Dataset Information

A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients.

ABSTRACT:

Background and aim

The presence of microvascular invasion (MVI) will impair the surgical outcome of hepatocellular carcinoma (HCC). Adipose and muscle tissues have been confirmed to be associated with the prognosis of HCC. We aimed to develop and validate a nomogram based on adipose and muscle related-variables for preoperative prediction of MVI in HCC.

Methods

One hundred fifty-eight HCC patients from institution A (training cohort) and 53 HCC patients from institution B (validation cohort) were included, all of whom underwent preoperative CT scan and curative resection with confirmed pathological diagnoses. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to data dimensionality reduction and screening. Nomogram was constructed based on the independent variables, and evaluated by external validation, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA).

Results

Histopathologically identified MVI was found in 101 of 211 patients (47.9%). The preoperative imaging and clinical variables associated with MVI were visceral adipose tissue (VAT) density, intramuscular adipose tissue index (IMATI), skeletal muscle (SM) area, age, tumor size and cirrhosis. Incorporating these 6 factors, the nomogram achieved good concordance index of 0.79 (95%CI: 0.72-0.86) and 0.75 (95%CI: 0.62-0.89) in training and validation cohorts, respectively. In addition, calibration curve exhibited good consistency between predicted and actual MVI probabilities. ROC curve and DCA of the nomogram showed superior performance than that of models only depended on clinical or imaging variables. Based on the nomogram score, patients were divided into high (> 273.8) and low (< = 273.8) risk of MVI presence groups. For patients with high MVI risk, wide-margin resection or anatomical resection could significantly improve the 2-year recurrence free survival.

Conclusion

By combining 6 preoperative independently predictive factors of MVI, a nomogram was constructed. This model provides an optimal preoperative estimation of MVI risk in HCC patients, and may help to stratify high-risk individuals and optimize clinical decision making.

SUBMITTER: Mao XC

PROVIDER: S-EPMC10548702 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients.

Mao Xin-Cheng XC Shi Shuo S Yan Lun-Jie LJ Wang Han-Chao HC Ding Zi-Niu ZN Liu Hui H Pan Guo-Qiang GQ Zhang Xiao X Han Cheng-Long CL Tian Bao-Wen BW Wang Dong-Xu DX Tan Si-Yu SY Dong Zhao-Ru ZR Yan Yu-Chuan YC Li Tao T

Biomarker research 20231004 1

<h4>Background and aim</h4>The presence of microvascular invasion (MVI) will impair the surgical outcome of hepatocellular carcinoma (HCC). Adipose and muscle tissues have been confirmed to be associated with the prognosis of HCC. We aimed to develop and validate a nomogram based on adipose and muscle related-variables for preoperative prediction of MVI in HCC.<h4>Methods</h4>One hundred fifty-eight HCC patients from institution A (training cohort) and 53 HCC patients from institution B (validat ...[more]

PMID: 37794517

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Project description:ObjectivesIn patients with hepatocellular carcinoma (HCC), accurately predicting the preoperative microvascular invasion (MVI) status is crucial for improving survival rates. This study proposes a multi-modal domain-adaptive fusion model based on deep learning methods to predict the preoperative MVI status in HCC.Materials and methodsFrom January 2008 to May 2022, we collected 163 cases of HCC from our institution and 42 cases from another medical facility, with each case including Computed Tomography (CT) images from the pre-contrast phase (PCP), arterial phase (AP), and portal venous phase (PVP). We divided our institution's dataset (n=163) into training (n=119) and test sets (n=44) in an approximate 7:3 ratio. Additionally, we included cases from another institution (n=42) as an external validation set (test1 set). We constructed three single-modality models, a simple concatenated multi-modal model, two current state-of-the-art image fusion model and a multi-modal domain-adaptive fusion model (M-DAFM) based on deep learning methods. We evaluated and analyzed the performance of these constructed models in predicting preoperative MVI using the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI) methods.ResultsIn comparison with all models, M-DAFM achieved the highest AUC values across the three datasets (0.8013 for the training set, 0.7839 for the test set, and 0.7454 for the test1 set). Notably, in the test set, M-DAFM's Decision Curve Analysis (DCA) curves consistently demonstrated favorable or optimal net benefits within the 0-0.65 threshold probability range. Additionally, the Net Reclassification Improvement (NRI) values between M-DAFM and the three single-modal models, as well as the simple concatenation model, were all greater than 0 (all p < 0.05). Similarly, the NRI values between M-DAFM and the two current state-of-the-art image fusion models were also greater than 0. These findings collectively indicate that M-DAFM effectively integrates valuable information from multi-phase CT images, thereby enhancing the model's preoperative predictive performance for MVI.ConclusionThe M-DAFM proposed in this study presents an innovative approach to improve the preoperative predictive performance of MVI.

Project description:BackgroundMicrovascular invasion (MVI) is a key prognostic factor in solitary hepatocellular carcinoma (HCC), significantly affecting treatment decisions and outcomes. Early prediction of MVI is crucial for enhancing clinical decision-making.ObjectivesThis study aimed to develop and evaluate four predictive models for MVI: one based on clinical indicators, one on MRI assessments, one using radiomics, and a combined model integrating all data across multiple medical centers.MethodsThe study included patients with solitary HCC from three centers (Mengchao Hepatobiliary Hospital, The Second Hospital of Nanping, and Datian County General Hospital). The dataset was divided into an internal training set, validation set, and two external validation sets. Predictive models were built using clinical indicators, MRI, radiomics, and a combination of these. Model performance was assessed through ROC curves, calibration curves, and decision curve analysis (DCA). Lasso regression identified significant features, and SHAP analysis interpreted the model predictions.ResultsA total of 319 patients were analyzed: 199 from the internal center, 67 from Nanping, and 53 from Datian. The combined model, which integrated clinical, MRI, and radiomics features, showed superior performance, with an AUC of 0.95(95%CI:0.92-0.98) in the internal training set, 0.92(95%CI:0.83-1.00) in the internal validation set, 0.96(95%CI:0.92-1.00) in Nanping, and 0.94(95%CI:0.88-0.99) in Datian. Calibration curves confirmed the model's accuracy, and NRI/IDI analyses highlighted its advantage over individual models. Key predictive indicators included pseudocapsule, peritumoral enhancement, and wavelet-based MRI features.ConclusionThis multi-center study demonstrates the effectiveness of combining clinical, MRI, and radiomics data in predicting MVI in solitary HCC, with robust results across different medical centers. These models have potential to improve patient management and treatment planning.

Dataset Information

A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients.

Background and aim

Methods

Results

Conclusion

Publications

A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients.

Similar Datasets

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