The role of PET radiomic features in prostate cancer: a systematic review

Aim: This systematic review aims to present the available evidence on the use of radiomic features (RFs) extracted from PET imaging in patients with prostate cancer (PCa). Materials and methods: A comprehensive literature search of studies on the utility of PET-derived RFs in patients with PCa was performed in the PubMed/MEDLINE database through February 24th, 2021 using the following search string: [“positron-emission tomography” (MeSh terms) OR “positron emission tomography computed tomography” (MeSh terms) OR “positron-emission tomography” (all fields) OR “positron emission tomography computed tomography” (all fields) OR “PET” (all fields)] AND [“radiomics” (all fields) OR “radiomic” (all fields) OR “radiogenomics” (all fields) OR “radiogenomic”(all fields) OR “machine learning”(all fields) OR “machine learning”(MeSh terms) OR “artificial intelligence”(MeSh terms) OR “artificial intelligence”(all fields)] AND [“prostatic neoplasms” (MeSh terms) OR “prostate cancer”(all fields) OR “prostatic carcinoma” (all fields) OR “prostate carcinoma” (all fields) OR “prostatic tumor” (all fields) OR “prostatic tumour” (all fields)]. The Google scholar database was interrogated to find additional studies. Results: Seven studies were ultimately included in the systematic review and summarized in two relevant clinical sections: (1) primary staging and (2) restaging. In primary staging, RFs, extracted from 68 Ga-prostate-specific membrane antigen (PSMA) PET may characterize intraprostatic radiotracer hotspots in patients with high- and intermediate-risk, discriminate between Gleason Score (GS) 7 and ≥ 8 and between pN1 and pN0 disease, and suggest presence of intraprostatic lesions missed at visual PET examination. Machine learning (ML) may help selecting RFs able to predict risk classification (low vs. high), lymph node involvement, presence of nodal or distant metastasis, GS and extracapsular extension. At restaging, PET_Kurtosis may correlate with OS in patients with advanced PCa scheduled for 177Lu-PSMA treatment, whereas ML may assist discrimination of malignant lesions from physiologic/unspecific tracer accumulation, and predict disease progression. Conclusion: To date, although PET literature appears still too narrow to draw definitive conclusions, PET-derived RFs appear promising in PCa. ML seems an important tool that may contribute to the widespread use of radiomics and subsequent implementation in the clinical setting