AIMC Topic: Adenocarcinoma of Lung

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Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma.

Radiology Jul 5, 2022
Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To...
Female Aged Male Humans Retrospective Studies Tomography, X-Ray Computed Adenocarcinoma ErbB Receptors Adenocarcinoma of Lung Middle Aged Deep Learning Lung Neoplasms
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Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.

International journal of computer assisted radiology and surgery Jun 28, 2022
PURPOSE: Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized ...
Humans Retrospective Studies Tomography, X-Ray Computed Deep Learning Lung Neoplasms Adenocarcinoma of Lung
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A deep learning based CT image analytics protocol to identify lung adenocarcinoma category and high-risk tumor area.

STAR protocols Jun 22, 2022
We present a protocol which implements deep learning-based identification of the lung adenocarcinoma category with high accuracy and generalizability, and labeling of the high-risk area on Computed Tomography (CT) images. The protocol details the exe...
Humans Image Processing, Computer-Assisted Lung Neoplasms Adenocarcinoma of Lung Tomography, X-Ray Computed Deep Learning
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Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Scientific reports Feb 3, 2022
Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackl...
Biopsy Image Interpretation, Computer-Assisted Datasets as Topic Carcinoma, Non-Small-Cell Lung Area Under Curve Eosine Yellowish-(YS) Hematoxylin Histocytochemistry Small Cell Lung Carcinoma Adenocarcinoma of Lung Humans Neural Networks, Computer Carcinoma, Squamous Cell Lung Lung Neoplasms
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Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis.

International journal of molecular sciences Nov 25, 2021
BACKGROUND: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell.
Humans Cluster Analysis Single-Cell Analysis Neural Networks, Computer Exome Sequencing Lung Neoplasms Machine Learning Adenocarcinoma of Lung Computational Biology
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Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor.

Thoracic cancer Nov 23, 2021
BACKGROUND: Rapid intraoperative diagnosis for unconfirmed pulmonary tumor is extremely important for determining the optimal surgical procedure (lobectomy or sublobar resection). Attempts to diagnose malignant tumors using mass spectrometry (MS) hav...
Male Lung Neoplasms Mass Spectrometry Adenocarcinoma of Lung Female Aged Humans Colorectal Neoplasms Artificial Intelligence
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Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.

Communications biology Nov 12, 2021
Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosi...
Humans Male Neural Networks, Computer Middle Aged Cohort Studies Lung Neoplasms Prognosis Female Adenocarcinoma of Lung Tomography, X-Ray Computed Risk Factors Aged
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Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.

Medical physics Nov 7, 2021
PURPOSE: This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subtypes in patients with lung adenocarcinoma.
Adenocarcinoma of Lung Lung Neoplasms Mutation ErbB Receptors Retrospective Studies Deep Learning Humans
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3D multi-scale, multi-task, and multi-label deep learning for prediction of lymph node metastasis in T1 lung adenocarcinoma patients' CT images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Sep 30, 2021
The diagnosis of preoperative lymph node (LN) metastasis is crucial to evaluate possible therapy options for T1 lung adenocarcinoma patients. Radiologists preoperatively diagnose LN metastasis by evaluating signs related to LN metastasis, like spicul...
Adenocarcinoma of Lung Deep Learning Humans Lung Neoplasms Tomography, X-Ray Computed Lymphatic Metastasis
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Highly accurate diagnosis of lung adenocarcinoma and squamous cell carcinoma tissues by deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Sep 14, 2021
Intraoperative detection of the marginal tissues is the last and most important step to complete the resection of adenocarcinoma and squamous cell carcinoma. However, the current intraoperative diagnosis is time-consuming and requires numerous steps ...
Adenocarcinoma Adenocarcinoma of Lung Humans Carcinoma, Squamous Cell Deep Learning Lung Neoplasms Spectrum Analysis, Raman
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