  Patients diagnosed with metastatic breast cancer (mBC) typically undergo
several radiographic assessments during their treatment. mBC often involves
multiple metastatic lesions in different organs, it is imperative to accurately
track and assess these lesions to gain a comprehensive understanding of the
disease's response to treatment. Computerized analysis methods that rely on
lesion-level tracking have often used manual matching of corresponding lesions,
a time-consuming process that is prone to errors. This paper introduces an
automated lesion correspondence algorithm designed to precisely track both
targets' lesions and non-targets' lesions in longitudinal data. Here we
demonstrate the applicability of our algorithm on the anonymized data from two
Phase III trials. The dataset contains imaging data of patients for different
follow-up timepoints and the radiologist annotations for the patients enrolled
in the trials. Target and non-target lesions are annotated by either one or two
groups of radiologists. To facilitate accurate tracking, we have developed a
registration-assisted lesion correspondence algorithm. The algorithm employs a
sequential two-step pipeline: (a) Firstly, an adaptive Hungarian algorithm is
used to establish correspondence among lesions within a single volumetric image
series which have been annotated by multiple radiologists at a specific
timepoint. (b) Secondly, after establishing correspondence and assigning unique
names to the lesions, three-dimensional rigid registration is applied to
various image series at the same timepoint. Registration is followed by ongoing
lesion correspondence based on the adaptive Hungarian algorithm and updating
lesion names for accurate tracking. Validation of our automated lesion
correspondence algorithm is performed through triaxial plots based on axial,
sagittal, and coronal views, confirming its efficacy in matching lesions.
