10 research outputs found
The correlation between the density of TILs assessed by an observer and that assessed by an automated imaging software program.
<p>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).</p
The correlations between the clinicopathological factors and the area of tumor infiltrating lymphocytes.
<p>The correlations between the clinicopathological factors and the area of tumor infiltrating lymphocytes.</p
The results of the univariate and multivariate analyses of the prognostic factors for overall survival.
<p>The results of the univariate and multivariate analyses of the prognostic factors for overall survival.</p
Survival curves.
<p><b>(A)</b>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).</p
The results of the univariate and multivariate analyses of the prognostic factors for relapse-free survival.
<p>The results of the univariate and multivariate analyses of the prognostic factors for relapse-free survival.</p
Examples of the evaluation of tumor infiltrating lymphocytes.
<p>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.</p
The method of identifying the area of TILs by the automated imaging software program.
<p><b>(A)</b> 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.</p
The Kaplan-Mayer survival curves for the relapse-free survival (RFS).
<p>A) The survival curves according to the CONUT score. The relapse-free survival rates were significantly worse in the high CONUT group compared to the low CONUT group (p = 0.0018). B) The survival curves according to the PNI. The relapse-free survival rates were significantly worse in the low PNI group compared to the high PNI group (p = 0.0162).</p
The receiver operating characteristic (ROC) curve for the controlling nutritional status (CONUT).
<p>We used the continuous variable Controlling Nutritional Status (CONUT) as the test variable and 5-year cancer-specific survival as the state variable. An investigation of the cut-off value for the CONUT score using the receiver operating characteristic (ROC) curve showed the most appropriate cut-off value for the CONUT score to be 3 (AUC; 0.624, 95%CI: 0.476–0.771, p = 0.076, the sensitivity was 0.5263 and the specificity was 0.7622.). We indicated the ROC curve on Fig 1. Therefore, we set 3 as the cut-off value for the CONUT score in this study and classified the patients into high CONUT (≥3) and low CONUT (≤2) groups.</p