Predicting Surgical Safety Margins in Osteosarcoma Knee Resections: An Unsupervised Approach

TL;DR

This paper introduces an unsupervised learning method utilizing MRI and X-ray data to automatically estimate surgical safety margins in osteosarcoma knee resections, aiming to improve precision and patient outcomes.

Contribution

It presents a novel unsupervised approach combining digital image processing and clustering algorithms for tumor boundary detection in osteosarcoma surgery.

Findings

Automated tumor boundary detection using MRI and X-ray data.

Potential for patient-specific safety margin estimation.

Effective use of k-means clustering in medical image analysis.

Abstract

According to the Pan American Health Organization, the number of cancer cases in Latin America was estimated at 4.2 million in 2022 and is projected to rise to 6.7 million by 2045. Osteosarcoma, one of the most common and deadly bone cancers affecting young people, is difficult to detect due to its unique texture and intensity. Surgical removal of osteosarcoma requires precise safety margins to ensure complete resection while preserving healthy tissue. Therefore, this study proposes a method for estimating the confidence interval of surgical safety margins in osteosarcoma surgery around the knee. The proposed approach uses MRI and X-ray data from open-source repositories, digital processing techniques, and unsupervised learning algorithms (such as k-means clustering) to define tumor boundaries. Experimental results highlight the potential for automated, patient-specific determination of safety margins.