Dataset Information

CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment.

ABSTRACT:

Background

Proliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment.

Methods

Patients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinicoradiological features to predict proliferative HCC was constructed using the training cohort (n = 184), and validated using an internal test cohort (n = 80) and an external test cohort (n = 89). The predictive performance of the nomogram for clinical outcomes was evaluated for HCC patients who underwent surgery (n = 201) or received transarterial chemoembolization (TACE, n = 104). RNA sequencing data and histological tissue slides from The Cancer Imaging Archive database were used to perform transcriptomics and pathomics analysis.

Results

The areas under the receiver operating characteristic curve of the radiomics nomogram to predict proliferative HCC were 0.84, 0.87, and 0.85 in the training, internal test, and external test cohorts, respectively. The radiomics nomogram could stratify early recurrence-free survivals in the surgery outcome cohort (hazard ratio [HR] = 2.25; P < 0.001) and progression-free survivals in the TACE outcome cohort (HR = 2.21; P = 0.03). Transcriptomics and pathomics analysis indicated that the radiomics nomogram was associated with carbon metabolism, immune cells infiltration, TP53 mutation, and heterogeneity of tumor cells.

Conclusion

The CT-based radiomics nomogram could predict proliferative HCC, stratify clinical outcomes, and measure a pro-tumor microenvironment.

SUBMITTER: Wang G

PROVIDER: S-EPMC11367757 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment.

Wang Gongzheng G Ding Feier F Chen Kaige K Liang Zhuoshuai Z Han Pengxi P Wang Linxiang L Cui Fengyun F Zhu Qiang Q Cheng Zhaoping Z Chen Xingzhi X Huang Chencui C Cheng Hongxia H Wang Ximing X Zhao Xinya X

Journal of translational medicine 20240902 1

<h4>Background</h4>Proliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment.<h4>Methods</h4>Patients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinico ...[more]

PMID: 39218938

Similar Datasets

Project description:ObjectiveTo construct a prediction model based on peritumoral radiomics signatures from CT images and investigate its efficiency in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after curative treatment.Materials and methodsIn total, 156 patients with primary HCC were randomly divided into the training cohort (109 patients) and the validation cohort (47 patients). From the pretreatment CT images, we extracted 3-phase two-dimensional images from the largest cross-sectional area of the tumor. A region of interest (ROI) was manually delineated around the lesion for tumoral radiomics (T-RO) feature extraction, and another ROI was outlined with an additional 2 cm peritumoral area for peritumoral radiomics (PT-RO) feature extraction. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. The T-RO and PT-RO models were constructed. In the validation cohort, the prediction efficiencies of the two models and peritumoral enhancement (PT-E) were evaluated qualitatively by receiver operating characteristic (ROC) curves, calibration curves and decision curves and quantitatively by area under the curve (AUC), the category-free net reclassification index (cfNRI) and integrated discrimination improvement values (IDI).ResultsBy comparing AUC values, the prediction accuracy in the validation cohort was good for the PT-RO model (0.80 vs. 0.79, P = 0.47) but poor for the T-RO model (0.82 vs. 0.62, P < 0.01), which was significantly overfitted. In the validation cohort, the ROC curves, calibration curves and decision curves indicated that the PT-RO model had better calibration efficiency and provided greater clinical benefits. CfNRI indicated that the PT-RO model correctly reclassified 47% of ER patients and 32% of non-ER patients compared to the T-RO model (P < 0.01); additionally, the PT-RO model correctly reclassified 24% of ER patients and 41% of non-ER patients compared to PT-E (P = 0.02). IDI indicated that the PT-RO model could improve prediction accuracy by 0.22 (P < 0.01) compared to the T-RO model and by 0.20 (P = 0.01) compared to PT-E.ConclusionThe CT-based PT-RO model can effectively predict the ER of HCC and is more efficient than the T-RO model and the conventional imaging feature PT-E.

Project description:BackgroundThis study aimed to develop a computed tomography (CT) model to predict Ki-67 expression in hepatocellular carcinoma (HCC) and to examine the added value of radiomics to clinico-radiological features.MethodsA total of 208 patients (training set, n = 120; internal test set, n = 51; external validation set, n = 37) with pathologically confirmed HCC who underwent contrast-enhanced CT (CE-CT) within 1 month before surgery were retrospectively included from January 2014 to September 2021. Radiomics features were extracted and selected from three phases of CE-CT images, least absolute shrinkage and selection operator regression (LASSO) was used to select features, and the rad-score was calculated. CE-CT imaging and clinical features were selected using univariate and multivariate analyses, respectively. Three prediction models, including clinic-radiologic (CR) model, rad-score (R) model, and clinic-radiologic-radiomic (CRR) model, were developed and validated using logistic regression analysis. The performance of different models for predicting Ki-67 expression was evaluated using the area under the receiver operating characteristic curve (AUROC) and decision curve analysis (DCA).ResultsHCCs with high Ki-67 expression were more likely to have high serum α-fetoprotein levels (P = 0.041, odds ratio [OR] 2.54, 95% confidence interval [CI]: 1.04-6.21), non-rim arterial phase hyperenhancement (P = 0.001, OR 15.13, 95% CI 2.87-79.76), portal vein tumor thrombus (P = 0.035, OR 3.19, 95% CI: 1.08-9.37), and two-trait predictor of venous invasion (P = 0.026, OR 14.04, 95% CI: 1.39-144.32). The CR model achieved relatively good and stable performance compared with the R model (AUC, 0.805 [95% CI: 0.683-0.926] vs. 0.678 [95% CI: 0.536-0.839], P = 0.211; and 0.805 [95% CI: 0.657-0.953] vs. 0.667 [95% CI: 0.495-0.839], P = 0.135) in the internal and external validation sets. After combining the CR model with the R model, the AUC of the CRR model increased to 0.903 (95% CI: 0.849-0.956) in the training set, which was significantly higher than that of the CR model (P = 0.0148). However, no significant differences were found between the CRR and CR models in the internal and external validation sets (P = 0.264 and P = 0.084, respectively).ConclusionsPreoperative models based on clinical and CE-CT imaging features can be used to predict HCC with high Ki-67 expression accurately. However, radiomics cannot provide added value.

Project description:BackgroundCirculating tumor cells (CTCs), an indispensable liquid biopsy classifier, can provide extra information for the diagnosis and prognosis of hepatocellular carcinoma (HCC).MethodsWe systematically analyzed the peripheral blood of preoperative HCC patients (n = 270) for CTC number, Ki67 index reflecting the proliferative CTC percentage (PCP), and CTC clusters correlated with the characteristics of malignant HCC tumors.ResultsDriver gene mutations were found with high levels of consistency between CTCs and primary tumors (n = 73). CTC number and PCP were associated with tumor size, microvascular invasion (MVI), presence or absence of multiple tumors, and thrombosis significantly. CTC number and PCP robustly separated patients with and without relapse, with a sensitivity of 88.89%/81.48% and a specificity of 72.73%/94.81% in the training set (n = 104) and corresponding values of 80.00%/86.67% and 78.38%/89.19% in the validation set (n = 52), showing a better performance than that associated with the alpha-fetoprotein (AFP) level. CTC number, PCP, CTC clusters, and MVI were independent significant risk factors for HCC recurrence (P = 0.0375, P < 0.0001, P = 0.0017, and P = 0.0157). A nomogram model based on these risk factors showed a considerable prediction ability for HCC recurrence (area under the curve = 0.947). PCP (training: log-rank P < 0.0001; hazard ratio [HR] 30.13, 95% confidence interval [CI] = 11.12-81.62; validation: log-rank P < 0.0001; HR 25.73, 95% CI = 5.28-106.60) and CTC clusters (training: log-rank P < 0.0001; HR 17.34, 95% CI = 7.46-40.30; validation: log-rank P < 0.0001; HR 9.92, 95% CI = 2.55-38.58) were more significantly correlated with worse recurrence-free survival than CTC number (training: log-rank P < 0.0001; HR 14.93, 95% CI = 4.48-49.78; validation: log-rank P = 0.0007; HR 9.03, 95% CI = 2.53-32.24).ConclusionPCP and CTC clusters may predict HCC recurrence and improve the performance of the serum biomarker AFP.

Project description:BackgroundRadiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsTwo hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility.ResultsHigher α-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.ConclusionsThe nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Dataset Information

CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment.

Background

Methods

Results

Conclusion

Publications

CT-based radiomics nomogram to predict proliferative hepatocellular carcinoma and explore the tumor microenvironment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets