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To evaluate the accuracy of computer aided differential diagnosis (CADD) between usual interstitial pneumonia (UIP)
and nonspecific interstitial pneumonia (NSIP) at HRCT in comparison with that of a radiologist's decision.
A computerized classification for six local disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity,
RO; honeycombing, HC; emphysema, EM; and consolidation, CON) using texture/shape analyses and a SVM classifier
at HRCT was used for pixel-by-pixel labeling on the whole lung area. The mode filter was applied on the results to
reduce noise. Area fraction (AF) of each pattern, directional probabilistic density function (pdf) (dPDF: mean, SD,
skewness of pdf /3 directions: superior-inferior, anterior-posterior, central-peripheral), regional cluster distribution
pattern (RCDP: number, mean, SD of clusters, mean, SD of centroid of clusters) were automatically evaluated. Spatially
normalized left and right lungs were evaluated separately. Disease division index (DDI) on every combination of AFs
and asymmetric index (AI) between left and right lung ((left-right)/left) were also evaluated. To assess the accuracy of
the system, fifty-four HRCT data sets in patients with pathologically diagnosed UIP (n=26) and NSIP (n=28) were used.
For a classification procedure, a CADD-SVM classifier with internal parameter optimization, and sequential forward
floating feature selection (SFFS) were employed. The accuracy was assessed by a 5-folding cross validation with 20-
times repetition. For comparison, two thoracic radiologists reviewed the whole HRCT images without clinical
information and diagnose each case either as UIP or NSIP.
The accuracies of radiologists' decision were 0.75 and 0.87, respectively. The accuracies of the CADD system using the
features of AF, dPDF, AI of dPDF, RDP, AI of RDP, DDI were 0.70, 0.79, 0.77, 0.80, 0.78, 0.81, respectively. The
accuracy of optimized CADD using all features after SFFS was 0.91.
We developed the CADD system to differentiate between UIP and NSIP using automated assessment of the extent and
distribution of regional disease patterns at HRCT.
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