Importancia y necesidad del reconocimiento pericial dactilar

The delineation of the MTV on a PET image, for which the ground truth (GT) contour is unknown, requires the estimation of the parameters incor- porated into the developed ATLAAS training model. Estimated parameters are acquired from an estimation of the MTV, which is delineated by applying a PET-AS algorithm to the PET image. Estimated tumour characteristics are used as input to the DTs, which output the predicted DSC for each PET-AS algorithm included in the training stage of ATLAAS. The PET-AS method with the highest predicted DSC is then used to delineate the final MTV.

Region of Interest definition

For accurate estimation of the MTV, the definition of a region of interest (ROI) is required. A ROI limits over-contouring of the estimated MTV, allows avoidance of areas of erroneous uptake as well as improving estimation of the training model parameters. A variety of approaches exist for the

definition of the ROI:

• Manual definition requires a user to select which voxels to investigate as potentially being the tumour. This is typically done by “painting” the ROI on the PET image. This process can be time consuming and has the same limitations as manual definition of the MTV.

• Existing contour expansion; if a contour has been pre-defined due to being involved in a retrospective study or RT planning, it is possible to expand this contour by a user defined measurement to use as the ROI. • Semi-automated definition of the ROI typically requires the user to place a sphere or cube of a user defined size around the area to de- lineate. More advanced semi-automated processes exist, in which the user defines a limited number of seed points in the saggital, axial and transverse planes. Seed points are converted to a spheroid around the centre of mass, which AT is then applied to. The resulting delineation is expanded. The advantage of this more advanced process is a re- duced time to define the ROI as it requires only the definition of two seed points, and a ROI which is clinically relevant to the data in the PET scan is generated allowing for the avoidance of contouring in areas of erroneous uptake.

This chapter has discussed the multitude of PET-AS algorithms that have been proposed for accurate MTV delineation, from simple threshold-based techniques to more advanced methodologies; including the development of decision trees from a 18_{F-FDG PET based training dataset. The follow-}

ing chapter aims to demonstrate the impact MTV delineation has on a pa- tient’s OS and risk stratification, thereby potentially affecting their quality of life.

(a)

(b)

(c)

Figure 2.1: a) The first stage of Watershed segmentation. The image is considered as a topographical height map and local minima (orange points) and maxima (red points) are defined from which flooding of the height map starts. b) From the minima selected in the image, the image is flooded until a singular maxima is left. b) The resulting watershed (black arrow) is considered to be the final image contour and segmentation.

(a) (b)

(c) (d)

Figure 2.2: a) The first stage of active contouring is to define a loose contour (red line) around the object to segment (blue T). b) The defined contour is attracted to changes in image intensity. c) The active contour is continuously modified until it reaches a stable state. d) The final segmentation once the active contour has reached a stable state.

Clinical Data ATLAAS Segmentation Boundary box definition TBR, MTV, NI calculated Training Data Ground Truth contour TBR, MTV, NI calculated PET-AS Contour DSC Calculated for PET-AS vs GT Decision Trees developed Predictive Model Predictive Model Predicted PET-AS Estimated MTV ATLAAS Predictive model builder

Chapter 3

Impact of metabolic tumour

volume segmentation on

patient overall survival

OC is the eighth most common [2] diagnosed cancer worldwide, with ex- tremely high rates of mortality. It is hoped that the development of prognos- tic models, combined with precision medicine, may improve the patient rate of mortality from 1— and 5— year OS rates of 44% and 15% respectively [11]. Traditionally, prognostic models are developed from patient-specific infor- mation including age, pathological subtype, molecular characterisation and tumour staging, resulting in a clinical model, which characterises a patient’s

advanced quantitative analysis of medical imaging and clinically developed prognostic models, it is hoped that the performance of prognostic models can be improved. The advanced quantiative analysis of medical imaging modalities is known as radiomics. Radiomic features aim to identify tumour biomarkers and heterogeniety, through the extraction of high-dimensional data, [15] that can be associated with metastatic growth, recurrence and OS [92]. Radiomic features may also have significant prognostic value in the management of cancer [93]. However, the extraction of radiomic features and the results of radiomic analysis are dependent upon the method used to delineate the MTV [17], with relatively few studies comparing the results of radiomic analysis derived from each PET-AS method (cf. [94] and references therein). Further, few studies have investigated the effect of radiomic analy- sis from PET-AS methods on patient risk stratification [18,95,96]. Therefore, this chapter aimed to evaluate the influence of PET-AS method MTV de- lineation on patient risk stratification and the resulting patient OS, in OC, by developing a series of prognostic models in the same patient cohort, with identical clinical data and standardised radiomic features derived from dif- ferent PET-AS methods. The following sections describe the materials & methods used to achieve the aims of this chapter.