Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)

Haniff, Nurin Syazwina Mohd; Karim, Muhammad Khalis Abdul; Osman, Nurul Huda; Saripan, M. Iqbal; Isa, Iza Nurzawani Che; Ibahim, Mohammad Johari

doi:10.3390/diagnostics11091573

Cited by 20 publications

(16 citation statements)

References 36 publications

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“…Two senior radiologists (HSS and JLZ) were responsible for checking all tumor segmentations, and any deviations were addressed with additional corrections. As reported in a recent publication (22), the semi-automatic segmentation from the 3D-Slicer is a better alternative to manual segmentation, as it can produce more robust and reproducible radiomic features. In addition, we randomly selected 40 cases at a ratio of 1:10 of all sample (396 cases) for estimating intraclass correlation coefficients (ICCs) analysis, which could ensure the precision and assurance of results (23).…”

Section: Radiomics Feature Extractionmentioning

confidence: 98%

The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma

et al. 2022

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ObjectivesSpread through air spaces (STAS), a new invasive pattern in lung adenocarcinoma (LUAD), is a risk factor for poor outcome in early-stage LUAD. This study aimed to develop and validate a CT-based radiomics model for predicting STAS in stage IA LUAD.MethodsA total of 395 patients (169 STAS positive and 226 STAS negative cases, including 316 and 79 patients in the training and test sets, respectively) with stage IA LUAD before surgery were retrospectively included. On all CT images, tumor size, types of nodules (solid, mix ground-glass opacities [mGGO] and pure GGO [pGGO]), and GGO percentage were recorded. Region of interest (ROI) segmentation was performed semi-automatically, and 1,037 radiomics features were extracted from every segmented lesion. Intraclass correlation coefficients (ICCs), Pearson’s correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were used to filter unstable (ICC < 0.75) and redundant features (r > 0.8). A temporary model was established by multivariable logistic regression (LR) analysis based on selected radiomics features. Then, seven radiomics features contributing the most were selected for establishing the radiomics model. We then built two predictive models (clinical-CT model and MixModel) based on clinical and CT features only, and the combination of clinical-CT and Rad-score, respectively. The performances of these three models were assessed.ResultsThe radiomics model achieved good performance with an area under of curve (AUC) of 0.812 in the training set, versus 0.850 in the test set. Furthermore, compared with the clinical-CT model, both radiomics model and MixModel showed higher AUC and better net benefit to patients in the training and test cohorts.ConclusionThe CT-based radiomics model showed satisfying diagnostic performance in early-stage LUAD for preoperatively predicting STAS, with superiority over the clinical-CT model.

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Section: Radiomics Feature Extractionmentioning

confidence: 98%

The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma

et al. 2022

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“…Radiomics, on the other hand, is the technique of obtaining multiple quantitative features of digital medical images by converting it into high-dimensional data [ 17 , 18 , 19 , 20 ]. Radiomics is a combination of the term ‘radio’ which indicates medical images, and the term ‘omics’ which refers to various fields including genomics and proteomics that contribute to our understanding of various medical conditions [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA)

et al. 2022

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Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features from Coronary Computed Tomography Angiography (CCTA) images and to evaluate the performance of automated machine learning (AutoML) model in classifying the atherosclerotic plaques. In total, 202 patients who underwent CCTA examination at Institut Jantung Negara (IJN) between September 2020 and May 2021 were selected as they met the inclusion criteria. Three primary coronary arteries were segmented on axial sectional images, yielding a total of 606 volume of interest (VOI). Subsequently, the first order, second order, and shape order of radiomic characteristics were extracted for each VOI. Model 1, Model 2, Model 3, and Model 4 were constructed using AutoML-based Tree-Pipeline Optimization Tools (TPOT). The heatmap confusion matrix, recall (sensitivity), precision (PPV), F1 score, accuracy, receiver operating characteristic (ROC), and area under the curve (AUC) were analysed. Notably, Model 1 with the first-order features showed superior performance in classifying the normal coronary arteries (F1 score: 0.88; Inverse F1 score: 0.94), as well as in classifying the calcified (F1 score: 0.78; Inverse F1 score: 0.91) and mixed plaques (F1 score: 0.76; Inverse F1 score: 0.86). Moreover, Model 2 consisting of second-order features was proved useful, specifically in classifying the non-calcified plaques (F1 score: 0.63; Inverse F1 score: 0.92) which are a key point for prediction of cardiac events. Nevertheless, Model 3 comprising the shape-based features did not contribute to the classification of atherosclerotic plaques. Overall, TPOT shown promising capabilities in terms of finding the best pipeline and tailoring the model using CCTA-based radiomic datasets.

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“…The advantages of semiautomated segmentation include allowing the viewer to detect those areas of interest which have about the same pixel value beside it. This has been proven beneficial to apply, especially in detecting disease in small areas [ 34 ]. This will be one of the reasons why semi-automated segmentation has lower repeatability as compared to manual segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…These features are extracted by using an advanced mathematical algorithm that describes phenotypes of lesions that might not be visible to the naked eye. Apart from the selection of segmentation algorithms, medical-image-segmentation accuracy will also be affected by other extrinsic factors such as image analysis, the observers’ educational background, image-segmentation-related experience, and the level of familiarity with the segmentation software [ 27 , 34 ]. To obtain accurate data regarding lesion characteristics, a robust image segmentation algorithm is required.…”

Section: Introductionmentioning

confidence: 99%

Reproducibility and Repeatability of Coronary Computed Tomography Angiography (CCTA) Image Segmentation in Detecting Atherosclerosis: A Radiomics Study

et al. 2022

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Atherosclerosis is known as the leading factor in heart disease with the highest mortality rate among the Malaysian population. Usually, the gold standard for diagnosing atherosclerosis is by using the coronary computed tomography angiography (CCTA) technique to look for plaque within the coronary artery. However, qualitative diagnosis for noncalcified atherosclerosis is vulnerable to false-positive diagnoses, as well as inconsistent reporting between observers. In this study, we assess the reproducibility and repeatability of segmenting atherosclerotic lesions manually and semiautomatically in CCTA images to identify the most appropriate CCTA image segmentation method for radiomics analysis to quantitatively extract the atherosclerotic lesion. Thirty (30) CCTA images were taken retrospectively from the radiology image database of Hospital Canselor Tuanku Muhriz (HCTM), Kuala Lumpur, Malaysia. We extract 11,700 radiomics features which include the first-order, second-order and shape features from 180 times of image segmentation. The interest vessels were segmentized manually and semiautomatically using LIFEx (Version 7.0.15, Institut Curie, Orsay, France) software by two independent radiology experts, focusing on three main coronary blood vessels. As a result, manual segmentation with a soft-tissuewindowing setting yielded higher repeatability as compared to semiautomatic segmentation with a significant intraclass correlation coefficient (intra-CC) 0.961 for thefirst-order and shape features; intra-CC of 0.924 for thesecond-order features with p < 0.001. Meanwhile, the semiautomatic segmentation has higher reproducibility as compared to manual segmentation with significant interclass correlation coefficient (inter-CC) of 0.920 (first-order features) and a good interclass correlation coefficient of 0.839 for the second-order features with p < 0.001. The first-order, shape order and second-order features for both manual and semiautomatic segmentation have an excellent percentage of reproducibility and repeatability (intra-CC > 0.9). In conclusion, semi-automated segmentation is recommended for inter-observer study while manual segmentation with soft tissue-windowing can be used for single observer study.

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Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC)

Cited by 20 publications

References 36 publications

The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma

The Value of CT-Based Radiomics for Predicting Spread Through Air Spaces in Stage IA Lung Adenocarcinoma

Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA)

Reproducibility and Repeatability of Coronary Computed Tomography Angiography (CCTA) Image Segmentation in Detecting Atherosclerosis: A Radiomics Study

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