Construction and Validation of TACE Therapeutic Efficacy by ALR Score and Nomogram: A Large, Multicenter Study

Li, Han; Guo, Lu; Su, Ke; Li, Changlun; Jiang, Yi; Wang, Pan; Chen, Jiali; Wu, Zhenying; Xu, Ke; Gu, Tao; Zeng, Hao; He, Kun; Chi, Hao; Zhao, Wenjing; Liu, Han; Han, Yunwei

doi:10.2147/jhc.s414926

Cited by 14 publications

(12 citation statements)

References 24 publications

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“…For instance, both alkaline phosphatase (ALP) 14 and albumin-bilirubin (ALBI) 12 nomogram model demonstrated good performance for predicting survival rate for HCC. Similarly, Li et al 15 introduced an aspartate amino-transferase lymphocyte rate (ASR score) nomogram model, which presented valuable performance to predict the prognosis of HCC treated with TACE or TACE combined with systemic therapy. In this study, the nomogram model that combining CEMRI-based radiomics with LI-RADS features provides an optimal clinical decision approach for individualized clinical diagnosis of HCC differentiation, indicating the potential value of nomogram model.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation

Liu,

Lu,

Wang

et al. 2023

JHC

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Purpose To develop and compare various machine learning (ML) classifiers that employ radiomics extracted from contrast-enhanced magnetic resonance imaging (CEMRI) for diagnosing pathological differentiation of hepatocellular carcinoma (HCC), and validate the performance of the best model. Methods A total of 251 patients with HCCs (n = 262) were assigned to a training (n = 200) cohort and a validation (n = 62) cohort. A collection of 5502 radiomics signatures were extracted from the CEMRI images for each HCC nodule. To reduce redundancy and dimensionality, Spearman rank correlation, minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) approach were employed. Eight ML classifiers were trained to obtain the best radiomics model. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUC). The radiomics model was integrated with liver imaging reporting and data system (LI-RADS) features to design a combined model. Results The eXtreme Gradient Boosting (XGBoost)-based radiomics model outperformed other ML classifiers in evaluating pHCC, achieving an AUC of 1.00 and accuracy of 1.00 in the training cohort. The LI-RADS model demonstrated an AUC value of 0.77 and 0.82 in the training and validation cohorts. The combined model exhibited best performance in both the training and validation cohorts, with AUCs of 1.00 and 0.86 for evaluating HCC differentiation, respectively. Conclusion CEMRI radiomics integrating LI-RADS features demonstrated excellent performance in evaluating HCC differentiation, suggesting an optimal clinical decision tool for individualized diagnosis of HCC differentiation.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation

Liu,

Lu,

Wang

et al. 2023

JHC

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“…For early-stage disease, curative intent treatments include surgery, radiofrequency ablation, and liver transplant. 63 , 64 Meanwhile, for advanced-stage disease, conventional cytotoxic chemotherapy is ineffective with limited clinical benefits. Treatment with multikinase inhibitors is expected to enhance the OS of patients with advanced HCC by only a few months.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Novel Prognosis Model Based on a Panel of Three Immunogenic Cell Death-Related Genes for Non-Cirrhotic Hepatocellular Carcinoma

Gong,

Yu,

et al. 2023

JHC

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The accurate prediction of non-cirrhotic hepatocellular carcinoma (NCHCC) risk facilitates improved surveillance strategy and decreases cancer-related mortality. This study aimed to explore the correlation between immunogenic cell death (ICD) and NCHCC prognosis using The Cancer Genome Atlas (TCGA) datasets, and the potential prognostic value of ICD-related genes in NCHCC. Methods: Clinical and transcriptomic data of patients with NCHCC patients were retrieved from TCGA database. Weighted gene coexpression network analysis was performed to obtain the NCHCC phenotype-related module genes. Consensus clustering analysis was performed to classify the patients into two clusters based on intersection genes among differentially expressed genes (DEGs) between cancer and adjacent tissues, NCHCC phenotype-related genes, and ICD-related genes. NCHCC-derived tissue microarray was used to evaluate the correlation of the expression levels of key genes with NCHCC prognosis using immunohistochemical staining. Results: Cox regression analyses were performed to construct a prognostic risk score model comprising three genes (TMC7, GRAMD1C, and GNPDA1) based on DEGs between two clusters. The model stratified patients with NCHCC into two risk groups. The overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. Univariable and multivariable Cox regression analyses revealed that these signature genes are independent predictors of OS. Functional analysis revealed differential immune status between the two risk groups. Next, a nomogram was constructed, which demonstrated the potent distinguishing ability of the developed model based on receiver operating characteristic curves. In vitro functional validation revealed that the migration and invasion abilities of HepG2 and Huh7 cells were upregulated upon GRAMD1C knockdown but downregulated upon TMC7 knockdown. Conclusion:This study developed a prognostic model comprising three genes, which can aid in predicting the survival of patients with NCHCC and guide the selection of drugs and molecular markers for NCHCC.

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“…At the same time, certain biomarkers can effectively predict patient prognosis, such as ALP, ALBI, and ALR scores, among others. They can further assist researchers in assessing prognosis ( 29 – 31 ). Therefore, there will be a greater focus on immunotherapy combinations and response markers in the future ( 32 – 37 ).…”

Section: Discussionmentioning

confidence: 99%

Postoperative adjuvant immunotherapy for high-risk hepatocellular carcinoma patients

Zhang,

Tan

et al. 2023

Front. Oncol.

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Background and aimStandardized approach to postoperative adjuvant therapy for hepatocellular carcinoma (HCC) remains elusive. This study endeavors to examine the effects of postoperative PD-1 adjuvant therapy on the short-term and long-term prognosis of patients at a heightened risk of post-surgical recurrence.MethodsThe data of HCC patients who underwent hepatectomy at our center from June 2018 to March 2023 were collected from the hospital database. Propensity score matching (PSM) was employed to perform a 1:1 match between the postoperative anti-PD-1 antibody group and the postoperative non-anti-PD-1 antibody group. Kaplan-Meier method was utilized to compare the overall survival (OS) and recurrence-free survival (RFS) between the two groups. Cox regression analysis was conducted to identify the prognostic factors affecting patient outcomes. Subgroup analyses were performed for different high-risk factors.ResultsAmong the 446 patients included in the study, 122 patients received adjuvant therapy with postoperative anti-PD-1 antibodies. After PSM, the PD-1 group had postoperative 1-year, 2-year, 3-year, and 4-year OS rates of 93.1%, 86.8%, 78.2%, and 51.1%, respectively, while the non-PD-1 group had rates of 85.3%, 70.2%, 47.7%, and 30.0%. The PD-1 group had postoperative 1-year, 2-year, 3-year, and 4-year RFS rates of 81.7%, 77.0%, 52.3%, and 23.1%, respectively, whereas the non-PD-1 group had rates of 68.4%, 47.7%, and 25.8% in 1-year, 2-year, 3-year. A multifactorial Cox regression analysis revealed that postoperative PD-1 use was a prognostic protective factor associated with OS and RFS. Subgroup analysis results indicated that HCC patients with high recurrence risks significantly benefited from postoperative anti-PD-1 antibody treatment in terms of OS and RFS.ConclusionFor HCC patients with high-risk recurrence factors and undergoing hepatectomy, postoperative adjuvant therapy with anti-PD-1 antibodies can effectively improve their survival prognosis.

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Construction and Validation of TACE Therapeutic Efficacy by ALR Score and Nomogram: A Large, Multicenter Study

Cited by 14 publications

References 24 publications

Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation

Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation

Development and Validation of a Novel Prognosis Model Based on a Panel of Three Immunogenic Cell Death-Related Genes for Non-Cirrhotic Hepatocellular Carcinoma

Postoperative adjuvant immunotherapy for high-risk hepatocellular carcinoma patients

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