Experiment 24: Is it possible to define a sub region of the data plot that relates to tumour?

Aim: The aim of this experiment was to see if it was possible to ensure that the tumour appeared in the ROI defined in the methods.

Methods: 8 patients were selected for this experiment. These were patients who did not recur within the first year after having a primary lung tumour treated with SABR. These patients all had a post treatment CT scan with IV contrast at 6 or 7 months.

Inclusion criteria:

1. Patients previously treated with Stereotactic Ablative Lung Radiotherapy (SABR) for Non-Small Cell Lung Cancer (NSCLC).

2. Patients either had biopsy confirmed NSCLC or a clinical decision has been made that there is a high probability of NSCLC suitable for SABR.

3. Patients had a pre-treatment pet available, with a minimum of 12 months follow up with a minimum of 2 CT scans.

4. Patient had not recurred after SABR 5. Patient had CT planning scan available Exclusion criteria

1. Patients not had SABR for primary lung cancer or did not receive the full dose/course of radiotherapy

2. Were treated for a lung metastasis from a different primary cancer.

3. Patients had less than 1 years follow up, less than 2 scans available or no pre- treatment PET-CT scan

4. Patients had recurred after SABR

The first step was to plot the high density low entropy sub-region (trunk) of the elephant plot, defined as a density score of 900-1200 and an entropy score of 2 or less. It was suspected that some of these sub regions would contain more than 1 data cluster. These were then plotted individually.

Results: Table 1 shows all of the data plots from the whole lung analysis of 8 patients. It also shows the feature plot for the tumour and a sub-region relating to the tip of the trunk, from

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the whole lung elephant plot discussed in the methods (density 900-1200 and entropy 2 or less). The right hand column of table 1 shows the sub-region (tip of the elephant’s trunk) that contains tumour. When analysing this region of interest, data points correlating to tumour appeared in 7 of the 8 sub-region analyses. Figures 1-4 show the different data clusters within the high density, low entropy region of the elephant plotted back into the image.

Discussion: This experiment aimed to understand what structures appear in the ROI where tumour appears, within the elephant plot. 7 of the 8 patients analysed had data points relating to the tumour, within the defined ROI. Only pt 16 did not appear to have the typical appearance of other tumours, seen in table 1, as there is no data cluster in the tip of the trunk, which represents the high density, low entropy region consistent with tumour. The data plot for the tumour of patient 16 does not contain these data points. It does appear to have data points with an entropy score of 3 or less (seen in table 1). It may be that future analyses may need to include voxels with an entropy score of 3 or less, rather than 2 or less.

Figure 1: sub region analysis, replotting data points within the high density low entropy region (tip of the trunk of the elephant plot) using CT spatial co-ordinates. All of the tip of the trunk of the elephant plot is plotted within the tumour.

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Table 1: data plots for 8 patients, for whole lung containing tumour, tumour and a sub region analysis of tip of elephant trunk (region of interest including data points with a density of 900- 1200 and entropy of 2 or less). X axis= density score. Y axis= entropy score.

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Figure 2: Sub region analysis for patients 7 + 8. For patient 7 left sided data cluster is consistent with contrast and the larger right sided cluster is consistent with the tumour. For patient 8, the larger left sided data cluster includes both tumour and contrast, the smaller right sided cluster includes contrast.

Figure 3: sub-region analysis for patients 16, 18 and 22. The region analysed containing data points relating to tumour in 2 of the 3 patients analysed in this figure and 7 out of 8 overall.

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Figure 4: sub region analysis for patient 6. The sub region shows the 3 peaks relate to regions of interest in the texture map. Left peak = liver edge, middle peak = tumour and right peak = IV contrast in a blood vessel

Of the 8 patients 7 contained more than one data cluster within the ROI. Patient 5 had a single obvious data cluster in the ROI. Figure 1 shows the data points within the ROI for patient 5. When these data points are replotted back into the scan, it shows that all of the data points are within the tumour. Patients 7, 8, 16, 18 and 22 had 2 data clusters. Patients 6 and 17 had 3 data clusters.

Figures 2 and 3 show 5 different patients, who each have 2 data clusters in the ROI representing the tip of the trunk. It shows the 2 separate data clusters replotted back into the original image. Of the 5 different patients illustrated in these analyses, different regions consistently relate to tumour and contrast. The dominant data cluster that represents tumour

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appears to consistently have a density score (on the x axis) of between 1000-1150. The density score = Hounsfield Units plus 1000.

Other sub regions show multiple clusters of data. Figure 4 shows that the sub region for patient 6 has 3 obvious clusters of data. These correspond to tumour, a vessel containing IV contrast and the edge of the liver. This suggests that the density range used in the sub-region could potentially be further narrowed.

In summary for the majority of tumours analysed in this small data set, the tumour lies within the set boundaries of the ROI. This would need to be confirmed in larger data sets. In Figures 2-4 it can be seen that the initial contouring is can affect the analysis of the sub region of interest. It shows that confounders such as blurred diaphragm/liver edge or IV contrast can potentially impact on elephant plot analysis.

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6.3 Experiment 25: Does the tumour volume correlate with the volume of Radiation Induced