A data-driven approach to model spatial dose characteristics for catheter placement of high dose-rate brachytherapy for prostate cancer.

BACKGROUND

High dose rate brachytherapy (HDR BT) is a common treatment modality for cancer. In HDR BT, a radioactive source is placed inside or close to a tumor, aiming to give a high enough dose to the tumor, while sparing nearby healthy tissue and organs at risk. Treatment planning of HDR BT for prostate cancer consists of two types of decisions, placement of catheters, modeled with binary variables, and dwell times, modeled with continuous non-negative variables. Optimal spatial placement of catheters is important for avoiding local recurrence and complications, but such characteristics have not been modeled for the combined treatment planning problem of catheter placement and dwell time optimization.

METHOD: We propose a data-driven approach using linear regression, mutual information, and random forests to find convex estimates of spatial dose characteristics that correlate well with contiguous volumes receiving a too-high (hot spots) or too-low dose (cold spots). These estimates were incorporated in retrospective treatment plan optimization of 28 prostate cancer patients.

RESULTS

The proposed hot-spot terms reduced the volume receiving twice the prescribed dose by 29% at 14 catheters. Also, the results illustrate the trade-offs between the number of catheters and spatial dose characteristics.

CONCLUSIONS

Our study demonstrates that incorporating a term for hot spots in the objective function of the treatment planning model is more effective in reducing hot spots than catheter placements that are not optimized for hot spots.