Global trends of targeted therapy for hepatocellular carcinoma: A bibliometric and visualized study from 2008 to 2022.

BACKGROUND: Hepatocellular carcinoma (HCC) is an exceedingly prevalent malignancy with an exceptionally poor prognosis. Targeted therapy is an effective treatment option for patients with advanced HCC. However, there have been no bibliometric analyses of targeted therapies for HCC. METHODS: This study aimed to assess the current status and future directions of targeted therapy for HCC to provide future scholars with clearer research contents and popular themes. Methods: Literature on targeted therapy for HCC from 2008 to 2022 was obtained from the Web of Science (WoS) and assessed using bibliometric methodology. Additionally, the VOS viewer was applied in the visualization study to conduct bibliographic coupling, co-authorship, co-citation, and co-occurrence analyses of publications. RESULTS: A total of 10,779 papers were subsequently selected. Over the past 15 years, there has been a progressive increase in the number of publications on an annualized basis. China released the most publications in the field, whereas the United States had the highest H-index. Cancers published the most papers. Fudan University had the greatest sway in this area. Studies could be divided into 5 clusters: "Gene and expression research," "Mechanism study," "Nanoparticle study," "Targeted drug research," and "Clinical study." CONCLUSIONS: In the upcoming years, more papers on targeted therapy for HCC are expected to be released, demonstrating the potential for this topic to flourish. Particularly, "Clinical study" is the following trendy topic in this field. Other research subfields may likewise exhibit a continuous tendency towards balanced development.

Machine learning-based prediction of surgical benefit in borderline resectable and locally advanced pancreatic cancer.

INTRODUCTION: Surgery represents a primary therapeutic approach for borderline resectable and locally advanced pancreatic cancer (BR/LAPC). However, BR/LAPC lesions exhibit high heterogeneity and not all BR/LAPC patients who undergo surgery can derive beneficial outcomes. The present study aims to employ machine learning (ML) algorithms to identify those who would obtain benefits from the primary tumor surgery. METHODS: We retrieved clinical data of patients with BR/LAPC from the Surveillance, Epidemiology, and End Results (SEER) database and classified them into surgery and non-surgery groups based on primary tumor surgery status. To eliminate confounding factors, propensity score matching (PSM) was employed. We hypothesized that patients who underwent surgery and had a longer median cancer-specific survival (CSS) than those who did not undergo surgery would certainly benefit from surgical intervention. Clinical and pathological features were utilized to construct six ML models, and model effectiveness was compared through measures such as the area under curve (AUC), calibration plots, and decision curve analysis (DCA). We selected the best-performing algorithm (i.e., XGBoost) to predict postoperative benefits. The SHapley Additive exPlanations (SHAP) approach was used to interpret the XGBoost model. Additionally, data from 53 Chinese patients prospectively collected was used for external validation of the model. RESULTS: According to the results of the tenfold cross-validation in the training cohort, the XGBoost model yielded the best performance (AUC = 0.823, 95%CI 0.707-0.938). The internal (74.3% accuracy) and external (84.3% accuracy) validation demonstrated the generalizability of the model. The SHAP analysis provided explanations independent of the model, highlighting important factors related to postoperative survival benefits in BR/LAPC, with age, chemotherapy, and radiation therapy being the top three important factors. CONCLUSION: By integrating of ML algorithms and clinical data, we have established a highly efficient model to facilitate clinical decision-making and assist clinicians in selecting the population that would benefit from surgery.

A population-based study of synchronous distant metastases and prognosis in patients with PDAC at initial diagnosis.

Objective: Cancer of the pancreas is a life-threatening condition and has a high distant metastasis (DM) rate of over 50% at diagnosis. Therefore, this study aimed to determine whether patterns of distant metastases correlated with prognosis in pancreatic ductal adenocarcinoma (PDAC) with metastatic spread, and build a novel nomogram capable of predicting the 6, 12, 18-month survival rate with high accuracy. Methods: We analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database for cases of PDAC with DM. Kaplan-Meier analysis, log-rank tests and Cox-regression proportional hazards model were used to assess the impact of site and number of DM on the cancer-specific survival (CSS) and over survival (OS). A total of 2709 patients with DM were randomly assigned to the training group and validation group in a 7:3 ratio. A nomogram was constructed by the dependent risk factors which were determined by multivariate Cox-regression analysis. An assessment of the discrimination and ability of the prediction model was made by measuring AUC, C-index, calibration curve and decision curve analysis (DCA). In addition, we collected 98 patients with distant metastases at the time of initial diagnosis from Ningbo University Affiliated LiHuili Hospital to verify the efficacy of the prediction model. Results: There was a highest incidence of liver metastases from pancreatic cancer (2387,74.36%), followed by lung (625,19.47%), bone (190,5.92%), and brain (8,0.25%). The prognosis of liver metastases differed from that of lung metastases, and the presence of multiple organ metastases was associated with poorer prognosis. According to univariate and multivariate Cox-regression analyses, seven factors (i.e., diagnosis age, tumor location, grade of tumor differentiation, T-stage, receipt of surgery, receipt of chemotherapy status, presence of multiple organ metastases) were included in our nomogram model. In internal and external validation, the ROC curves, C-index, calibration curves and DCA were calculated, which confirmed that this nomogram can precisely predict prognosis of PDAC with DM. Conclusion: Metastatic PDAC patients with liver metastases tended to have a worse prognosis than those with lung metastases. The number of DM had significant effect on the overall survival rate of metastatic PDAC. This study had a high prediction accuracy, which was helpful clinicians to analyze the prognosis of PDAC with DM and implement individualized diagnosis and treatment.