Fig 1.
Examples of the evaluation of tumor infiltrating lymphocytes.
Examples of the evaluation of tumor infiltrating lymphocytes(TILs) in the present study. The mean percentage of the area occupied by TILs in 5 areas per section was reported as the density of TILs.
Fig 2.
The method of identifying the area of TILs by the automated imaging software program.
(A) A hematoxylin and eosin-stained section (×200). (B) Examples of the evaluating the area of TILs using the automated imaging software program (×400). The area in the square in Figure (A) was subjected to an image analysis and is shown in Figure (B). We set the depth of color of TILs. The cells of the same color were then extracted. The ratio of the white area to the extracted area was determined.
Table 1.
The patient characteristics.
Fig 3.
The receiver operating characteristic (ROC) curve for the density of tumor infiltrating lymphocytes (TILs).
We treated the five-year relapse-free survival as the state variable and the percentage of tumor infiltrating lymphocytes (TILs) as the test variable. The most appropriate cut-off value for the percentage of TILs to be 42% was shown by the investigation of the cut-off value of percentage of TILs using the receiver operating characteristic (ROC) curve (AUC, 0.5781; sensitivity, 0.8636; specificity, 0.3103).
Table 2.
The correlations between the clinicopathological factors and the area of tumor infiltrating lymphocytes.
Fig 4.
(A)The survival curves for relapse-free survival. The relapse-free survival rates of the low-TIL group were significantly worse in comparison to the high-TIL group (p = 0.0251). (B)The survival curves for overall survival. The overall survival rates in the low-TIL group were significantly worse in comparison to the high-TIL group (p = 0.0059).
Table 3.
The results of the univariate and multivariate analyses of the prognostic factors for relapse-free survival.
Table 4.
The results of the univariate and multivariate analyses of the prognostic factors for overall survival.
Fig 5.
The correlation between the density of TILs assessed by an observer and that assessed by an automated imaging software program.
The correlation between the density of TILs assessed by an observer and the density of TILs assessed by the imaging software program are shown. A significant positive correlation was observed (r = 0.7071, p<0.0001).