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Automated Radiochemical Synthesis of [18F]3F4AP: A Novel PET Tracer for Imaging Demyelinating Diseases

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Evaluation of prognostic models developed using standardised image features from different PET automated segmentation

Craig Parkinson¹, Kieran Foley², Philip Whybra¹

¹School of Engineering, Cardiff University, Queen's Buildings, 14-17 The Parade, Cardiff, CF24 3AA, UK.

EJNMMI Research

|April 13, 2018

Summary

The method used to define tumor size on PET scans significantly impacts esophageal cancer patient risk classification. Accurate segmentation is crucial for reliable prognostic models and personalized treatment strategies.

Keywords:

Automated segmentation Esophageal cancer PET/CT Prognostic model

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Area of Science:

Oncology
Medical Imaging
Radiomics

Background:

Esophageal cancer (OC) has a poor prognosis, with a 5-year survival rate around 15%.
Personalized medicine aims to improve survival rates in OC.
Quantitative analysis of Positron Emission Tomography (PET) is a promising prognostic tool, but requires precise metabolic tumor volume definition.

Purpose of the Study:

To compare prognostic models developed using different PET segmentation algorithms in the same OC patient cohort.
To assess the impact of various segmentation methods on patient risk stratification.

Main Methods:

Nine automatic PET segmentation methods were evaluated in 427 OC patients staged with PET/CT.
Methods with <90% accuracy were excluded after subjective contour analysis.
Standardized image features were calculated, and prognostic models were built using identical clinical data.

Main Results:

Four methods (KM2, GCM3, AT, WT) were included for analysis.
Clinical factors like age, treatment, and staging were significant in all models.
Patient risk stratification varied by segmentation method, with up to 17.1% of patients changing groups.

Conclusions:

Prognostic models incorporating quantitative PET features are highly dependent on the tumor delineation method.
The choice of image segmentation significantly affects patient risk stratification in esophageal cancer.