CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma

Zhou, Ying; He, Lan; Huang, Yanqi; Chen, Shuting; Wu, Penqi; Ye, Weitao; Liu, Zaiyi; Liang, Changhong

doi:10.1007/s00261-017-1072-0

Cited by 185 publications

(155 citation statements)

References 41 publications

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“…Previous reports have indicated that radiomics features could potentially be used as predictive or prognostic biomarkers (19)(20)(21). Although a radiomics signature based on CT textural features has been reported as a biomarker for the preoperative prediction of early recurrence in patients with HCC (22), to the best of our knowledge, no study to date has proposed that presence of MVI can be accurately predicted based on a radiomics signature.…”

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confidence: 93%

A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma

Peng

Zhang

et al. 2018

Diagn Interv Radiol

152

124

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H epatocellular carcinoma (HCC) is the sixth most common type of cancer and ranks third as a cause of cancer-related death globally. Worldwide, approximately 630 000 new cases of HCC occur annually, with more than half of these cases occurring in China (1). This disease is related to hepatitis B virus (HBV) infection in almost 70%-90% of cases in the highly endemic Asia-Pacific regions, especially in China (2, 3). Partial hepatectomy and liver transplantation are the most effective curative treatments, albeit in a limited number of cases (4). Indeed, the 5-year recurrence rates after surgical treatment and liver transplantation are as high as 70% and 35%, respectively (5-7). It is, therefore, necessary to find effective biomarkers that can identify aggressive behavior and predict tumor recurrence after liver resection and transplantation.In HCC, the presence of microvascular invasion (MVI) is a histopathologic feature indicative of aggressive behavior (8). Previous studies have identified MVI as a major risk factor for early recurrence in the first two years after liver resection and transplantation (9). Precise identification of MVI involvement in patients with HCC is critical to develop treatment strategies and arrive at prognoses. Over the past decade, researchers have made persistent endeavors towards the preoperative prediction of MVI (10-12). Although several radiologic features on contrast-enhanced magnetic resonance imaging (MRI) and computed tomography (CT) images (such as tumor margin, internal arteries, and hypodense halos) are known PURPOSE We aimed to develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). METHODSA total of 304 eligible patients with HCC were randomly divided into training (n=184) and independent validation (n=120) cohorts. Portal venous and arterial phase computed tomography data of the HCCs were collected to extract radiomic features. Using the least absolute shrinkage and selection operator algorithm, the training set was processed to reduce data dimensions, feature selection, and construction of a radiomics signature. Then, a prediction model including the radiomics signature, radiologic features, and alpha-fetoprotein (AFP) level, as presented in a radiomics nomogram, was developed using multivariable logistic regression analysis. The radiomics nomogram was analyzed based on its discrimination ability, calibration, and clinical usefulness. Internal cohort data were validated using the radiomics nomogram. RESULTSThe radiomics signature was significantly associated with MVI status (P < 0.001, both cohorts). Predictors, including the radiomics signature, nonsmooth tumor margin, hypoattenuating halos, internal arteries, and alpha-fetoprotein level were reserved in the individualized prediction no- CONCLUSIONThe radiomics nomogram, as a noninvasive preoperative prediction method, shows a favorable predictive accuracy for MVI status in patients with HBV-related HCC.

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confidence: 93%

A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma

Peng

Zhang

et al. 2018

Diagn Interv Radiol

152

124

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“…Radiomic biomarkers have been shown to be associated with several clinical endpoints, including survival (11,14–16), nodule malignancy (17,18), pathological response (19,20), recurrence and distant metastasis (21–23), as well as tumor gene expression patterns (11,14,21). A natural extension of this observation is that tumor phenotype should be linked to the tumor genotype.…”

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confidence: 99%

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

et al. 2017

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Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n ¼ 353) and verified them in an independent validation cohort (n ¼ 352). All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter).We found a radiomic signature related to radiographic heterogeneity that successfully discriminated between EGFR þ and EGFR Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics. This work has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost.

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“…Objective and quantitative imaging descriptors can improve the accuracy in diagnosis, evaluation of prognosis, and prediction of therapeutic response . According to previous studies, radiomic mineable quantitative data could serve as predictive or prognostic biomarkers . In the field of oncology, especially in lung cancer, radiomics is mostly used to facilitate the improvement diagnostic accuracy as well as making treatment decisions .…”

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confidence: 99%

“…10 According to previous studies, radiomic mineable quantitative data could serve as predictive or prognostic biomarkers. [13][14][15][16] In the field of oncology, especially in lung cancer, radiomics is mostly used to facilitate the improvement diagnostic accuracy as well as making treatment decisions. [16][17][18][19] Therefore, the objective of our study was to build and evaluate an MR-based radiomics nomogram that incorporates a radiomics signature and clinical parameters for preoperatively predicting the LVSI of cervical cancer.…”

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confidence: 99%

MR‐Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph‐Vascular Space Invasion preoperatively

Wang

et al. 2018

Magnetic Resonance Imaging

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Background Lymph‐vascular space invasion (LVSI) is an unfavorable prognostic factor in cervical cancer. Unfortunately, there are no current clinical tools for the preoperative prediction of LVSI. Purpose To develop and validate an axial T 1 contrast‐enhanced (CE) MR‐based radiomics nomogram that incorporated a radiomics signature and some clinical parameters for predicting LVSI of cervical cancer preoperatively. Study Type Retrospective. Population In all, 105 patients were randomly divided into two cohorts at a 2:1 ratio. Field Strength/Sequence T 1 CE MRI sequences at 1.5T. Assessment Univariate analysis was performed on the radiomics features and clinical parameters. Multivariate analysis was performed to determine the optimal feature subset. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of prediction model and radiomics nomogram. Statistical Tests The Mann–Whitney U ‐test and the chi‐square test were used to evaluate the performance of clinical characteristics and LVSI status by pathology. The minimum‐redundancy/maximum‐relevance and recursive feature elimination methods were applied to select the features. The radiomics model was constructed using logistic regression. Results Three radiomics features and one clinical characteristic were selected. The radiomics nomogram showed favorable discrimination between LVSI and non‐LVSI groups. The AUC was 0.754 (95% confidence interval [CI], 0.6326–0.8745) in the training cohort and 0.727 (95% CI, 0.5449–0.9097) in the validation cohort. The specificity and sensitivity were 0.756 and 0.828 in the training cohort and 0.773 and 0.692 in the validation cohort. Data Conclusion T 1 CE MR‐based radiomics nomogram serves as a noninvasive biomarker in the prediction of LVSI in patients with cervical cancer preoperatively. Level of Evidence : 4 Technical Efficacy : Stage 2 J. Magn. Reson. Imaging 2019;49:1420–1426.

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CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma

Abstract: The radiomics signature was a significant predictor for early recurrence in HCC. Incorporating radiomics signature into conventional clinical factors performed better for preoperative estimation of early recurrence than with clinical variables alone.

Cited by 185 publications

References 41 publications

A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma

A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

MR‐Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph‐Vascular Space Invasion preoperatively

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