Generating lesion change prediction data for medical scans

A method comprising:

• generating, in a first temporal period, a longitudinal lesion model by performing a training step utilizing a plurality of sets of longitudinal data, wherein dates of medical scans of different ones of the plurality of sets of longitudinal data have relative time differences corresponding to different time spans, wherein each set of the plurality of sets of longitudinal data corresponds to one of a plurality of different patients, and wherein generating the longitudinal lesion model includes:
• generating a first lesion volume measurement of a first lesion detected in a first medical scan of one set of longitudinal data corresponding to a first patient;
• and
• generating a second lesion volume measurement of the first lesion detected in a second medical scan of the one set of longitudinal data, wherein the second medical scan has a more recent scan date than the first medical scan;
• receiving, in a second temporal period that is strictly after the first temporal period, a third medical scan that is associated with a second patient that is distinct from the first patient;
• utilizing the longitudinal lesion model to perform an inference step on the third medical scan to generate, for a second lesion detected in the third medical scan, a plurality of lesion change prediction data for a corresponding plurality of different projected time spans ending after a current date based on the different time spans;
• and
• transmitting at least one lesion change prediction data of the plurality of lesion change prediction data for display.

A lesion tracking system comprising:

• at least one processor;
• and
• a memory that stores operational instructions that, when executed by the at least one processor, cause the lesion tracking system to:
• generate, in a first temporal period, a longitudinal lesion model by performing a training step on a plurality of sets of longitudinal data, wherein dates of medical scans of different ones of the plurality of sets of longitudinal data have relative time differences corresponding to different time spans, wherein each set of the plurality of sets of longitudinal data corresponds to one of a plurality of different patients wherein one set of longitudinal data of the plurality of sets of longitudinal data corresponds a first patient and is determined based on:
• a first lesion volume measurement of a first lesion detected in a first medical scan of the one set of longitudinal data;
• and
• a second lesion volume measurement of the first lesion detected in a second medical scan of the one set of longitudinal data, wherein the second medical scan has a more recent scan date than the first medical scan;
• receive, in a second temporal period that is strictly after the first temporal period, a third medical scan that is associated with a second patient that is distinct from the first patient;
• utilize the longitudinal lesion model to perform an inference step on the third medical scan to generate, for a second lesion detected in the third medical scan, a plurality of lesion change prediction data for a corresponding plurality of different projected time spans ending after a current date based on the different time spans;
• and
• transmit at least one lesion change prediction data of the plurality of lesion change prediction data for display.