Three-dimensional (3D) printing is a process in which materials are added together in a layer-by-layer manner to construct customized products. Many different techniques of 3D printing exist, which vary in materials used, cost, advantages, and drawbacks. Medicine is increasingly benefiting from this transformative technology, and the field of ophthalmology is no exception. The possible 3D printing applications in eyecare are vast and have been explored in the literature, such as 3D-printed ocular prosthetics, orbital implants, educational and anatomical models, as well as surgical planning and training. Novel drug-delivery platforms have also emerged because of 3D printing, offering improved treatment modalities for several ocular pathologies. Innovative research in 3D bioprinting of viable tissues, including the cornea, retina, and conjunctiva, is presenting an avenue for regenerative ophthalmic therapies in the future. Although further development in printing capabilities and suitable materials is required, 3D printing represents a powerful tool for enhancing eye health.

Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare costs. Early identification of patients at risk for severe RD can facilitate preventive management and reduce severe symptoms. This study evaluated the utility of subjective and objective factors, such as patient-reported outcomes (PROs) and serum cytokines, for predicting RD in breast cancer patients. The performance of machine learning (ML) and logistic regression-based models were compared. Data from 147 breast cancer patients who underwent radiotherapy was analyzed to develop prognostic models. ML algorithms, including neural networks, random forest, XGBoost, and logistic regression, were employed to predict clinically significant Grade 2+ RD. Clinical factors, PROs, and cytokine biomarkers were incorporated into the risk models. Model performance was evaluated using nested cross-validation with separate loops for hyperparameter tuning and calculating performance metrics. Feature selection identified 18 predictors of Grade 2+ RD including smoking, radiotherapy boost, reduced motivation, and the cytokines interleukin-4, interleukin-17, interleukin-1RA, interferon-gamma, and stromal cell-derived factor-1a. Incorporating these predictors, the XGBoost model achieved the highest performance with an area under the curve (AUC) of 0.780 (95% CI: 0.701-0.854). This was not significantly improved over the logistic regression model, which demonstrated an AUC of 0.714 (95% CI: 0.629-0.798). Clinical risk factors, PROs, and serum cytokine levels provide complementary prognostic information for predicting severe RD in breast cancer patients undergoing radiotherapy. ML and logistic regression models demonstrated comparable performance for predicting clinically significant RD with AUC>0.70.

Lymphedema is a chronic complication of breast cancer treatment, and early intervention is crucial to reduce morbidity. This study evaluated the role of blood-based cytokine biomarkers in the prognostication of breast cancer–related lymphedema (BCRL) to improve risk prediction. A secondary analysis of inflammatory biomarkers for BCRL was performed using a previously published cohort of 147 patients with breast cancer who had undergone serum cytokine profiling during their treatment at the Princess Margaret Cancer Centre from 2010 to 2014. Prognostic cytokine variables for lymphedema were selected by regression analysis and independence from known clinical risk factors. Regression-based modeling was employed to integrate prognostic variables for the prediction of lymphedema occurrence. We identified the immunostimulatory cytokine IFNα2A as a potential biomarker for lymphedema development [OR, 3.10; 95% confidence interval (CI), 1.05–9.51; P = 0.042], independent from known clinical risk factors. Furthermore, Kaplan–Meier analysis demonstrated 3-year lymphedema-free survival of 95% (90%–100%) versus 85% (77%–94%) for below versus above median concentrations of IFNα2A (P = 0.026). In combination with an established clinical risk regression-based model, patients identified as high risk based on clinical factors alone were able to be correctly reclassified as low risk by IFNα2A in 31% (eight of 26) of cases. Our combined logistic regression model using both IFNα2A and clinical risk score achieved an AUC of 0.895 (95% CI, 0.796–0.971) and Brier score of 0.101 (95% CI, 0.061–0.149), representing a favorable improvement compared with the logistic regression model using clinical risk factors alone. IFNα2A in combination with established clinical risk factors may be useful for improving BCRL prognostication. Significance: The cytokine IFNα2A was identified as a potentially complementary biomarker to improve the stratification of high- and low-risk patients for BCRL. This will help enable earlier intervention to reduce long-term morbidity for those at high risk for lymphedema while minimizing burdensome interventions for those at low risk.

Table S1. Summary of baseline serum cytokine levels measured at pre-radiotherapy.

IFNα2 levels in patients with breast cancer. Preradiotherapy IFNα2 was measured in 62 patients with breast cancer. Patients were grouped by their lymphedema (LE) status at 18 months after cancer treatment. Data are plotted as median with IQR. Two-tailed P value is 0.157 (Mann–Whitney test).