The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI

Abstract

Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters.

Wait List Strategies for CT and MRI Scans

“…Among the Canadian publications, some were from pan-Canadian groups or were relevant to Canada in general 3,8,[10][11][12][13][14][15][16][17][18] ; others were action plans, initiatives, or studies from specific provinces, including British Columbia, [19][20][21] Alberta, 6,22 Saskatchewan, 23,24 Manitoba, 25,26 Ontario, [27][28][29][30][31][32][33][34][35][36][37][38] Quebec, 39 Prince Edward Island, 16 Newfoundland, 40 Nova Scotia, 41,42 New Brunswick, 43 and Yukon. 44 Publications were also identified from Australia, 45,46 China, 47,48 the European Union, 49 France, 50,51 India, 52 Ireland, 53 Israel, 54,…”

Potential roles of transformers in brain tumor diagnosis and treatment

“…This problem has been corrected using image transform, such as wavelet transform, [95] but the scope of the extracted feature is still limited. Deep AI learning is a promising data‐driven approach that can overcome this challenge [96] . Increased success using this approach has been reported for medical assessments, for instance, the characterization of brain metastases and the prediction of their outcomes [40] .…”

“…Deep AI learning is a promising data-driven approach that can overcome this challenge. [96] Increased success using this approach has been reported for medical assessments, for instance, the characterization of brain metastases and the prediction of their outcomes. [40] Deep AI learning has recently achieved immense success in identifying primary and metastasized tumors and categorizing them according to their origin site based on whole-slide histological data.…”

A transformer-based deep-learning approach for classifying brain metastases into primary organ sites using clinical whole-brain MRI images

“…Deep learning, as the mainstream of artificial intelligence, is a data-driven approach, holding a great promise to address this challenge. 15 Great successes have been made by deep learning for medical tasks such as brain tumor segmentation, 16 medical imaging modality transfer, 17 and brain metastases characterization and outcome prediction. 18 Recently, deep learning has been widely used in radiomics for many tasks, such as cancer prognostication and cancer radiotherapy failure rate prediction.…”