Can We Predict Skeletal Lesion on Bone Scan Based on Quantitative PSMA PET/CT Features?

Simple Summary: Treating patients for metastatic prostate cancer based on the information of PSMA-PET bears the risk to "under- or overtreat" patients, given that many lesions seen only on PSMA-PET but not on conventional imaging (CI) could alter their management. It is not possible to predict the disease status using CI with PSMA-PET/CT, because bone lesions can be positive on bone scintigraphy (BS), without evidence of the disease on CT. Some authors suggested using clinical parameters to predict BS results, but this does not reach enough accuracy to adjust the therapy. If an algorithm based on PSMA-PET/CT data were able to predict if lesions are visible on BS, this might be a possible way to adjust patient management based on CI-based guidelines in light of PSMA-PET/CT. Therefore, we aimed to develop a model to predict the visibility of bone lesions on BS based on PSMA-PET/CT data. Objective: The increasing use of PSMA-PET/CT for restaging prostate cancer (PCa) leads to a patient shift from a non-metastatic situation based on conventional imaging (CI) to a metastatic situation. Since established therapeutic pathways have been designed according to CI, it is unclear how this should be translated to the PSMA-PET/CT results. This study aimed to investigate whether PSMA-PET/CT and clinical parameters could predict the visibility of PSMA-positive lesions on a bone scan (BS). Methods: In four different centers, all PCa patients with BS and PSMA-PET/CT within 6 months without any change in therapy or significant disease progression were retrospectively selected. Up to 10 non-confluent clear bone metastases were selected per PSMA-PET/CT and SUVmax, SUVmean, PSMA(tot), PSMA(vol), density, diameter on CT, and presence of cortical erosion were collected. Clinical variables (age, PSA, Gleason Score) were also considered. Two experienced double-board physicians decided whether a bone metastasis was visible on the BS, with a consensus readout for discordant findings. For predictive performance, a random forest was fit on all available predictors, and its accuracy was assessed using 10-fold cross-validation performed 10 times. Results: A total of 43 patients were identified with 222 bone lesions on PSMA-PET/CT. A total of 129 (58.1%) lesions were visible on the BS. In the univariate analysis, all PSMA-PET/CT parameters were significantly associated with the visibility on the BS (p < 0.001). The random forest reached a mean accuracy of 77.6% in a 10-fold cross-validation. Conclusions: These preliminary results indicate that there might be a way to predict the BS results based on PSMA-PET/CT, potentially improving the comparability between both examinations and supporting decisions for therapy selection.