Automated resection planning for bone tumor surgery

Dave Hill; Tom Williamson; Chow Yin Lai; Martin Leary; Milan Brandt; Peter Choong

doi:10.1016/j.compbiomed.2021.104777

Automated resection planning for bone tumor surgery

Comput Biol Med. 2021 Oct:137:104777. doi: 10.1016/j.compbiomed.2021.104777. Epub 2021 Aug 25.

Authors

Dave Hill¹, Tom Williamson², Chow Yin Lai³, Martin Leary¹, Milan Brandt¹, Peter Choong⁴

Affiliations

¹ Centre for Additive Manufacturing, School of Engineering, RMIT University, 58 Cardigan St, Carlton, 3001, Australia.
² Centre for Additive Manufacturing, School of Engineering, RMIT University, 58 Cardigan St, Carlton, 3001, Australia. Electronic address: tom.williamson@rmit.edu.au.
³ Department of Electronic and Electrical Engineering, University College London, Malet Place and Torrington Place, Roberts Building, Level 7, London, WC1E 7JE, United Kingdom.
⁴ Department of Surgery, University of Melbourne, Level 2, Clinical Sciences Building, 29 Regent Street, Fitzroy, 3065, Australia.

PMID: 34492517
DOI: 10.1016/j.compbiomed.2021.104777

Abstract

Planning for bone tumor resection surgery is a technically demanding and time-consuming task, reliant on manual positioning of cutting planes (CPs). This work describes an automated approach for generating bone tumor resection plans, where the volume of healthy bone collaterally resected with the tumor is minimized through optimized placement of CPs. Particle swarm optimization calculates the optimal position and orientation of the CPs by introducing a single new CP to an existing resection, then optimizing all CPs to find the global minima. The bone bounded by all CPs is collaterally resected with the tumor. The approach was compared to manual resection plans from an experienced surgeon for 20 tumor cases. It was found that a greater number of CPs reduce the collaterally resected healthy bone, with diminishing returns on this improvement after five CPs. The algorithm-generated resection plan with equivalent number of CPs resulted in a statistically significant improvement over manual plans (paired t-test, p < 0.001). The described approach has potential to improve patient outcomes by reducing loss of healthy bone in tumor surgery while offering a surgeon multiple resection plan options.

Keywords: Automated planning; Bone tumors; Orthopedic oncology; Surgical planning.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bone Neoplasms* / diagnostic imaging
Bone Neoplasms* / surgery
Humans
Radiotherapy Planning, Computer-Assisted