically distinct 2D and 3D intratumoral heterogeneity scores for preoperatively predicting invasiveness in stage I lung adenocarcinoma: A multicenter study.

This multicenter study aims to enhance the preoperative prediction of pathological invasiveness in clinical stage I lung adenocarcinoma (LUAD) by deve...

Driver gene mutations and clinical features predict bone metastasis risk in NSCLC: a logistic regression model.

Bone metastasis affects approximately 40% of patients with non-small cell lung cancer (NSCLC), significantly impacting patient survival and prognosis....

Non-invasive prediction of Ki-67 expression in gastric cancer using AI-based dual-energy CT: a multicenter study.

OBJECTIVE: To develop and validate a machine learning model based on quantitative parameters of dual-energy CT (DECT) virtual monoenergetic images (VM...

Flow rate determination in a two-phase system using radioactive particle tracking and deep learning.

In the oil industry, accurate flow rate determination and control in pipelines are critical for ensuring operational efficiency. However, most convent...

Non-invasive screening for ovarian cancer by combining serum SERS with interpretable machine learning models.

Early identification of malignant ovarian tumors is critical for informing treatment decisions and enhancing patients' quality of life. As the third m...

Neoadjuvant Radiation is Causally Linked to Increased Operative Time and Perioperative Blood Transfusion in Pancreatic Ductal Adenocarcinoma.

INTRODUCTION: Blood transfusion in patients undergoing surgical resection for pancreatic ductal adenocarcinoma (PDAC) is associated with worse outcome...

Histopathological diagnosis of Ovine Pulmonary Adenocarcinoma (OPA) based on ensemble model.

ASO Visual Abstract: CT-Based Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma ≤ 3 cm: Enhancing Deep Learning Specificity by Waiving Chest Wall Information.

Screening for Clinically Significant Nephrolithiasis Based on Simple Health Checkup Clinical and Urine Parameters in General Populations: Multicenter Machine Learning Study.

BACKGROUND: Nephrolithiasis affects approximately 15% of the population and often remains undetected in asymptomatic individuals. Current diagnostic a...

Development and Validation of a Frailty Risk Prediction Model for Preoperative Non-Small-Cell Lung Cancer Patients: A Cross-Sectional Study.

PURPOSE: Frailty is increasingly recognized as a predictor of poor surgical outcomes, yet its preoperative assessment in patients with non-small-cell ...

Prediction of Symptomatic Radiation Pneumonitis in Lung Cancer Patients: A Radiomics and Dosiomics Machine Learning Approach Using the Prospective Multicenter RTOG 0617 and REQUITE Trials.

PURPOSE: Radiation-induced pneumonitis (RP) is a side effect after thoracic radiation therapy (RT). The ability to predict RP would facilitate treatme...

Concurrent driver mutations induce distinct tumour morphologies on radiological imaging.

BACKGROUND: Most radiogenomic studies examine single driver mutations in isolation, despite evidence that co-occurring driver alterations are common a...

Contemporary Management of Radiation Necrosis: Insights and Avenues.

Radiation necrosis (RN) remains a challenging complication of upfront radiation therapy for both brain metastases and primary tumors. Despite developm...