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.

Highlights

  • With advances in computer science and medical imaging, researchers have begun to explore new avenues for making the most of the information buried in medical images

  • This study evaluated and compared six conventional learning models and one deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recurrence

  • 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 more of the tumour itself

Read more

Summary

Introduction

With advances in computer science and medical imaging, researchers have begun to explore new avenues for making the most of the information buried in medical images. Radiomics is the extraction of a massive amount of data from conventional medical images, such as standard X-rays, ultrasound, CT scan, MRI, or even PET-scan, in correlation with the diagnosis, the stage of the disease, the therapeutic response, the genomic data, or relatively simple the prognosis [1]. It essentially emerged from cancerology, where providing specific information for personalized therapy is essential. The present study evaluated and compared the predictive potential of conventional and deep learning algorithms applied to MRI scans of patients with locally advanced rectal tumours correlated with recurrence

Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call