Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [F]F-FDG Tracers Part II. [F]F-FLT, [F]F-FET, [C]C-MET and Other Less-Commonly Used Radiotracers.

Following the previous part of the narrative review on artificial intelligence (AI) applications in positron emission tomography (PET) using tracers rather than F-fluorodeoxyglucose ([F]F-FDG), in this part we review the impact of PET-derived radiomics data on the diagnostic performance of other PET radiotracers, F-O-(2-fluoroethyl)-L-tyrosine ([F]F-FET), F-Fluorothymidine ([F]F-FLT) and C-Methionine ([C]C-MET). [F]F-FET-PET, using an artificial amino acid taken up into upregulated tumoral cells, showed potential in lesion detection and tumor characterization, especially with its ability to reflect glioma heterogeneity. [F]F-FET-PET-derived textural features appeared to have the potential to reveal considerable information for accurate delineation for guiding biopsy and treatment, differentiate between low-grade and high-grade glioma and related wild-type genotypes, and distinguish pseudoprogression from true progression. In addition, models built using clinical parameters and [F]F-FET-PET-derived radiomics features showed acceptable results for survival stratification of glioblastoma patients. [F]F-FLT-PET-based characteristics also showed potential in evaluating glioma patients, correlating with Ki-67 and patient prognosis. AI-based PET-volumetry using this radiotracer as a proliferation marker also revealed promising preliminary results in terms of guide-targeting bone marrow-preserving adaptive radiation therapy. Similar to [F]F-FET, the other amino acid tracer which reflects cellular proliferation, [C]C-MET, has also shown acceptable performance in predicting tumor grade, distinguishing brain tumor recurrence from radiation necrosis, and treatment monitoring by PET-derived radiomics models. In addition, PET-derived radiomics features of various radiotracers such as [F]F-DOPA, [F]F-FACBC, [F]F-NaF, [Ga]Ga-CXCR-4 and [F]F-FMISO may also provide useful information for tumor characterization and predict of disease outcome. In conclusion, AI using tracers beyond [F]F-FDG could improve the diagnostic performance of PET-imaging for specific indications and help clinicians in their daily routine by providing features that are often not detectable by the naked eye.