Impact of Image Enhancement on the Radiomics Stability of Diffusion-Weighted MRI Images of Cervical Cancer

Impact of Image Enhancement on the Radiomics Stability of Diffusion-Weighted MRI Images of Cervical Cancer

The diffusion-weighted imaging (DWI) technique is known for its capability to differentiate the diffusion of water molecules between cancerous and non-cancerous cervix tissues, which enhances the accuracy of detection. Despite the potential of DWI-MRI, its accuracy is limited by technical factors in...

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Bibliographic Details
Published in:Curēus (Palo Alto, CA) Vol. 16; no. 1; p. e52132
Main Authors: Ramli, Zarina, Farizan, Aishah, Tamchek, Nizam, Haron, Zaharudin, Abdul Karim, Muhammad Khalis
Format: Journal Article
Language:English
Published: United States Cureus Inc 11-01-2024
Cureus
Subjects:
Automation
Cancer therapies
Cervical cancer
Disease prevention
Medical diagnosis
Medical Physics
Medical screening
Mortality
Patients
Radiation Oncology
Radiology
Radiomics
Reproducibility
Womens health
Malaysia
clahe
cervical cancer diagnosis
magnetic resonance imaging (mri)
standardized radiomics
diffusion-weighted imaging (dwi)
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Summary:The diffusion-weighted imaging (DWI) technique is known for its capability to differentiate the diffusion of water molecules between cancerous and non-cancerous cervix tissues, which enhances the accuracy of detection. Despite the potential of DWI-MRI, its accuracy is limited by technical factors influencing in vivo data acquisition, thus impacting the quantification of radiomics features. This study aimed to measure the radiomics stability of manual and semi-automated segmentation on contrast limited adaptive histogram equalization (CLAHE)-enhanced DWI-MRI cervical images. Eighty diffusion-weighted MRI images were obtained from patients diagnosed with cervical cancer, and an active contour model was used to analyze the data. Radiomics analysis was conducted to extract the first statistical order, shape, and textural features with intraclass correlation coefficient (ICC) measurement. The results of the CLAHE segmentation approach showed a marked improvement when compared to the manual and semi-automated segmentation methods, with an ICC value of 0.990 ± 0.005 (p<0.05), compared to 0.864 ± 0.033 (p<0.05) and 0.554 ± 0.185 (p>0.05), respectively. The CLAHE segmentation displayed a higher level of robustness than the manual groups in terms of the features present in both categories. Thus, CLAHE segmentation is owing to its potential to generate radiomics features that are more durable and consistent.
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ISSN:2168-8184
2168-8184
DOI:10.7759/cureus.52132