AIMC Topic: Lung Neoplasms
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Completion of Pembrolizumab in Advanced Non-Small Cell Lung Cancer-Real World Outcomes After Two Years of Therapy (COPILOT).
Clinical lung cancer
Apr 15, 2024
BACKGROUND: Seminal trials with first-line pembrolizumab for metastatic non-small cell lung cancer (NSCLC) mandated a maximum two-years treatment. We describe real-world outcomes of a multi-site Australian cohort of patients who completed two-years o...
Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Retrospective Study.
Clinical lung cancer
Apr 12, 2024
BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung cancer (NSCLC), yet reliable noninvasive predictive markers are lacking. This study aimed to develop a radiomics model predicting pathological complete re...
The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung.
BMC cancer
Apr 11, 2024
OBJECTIVE: To explore the value of six machine learning models based on PET/CT radiomics combined with EGFR in predicting brain metastases of lung adenocarcinoma.
Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature.
Environmental toxicology
Apr 9, 2024
The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promising therapeutic modality, demonstrating remarkable potential for substantially prolonging the overall survival of individuals afflicted with LUAD. How...
A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.
Computers in biology and medicine
Apr 9, 2024
BACKGROUND: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effecti...
Diagnostic performance of a deep-learning model using F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer.
Annals of nuclear medicine
Apr 8, 2024
OBJECTIVE: We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.
Lung Cancer Diagnosis on Virtual Histologically Stained Tissue Using Weakly Supervised Learning.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Apr 7, 2024
Lung adenocarcinoma (LUAD) is the most common primary lung cancer and accounts for 40% of all lung cancer cases. The current gold standard for lung cancer analysis is based on the pathologists' interpretation of hematoxylin and eosin (H&E)-stained ti...
A muti-modal feature fusion method based on deep learning for predicting immunotherapy response.
Journal of theoretical biology
Apr 6, 2024
Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few years, but only a small number of patients respond to ICT. To predict ICT response, we developed a multi-modal feature fusion model based on deep lea...
A Deep Learning-Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non-Small Cell Lung Carcinoma: Does it Help Pathologists Score?
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Apr 6, 2024
Several studies have developed various artificial intelligence (AI) models for immunohistochemical analysis of programmed death ligand 1 (PD-L1) in patients with non-small cell lung carcinoma; however, none have focused on specific ways by which AI-a...
Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer.
Frontiers in public health
Apr 5, 2024
BACKGROUND AND OBJECTIVE: Accurately predicting the extent of lung tumor infiltration is crucial for improving patient survival and cure rates. This study aims to evaluate the application value of an improved CT index combined with serum biomarkers, ...
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