AIMC Topic: Lung Neoplasms
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An Artificial Intelligence Platform for the Radiologic Diagnosis of Pulmonary Sarcoidosis: An Initial Pilot Study of Chest Computed Tomography Analysis to Distinguish Pulmonary Sarcoidosis from a Negative Lung Cancer Screening Scan.
Lung
Nov 14, 2023
PURPOSE: To determine the reliability of an artificial intelligence, deep learning (AI/DL)-based method of chest computer tomography (CT) scan analysis to distinguish pulmonary sarcoidosis from negative lung cancer screening chest CT scans (Lung Imag...
CT-Based Deep-Learning Model for Spread-Through-Air-Spaces Prediction in Ground Glass-Predominant Lung Adenocarcinoma.
Annals of surgical oncology
Nov 13, 2023
BACKGROUND: Sublobar resection is strongly associated with poor prognosis in early-stage lung adenocarcinoma, with the presence of tumor spread through air spaces (STAS). Thus, preoperative prediction of STAS is important for surgical planning. This ...
DBPNDNet: dual-branch networks using 3DCNN toward pulmonary nodule detection.
Medical & biological engineering & computing
Nov 10, 2023
With the advancement of artificial intelligence, CNNs have been successfully introduced into the discipline of medical data analyzing. Clinically, automatic pulmonary nodules detection remains an intractable issue since those nodules existing in the ...
A comparative study of robot-assisted thoracoscopic surgery and conventional approaches for short-term outcomes of anatomical segmentectomy.
General thoracic and cardiovascular surgery
Nov 7, 2023
OBJECTIVES: Since anatomical segmentectomy requires meticulous dissection of the segmental pulmonary vessels and bronchus, robot-assisted thoracoscopic surgery (RATS) has been widely adopted in recent years. We investigated the usefulness of RATS seg...
A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis.
Journal of translational medicine
Nov 7, 2023
BACKGROUND: The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks prese...
An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning.
Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Nov 4, 2023
PURPOSE: The purpose of this study was to develop a model for predicting chemoradiation response in non-small cell lung cancer (NSCLC) patients by integrating radiomics and deep-learning features and combined intra- and peritumoral regions with pre-t...
Utilizing Deep Learning and Computed Tomography to Determine Pulmonary Nodule Activity in Patients With Nontuberculous Mycobacterial-Lung Disease.
Journal of thoracic imaging
Nov 2, 2023
PURPOSE: To develop and evaluate a deep convolutional neural network (DCNN) model for the classification of acute and chronic lung nodules from nontuberculous mycobacterial-lung disease (NTM-LD) on computed tomography (CT).
DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence.
Computer methods and programs in biomedicine
Oct 24, 2023
BACKGROUND AND OBJECTIVE: Artificial intelligence (AI) has several uses in the healthcare industry, some of which include healthcare management, medical forecasting, practical making of decisions, and diagnosis. AI technologies have reached human-lik...
Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy.
Cancer imaging : the official publication of the International Cancer Imaging Society
Oct 22, 2023
OBJECTIVES: This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment.
Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.
Surgery today
Oct 20, 2023
PURPOSE: To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically.
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