Extravasation or partial extravasation of the positron emission tomography (PET) tracer negatively effects image quality in PET and the accuracy of the standard uptake value (SUV). A commercially available topical sensor has been validated using a number of metrics to characterise injection quality. This evaluation explores contributing factors for extravasation and refines metrics to predict extravasation based on the time-activity-curves (TAC) of the topical sensor device. Methods: A multi-site, multi-national pooling of 18F FDG PET/CT data was undertaken with 863 patients from 6 sites in the USA and 2 sites in Australia. A number of dose migration metrics determined with topical application of Lara sensors were retrospectively analysed using conventional statistical analysis. Deeper insights into the complex relationship between variables was further explored using an artificial neural network. Results: Extravasation was independently predicted by the time taken for the injection sensor counts to reach double the counts of the reference sensor (tc50), the normalised difference between injection and reference sensors counts at 4 min post injection (ndAvgN), or the ratio of injection sensor counts to reference sensor counts at the end of data collection (CEnd ratio). The algorithm developed using the artificial neural network produced 100% sensitivity and 100% specificity against grounded truth for detecting extravasation by weighting and scaling these 3 key metrics; CEnd ratio, ndAvgN and tc50. Conclusion: Partial extravasation of a PET dose is readily detected and differentiated using TAC metrics and these metrics could provide deeper insight into the impact of partial extravasation on image quality or quantitation. Further validation of key metrics developed in this study are recommended in a larger and more diverse cohort. Implications for practice: Partial extravasation undermines image quality and accuracy of quantitation, impacting efficacy of outcomes for patients. Characterisation of extravasation informs decision making to optimise protocol and procedure, enhancing patient outcomes. Awareness provides the opportunity for education and training to minimise impact. The information can be used to drive policy and regulations to support improved techniques in practice.