Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI

Ramli, Zarina; Karim, Muhammad Khalis Abdul; Effendy, Nuraidayani; Rahman, Mohd Amiruddin Abd; Kechik, Mohd Mustafa Awang; Ibahim, Mohammad Johari; Haniff, Nurin Syazwina Mohd

doi:10.3390/diagnostics12123125

Cited by 11 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Radiomics features are not stable between the region of interest sizes and volumes on computed tomography and MRI, which was reported in a study using a homogenous phantom without any texture differences [19,20]. Ensuring the stability of radiomics features is crucial for the accuracy of image-based prognostication and external generalization of prognostic models [21][22][23][24][25][26][27][28][29][30][31]. In this study, ICCs were used to evaluate the repeatability and reproducibility of the radiomics features.…”

Section: Discussionmentioning

confidence: 99%

Interobserver Agreement in Automatic Segmentation Annotation of Prostate Magnetic Resonance Imaging

Jin,

Ma,

et al. 2023

Bioengineering

View full text Add to dashboard Cite

We aimed to compare the performance and interobserver agreement of radiologists manually segmenting images or those assisted by automatic segmentation. We further aimed to reduce interobserver variability and improve the consistency of radiomics features. This retrospective study included 327 patients diagnosed with prostate cancer from September 2016 to June 2018; images from 228 patients were used for automatic segmentation construction, and images from the remaining 99 were used for testing. First, four radiologists with varying experience levels retrospectively segmented 99 axial prostate images manually using T2-weighted fat-suppressed magnetic resonance imaging. Automatic segmentation was performed after 2 weeks. The Pyradiomics software package v3.1.0 was used to extract the texture features. The Dice coefficient and intraclass correlation coefficient (ICC) were used to evaluate segmentation performance and the interobserver consistency of prostate radiomics. The Wilcoxon rank sum test was used to compare the paired samples, with the significance level set at p < 0.05. The Dice coefficient was used to accurately measure the spatial overlap of manually delineated images. In all the 99 prostate segmentation result columns, the manual and automatic segmentation results of the senior group were significantly better than those of the junior group (p < 0.05). Automatic segmentation was more consistent than manual segmentation (p < 0.05), and the average ICC reached >0.85. The automatic segmentation annotation performance of junior radiologists was similar to that of senior radiologists performing manual segmentation. The ICC of radiomics features increased to excellent consistency (0.925 [0.888~0.950]). Automatic segmentation annotation provided better results than manual segmentation by radiologists. Our findings indicate that automatic segmentation annotation helps reduce variability in the perception and interpretation between radiologists with different experience levels and ensures the stability of radiomics features.

show abstract

Section: Discussionmentioning

confidence: 99%

Interobserver Agreement in Automatic Segmentation Annotation of Prostate Magnetic Resonance Imaging

Jin,

Ma,

et al. 2023

Bioengineering

View full text Add to dashboard Cite

show abstract

“…The quantization of the tumor lesion was deemed accurate in comparison to manual segmentation based on the uniform color present within the area of interest. Radzi et al (2021) previously showed that in order to accurately quantify the ROI, the optimal segmentation method requires images with good contrast enhancement [ 17 , 28 ]. In comparison to manual segmentation, it has been observed that a significant proportion of the tumor first-order features demonstrate a higher degree of reproducibility.…”

Section: Discussionmentioning

confidence: 99%

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

Ramli,

Farizan,

Tamchek

et al. 2024

Cureus

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The flood-filling algorithm determines connection in an area in a multi-dimensional array with the help of the similarity of intensity voxels to the selected node determined by users. In this method, nodes are added around the tumor region using a mouse cursor and when the flood-fill tool is activated, the ROI is segmented based on similar voxel intensity (Haniff et al 2021, Ramli et al 2022.…”

Section: Flood Filling (Ff)mentioning

confidence: 99%

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

Philip,

Watts,

Moeini

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

Objective: Accurate and reproducible tumor delineation on Positron Emission Tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in Head and Neck Squamous Cell Carcinoma (HNSCC) PET images.Approach: We employed manual and six semi-automatic segmentation methods (Just Enough Interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual method versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics ¬- the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA) - to evaluate the agreement between the manual and semi-automatic methods.Main Results: Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91.Significance: This study demonstrated that the JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

show abstract

Stability and Reproducibility of Radiomic Features Based on Various Segmentation Techniques on Cervical Cancer DWI-MRI

Cited by 11 publications

References 29 publications

Interobserver Agreement in Automatic Segmentation Annotation of Prostate Magnetic Resonance Imaging

Interobserver Agreement in Automatic Segmentation Annotation of Prostate Magnetic Resonance Imaging

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

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

Contact Info

Product

Resources

About