Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery

Cancer therapy, even when highly targeted, typically fails because of the remarkable capacity of malignant cells to evolve effective adaptations. These evolutionary dynamics are both a cause and a consequence of cancer system heterogeneity at many scales, ranging from genetic properties of individual cells to large-scale imaging features. Tumors of the same organ and cell type can have remarkably diverse appearances in different patients. Furthermore, even within a single tumor, marked variations in imaging features, such as necrosis or contrast enhancement, are common. Similar spatial variations recently have been reported in genetic profiles. Radiologic heterogeneity within tumors is usually governed by variations in blood flow, whereas genetic heterogeneity is typically ascribed to random mutations. However, evolution within tumors, as in all living systems, is subject to Darwinian principles; thus, it is governed by predictable and reproducible interactions between environmental selection forces and cell phenotype (not genotype). This link between regional variations in environmental properties and cellular adaptive strategies may permit clinical imaging to be used to assess and monitor intratumoral evolution in individual patients. This approach is enabled by new methods that extract, report, and analyze quantitative, reproducible, and mineable clinical imaging data. However, most current quantitative metrics lack spatialness, expressing quantitative radiologic features as a single value for a region of interest encompassing the whole tumor. In contrast, spatially explicit image analysis recognizes that tumors are heterogeneous but not well mixed and defines regionally distinct habitats, some of which appear to harbor tumor populations that are more aggressive and less treatable than others. By identifying regional variations in key environmental selection forces and evidence of cellular adaptation, clinical imaging can enable us to define intratumoral Darwinian dynamics before and during therapy. Advances in image analysis will place clinical imaging in an increasingly central role in the development of evolution-based patient-specific cancer therapy. © RSNA, 2013.

The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by the human brain system, was developed by connecting layers with artificial neurons. However, due to the low computing power and insufficient learnable data, ANN has suffered from overfitting and vanishing gradient problems for training deep networks. The advancement of computing power with graphics processing units and the availability of large data acquisition, deep neural network outperforms human or other ML capabilities in computer vision and speech recognition tasks. These potentials are recently applied to healthcare problems, including computer-aided detection/diagnosis, disease prediction, image segmentation, image generation, etc. In this review article, we will explain the history, development, and applications in medical imaging