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Cancer is deadly, a genetic disorder in which invoke uncontrolled growth of living cells in a particular region. Cancer is usually invoked by many natural causes like exposure to UV rays, smoking tobacco, oil fogs, and exposure to radioactive frequencies. In this work, we concentrate on tobacco-induced lung cancer. There are many existing systems to detect lung cancer. But none are efficient enough...
4D-PET reconstruction has the potential to significantly increase the signal-to-noise ratio in dynamic PET by fitting smooth temporal functions during the reconstruction. However, the optimal choice of temporal function remains an open question. A 4D-PET reconstruction algorithm using adaptive-knot cubic B-splines is proposed. Using realistic Monte-Carlo simulated data from a digital patient phantom...
With the rapid growth of biomedical imaging data, manual delineation of gross tumor volume (GTV) for variety of cancers is becoming less practical due to its low efficiency, non-reproducibility, and inter-observer dependency. In this paper, we propose an automated co-segmentation method using a Bayesian decision theory to correlate the tumor and background similarities from PET and CT images. Our...
Accurate parenchymal lung tumor delineation with PET-CT can be problematic given the inherent tumor heterogeneity and proximity / involvement of extra-parenchymal tissue. In this paper, we propose a tumor delineation approach that is based on new tumor–background likelihood models in PET and CT. By incorporating the intensity downhill feature in PET as a distance cost into the background likelihood...
Combined PET-CT is now increasingly used for the clinical evaluation of cancer and is arguably the best tool to stage non-small cell lung cancer (NSCLC). We propose a framework to better delineate lung tumors which utilizes information from PET and CT images. The framework is based on a downhill region growing technique for PET and a Gaussian mixture model for CT images. We applied our framework in...
Despite various efforts to develop new predictive models for early detection of tumor local failure in locally advanced non-small cell lung cancer (NSCLC), many patients still suffer from a high local failure rate after radiotherapy. Based on recent studies of biomarker proteins' role in predicting tumor response following radiotherapy, we hypothesize that incorporation of physical and biological...
Due to the high fatality rate of patients with radiation pneumonitis (RP), a complication of the radiation therapy (radiotherapy), great attention has been paid to the treatment plan of individual RP patients. Therefore, not only technological advances in the development of treatment planning systems but also new prognostic models are urgently required to lessen the complication and to predict the...
Lung cancer has been the top leading cancer type for the past two decades and the overall survival rate for Non-Small Cell Lung Cancer (NSCLC) remains at a low rate of 15 percents. Current lung cancer prognosis using staging system has been studied and proven to be not accurate enough, especially on early stages. Therefore, a new prognostic model is desired. Using gene expression values of 442 Affymetrix...
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