Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches
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 co...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2021/4520450 |
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| author | Alaa Khadidos Adil Khadidos Olfat M. Mirza Tawfiq Hasanin Wegayehu Enbeyle Abdulsattar Abdullah Hamad |
| author_facet | Alaa Khadidos Adil Khadidos Olfat M. Mirza Tawfiq Hasanin Wegayehu Enbeyle Abdulsattar Abdullah Hamad |
| author_sort | Alaa Khadidos |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5554a446b5004ea2b89e38075661ec98 |
| institution | Kabale University |
| issn | 1754-2103 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-5554a446b5004ea2b89e38075661ec982025-02-03T06:05:44ZengWileyApplied Bionics and Biomechanics1754-21032021-01-01202110.1155/2021/4520450Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis ApproachesAlaa Khadidos0Adil Khadidos1Olfat M. Mirza2Tawfiq Hasanin3Wegayehu Enbeyle4Abdulsattar Abdullah Hamad5Department of Information SystemsDepartment of Information TechnologyDepartment of Computer ScienceDepartment of Information SystemsDepartment of StatisticsTikrit UniversityThe 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.http://dx.doi.org/10.1155/2021/4520450 |
| spellingShingle | Alaa Khadidos Adil Khadidos Olfat M. Mirza Tawfiq Hasanin Wegayehu Enbeyle Abdulsattar Abdullah Hamad Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches Applied Bionics and Biomechanics |
| title | Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches |
| title_full | Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches |
| title_fullStr | Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches |
| title_full_unstemmed | Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches |
| title_short | Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches |
| title_sort | evaluation of the risk of recurrence in patients with local advanced rectal tumours by different radiomic analysis approaches |
| url | http://dx.doi.org/10.1155/2021/4520450 |
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