Abstract
Lung cancer is the first killer among the cancer deaths. Malignant lung nodules have extremely high mortality while
some of the benign nodules don't need any treatment .Thus, the accuracy of diagnosis between benign or malignant
nodules diagnosis is necessary. Notably, although currently additional invasive biopsy or second CT scan in 3 months
later may help radiologists to make judgments, easier diagnosis approaches are imminently needed. In this paper, we
propose a novel CAD method to distinguish the benign and malignant lung cancer from CT images directly, which can
not only improve the efficiency of rumor diagnosis but also greatly decrease the pain and risk of patients in biopsy
collecting process. Briefly, according to the state-of-the-art radiomics approach, 583 features were used at the first step
for measurement of nodules' intensity, shape, heterogeneity and information in multi-frequencies. Further, with Random
Forest method, we distinguish the benign nodules from malignant nodules by analyzing all these features. Notably, our
proposed scheme was tested on all 79 CT scans with diagnosis data available in The Cancer Imaging Archive (TCIA)
which contain 127 nodules and each nodule is annotated by at least one of four radiologists participating in the project.
Satisfactorily, this method achieved 82.7% accuracy in classification of malignant primary lung nodules and benign
nodules. We believe it would bring much value for routine lung cancer diagnosis in CT imaging and provide
improvement in decision-support with much lower cost.
