To improve respiratory-gated radiotherapy accuracy, we developed a machine learning approach for markerless tumor tracking and evaluated it using lung cancer patient data. Digitally reconstructed radiography (DRR) datasets were generated using planni...
Survival data from 225 patients with resected pulmonary typical carcinoids were analyzed with Kaplan-Meier statistics (K-M) and "deep learning" methods to illustrate the difference between establishing "correlations" and "prognostications". Cases wer...
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is underdiagnosed in the community. Thoracic CT scans are widely used for diagnostic and screening purposes for lung cancer. In this proof-of-concept study, we aimed to evaluate a software pipe...
Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosize...
Lung squamous cell carcinoma (LUSC) is a common type of malignancy. The mechanism behind its tumor progression is not clear yet. The aim of this study is to use machine learning to identify the feature miRNAs, which can be reliably used as biomarkers...
Computational intelligence and neuroscience
Mar 30, 2020
The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classifica...
Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescri...
Clinical cancer research : an official journal of the American Association for Cancer Research
Mar 20, 2020
PURPOSE: Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib.
BACKGROUND: IBM Watson for Oncology (WFO) provides physicians with evidence-based treatment options. This study was designed to explore the concordance of the suggested therapeutic regimen for advanced non-small cell lung (NSCLC) cancer patients betw...
PURPOSE: Multiview two-dimensional (2D) convolutional neural networks (CNNs) and three-dimensional (3D) CNNs have been successfully used for analyzing volumetric data in many state-of-the-art medical imaging applications. We propose an alternative mo...
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