Construction and validation of a nomogram for HBV-related hepatocellular carcinoma: A large, multicenter study

Su, Ke; Shen, Qiuni; Tong, Jian; Gu, Tao; Xu, Ke; Li, Han; Chi, Hao; Liu, Yanlin; Li, Xueting; Wen, Lianbin; Song, Yanqiong; Guo, Qulian; Chen, Jiali; Wu, Zhenying; Jiang, Yi; He, Kun; Guo, Lu; Han, Yunwei

doi:10.1016/j.aohep.2023.101109

Cited by 19 publications

(15 citation statements)

References 32 publications

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“…To improve the prognosis and effectiveness monitoring of HCC, clinical studies have explored programmed death-1 (PD-1) inhibitors with antiangiogenic therapy through TACE, 12 as well as the combination of radiotherapy with sorafenib. 13 These studies have shown promising therapeutic outcomes in cases of unresectable HCC, providing valuable guidance for selecting the most effective treatment approach for HCC in the future. Moreover, an increasing number of studies have been conducted to select more accurate biomarkers for predicting treatment response and survival prognosis in HCC patients by developing a 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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“…Moreover, using this blood-tumor barrier (BTB) model could aid the introduction of novel therapeutic biomarkers that predict optimized response to therapy. Prediction of therapeutic responses for oncologic purposes possesses great significance when translated into clinical stages for all types of cancers ( 54 , 55 ). this model could serve as a future backbone for studies focusing on the interactions of the therapies on BTB and the biomarkers predicting tumoral cell death and therapy success in future oncological practice.…”

Section: Discussionmentioning

confidence: 99%

A dynamic study of VEGF-A siDOX-EVs trafficking through the in-vitro insert co-culture blood-brain barrier model by digital holographic microscopy

Shamshiripour,

Rahnama,

Nikoobakht

et al. 2024

Front. Oncol.

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IntroductionModeling the blood-brain barrier has long been a challenge for pharmacological studies. Up to the present, numerous attempts have been devoted to recapitulating the endothelial barrier in vitro to assess drug delivery vehicles’ efficiency for brain disorders. In the current work, we presented a new approach for analyzing the morphometric parameters of the cells of an insert co-culture blood-brain barrier model using rat brain astrocytes, rat brain microvascular endothelial cells, and rat brain pericytes. This analytical approach could aid in getting further information on drug trafficking through the blood-brain barrier and its impact on the brain indirectly.MethodsIn the current work, we cultured rat brain astrocytes, rat brain microvascular endothelial cells, and rat brain pericytes and then used an insert well to culture the cells in contact with each other to model the blood-brain barrier. Then, the morphometric parameters of the porous membrane of the insert well, as well as each cell type were imaged by digital holographic microscopy before and after cell seeding. At last, we performed folate conjugation on the surface of the EVs we have previously tested for glioma therapy in our previous work called VEGF-A siDOX-EVs and checked how the trafficking of EVs improves after folate conjugation as a clathrin-mediated delivery setup. the trafficking and passage of EVs were assessed by flow cytometry and morphometric analysis of the digital holographic microscopy holograms.ResultsOur results indicated that EVs successfully entered through the proposed endothelial barrier assessed by flow cytometry analysis and furthermore, folate conjugation significantly improved EV passage through the blood-brain barrier. Moreover, our results indicated that the VEGF-A siDOX-EVs insert cytotoxic impact on the cells of the bottom of the culture plate.Conclusionfolate-conjugation on the surface of EVs improves their trafficking through the blood-brain barrier and by using digital holographic microscopy analysis, we could directly assess the morphometric changes of the blood-brain barrier cells for pharmacological purposes as an easy, label-free, and real-time analysis.

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“…Studies have shown that breast cancer patients with concurrent bone and brain metastasis often suffer from severe symptoms such as pain, intracranial pressure, pathological fractures, etc. Radiotherapy is not only an effective treatment for improving local symptoms, relieving pain, and reducing the incidence of pathological fractures ( 24 , 25 ), but also provides a possibility for the application of other anti-tumor measures ( 26 , 27 ). Additionally, the application of radiotherapy can significantly alter the tumor microenvironment, a process mediated by the mechanisms of ionizing radiation release.…”

Section: Discussionmentioning

confidence: 99%

Survival analysis of palliative radiotherapy in patients with HER-2+ metastatic breast cancer

Li,

Zhong,

et al. 2024

Front. Endocrinol.

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BackgroundWhether radiotherapy can improve the long-term survival of HER-2+ metastatic breast cancer remains unclear. We launched this study to explore the effect of HER-2+ metastatic breast cancer patients through anti-HER-2 targeted therapy + radiotherapy.Methods488 HER-2 + metastatic breast cancer patients who received anti-HER2 targeted ± local radiotherapy from March 2006 to September 2021 were retrospectively collected. Patients were divided into a radiotherapy group (n=207) and a non-radiotherapy group (n=281) based on whether they received radiotherapy or not. 1: 1 propensity matching analysis was used to determine two groups of patients with similar baselines.ResultsBefore matching, the radiotherapy group (n=207) had a median overall survival (mOS) of 51.7 months (48.8-63.8), which was superior to the non-radiotherapy group’s (n=281) mOS of 33.9 months (27.9-39.9) (P < 0.0001). Moreover, the radiotherapy group exhibited better 1-year (94.6% vs 83.9%), 3-year (70.8% vs 45.5%), and 5-year (43.3% vs 25.0%) survival rates compared to the control group. Propensity score matching analysis identified 135 pairs of baseline-matched patients. In the matched groups, the mOS was 57.2 (44.5-69.8) months in the radiotherapy group (n=135) and 34.1 (27.5-40.6) months in the non-radiotherapy group (n=135), showing a statistically significant difference (P < 0.0001). Additionally, the radiotherapy group demonstrated 1-, 3-, and 5-year survival rates of 93.2%, 71.5%, and 46.9%, respectively, while those in the non-radiotherapy group were 89.4%, 45.8%, and 22.2%, respectively. Multivariate Cox analysis revealed that the presence of brain metastasis, liver metastasis, and radiotherapy were identified as independent predictive factors significantly associated with OS.ConclusionIn patients with HER-2 positive metastatic breast cancer, radiotherapy was associated with better survival benefits compared to those who did not receive radiotherapy.

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Construction and validation of a nomogram for HBV-related hepatocellular carcinoma: A large, multicenter study

Cited by 19 publications

References 32 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

A dynamic study of VEGF-A siDOX-EVs trafficking through the in-vitro insert co-culture blood-brain barrier model by digital holographic microscopy

Survival analysis of palliative radiotherapy in patients with HER-2+ metastatic breast cancer

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