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A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

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Automated resection planning for bone tumor surgery.

Dave Hill¹, Tom Williamson¹, Chow Yin Lai²

¹Centre for Additive Manufacturing, School of Engineering, RMIT University, 58 Cardigan St, Carlton, 3001, Australia.

Computers in Biology and Medicine

|September 7, 2021

Summary

This study introduces an automated method for bone tumor surgery planning, optimizing cutting plane placement to minimize healthy bone removal. The algorithm significantly outperforms manual planning, improving patient outcomes in tumor resections.

Keywords:

Automated planning Bone tumors Orthopedic oncology Surgical planning

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Published on: April 11, 2018

Area of Science:

Orthopedic Surgery
Computational Biology
Medical Imaging

Background:

Manual planning of bone tumor resections is complex and time-consuming.
Current methods often lead to collateral resection of healthy bone, impacting patient outcomes.

Purpose of the Study:

To develop an automated approach for generating bone tumor resection plans.
To minimize the volume of healthy bone resected alongside the tumor through optimized cutting plane placement.

Main Methods:

Utilized particle swarm optimization to determine optimal positions and orientations of cutting planes (CPs).
The algorithm iteratively introduced and optimized CPs to find global minima, defining the resection volume.
Compared algorithm-generated plans against manual plans from experienced surgeons across 20 tumor cases.

Main Results:

Increasing the number of CPs reduced collateral healthy bone resection, with diminishing returns after five CPs.
Algorithm-generated plans showed a statistically significant improvement over manual plans (p < 0.001).
The automated approach offers surgeons multiple optimized resection plan options.

Conclusions:

Automated planning significantly reduces healthy bone loss during bone tumor resection surgery.
This approach has the potential to enhance patient outcomes by preserving more healthy bone.
The method provides a valuable tool for optimizing surgical planning in orthopedic oncology.