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Carcinoma, Non-Small-Cell Lung

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A deep-learning model using enhanced chest CT images to predict PD-L1 expression in non-small-cell lung cancer patients.

Clinical radiology
AIM: To develop a deep-learning model using contrast-enhanced chest computed tomography (CT) images to predict programmed death-ligand 1 (PD-L1) expression in patients with non-small-cell lung cancer (NSCLC).

Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning.

ACS sensors
Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutations rapidly. However, it has low diagnostic accuracy since there are more normal proteins than mutate...

Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.

The Lancet. Digital health
BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tu...

Outcome-Supervised Deep Learning on Pathologic Whole Slide Images for Survival Prediction of Immunotherapy in Patients with Non-Small Cell Lung Cancer.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Although programmed death-(ligand) 1 (PD-(L)1) inhibitors are marked by durable efficacy in patients with non-small cell lung cancer (NSCLC), approximately 60% of the patients still suffer from recurrence and metastasis after PD-(L)1 inhibitor treatm...

Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment F-FDG PET/CT Using Deep Learning.

Academic radiology
RATIONALE AND OBJECTIVES: To develop an end-to-end deep learning (DL) model for non-invasively predicting non-small cell lung cancer (NSCLC) pathological subtypes based on F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (P...

Deep Learning With an Attention Mechanism for Differentiating the Origin of Brain Metastasis Using MR images.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear.

Robot-assisted thoracic surgery for stages IIB-IVA non-small cell lung cancer: retrospective study of feasibility and outcome.

Journal of robotic surgery
Robot-assisted thoracic surgery (RATS) for higher stages non-small cell lung carcinoma (NSCLC) remains controversial. This study reports the feasibility of RATS in patients with stages IIB-IVA NSCLC. A single-institute, retrospective study was conduc...

Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours.

Sensors (Basel, Switzerland)
Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during l...

Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients.

BMC bioinformatics
BACKGROUND: Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of lung cancers are non-small cell lung cancer (NSCLC), accounting for approximately 85% of all lung cancer types. The Cox proportional hazards model (CPH),...