AIMC Topic: Positron-Emission Tomography

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Monitoring vascular normalization induced by antiangiogenic treatment with (18)F-fluoromisonidazole-PET.

Molecular oncology
BACKGROUND: Rationalization of antiangiogenics requires biomarkers. Vascular re-normalization is one widely accepted mechanism of action for this drug class. The interstitium of tumors with abnormal vasculature is hypoxic. We sought to track vascular...

Graph-guided joint prediction of class label and clinical scores for the Alzheimer's disease.

Brain structure & function
Accurate diagnosis of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, is very important for early treatment. Over the last decade, various machine learning methods have been proposed to predict disease status and clinica...

Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

PloS one
Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy....

Enhancing spatial resolution of (18)F positron imaging with the Timepix detector by classification of primary fired pixels using support vector machine.

Physics in medicine and biology
Position-sensitive positron cameras using silicon pixel detectors have been applied for some preclinical and intraoperative clinical applications. However, the spatial resolution of a positron camera is limited by positron multiple scattering in the ...

Transoral robotic surgery for the management of head and neck squamous cell carcinoma of unknown primary.

Acta oto-laryngologica
CONCLUSION: The addition of transoral robotic surgery (TORS) in the diagnostic management of patients classified with head and neck squamous cell carcinoma of unknown primary (SCCUP) is promising and appears to improve detection rates of the primary ...

Combined unsupervised-supervised classification of multiparametric PET/MRI data: application to prostate cancer.

NMR in biomedicine
Multiparametric medical imaging data can be large and are often complex. Machine learning algorithms can assist in image interpretation when reliable training data exist. In most cases, however, knowledge about ground truth (e.g. histology) and thus ...

Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis.

Brain structure & function
Recently, neuroimaging-based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis has attracted researchers in the field, due to the increasing prevalence of the diseases. Unfortunately, the unfavorable high-dimensional nature of neu...

A Robust Deep Model for Improved Classification of AD/MCI Patients.

IEEE journal of biomedical and health informatics
Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many rese...

Manifold regularized multitask feature learning for multimodality disease classification.

Human brain mapping
Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint select...