Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Animals (1)
- Brain neoplasms (1)
- Diagnosis (1)
- Fluorescence (1)
- Fluorescent dyes (1)
-
- Genes (1)
- Genome (1)
- Genomics (1)
- Glioma (1)
- Human (1)
- Imaging (1)
- Instrumentation (1)
- Intraoperative period (1)
- Logistic models (1)
- Male (1)
- Methods (1)
- Models (1)
- Neoplasm (1)
- Optical imaging (1)
- Phantoms (1)
- Predictive value of tests (1)
- Prostatic neoplasms (1)
- Protoporphyrins (1)
- Rats (1)
- Statistical (1)
- Surgery (1)
Articles 1 - 2 of 2
Full-Text Articles in Oncology
Building A Statistical Model For Predicting Cancer Genes, Ivan P. Gorlov, Christopher J. Logothetis, Shenying Fang, Olga Y. Gorlova, Christopher Amos
Building A Statistical Model For Predicting Cancer Genes, Ivan P. Gorlov, Christopher J. Logothetis, Shenying Fang, Olga Y. Gorlova, Christopher Amos
Dartmouth Scholarship
More than 400 cancer genes have been identified in the human genome. The list is not yet complete. Statistical models predicting cancer genes may help with identification of novel cancer gene candidates. We used known prostate cancer (PCa) genes (identified through KnowledgeNet) as a training set to build a binary logistic regression model identifying PCa genes. Internal and external validation of the model was conducted using a validation set (also from KnowledgeNet), permutations, and external data on genes with recurrent prostate tumor mutations. We evaluated a set of 33 gene characteristics as predictors. Sixteen of the original 33 predictors were …
Quantitative, Spectrally-Resolved Intraoperative Fluorescence Imaging, Pablo A. Valdés, Frederic Leblond, Valerie L. Jacobs, Brian C. Wilson, Keith D. Paulsen, David W. Roberts
Quantitative, Spectrally-Resolved Intraoperative Fluorescence Imaging, Pablo A. Valdés, Frederic Leblond, Valerie L. Jacobs, Brian C. Wilson, Keith D. Paulsen, David W. Roberts
Dartmouth Scholarship
Intraoperative visual fluorescence imaging (vFI) has emerged as a promising aid to surgical guidance, but does not fully exploit the potential of the fluorescent agents that are currently available. Here, we introduce a quantitative fluorescence imaging (qFI) approach that converts spectrally-resolved data into images of absolute fluorophore concentration pixel-by-pixel across the surgical field of view (FOV). The resulting estimates are linear, accurate, and precise relative to true values, and spectral decomposition of multiple fluorophores is also achieved. Experiments with protoporphyrin IX in a glioma rodent model demonstrate in vivo quantitative and spectrally-resolved fluorescence imaging of infiltrating tumor margins for the …