Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection.
Yoshiki KawataNoboru NikiHironobu OhmatsuM. SatakeMasahiko KusumotoTakaaki TsuchidaKeiju AokageKenji EguchiM. KanekoNoriyuki MoriyamaPublished in: Medical Imaging: Computer-Aided Diagnosis (2014)
Keyphrases
- ct images
- breast cancer
- lung cancer
- ct scans
- computed tomography
- ct data
- ovarian cancer
- gene expression profiles
- computer aided diagnosis
- early detection
- medical images
- risk factors
- pulmonary nodules
- lung parenchyma
- lymph nodes
- gastric cancer
- image guided
- pulmonary embolism
- lung nodules
- risk assessment
- ground glass opacity
- risk management
- medical imaging
- computer tomography
- x ray
- radiation doses
- treatment planning
- surgical procedures
- target registration error
- pet ct
- text categorization
- logistic regression
- prostate cancer
- computed tomography images
- domain theory
- image reconstruction
- automatic segmentation
- cancer classification
- topic models
- three dimensional
- patient specific
- magnetic resonance imaging