AIMC Topic: Adenocarcinoma of Lung

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Identification and validation of an immune-derived multiple programmed cell death index for predicting clinical outcomes, molecular subtyping, and drug sensitivity in lung adenocarcinoma.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico Apr 2, 2024
OBJECTIVES: Comprehensive cross-interaction of multiple programmed cell death (PCD) patterns in the patients with lung adenocarcinoma (LUAD) have not yet been thoroughly investigated.
Female Aged Biomarkers, Tumor Humans Machine Learning Prognosis Nomograms Drug Resistance, Neoplasm Adenocarcinoma of Lung Male Middle Aged Lung Neoplasms
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Deceptive learning in histopathology.

Histopathology Mar 31, 2024
AIMS: Deep learning holds immense potential for histopathology, automating tasks that are simple for expert pathologists and revealing novel biology for tasks that were previously considered difficult or impossible to solve by eye alone. However, the...
Lung Neoplasms Mutation Adenocarcinoma of Lung Humans Neural Networks, Computer Deep Learning
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Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma.

Apoptosis : an international journal on programmed cell death Mar 22, 2024
Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging studies demonstrated NETs contributed to cancer progression and metastases in multiple ways. This study intends to provide a prognostic NETs signature an...
Gene Expression Regulation, Neoplastic Extracellular Traps Neutrophils Adenocarcinoma of Lung Phospholipid Transfer Proteins Lung Neoplasms Mice Immunotherapy Tumor Microenvironment Animals Cell Line, Tumor Humans Machine Learning Prognosis
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Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma.

La Radiologia medica Mar 21, 2024
PURPOSE: To investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC).
Adenocarcinoma of Lung Middle Aged Lung Neoplasms Female Deep Learning Neoplasm Invasiveness Aged Imaging, Three-Dimensional Neural Networks, Computer Predictive Value of Tests Male Retrospective Studies Tomography, X-Ray Computed Humans
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A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma.

Scientific reports Mar 16, 2024
This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological data and whole slide images from 164 LUAD cases were collected and us...
Adenocarcinoma of Lung Deep Learning Lung Neoplasms Neoplasm Recurrence, Local Humans Risk Factors
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Transformaer-based model for lung adenocarcinoma subtypes.

Medical physics Mar 1, 2024
BACKGROUND: Lung cancer has the highest morbidity and mortality rate among all types of cancer. Histological subtypes serve as crucial markers for the development of lung cancer and possess significant clinical values for cancer diagnosis, prognosis,...
Humans Neural Networks, Computer Image Processing, Computer-Assisted Lung Neoplasms Adenocarcinoma of Lung
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Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model.

Cancer imaging : the official publication of the International Cancer Imaging Society Jan 12, 2024
BACKGROUND: In solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment strategies. This study seeks to develop and validate a fusion model combining radiomics and deep learni...
Lymphatic Metastasis Adenocarcinoma of Lung Radiomics Deep Learning Humans Lung Neoplasms Retrospective Studies
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Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico Jan 9, 2024
BACKGROUND: Lung adenocarcinoma is a common cause of cancer-related deaths worldwide, and accurate EGFR genotyping is crucial for optimal treatment outcomes. Conventional methods for identifying the EGFR genotype have several limitations. Therefore, ...
Deep Learning Lung Neoplasms Mutation Adult Male Humans Middle Aged Tomography, X-Ray Computed Female ErbB Receptors Aged Adenocarcinoma of Lung
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CT-Based Super-Resolution Deep Learning Models with Attention Mechanisms for Predicting Spread Through Air Spaces of Solid or Part-Solid Lung Adenocarcinoma.

Academic radiology Jan 5, 2024
RATIONALE AND OBJECTIVES: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma (LUAD), and preoperative knowledge of STAS status is helpful in choosing an appropriate surgical approach.
Lung Neoplasms Neoplasm Invasiveness Adenocarcinoma of Lung Male Retrospective Studies Middle Aged Deep Learning Tomography, X-Ray Computed Aged Female Adult Sensitivity and Specificity Humans
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Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.

Scientific reports Jan 4, 2024
Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational auto...
Humans Prognosis Biomarkers, Tumor Deep Learning Lung Neoplasms Adenocarcinoma of Lung
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