Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI

Feng, Shi‐Ting; Jia, Yulin; Liao, Bing; Huang, Bingsheng; Zhou, Qian; Li, Xin; Wei, Kaikai; Chen, Lili; Li, Bin; Wang, Wei; Chen, Shu‐Ling; He, Xiaofang; Wang, Haibo; Peng, Sui; Chen, Zebin; Tang, Mimi; Chen, Zhihang; Hou, Yang; Peng, Zhenwei; Kuang, Ming

doi:10.1007/s00330-018-5935-8

Cited by 151 publications

(169 citation statements)

References 32 publications

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“…However, the diagnostic accuracy analysis of this previous study showed relatively high specificity (0.90-0.94), low sensitivity (0.29-0.40) in assessing MVI (31). In another study, three radiomic models were built by extracting radiomic features from both intra-tumoral and peri-tumoral regions of Gd-EOB-DTPA-enhanced MRI images, which yielded an AUC value of 0.83 in predicting MVI (23). Until now, no study has ever extracted PTR radiomic signatures based on grayscale ultrasound for predicting MVI status.…”

Section: Discussionmentioning

confidence: 72%

“…A noninvasive imaging method which could accurately diagnosing MVI preoperatively would be help to better stratify HCC patients for clinical management (38). 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).…”

Section: Discussionmentioning

confidence: 99%

“…The radiomic method is a brand new imaging technique with the assistance of artificial intelligence software in performing high-throughput extraction of advanced quantitative features (21)(22)(23). By extracting high-dimensional features to quantify tumor heterogeneity from radiological images, preoperative MVI assessment in HCC can be hopefully realized (22,(24)(25)(26)(27).…”

Section: Introductionmentioning

confidence: 99%

“…It acts as the main blood dissemination path to portal venous thrombosis, as well as metastases in both intrahepatic and extrahepatic areas (31). Therefore, comparing to the tumor area, imaging features involving peri-tumoral areas may reveal a more direct association with MVI (23).…”

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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Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…Radiomics features can be extracted in 2D or 3D using inhouse software (6,(9)(10)(11)(12)(13) or commercial software (14,15 (16), the analysis of high-dimensional features may lead to problems of multicollinearity and overfitting. A recent phantom study revealed that the information provided by multiple radiomics features could be summarized using only 10 features because of redundancy (16).…”

Section: Process Of Radiomics Analysismentioning

confidence: 99%

Radiomics and Deep Learning: Hepatic Applications

2020

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Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.

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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 prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI

Cited by 151 publications

References 32 publications

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

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

Radiomics and Deep Learning: Hepatic Applications

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

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