In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images, which include steps in detecting and segmenting suspicious regions or tumors, followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors. However, due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors, segmenting subtle regions is often difficult and unreliable. Additionally, ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches. In our recent studies, we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis. We trained and tested several models using images obtained from full-field digital mammography, magnetic resonance imaging, and computed tomography of breast, lung, and ovarian cancers. Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice. Furthermore, the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis. Therefore, the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.

中文翻译：

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开发全球图像特征分析模型来预测癌症风险和预后

为了开发精准或个性化医疗，识别新的定量成像标志物和建立机器学习模型来预测癌症风险和预后最近吸引了广泛的研究兴趣。这些研究方法中的大多数使用与传统医学图像计算机辅助检测方案相似的概念，其中包括检测和分割可疑区域或肿瘤的步骤，然后基于多个图像特征的融合来训练机器学习模型。分割区域或肿瘤。然而，由于可疑区域或肿瘤的异质性和边界模糊性，分割细微区域往往是困难和不可靠的。此外，忽略全局和/或背景实质组织特征也可能是传统方法的局限性。在我们最近的研究中，我们研究了开发新的计算机辅助方案的可行性，这些方案是通过机器学习模型实现的，这些模型通过全局图像特征进行训练，以预测癌症风险和预后。我们使用从乳腺癌、肺癌和卵巢癌的全场数字乳房 X 光检查、磁共振成像和计算机断层扫描获得的图像训练和测试了几个模型。研究结果表明，与当前临床实践中使用的其他方法相比，许多这些新模型产生了更高的性能。此外，计算的全局图像特征还包含来自预测癌症预后的分割区域或肿瘤计算的特征的补充信息。