Purpose: We hypothesised that applying radiomics to [F]PSMA-1007 PET/CT images could help distinguish Unspecific Bone Uptakes (UBUs) from bone metastases in prostate cancer (PCa) patients. We compared the performance of radiomic features to human visual interpretation.
Materials And Methods: We retrospectively analysed 102 hormone-sensitive PCa patients who underwent [F]PSMA-1007 PET/CT and exhibited at least one focal bone uptake with known clinical follow-up (reference standard). Using matRadiomics, we extracted features from PET and CT images of each bone uptake and identified the best predictor model for bone metastases using a machine-learning approach to generate a radiomic score. Blinded PET readers with low (n = 2) and high (n = 2) experience rated each bone uptake as either UBU or bone metastasis. The same readers performed a second read three months later, with access to the radiomic score.
Results: Of the 178 [F]PSMA-1007 bone uptakes, 74 (41.5%) were classified as PCa metastases by the reference standard. A radiomic model combining PET and CT features achieved an accuracy of 84.69%, though it did not surpass expert PET readers in either round. Less-experienced readers had significantly lower diagnostic accuracy at baseline (p < 0.05) but improved with the addition of radiomic scores (p < 0.05 compared to the first round).
Conclusion: Radiomics might help to differentiate bone metastases from UBUs. While it did not exceed expert visual assessments, radiomics has the potential to enhance the diagnostic accuracy of less-experienced readers in evaluating [F]PSMA-1007 PET/CT bone uptakes.
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