Summary Table 1

Summary Table by Grade

Summary Table by GBM vs Non-GBM

Summary table by most common tumors

Grade

The Flipped Bar- plot above shows Counts(X axis) and percentages(annotated within bar) of various categories. The top 2 sub-groups are as follows : 19/50(38 %) patients are in sub-group 3 15/50(30 %) patients are in sub-group 4 The Full details of distribution is in table below.

Diagnosis

The Flipped Bar- plot above shows Counts(X axis) and percentages(annotated within bar) of various categories. The top 2 sub-groups are as follows : 11/50(22 %) patients are in sub-group ANAPLASTIC OLIGODENDROGLIOMA 11/50(22 %) patients are in sub-group GLIOBLASTOMA MULTIFORME The Full details of distribution is in table below.

Symptoms

The Flipped Bar- plot above shows Counts(X axis) and percentages(annotated within bar) of various categories. The top 2 sub-groups are as follows : 28/50(56 %) patients are in sub-group HEADACHE 20/50(40 %) patients are in sub-group SEIZURE The Full details of distribution is in table below.

Sex

The Flipped Bar- plot above shows Counts(X axis) and percentages(annotated within bar) of various categories. The top 2 sub-groups are as follows : 38/50(76 %) patients are in sub-group MALE 12/50(24 %) patients are in sub-group FEMALE The Full details of distribution is in table below.

Duration Of Symptoms

The plot above shows a flipped bar plot of Counts(X axis) and percentages(annotated within bar) of various categories. The top 2 tos categories are as follows : 20/50(40%) patients are in group 3 - 6 . 13/50(26%) patients are in group < 3 . The Full details of distribution is in table below.

Age Distribution

we found a Significant association between category and age_grp. The chi-square statistic was 30.61 . The degree of freedom was 5 and P value was <0.001 .Contingency and Proportion table are shown below

we found a Significant association between category and age_grp. The chi-square statistic was 30.61 . The degree of freedom was 5 and P value was <0.001 .Contingency and Proportion table are shown below

In this Figure we see Box plot of age in 2 sub-groups of category : Non-GBM and GBM respectively .The individual jittered data points of age are overlaid over transparent Boxplot for better visualisation. We see distribution of data in individual sub-groups of category based on these box-plots. The lower edge of box plot represents -first quartile (Q1), Horizontal bar represents the median, Upper edge represnts third quartile (Q3), Two black lines (whiskers) emanating from box-plots signify range of non-outlier data for the particular sub-group. Lower whisker represents minimum(Q1- 1.5 interquartile range) non-outlier limit of age and upper whisker represnts maximum(Q1+1.5interquartile range) of age .Any data beyond whiskers of box-plots represents outliers in the sub-groups The big brown point in the box-plots represents mean age of 2 groups and it has been annotated in the figure itself Summary Statistics of the groups is presented in table below

The mean in Group GBM [ 54.82 ± 15.13 ] was significantly higher than Group Non- GBM [ 35.15 ± 12.56 ] . The mean difference was 19.66 and 95 % confidence interval of the difference was ( 8.98 - 30.35 ) . The p value was <0.001 . The t statistic was 3.94 and degree of freedom of the Welch unpaired two-sample t test was 14.12 .In Formal statistical notation this result is expressed as : t(14.12) = 3.94, p= <0.001.

Gender distribution

we found a Non-significant association between category and sex. The chi-square statistic was 0.28 . The degree of freedom was 1 and P value was 0.6 .Contingency and Proportion table are shown below

GFAP as prognostic marker

This is a trace-plot of variation in GFAP (on Y axis) with passage of of time on X-axis , GFAP across various time are connected by distinct colored trace-line . Individual readings have been plotted on graph and points have been jittered for better clarity . The raw data points representing two groups have different shapes as indicated in the legend. The points across the trace represent the mean readings in two groups and whiskers represent Standard error of measurements.

we can clearly see a negative trend/slope indicating a fall in GFAP with passage of Time.we decided to explore this intuitive graphical relationship with formal statistical tests.

On Formal Statistical analysis there is a non-significant fall on repeated measures Anova. Adjusted pairwise differences are given in table below.

Variation Of tumor Volume with Time

This is a trace-plot of variation in Tumor volume (on Y axis) with passage of of time on X-axis , Tumor Volume across various times are connected by distinct colored trace-line . Individual readings have been plotted on graph and points have been jittered for better clarity . The raw data points representing two groups have different shapes as indicated in the legend. The points across the trace represent the mean readings in two groups and whiskers represent Standard error of measurements.

we can clearly see an initial fall post surgery followed by a rise at 3 months .we decided to explore this intuitive graphical relationship with formal statistical tests.

On Formal Statistical analysis there is a significant fall on repeated measures Anova. Adjusted pairwise differences are given in table below.

GFAP as diagnostic marker

The scatter plots above show relationship between Tumour Grade on X axis and GFAP_preop on Y axis. Graphically, we see that as Grade increases, GFAP_preop also increases . On a formal statistical linear regression analysis, we that line of best fit (blue line signifying line with least square difference) also has a positive slope implying a positive correlation. The gray shaded error around blue line signifies 95% confidence interval of linear regression line of best fit. The correlation between two variables is Significant . The Pearson’s correlation between Grade and GFAP_preop is 0.56 with 95% Confidence Interval of 0.33 to 0.72. the t statistic is 4.63 The p value is <0.001 .The degree of freedom is 48. In formal statistical notation this expressed as t(48)= 4.63, P= <0.001. r(Pearson) = 0.56 95% C.I. [0.33-0.72]. n= 50. The correlation is summmarised in table below

Discriminating Value of GFAP in GBM vs non GBM tumours

The ridgeplot above shows density distribution of Preoperative GFAP in GBM group and Non -GBM tumours. We see most of GBM tumours have GFAP greater than 0.05

Thus cut off 0.049 has best combination of sensitivity (100%) and specifcity (84.6%) for GBM detection with AUC of 0.923

cut off >0.05

         
          FALSE TRUE
  GBM         2    9
  Non-GBM    33    6

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