Risk factors for intraocular metastasis of primary liver cancer in diabetic patients: Alpha-fetoprotein and cancer antigen 125

Rapid progress of an iris metastasis from esophageal cancer: a case report and review of literature

This case report details a rare instance of rapid iris metastasis from esophageal cancer in a 59-year-old man. A literature review was conducted to explore recent advances in detecting, diagnosing, and treating intraocular metastatic malignancies. Positron emission tomography-computed tomography played a crucial role in identifying primary sites and systemic metastases. Local treatment combined with systemic therapy effectively reduced tumor size, preserved useful vision, and improved the patient's survival rate. A comparison was made of the characteristics of iris metastases from esophageal cancer and lung cancer, including age, gender, tumor characteristics, and treatment. The challenges associated with diagnosis and treatment are discussed, highlighting the implications for clinical practice.

Prediction model of ocular metastasis from primary liver cancer: Machine learning-based development and interpretation study

BACKGROUND

Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML).

METHODS

We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non-ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10-fold cross-validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM.

RESULTS

Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA.

CONCLUSION

We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.

A Review on CNTs-Based Electrochemical Sensors and Biosensors: Unique Properties and Potential Applications

Carbon nanotubes (CNTs), are safe, biocompatible, bioactive, and biodegradable materials, and have sparked a lot of attention due to their unique characteristics in a variety of applications, including medical and dye industries, paper manufacturing and water purification. CNTs also have a strong film-forming potential, permitting them to be widely employed in constructing sensors and biosensors. This review concentrates on the application of CNT-based nanocomposites in the production of electrochemical sensors and biosensors. It emphasizes the synthesis and optimization of CNT-based sensors for a range of applications and outlines the benefits of using CNTs for biomolecule immobilization. In addition, the use of molecularly imprinted polymer (MIP)-CNTs in the production of electrochemical sensors is also discussed. The challenges faced by the current CNTs-based sensors, along with some the future perspectives and their future opportunities, are also briefly explained in this paper.