Life (Feb 2021)
Radiomics Analysis for <sup>177</sup>Lu-DOTAGA-(l-y)fk(Sub-KuE) Targeted Radioligand Therapy Dosimetry in Metastatic Prostate Cancer—A Model Based on Clinical Example
Abstract
177Lu-DOTAGA-(l-y)fk(Sub-KuE) a.k.a. 177Lu-PSMA I&T is currently used for radioligand therapy (RLT) of metastatic castration-resistant prostate cancer (mCRPC) in several centers in Europe. Background: Dosimetry is mandatory according to EU guidelines, although routine methods for dosimetry, i.e., absorbed radiation dose calculations for radiopharmaceuticals, are missing. Methods: We created a model of dosimetric analysis utilizing voxel-based dosimetry and intra-lesion radiomics to assess their practicality in routine dosimetry. Results: As an example for the model, our patient with mCRPC had excellent therapy response; quantitatively more than 97% of the metastatic tumor burden in local and distant lymph nodes and skeleton was destroyed by four cycles of RLT. The absorbed radiation doses in metastases decreased towards later cycles of RLT. Besides the change of prostate-specific membrane antigen (PSMA) concentration and absorbed doses in the tumor, further response to RLT could be predicted from biomarker changes, such as LDH and PSA. Conclusions: Individual dosimetry is needed to understand large variations in tumor doses and mixed responses; for that purpose, routine tools should be developed. The Dosimetry Research Tool (DRT) fluently performed automated organ delineation and absorbed radiation dose calculations in normal organs, and the results in our patient were in good concordance with the published studies on 177Lu-PSMA dosimetry. At the same time, we experienced considerable challenges in voxel-based dosimetry of tumor lesions. Measurements of 177Lu-PSMA activity concentrations instead of absorbed radiation dose calculations could make routine dosimetry more flexible. The first cycle of RLT seems to have quantitatively the biggest impact on the therapy effect. Radiomics analyses could probably aid in the treatment optimization, but it should be tested in large patient populations.
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