Zheng Bin, Qiu Yuchen, Aghaei Faranak, Mirniaharikandehei Seyedehnafiseh, Heidari Morteza, Danala Gopichandh
School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Suite 1001, Norman, OK 73019 USA.
Vis Comput Ind Biomed Art. 2019;2(1):17. doi: 10.1186/s42492-019-0026-5. Epub 2019 Nov 19.
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.
为了开发精准医学或个性化医学，识别新的定量成像标志物并构建机器学习模型来预测癌症风险和预后，近来已引起广泛的研究兴趣。这些研究方法大多采用与医学图像传统计算机辅助检测方案类似的概念，其中包括检测和分割可疑区域或肿瘤的步骤，随后基于从分割区域或肿瘤计算出的多个图像特征融合来训练机器学习模型。然而，由于可疑区域或肿瘤的异质性和边界模糊性，分割细微区域往往困难且不可靠。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一个局限。在我们最近的研究中，我们探讨了开发新的计算机辅助方案的可行性，该方案采用通过全局图像特征训练的机器学习模型来预测癌症风险和预后。我们使用从全视野数字乳腺摄影、磁共振成像以及乳腺癌、肺癌和卵巢癌的计算机断层扫描获得的图像训练和测试了多个模型。研究结果表明，这些新模型中的许多在性能上优于当前临床实践中使用的其他方法。此外，计算出的全局图像特征在预测癌症预后方面也包含来自分割区域或肿瘤计算出的特征的补充信息。因此，全局图像特征可单独用于开发新的基于病例的预测模型，或可添加到当前基于肿瘤的模型中以提高其鉴别能力。
