The purpose of our research is to make clear the mechanism that a reader (physician or radiological technologist) effectively identify abnormal findings in CT images of lung cancer screening by using with CAD system. A method guessing the 2X2 decision matrix between reader / CAD and reader / reader with CAD was investigated. We suppose the next scene to be it. At first, a reader judges whether abnormal findings per one patient per one CT image are present (1) or absent (0) without CAD results. The second, a reader judges whether abnormal findings are present (1) or absent (0) with CAD results. We expresses the correlation between diagnoses by a reader and CAD system for abnormal cases and for normal cases by following formula using phi correlation coefficient:φ=(cd-ab)/√(a+c)(b+d)(b+c)(a+d). a,b,c,d: 2X2 decision matrix parameters. If TPR1=(a+c)/n, TPR2=(b+c)/n and TPR3=(a+b+c)/n for abnormal cases, TPR3=TPR1+TPR2 - TPR1×TRR2 - φ√TPR1(1-TPR1)TPR2(1-TPR2). Therefore, a=n (TPR3 - TPR1), b=n (TPR3 - TPR2), c=n (TPR1 + TPR2 -TPR3), d=n (1.0 - TPR3). This theory was applied for the experimental data. The 41 students interpreted the same CT images [no training]. A second interpretation was performed after they had been instructed on how to interpret CT images [training], and third was assisted by a virtual CAD [training + CAD]. The mechanism that makes up for a good point of a reader and a CAD with CAD in interpreting CT images was theoretically and experimentally investigated. We concluded that a method guessing the decision matrix (2X2) between a reader and a CAD decided the "presence" or "absence" of abnormal findings explain the improvement mechanism of diagnostic performance with CAD system.