Advanced intra-tumoural structural characterisation of hepatocellular carcinoma utilising FDG-PET/CT: a comparative study of radiomics and metabolic features in 3D and 2D

Houseni, Mohamed; Mahmoud, Menna Allah; Saad, Salwa; Elhussiny, F.; Shihab, Mohammed

doi:10.5114/pjr.2021.103239

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…of Average tumors maximum diameters = 7.85 cm. Part of this patient data set was included in our previous study [13].…”

Section: Methodsmentioning

confidence: 99%

“…F-value as calculated from one way ANOVA test for radiomics value, the blue cells are significantly different values. In our previous work we studied the correlation of radiomics features in liver cancer FDG/PET with tumor characteristics in both three dimensional and two dimensional mode (3D and 2D) [13] so in this work we extended our work to study correlation between radiomics features and tumor characteristics at different discretization values.…”

Section: Radiomics Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Effect of Standardized Uptake Value Discretization on Radiomics Features of Liver Tumors Using 18fdg-Pet/Ct Scan

Mahmoud¹,

Shihab²,

Saad³

et al. 2021

Rejr

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In this contribution, we study the dependence of radiomics features extracted from 18F-fluorodeoxyglucose -positron emission tomography/computed tomography (18F-FDG PET/CT) on different SUV discretization intensity values in patients with liver cancer type hepatocellular carcinoma (HCC). Materials and methods. 35 radiomic features were extracted from PET images using semiautomatic method through 4 discretization intensity values with number of grey levels = (32, 64, 128, 256). Determination of bin within dour different. One way analysis of variance (ANOVA) was used to obtain whether there is significant difference between radiomics features at the four discretization values.Results. By comparing the average and variance of radiomics through the four discretization values, only the first order skewness and kurtosis are stable through the different discretization values, where all the other features demonstrated significant difference between features in the 4 values. Spearman correlation coefficient between radiomics features and tumors parameters calculated.Conclusion. The discretization level of 256 have been found to be the optimum discretization value to extract the radiomics features.

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“…of Average tumors maximum diameters = 7.85 cm. Part of this patient data set was included in our previous study [13].…”

Section: Methodsmentioning

confidence: 99%

Section: Radiomics Featuresmentioning

confidence: 99%

Effect of Standardized Uptake Value Discretization on Radiomics Features of Liver Tumors Using 18fdg-Pet/Ct Scan

Mahmoud¹,

Shihab²,

Saad³

et al. 2021

Rejr

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“…They found that the latter features performed better than the former features (AUCs: 0.824 vs. 0.740) and that combined features did not show better performance than either type of features alone (AUC = 0.813). Xu et al [ 62 ] found that 3D radiomics features showed better predictive performance than 2D radiomics features in a study of multi-organ cancer, as unlike the latter, the former was significantly correlated with total lesion glycolysis, tumor volume, and staging [ 63 ]. However, Shen et al [ 60 ] demonstrated that compared with 3D radiomics features, 2D radiomics features of CT images of non-small cell lung cancer (NSCLC) performed slightly better, and Zhu et al [ 64 ] reached the same conclusion.…”

Section: Ai-driven Radiomics Studiesmentioning

confidence: 99%

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

Zhang

Cheng

et al. 2023

Military Med Res

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Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’ anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.

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“…The areas of greatest interest were related to the characterization of the lesions [167][168][169][170][171][172][173] and the evaluation of the response to therapy [174][175][176][177][178][179][180][181][182].…”

Section: Radiomics Analysismentioning

confidence: 99%

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

Granata

Grassi

Fusco³

et al. 2021

Infect Agents Cancer

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This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.

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Advanced intra-tumoural structural characterisation of hepatocellular carcinoma utilising FDG-PET/CT: a comparative study of radiomics and metabolic features in 3D and 2D

Cited by 8 publications

References 25 publications

Effect of Standardized Uptake Value Discretization on Radiomics Features of Liver Tumors Using 18fdg-Pet/Ct Scan

Effect of Standardized Uptake Value Discretization on Radiomics Features of Liver Tumors Using 18fdg-Pet/Ct Scan

Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

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