AIMC Topic: Lung Neoplasms

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Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, which is difficult because of respiratory motion and the latency of linear accelerator (LINAC) systems. ...

Automatic detect lung node with deep learning in segmentation and imbalance data labeling.

Scientific reports
In this study, a novel method with the U-Net-based network architecture, 2D U-Net, is employed to segment the position of lung nodules, which are an early symptom of lung cancer and have a high probability of becoming a carcinoma, especially when a l...

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis.

Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation.

Medical physics
PURPOSE: Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reli...

Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography.

Nature communications
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Ou...

Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.

Radiology
Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening. Purpose To develop and validate a deep learning (DL) algorithm for malignancy risk estimation of pul...

Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.

Scientific reports
Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination o...

Dual energy CT image prediction on primary tumor of lung cancer for nodal metastasis using deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Lymph node metastasis (LNM) identification is the most clinically important tasks related to survival and recurrence from lung cancer. However, the preoperative prediction of nodal metastasis remains a challenge to determine surgical plans and pretre...