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Radiomics analysis and supervised machine learning model for classification of cervical cancer images using diffusion weighted imaging-MRI


Citation

Ramli, Zarina (2024) Radiomics analysis and supervised machine learning model for classification of cervical cancer images using diffusion weighted imaging-MRI. Doctoral thesis, Universiti Putra Malaysia.

Abstract

Cervical cancer is the third most prevalent cause of mortality among women in Malaysia. Early detection, especially in high-risk populations, can reduce mortality rates and enable timely treatment. This study investigates the efficacy of staging classification using diffusion-weighted imaging magnetic resonance imaging (DWIMRI) through radiomic analysis and machine learning. Data were retrospectively analyzed from the picture archiving and communication system (PACS) at Institut Kanser Negara (IKN) in Putrajaya, Malaysia. The first objective involved 30 patients to evaluate the repeatability and reproducibility of manual and semi-automated segmentation methods on DWI-MRI images. Intra-class correlation coefficient (ICC) analyses were performed on 662 radiomic features encompassing texture, shape, and first-order statistics. The semi-automated active contour model (ACM) algorithm (average ICC = 0.952 ± 0.009, p > 0.05) was found to be more robust and reproducible than fully manual segmentation (average ICC = 0.897 ± 0.011, p > 0.05). The second objective assessed the stability of radiomic features using contrast-limited adaptive histogram equalization (CLAHE) for image enhancement of 80 DWI-MRI images, enhanced images exhibited improved stability in radiomic features (ICC = 0.990 ± 0.005, p < 0.05), outperforming both semi-automated (ICC = 0.864 ± 0.033, p < 0.05) and manual methods (ICC = 0.554 ± 0.185, p > 0.05). The third objective focused on classifying cervical cancer stages using DWI-MRI radiomic features. A support vector machine (SVM) classifier yielded excellent performance metrics, accuracy of 0.77, and precision of 0.63, with an area under the curve (AUC) of 96%. Additionally, the SVM algorithm was evaluated based on its performance across different DWI bvalues, aiming to optimize scanning time. In conclusion, SVM-based models can develop accurate and reproducible software for classifying cervical cancer stages, significantly enhancing the role of radiology by enabling more quantitative MRI interpretations. This study underscores the potential of radiomic analysis to improve the accuracy of medical reports, reduce dependency on contrast agents, and enhance early detection of cervical cancer.


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Official URL or Download Paper: http://ethesis.upm.edu.my/id/eprint/18442

Additional Metadata

Item Type: Thesis (Doctoral)
Subject: Cervix uteri - Cancer - Diagnosis
Subject: Magnetic resonance imaging
Subject: Radiomics
Call Number: FS 2024 18
Chairman Supervisor: Associate Professor Muhammad Khalis bin Abdul Karim, PhD
Divisions: Faculty of Science
Keywords: Cervical cancer, DWI-MRI, Radiomic analysis, Supervised machine learning.
Depositing User: Ms. Rohana Alias
Date Deposited: 02 Sep 2025 07:17
Last Modified: 02 Sep 2025 07:17
URI: http://psasir.upm.edu.my/id/eprint/119334
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