BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy with a dismal prognosis. We aimed to identify predictors of survival among male and female MPM patients in the United States.
Technology in cancer research & treatment
Jan 1, 2021
Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. We propose the use of a deep learning and transfer learning appr...
Technology in cancer research & treatment
Jan 1, 2021
BACKGROUND: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient and tumor characteristics, dosimetric parameters, and treatment features are intertwined, and it is...
BACKGROUND: Every year, lung cancer contributes to a high percentage deaths in the world. Early detection of lung cancer is important for its effective treatment, and non-invasive rapid methods are usually used for diagnosis.
Combinatorial chemistry & high throughput screening
Jan 1, 2021
AIM AND OBJECTIVE: Lung nodule detection is critical in improving the five-year survival rate and reducing mortality for patients with lung cancer. Numerous methods based on Convolutional Neural Networks (CNNs) have been proposed for lung nodule dete...
We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in ...
Zhonghua bing li xue za zhi = Chinese journal of pathology
Nov 8, 2020
To establish an artificial intelligence (AI)-assisted diagnostic system for lung cancer via deep transfer learning. The researchers collected 519 lung pathologic slides from 2016 to 2019, covering various lung tissues, including normal tissues, ade...
Nan fang yi ke da xue xue bao = Journal of Southern Medical University
Oct 30, 2020
OBJECTIVE: To propose a probabilistic neural network classification method optimized by simulated annealing algorithm (SA-PNN) to discriminate lung cancer and adjacent normal tissues based on permittivity.
Delineation of organs at risk (OARs) is important but time consuming for radiotherapy planning. Automatic segmentation of OARs based on convolutional neural network (CNN) has been established for lung cancer patients at our institution. The aim of th...
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