Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT

Ma, Xiaohong; Wei, Jingwei; Gu, Dongsheng; Zhu, Yongjian; Feng, Bing; Liang, Meng; Wang, Shuang; Zhao, Xinming; Tian, Jie

doi:10.1007/s00330-018-5985-y

Cited by 173 publications

(153 citation statements)

References 40 publications

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“…We used venous-phase images rather than non-enhanced or arterial-phase images because venous-phase images enable improved lesion visualization and accurate segmentation of anterior mediastinal neoplasms, which are surrounded by mediastinal fat tissue, large vessels, pleura, and lung parenchyma. Moreover, venous-phase images have previously been used to reveal enhancement heterogeneity for the radiomics analysis of soft-tissue neoplasms, such as gastric cancer, renal tumor, and hepatocellular cancer (30)(31)(32). We selected 3D radiomics features over 2D features because the former provide comprehensive information and improve the accuracy of radiomics-based predictions (33).…”

Section: Discussionmentioning

confidence: 99%

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

Chen

Bao

et al. 2020

Oncol Rep

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In the present study, we aimed to construct a radiomics model using contrast-enhanced computed tomography (CT) to predict the pathological invasiveness of thymic epithelial tumors (TETs). We retrospectively reviewed the records of 179 consecutive patients (89 females) with histologically confirmed TETs from two hospitals. The 82 low-and 97 high-risk TETs were assigned to training (90 tumors), internal validation (49 tumors) and external validation (40 tumors) cohorts. Radiomics features extracted from preoperative contrast-enhanced chest CT were selected using least absolute shrinkage and selection operator logistic regression. Three prediction models were developed using multivariate logistic regression analysis. Their performance and clinical utility were assessed using receiver operating characteristic curves and the DeLong test, respectively. Eight radiomics features with non-zero coefficients were used to develop a radiomics score, which significantly differed between low-and high-risk TETs (P<0.001). The subjective finding, infiltration, was independently associated with high-risk TETs. Prediction models based on infiltration alone, the radiomics signature alone, and both these parameters showed diagnostic accuracies of 72.2% [area under curve (AUC), 0.731; 95% confidence interval (CI): 0.627-0.819; sensitivity, 85.7%; specificity, 60.4%], 88.9% (AUC, 0.944; 95% CI: 0.874-0.981; sensitivity, 92.9%; specificity, 85.4%), and 90.0% (AUC, 0.953; 95% CI: 0.887-0.987; sensitivity, 92.9%; specificity, 87.5%), respectively. Decision-curve analysis showed that the combined model added more net benefit than the single-parameter models. In conclusion, a radiomics signature based on contrast-enhanced CT has the potential to differentiate between low-and high-risk TETs. The model incorporating the radiomics signature and subjective finding may facilitate the individualized, preoperative prediction of the pathological invasiveness of TETs.

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Section: Discussionmentioning

confidence: 99%

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

Chen

Bao

et al. 2020

Oncol Rep

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“…Extensive studies have shown that radiomics have great potential in predicting tumor biology and in improving implementation of precision medicine (18,23,28,29). Previously, some studies have established radiomic signatures for detecting the presence of MVI based on CT and MRI (11)(12)(13)(14). Radiomic signatures based on arterial phase and delay phase of contrast-enhanced CT have yielded AUCs of 0.684 and 0.490, respectively (11).…”

Section: Discussionmentioning

confidence: 99%

“…Extensive studies have been proposed to use various imaging methods to predict MVI in HCC. Current reports of MVI classification have been mainly based on computed tomography (CT) (11)(12)(13), magnetic resonance imaging (MRI) (14)(15)(16), and contrast-enhanced ultrasound (CEUS) (17,18). Several imaging features have been proposed as predictors of MVI, such as the status of tumor-internal arteries, hypodense halos on CT scans, arterial peritumoral enhancements, non-smooth tumor margins, and peritumoral hypointensities on gadoxetic-acid-enhanced MRI (16).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have shown that radiomics may potentially be applied via CT and MRI in classification of HCC grades, early recurrence prediction, and evaluation of biological characteristics in HCC patients (15,18,21,(28)(29)(30). Ma et al, established radiomic signatures based on contrast-enhanced CT to predict the status of MVI (11). Yang et al, constructed radiomic signatures based on MRI for prediction of MVI (14).…”

Section: Introductionmentioning

confidence: 99%

“…Grayscale ultrasound is the most commonly used first-line imaging method of HCC lesions before operation, which has unique advantages in terms of being a non-radiation, easy-toperform, and cost-effective imaging method. Recent studies have shown that radiomic analysis can also be applied to ultrasound images (11,14). Radiomic scores based on ultrasound images have potential to non-invasively predict the MVI status in HCC patients (18).…”

Section: Introductionmentioning

confidence: 99%

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Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

Liu

Xia

et al. 2020

Front. Oncol.

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Objectives: To establish a radiomic algorithm based on grayscale ultrasound images and to make preoperative predictions of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods: In this retrospective study, 322 cases of histopathologically confirmed HCC lesions were included. The classifications based on preoperative grayscale ultrasound images were performed in two stages: (1) classifier #1, MVI-negative and MVI-positive cases; (2) classifier #2, MVI-positive cases were further classified as M1 or M2 cases. The gross-tumoral region (GTR) and peri-tumoral region (PTR) signatures were combined to generate gross-and peri-tumoral region (GPTR) radiomic signatures. The optimal radiomic signatures were further incorporated with vital clinical information. Multivariable logistic regression was used to build radiomic models. Results: Finally, 1,595 radiomic features were extracted from each HCC lesion. At the classifier #1 stage, the radiomic signatures based on features of GTR, PTR, and GPTR showed area under the curve (AUC) values of 0.708 (95% CI, 0.603-0.812), 0.710 (95% CI, 0.609-0.811), and 0.726 (95% CI, 0.625-0.827), respectively. Upon incorporation of vital clinical information, the AUC of the GPTR radiomic algorithm was 0.744 (95% CI, 0.646-0.841). At the classifier #2 stage, the AUC of the GTR radiomic signature was 0.806 (95% CI, 0.667-0.944). Conclusions: Our radiomic algorithm based on grayscale ultrasound images has potential value to facilitate preoperative prediction of MVI in HCC patients. The GTR radiomic signature may be helpful for further discriminating between M1 and M2 levels among MVI-positive patients.

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Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study

Zhang

Ruan

Xiao

et al. 2020

Clinical & Translational Med

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Background The present study constructed and validated the use of contrast‐enhanced computed tomography (CT)‐based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC). Methods We enrolled 637 patients from two independent institutions. Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients. Patients from Institution II served as an independent validation set. The LASSO algorithm was used for the selection of 798 radiomics features. Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression. We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers. Results The developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of .780, .776, and .743 in the training, test, and independent validation cohorts, respectively. The final MVI status classifier that integrated two clinical factors (age and α‐fetoprotein level) achieved AUC of .806, .803, and .796 in the training, test, and independent validation cohorts, respectively. For MVI risk stratification, the AUCs of the radiomics signature were .746, .664, and .700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier‐integrated clinical stage were .783, .778, and .740, respectively. Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence. Conclusions Our CT radiomics‐based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool.

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Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT

Cited by 173 publications

References 40 publications

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Initial Application of a Radiomic Algorithm Based on Grayscale Ultrasound Images

Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study

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