Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches

Abstract

The word radiomics, like all domains of type omics, assumes the existence of a large amount of data. Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example). More recently, deep learning, a subdomain of machine learning, has emerged. Its applications are increasing, and the results obtained so far have demonstrated their remarkable effectiveness. Several previous studies have explored the potential applications of radiomics in colorectal cancer. These potential applications can be grouped into several categories like evaluation of the reproducibility of texture data, prediction of response to treatment, prediction of the occurrence of metastases, and prediction of survival. Few studies, however, have explored the potential of radiomics in predicting recurrence-free survival. In this study, we evaluated and compared six conventional learning models and a deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recidivism; in traditional learning, we compared 2D image analysis models vs. 3D image analysis models, models based on a textural analysis of the tumour versus models taking into account the peritumoural environment in addition to the tumour itself. In deep learning, we built a 16-layer convolutional neural network model, driven by a 2D MRI image database comprising both the native images and the bounding box corresponding to each image.

Applying Dynamic Systems to Social Media by Using Controlling Stability

“…It has been organized in the previous time, for example, a 3D auto system, which is not differ-isomorphic with the Lorenz attractor. In the arrangement of values for a parameter k , [ 10 ] has proposed another 3D attractor that shows chaotic behavior in distinct respects and not diffeomorphic with Lorenz [ 11 – 18 ]. The first chaotic nonlinear system has been suggested by Lorenz [ 19 – 22 ] in which is a generalization of the Lorenz system.…”

Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases

“…The various artificial intelligence tools were analyzed for the detection of phytosanitary diseases in agriculture, each of which has been of great importance; this process, which has been supported by artificial intelligence throughout history, has been able to represent precise results by using them, specifically in the area of agriculture, different disease diagnoses have been developed [ 21 , 22 ]. In this study carried out, the following artificial intelligence techniques, neural networks, expert systems, and multivalent systems, were bibliographically verified, considering the principle of each technique, based on fuzzy cognitive maps for decision-making in algorithms.…”

[Retracted] The Practices of Solid Waste Utility and Thriving Conditions of Logistics (a Case of Tepi Town): A Study to Treat the Healthy Environment

“…The polluted water chemicals will damage plants and fish in lakes. Inadequately managed landfills may cause air and other environmental pollution across the world [ 16 ]. The health, well-being of residents in cities and towns of urban areas, and the environmental protections were kept by adequate solid waste facilities [ 17 ].…”