Computer-aided Diagnosis for Lung Cancer

Publication date: Available online 16 January 2017
Source:Academic Radiology
Author(s): Mizuho Nishio, Chihiro Nagashima
Rationale and ObjectivesTo develop a computer-aided diagnosis system to differentiate between malignant and benign nodules.Materials and MethodsSeventy-three lung nodules revealed on 60 sets of computed tomography (CT) images were analyzed. Contrast-enhanced CT was performed in 46 CT examinations. The images were provided by the LUNGx Challenge, and the ground truth of the lung nodules was unavailable; a surrogate ground truth was, therefore, constructed by radiological evaluation. Our proposed method involved novel patch-based feature extraction using principal component analysis, image convolution, and pooling operations. This method was compared to three other systems for the extraction of nodule features: histogram of CT density, local binary pattern on three orthogonal planes, and three-dimensional random local binary pattern. The probabilistic outputs of the systems and surrogate ground truth were analyzed using receiver operating characteristic analysis and area under the curve. The LUNGx Challenge team also calculated the area under the curve of our proposed method based on the actual ground truth of their dataset.ResultsBased on the surrogate ground truth, the areas under the curve were as follows: histogram of CT density, 0.640; local binary pattern on three orthogonal planes, 0.688; three-dimensional random local binary pattern, 0.725; and the proposed method, 0.837. Based on the actual ground truth, the area under the curve of the proposed method was 0.81.ConclusionsThe proposed method could capture discriminative characteristics of lung nodules and was useful for the differentiation between malignant and benign nodules.



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