Polyp shape (sessile or pedunculated) may provide important clinical implication. However, the traditional way of
determining polyp shape is both invasive and subjective. We present a less-invasive and automated method to
predict the shape of colonic polyps on computed tomographic colonography (CTC) using the content-based image
retrieval (CBIR) approach. We classify polyps as either sessile (SS) or pedunculated (PS) in shape. The CBIR uses
numerical feature vectors generated from our CTC computer aided detection (CTC-CAD) system to describe the
polyps. These features relate to physical and visual characteristics of the polyp. Feature selection was done using a
support vector machine classifier on a training set of polyp shapes. The system is evaluated using an independent
test set. Using receiver operating curve (ROC) analysis, we showed our system is as accurate as a polyp shape
classifier. The area under the ROC curve was 0.86 (95% confidence interval [0.77, 0.93]).
Predicting the malignancy of colonic polyps is a difficult problem and in general requires an invasive polypectomy
procedure. We present a less-invasive and automated method to predict the histology of colonic polyps under computed
tomographic colonography (CTC) using the content-based image retrieval (CBIR) paradigm. For the purpose of
simplification, polyps annotated as hyperplastic or "other benign" were classified as benign polyps (BP) and the rest
(adenomas and cancers) were classified as malignant polyps (MP). The CBIR uses numerical feature vectors generated
from our CTC computer aided detection (CTC-CAD) system to describe the polyps. These features relate to physical and
visual characteristics of the polyp. A representative database of CTC-CAD polyp images is created. Query polyps are
matched with those in the database and the results are ranked based on the similarity to the query. Polyps with a majority
of representative MPs in their result set are predicted to be malignant and similarly those with a majority of BPs in their
results are benign. For evaluation, the system is compared to the typical optical colonoscopy (OC) size based
classification. Using receiver operating curve (ROC) analysis, we show our system is sufficiently better than the OC size
method.
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