The latest advances in computer-assisted precision medicine are making it feasible to move from population-wide models that are useful to discover aggregate patterns that hold for group-based analysis to patient-specific models that can drive patient-specific decisions with regard to treatment choices, and predictions of outcomes of treatment. Body Composition is recognized as an important driver and risk factor for a wide variety of diseases, as well as a predictor of individual patient-specific clinical outcomes to treatment choices or surgical interventions. 3D CT images are routinely acquired in the oncological worklows and deliver accurate rendering of internal anatomy and therefore can be used opportunistically to assess the amount of skeletal muscle and adipose tissue compartments. Powerful tools of artificial intelligence such as deep learning are making it feasible now to segment the entire 3D image and generate accurate measurements of all internal anatomy. These will enable the overcoming of the severe bottleneck that existed previously, namely, the need for manual segmentation, which was prohibitive to scale to the hundreds of 2D axial slices that made up a 3D volumetric image. Automated tools such as presented here will now enable harvesting whole-body measurements from 3D CT or MRI images, leading to a new era of discovery of the drivers of various diseases based on individual tissue, organ volume, shape, and functional status. These measurements were hitherto unavailable thereby limiting the field to a very small and limited subset. These discoveries and the potential to perform individual image segmentation with high speed and accuracy are likely to lead to the incorporation of these 3D measures into individual specific treatment planning models related to nutrition, aging, chemotoxicity, surgery and survival after the onset of a major disease such as cancer.
翻译:计算机辅助精密医学的最新进展使得从整个人口的模型中发现有助于发现总体模式的实用性,这些模型有助于发现用于进行群体分析的集成模式,形成能够推动患者就治疗选择做出特定决定的患者特定模型,并预测治疗结果; 身体构成被公认为是各种疾病的一个重要驱动因素和风险因素,以及个别病人特定临床结果的预测器,可转化为治疗选择或外科手术。 3DCT图像定期在肿瘤工作站中采集,并准确提供内部解剖治疗,因此可以被机会性地用于评估骨骼肌肉和脂肪组织包厢的准确度。 人工智能的强大工具,如深度学习,现在可以分割整个3D图像并生成所有内部解剖学的准确度。 这将使人们能够克服以前存在的严重的严重瓶颈,即手动分解的需要,这些无法推广到形成体积3D图象的数百种血清分解分解分解分解分解,因此可以用来评估骨质肌肉肌肉肌肉肌肉肌肉肌肉肌肉肌肉的精确度和脂肪包箱。 此处展示的自动化工具,现在可以将功能化为功能分解的分解的分解法,这些分解的分解方法,这些分解的分解为整个的分解的分解状态,这些分解结果的分解为3D,这些分解的分解的分解的分解的分解方式的分解的分解结果的分解结果的分解结果的分解方式的分解为3的分解状态,这些分解方式的分解为3D的分解方式的分解的分解的分解的分解,这些分解的分解的分解的分解方式的分解方式的分解方式的分解方式的分解方式的分解方式将使得的分解方式的分解的分解的分解状态将使得的分解为3。