A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma

Zhang, Rui; Xu, Lei; Wang, Xue; Zhang, Jiahui; Yang, Pengfei; Zhang, Lixia; Xue, Xindong; Wang, Xiaoli; Huang, Qiang; Guo, Chuangen; Shi, Yifeng; Niu, Tianye; Chen, Feng

doi:10.21037/qims.2019.09.07

Cited by 63 publications

(62 citation statements)

References 47 publications

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“…In line with the study by Rui Z et al. ( 50 ), this result indicated that the combination of peritumoral features provide more information of the tumor microenvironment, which can reflect the biological behavior of the tumor better.…”

Section: Discussionsupporting

confidence: 88%

“…And the Fusion Rad-score which consisted features from the peritumoral area exhibited better performance than the Tumor Rad-score. In line with the study by Rui Z et al (50), this result indicated that the combination of peritumoral features provide more information of the tumor microenvironment, which can reflect the biological behavior of the tumor better.…”

Section: Discussionsupporting

confidence: 87%

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Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

Yuan

Song

et al. 2021

Front. Immunol.

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BackgroundThere is no study accessible now assessing the prognostic aspect of radiomics for anti-PD-1 therapy for patients with HCC.AimThe aim of this study was to develop and validate a radiomics nomogram by incorporating the pretreatment contrast-enhanced Computed tomography (CT) images and clinical risk factors to estimate the anti-PD-1 treatment efficacy in Hepatocellular Carcinoma (HCC) patients.MethodsA total of 58 patients with advanced HCC who were refractory to the standard first-line of therapy, and received PD-1 inhibitor treatment with Toripalimab, Camrelizumab, or Sintilimab from 1st January 2019 to 31 July 2020 were enrolled and divided into two sets randomly: training set (n = 40) and validation set (n = 18). Radiomics features were extracted from non-enhanced and contrast-enhanced CT scans and selected by using the least absolute shrinkage and selection operator (LASSO) method. Finally, a radiomics nomogram was developed based on by univariate and multivariate logistic regression analysis. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.ResultsEight radiomics features from the whole tumor and peritumoral regions were selected and comprised of the Fusion Radiomics score. Together with two clinical factors (tumor embolus and ALBI grade), a radiomics nomogram was developed with an area under the curve (AUC) of 0.894 (95% CI, 0.797–0.991) and 0.883 (95% CI, 0.716–0.998) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) confirmed that nomogram had good consistency and clinical usefulness.ConclusionsThis study has developed and validated a radiomics nomogram by incorporating the pretreatment CECT images and clinical factors to predict the anti-PD-1 treatment efficacy in patients with advanced HCC.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 87%

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

Yuan

Song

et al. 2021

Front. Immunol.

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“…While radiomics provides an auxiliary alternative for radiologists to explore more hidden image patterns in characterizing diseases. Several studies have attempted to predict MVI status via radiomics analysis on DCE-CT or MRI ( 26 – 28 , 30 , 39 , 40 ). For instance, Ma et al.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, key image information beyond the tumor core might be lost since MVI often occurs in regions neighboring the tumor/non-tumor interface ( 6 , 29 ). Image texture from the peripheral liver parenchyma, such as a settled 5mm or 10mm distance from the tumor edge, have shown encouraging predictive ability in MVI ( 26 , 28 , 30 ). To our knowledge, no study has comprehensively reported on MVI predictive performances using different combinations of DCE-CT/MRI phase(s) with varying distances from the tumor margin.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Microvascular Invasion in Hepatocellular Carcinoma With a Multi-Disciplinary Team-Like Radiomics Fusion Model on Dynamic Contrast-Enhanced Computed Tomography

et al. 2021

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ObjectiveTo investigate microvascular invasion (MVI) of HCC through a noninvasive multi-disciplinary team (MDT)-like radiomics fusion model on dynamic contrast enhanced (DCE) computed tomography (CT).MethodsThis retrospective study included 111 patients with pathologically proven hepatocellular carcinoma, which comprised 57 MVI-positive and 54 MVI-negative patients. Target volume of interest (VOI) was delineated on four DCE CT phases. The volume of tumor core (Vtc) and seven peripheral tumor regions (Vpt, with varying distances of 2, 4, 6, 8, 10, 12, and 14 mm to tumor margin) were obtained. Radiomics features extracted from different combinations of phase(s) and VOI(s) were cross-validated by 150 classification models. The best phase and VOI (or combinations) were determined. The top predictive models were ranked and screened by cross-validation on the training/validation set. The model fusion, a procedure analogous to multidisciplinary consultation, was performed on the top-3 models to generate a final model, which was validated on an independent testing set.ResultsImage features extracted from Vtc+Vpt(12mm) in the portal venous phase (PVP) showed dominant predictive performances. The top ranked features from Vtc+Vpt(12mm) in PVP included one gray level size zone matrix (GLSZM)-based feature and four first-order based features. Model fusion outperformed a single model in MVI prediction. The weighted fusion method achieved the best predictive performance with an AUC of 0.81, accuracy of 78.3%, sensitivity of 81.8%, and specificity of 75% on the independent testing set.ConclusionImage features extracted from the PVP with Vtc+Vpt(12mm) are the most reliable features indicative of MVI. The MDT-like radiomics fusion model is a promising tool to generate accurate and reproducible results in MVI status prediction in HCC.

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“…Meanwhile, three studies demonstrated the effect of a combined model incorporating the contrast-enhanced MRI radiomics signature and clinical factors. The first study performed in 267 HCC patients reported an AUC of 0.858, with a sensitivity of 80.77% and a specificity of 68.09%, using precontrast and contrast-enhanced MRI with gadopentetate dimeglumine and two clinical factors (arterial peritumoral enhancement and AFP) [ 51 ]. According to the second study performed in patients with pathologically confirmed HCC (training cohort 146 patients, validation cohort 62 patients), the AUC was 0.861 (C-index of 0.864), with a sensitivity of 89.5%, a specificity of 81.4%, and an accuracy of 83.9%, using precontrast and contrast-enhanced MRI with gadoxetic acid and clinical factors including AFP, nonsmooth tumor margin, and arterial peritumoral enhancement [ 50 ].…”

Section: Diagnosis Of Hcc By Ct/mri Based Radiomicsmentioning

confidence: 99%

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

et al. 2021

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Hepatocellular carcinoma (HCC) is a common cancer worldwide. Recent international guidelines request an identification of the stage and patient background/condition for an appropriate decision for the management direction. Radiomics is a technology based on the quantitative extraction of image characteristics from radiological imaging modalities. Artificial intelligence (AI) algorithms are the principal axis of the radiomics procedure and may provide various results from large data sets beyond conventional techniques. This review article focused on the application of the radiomics-related diagnosis of HCC using radiological imaging (computed tomography, magnetic resonance imaging, and ultrasound (B-mode, contrast-enhanced ultrasound, and elastography)), and discussed the current role, limitation and future of ultrasound. Although the evidence has shown the positive effect of AI-based ultrasound in the prediction of tumor characteristics and malignant potential, posttreatment response and prognosis, there are still a number of issues in the practical management of patients with HCC. It is highly expected that the wide range of applications of AI for ultrasound will support the further improvement of the diagnostic ability of HCC and provide a great benefit to the patients.

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A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma

Cited by 63 publications

References 47 publications

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

Prediction of Microvascular Invasion in Hepatocellular Carcinoma With a Multi-Disciplinary Team-Like Radiomics Fusion Model on Dynamic Contrast-Enhanced Computed Tomography

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

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