  Assessing tumor response to systemic therapies is one of the main
applications of PET/CT. Routinely, only a small subset of index lesions out of
multiple lesions is analyzed. However, this operator dependent selection may
bias the results due to possible significant inter-metastatic heterogeneity of
response to therapy. Automated, AI based approaches for lesion tracking hold
promise in enabling the analysis of many more lesions and thus providing a
better assessment of tumor response. This work introduces a Siamese CNN
approach for lesion tracking between PET/CT scans. Our approach is applied on
the laborious task of tracking a high number of bone lesions in full-body
baseline and follow-up [68Ga]Ga- or [18F]F-PSMA PET/CT scans after two cycles
of [177Lu]Lu-PSMA therapy of metastatic castration resistant prostate cancer
patients. Data preparation includes lesion segmentation and affine
registration. Our algorithm extracts suitable lesion patches and forwards them
into a Siamese CNN trained to classify the lesion patch pairs as corresponding
or non-corresponding lesions. Experiments have been performed with different
input patch types and a Siamese network in 2D and 3D. The CNN model
successfully learned to classify lesion assignments, reaching a lesion tracking
accuracy of 83 % in its best configuration with an AUC = 0.91. For remaining
lesions the pipeline accomplished a re-identification rate of 89 %. We proved
that a CNN may facilitate the tracking of multiple lesions in PSMA PET/CT
scans. Future clinical studies are necessary if this improves the prediction of
the outcome of therapies.
