2024
DOI: 10.5772/intechopen.107497
|View full text |Cite
|
Sign up to set email alerts
|

Review of Cervix Cancer Classification Using Radiomics on Diffusion-Weighted Imaging

Abstract: Magnetic Resonance Imaging (MRI) is one of the most used imaging modalities for the identification and quantification of various types of cancers. MRI image analysis is mostly conducted by experts relying on the visual interpretation of the images and some basic semiquantitative parameters. However, it is well known that additional clinical information is available in these images and can be harvested using the field of radiomics. This consists of the extraction of complex unexplored features from these images… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 79 publications
0
1
0
Order By: Relevance
“…When performing a search on Pubmed database with the key words (deep learning) AND (cervix cancer) AND (Diffusion weighted imaging), only three publications were found that investigated DL for tumor segmentation [31,32] or the prediction of normal-sized lymph node metastasis in CC [33]. On the other hand, the use of radiomics analysis was more popular due to possible interpretation of the features in comparison to DL ones [34]. Only three studies were found to rely on DWI/ADC as input of the machine learning model [8][9][10].…”
Section: Discussionmentioning
confidence: 99%
“…When performing a search on Pubmed database with the key words (deep learning) AND (cervix cancer) AND (Diffusion weighted imaging), only three publications were found that investigated DL for tumor segmentation [31,32] or the prediction of normal-sized lymph node metastasis in CC [33]. On the other hand, the use of radiomics analysis was more popular due to possible interpretation of the features in comparison to DL ones [34]. Only three studies were found to rely on DWI/ADC as input of the machine learning model [8][9][10].…”
Section: Discussionmentioning
confidence: 99%