Using Naïve Bayesian Analysis to Determine Imaging Characteristics of KRAS Mutations in Metastatic Colon Cancer.

Genotype, particularly Ras status, greatly affects prognosis and treatment of liver metastasis in colon cancer patients. This pilot aimed to apply word frequency analysis and a naive Bayes classifier on radiology reports to extract distinguishing imaging descriptors of wild-type colon cancer patients and those with v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations. In this institutional-review-board-approved study, we compiled a SNaPshot mutation analysis dataset from 457 colon adenocarcinoma patients.

Automatically inserted technical details improve radiology report accuracy.

Purpose: To assess the effect of automatically inserted technical details on the concordance of a radiology report header with the actual procedure performed.

Methods And Materials: The study was IRB approved and informed consent was waived. We obtained radiology report audit data from the hospital's compliance office from the period of January 2005 through December 2009 spanning a total of 20 financial quarters.

Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm.

Purpose: To validate Bridge Burner, a new brain segmentation algorithm based on thresholding, connectivity, surface detection, and a new operator of constrained growing.

Materials And Methods: T1-weighted MR images were selected at random from three previous neuroimaging studies to represent a spectrum of system manufacturers, pulse sequences, subject ages, genders, and neurological conditions. The ground truth consisted of brain masks generated manually by a consensus of expert observers.