Osteoporosis is a significant health and economic issue, as it predisposes patients to a higher risk of bone fracture. Measuring bone mineral density has been shown to be an accurate way to assess the risk for osteoporosis. The most common way for bone density testing is a dual-energy X-ray absorptiometry (DEXA) scan, which may be recommended for patients with increased risk of osteoporosis. Radiograph imaging is widely available in clinical settings and acquired for many reasons, such as trauma or pain. The goal of this project is to extract radiomics information from pelvic X-rays (both the hip and femoral neck regions) to assess the risk of osteoporosis (triaging patients into “normal” vs. “at-risk”, or “low risk” vs. “high risk” categories). The motivation here is not to replace the DEXA scan but to proactively identify patients at risk for osteoporosis and appropriately refer them to management options. We apply machine learning-based radiomics techniques on a study cohort of 565 patients. Our preliminary results show that a correlation between the radiomics features extracted from pelvic X-rays and the level of osteoporosis risk derived from the DEXA test results.

中文翻译：

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使用 X 射线成像进行骨质疏松症风险预测的机器学习和放射组学

骨质疏松症是一个重要的健康和经济问题，因为它使患者更容易发生骨折的风险。测量骨矿物质密度已被证明是评估骨质疏松症风险的准确方法。最常见的骨密度检测方法是双能 X 射线骨密度仪 (DEXA) 扫描，推荐用于骨质疏松症风险增加的患者。射线照相成像在临床环境中广泛使用，并且由于许多原因而获得，例如创伤或疼痛。该项目的目标是从骨盆 X 射线（臀部和股骨颈区域）中提取放射组学信息，以评估骨质疏松症的风险（将患者分为“正常”与“有风险”或“低风险”与“高风险”类别）。这里的动机不是取代 DEXA 扫描，而是主动识别有骨质疏松症风险的患者，并适当地将他们推荐给管理方案。我们将基于机器学习的放射组学技术应用于 565 名患者的研究队列。我们的初步结果表明，从骨盆 X 射线中提取的放射组学特征与 DEXA 测试结果得出的骨质疏松症风险水平之间存在相关性。