Comput. Biol. Med. - Synergistic combination of clinical and imaging features predicts abnormal imaging patterns of pulmonary infections.

Tópicos

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Resumo

We designed and tested a novel hybrid statistical model that accepts radiologic image features and clinical variables, and integrates this information in order to automatically predict abnormalities in chest computed-tomography (CT) scans and identify potentially important infectious disease biomarkers. In 200 patients, 160 with various pulmonary infections and 40 healthy controls, we extracted 34 clinical variables from laboratory tests and 25 textural features from CT images. From the CT scans, pleural effusion (PE), linear opacity (or thickening) (LT), tree-in-bud (TIB), pulmonary nodules, ground glass opacity (GGO), and consolidation abnormality patterns were analyzed and predicted through clinical, textural (imaging), or combined attributes. The presence and severity of each abnormality pattern was validated by visual analysis of the CT scans. The proposed biomarker identification system included two important steps: (i) a coarse identification of an abnormal imaging pattern by adaptively selected features (AmRMR), and (ii) a fine selection of the most important features from the previous step, and assigning them as biomarkers, depending on the prediction accuracy. Selected biomarkers were used to classify normal and abnormal patterns by using a boosted decision tree (BDT) classifier. For all abnormal imaging patterns, an average prediction accuracy of 76.15% was obtained. Experimental results demonstrated that our proposed biomarker identification approach is promising and may advance the data processing in clinical pulmonary infection research and diagnostic techniques.

Resumo Limpo

design test novel hybrid statist model accept radiolog imag featur clinic variabl integr inform order automat predict abnorm chest computedtomographi ct scan identifi potenti import infecti diseas biomark patient various pulmonari infect healthi control extract clinic variabl laboratori test textur featur ct imag ct scan pleural effus pe linear opac thicken lt treeinbud tib pulmonari nodul ground glass opac ggo consolid abnorm pattern analyz predict clinic textur imag combin attribut presenc sever abnorm pattern valid visual analysi ct scan propos biomark identif system includ two import step coars identif abnorm imag pattern adapt select featur amrmr ii fine select import featur previous step assign biomark depend predict accuraci select biomark use classifi normal abnorm pattern use boost decis tree bdt classifi abnorm imag pattern averag predict accuraci obtain experiment result demonstr propos biomark identif approach promis may advanc data process clinic pulmonari infect research diagnost techniqu

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