Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches. (28th November 2021)

• Record Type: Journal Article
• Authors: Khadidos, Alaa; Khadidos, Adil; Mirza, Olfat M.; Hasanin, Tawfiq; Enbeyle, Wegayehu; Hamad, Abdulsattar Abdullah
• Other Names: Algalil Fahd Abd Academic Editor.
• Is Part Of: Applied bionics and biomechanics. Volume 2021(2021)
• Publication Date: 2021-11-28
• Subjects: Bionics -- Periodicals; Biomechanics -- Periodicals; Biomedical engineering -- Periodicals; 003.505
• DOI: 10.1155/2021/4520450
• Languages: English
• ISSNs: 1176-2322

Abstract

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.