用无监督学习自动预测骨肉瘤手术安全边界，提升精准度。
Predicting Surgical Safety Margins in Osteosarcoma Knee Resections: An Unsupervised Approach
- 结合MRI/X光数据与聚类算法，无监督划分肿瘤边界。
- 实现患者特异性安全边界的自动化估计，无需标注数据。
- 适合临床辅助决策，尤其在资源有限地区有潜力。
根据泛美卫生组织数据，拉丁美洲2022年癌症病例达420万，预计到2045年将升至670万。骨肉瘤是影响青少年的常见且致命的骨癌，因其独特的纹理和强度分布，难以检测。手术切除需精确的安全边界以确保完全切除同时保留健康组织。本研究提出一种方法，用于预测膝关节周围骨肉瘤手术的安全边际置信区间。该方法利用开源数据库中的MRI和X射线数据，结合数字图像处理技术与无监督学习算法（如k均值聚类），定义肿瘤边界。实验结果表明，该方法具备实现自动化、个性化安全边界判定的潜力。
原文摘要 · Abstract (English)
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.
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