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

Carolina Vargas-Ecos, Edwin Salcedo

TL;DR

The paper tackles the problem of defining reliable surgical safety margins for knee osteosarcoma by proposing an unsupervised, imaging-driven method that combines X-ray and MRI segmentation. It develops two segmentation pipelines, builds a dataset from open-source images, and integrates the results into a GUI to aid clinicians. Through X-ray and MRI analyses, it demonstrates strong correlations between tumor extent and surrounding tissue damage, enabling margin predictions at 0.25 cm increments and aligning with Enneking staging. The approach holds promise for automated, patient-specific margin estimation in data-limited settings, with future work expanding datasets and incorporating deep learning alongside unsupervised techniques.

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.

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

TL;DR

The paper tackles the problem of defining reliable surgical safety margins for knee osteosarcoma by proposing an unsupervised, imaging-driven method that combines X-ray and MRI segmentation. It develops two segmentation pipelines, builds a dataset from open-source images, and integrates the results into a GUI to aid clinicians. Through X-ray and MRI analyses, it demonstrates strong correlations between tumor extent and surrounding tissue damage, enabling margin predictions at 0.25 cm increments and aligning with Enneking staging. The approach holds promise for automated, patient-specific margin estimation in data-limited settings, with future work expanding datasets and incorporating deep learning alongside unsupervised techniques.

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.
Paper Structure (9 sections, 5 figures, 3 tables)

This paper contains 9 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Materials and Methods
  3. Data Collection and Exploration
  4. Lesion Segmentation in X-Ray Images
  5. Bone Tumor Segmentation on MRI
  6. Experimental Results and Discussion
  7. X-Ray Image Lesion Segmentation
  8. Bone Tumor Segmentation on MRI and Prediction of Surgical Safety Margins
  9. Conclusions

Figures (5)

  • Figure 1: Pipelines for X-Ray and MRI image segmentation.
  • Figure 2: Demographics of the collected dataset.
  • Figure 3: Correlation between the value of the segmentation of the bone tumor lesion and the involvement of soft tissues with tumor lesion in Sagittal MRI.
  • Figure 4: ImageJ software used for annotating lengths.
  • Figure 5: GUI based on PyQT, which integrated the proposed algorithms to identify the safety margins.