Value of texture analysis based on enhanced MRI for predicting an early therapeutic response to transcatheter arterial chemoembolisation combined with high-intensity focused ultrasound treatment in hepatocellular carcinoma

Yu, Jinhong; Zhang, H.P.; Tang, Zhuoyue; Zhou, Jun; He, Xiaojing; Liu, Y.Y.; Liu, X.J.; Guo, Dajing

doi:10.1016/j.crad.2018.04.013

Cited by 34 publications

(31 citation statements)

References 33 publications

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“…A radiomic signature then could allow to assess if prostate cancer is successfully ablated . A radiomic model was predictive of complete response after transcatheter arterial chemoembolisation combined with high‐intensity focused ultrasound treatment in hepatocellular carcinoma …”

Section: Overview Of Research and Clinical Applications Of Cancer Radmentioning

confidence: 99%

Machine and deep learning methods for radiomics

et al. 2020

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Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three‐dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open‐source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics‐based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.

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Section: Overview Of Research and Clinical Applications Of Cancer Radmentioning

confidence: 99%

Machine and deep learning methods for radiomics

et al. 2020

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“…With high-throughput computing, texture analysis enables extraction and evaluation of innumerable quantitative features which are unavailable to human naked-eyes (15). It has been considered as a noninvasive method of assessing intra-tumor heterogeneity and predicting tumor biologic behaviors (16,17). By comparing differences in texture parameters or constructing comprehensive classifier models, texture analysis could assist in HCC tumor diagnosis (18), biologic aggressiveness evaluation (19) and therapeutic response assessment (20).…”

Section: Introductionmentioning

confidence: 99%

Texture analysis on gadoxetic acid enhanced-MRI for predicting Ki-67 status in hepatocellular carcinoma: A prospective study

Zheng

Jiang

Chen

et al. 2019

Chinese Journal of Cancer Research

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ObjectiveTo investigate the value of whole-lesion texture analysis on preoperative gadoxetic acid enhanced magnetic resonance imaging (MRI) for predicting tumor Ki-67 status after curative resection in patients with hepatocellular carcinoma (HCC).MethodsThis study consisted of 89 consecutive patients with surgically confirmed HCC. Texture features were extracted from multiparametric MRI based on whole-lesion regions of interest. The Ki-67 status was immunohistochemical determined and classified into low Ki-67 (labeling index ≤15%) and high Ki-67 (labeling index >15%) groups. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were applied for generating the texture signature, clinical nomogram and combined nomogram. The discrimination power, calibration and clinical usefulness of the three models were evaluated accordingly. Recurrence-free survival (RFS) rates after curative hepatectomy were also compared between groups.ResultsA total of 13 texture features were selected to construct a texture signature for predicting Ki-67 status in HCC patients (C-index: 0.878, 95% confidence interval: 0.791−0.937). After incorporating texture signature to the clinical nomogram which included significant clinical variates (AFP, BCLC-stage, capsule integrity, tumor margin, enhancing capsule), the combined nomogram showed higher discrimination ability (C-index: 0.936vs. 0.795, P<0.001), good calibration (P>0.05 in Hosmer-Lemeshow test) and higher clinical usefulness by decision curve analysis. RFS rate was significantly lower in the high Ki-67 group compared with the low Ki-67 group after curative surgery (63.27%vs. 85.00%, P<0.05). ConclusionsTexture analysis on gadoxetic acid enhanced MRI can serve as a noninvasive approach to preoperatively predict Ki-67 status of HCC after curative resection. The combination of texture signature and clinical factors demonstrated the potential to further improve the prediction performance.

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“…15 Recently, magnetic resonance imaging (MRI) has been successfully applied in predicting the pathological grading of tumor, assessing the malignancy and treatment response due to its advantages of multiparametric, multiorientation imaging and the high contrast of soft tissues. [19][20][21][22] It was reported that the radiomics signature based on contrast-enhanced MRI could act as an imaging biomarker to predict the microvascular invasion of HCC. 23 However, to our best knowledge, few studies have attempted to identify GPC3-positive HCC with a radiomics signature.…”

mentioning

confidence: 99%

MRI‐Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3‐Positive Hepatocellular Carcinoma

Xie

Wei

et al. 2020

Magnetic Resonance Imaging

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Background: Glypican 3 (GPC3) expression has proved to be a critical risk factor related to prognosis in hepatocellular carcinoma (HCC) patients. Purpose: To investigate the performance of MRI-based radiomics signature in identifying GPC3-positive HCC. Study Type: Retrospective. Population: An initial cohort of 293 patients with pathologically confirmed HCC was involved in this study, and patients were randomly divided into training (195) and validation (98) cohorts. Field Strength/Sequences: Contrast-enhanced T 1-weight MRI was performed with a 1.5T scanner. Assessment: A total of 853 radiomic features were extracted from the volume imaging. Univariate analysis and Fisher scoring were utilized for feature reduction. Subsequently, forward stepwise feature selection and radiomics signature building were performed based on a support vector machine (SVM). Incorporating independent risk factors, a combined nomogram was developed by multivariable logistic regression modeling. Statistical Tests: The predictive performance of the nomogram was calculated using the area under the receive operating characteristic curve (AUC). Decision curve analysis (DCA) was applied to estimate the clinical usefulness. Results: The radiomics signature consisting of 10 selected features achieved good prediction efficacy (training cohort: AUC = 0.879, validation cohort: AUC = 0.871). Additionally, the combined nomogram integrating independent clinical risk factor α-fetoprotein (AFP) and radiomics signature showed improved calibration and prominent predictive performance with AUCs of 0.926 and 0.914 in the training and validation cohorts, respectively. Data Conclusion: The proposed MR-based radiomics signature is strongly related to GPC3-positive. The combined nomogram incorporating AFP and radiomics signature may provide an effective tool for noninvasive and individualized prediction of GPC3-positive in patients with HCC.

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Value of texture analysis based on enhanced MRI for predicting an early therapeutic response to transcatheter arterial chemoembolisation combined with high-intensity focused ultrasound treatment in hepatocellular carcinoma

Cited by 34 publications

References 33 publications

Machine and deep learning methods for radiomics

Machine and deep learning methods for radiomics

Texture analysis on gadoxetic acid enhanced-MRI for predicting Ki-67 status in hepatocellular carcinoma: A prospective study

MRI‐Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3‐Positive Hepatocellular Carcinoma

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