Yunfeng Li

Nanocomposite Hydrogel Bioinks for 3D Bioprinting of Tumor Models

In vitro tumor models were successfully constructed by 3D bioprinting; however, bioinks with proper viscosity, good biocompatibility, and tunable biophysical and biochemical properties are highly desirable for tumor models that closely recapitulated the main features of native tumors. Here, we developed a nanocomposite hydrogel bioink that was used to construct ovarian and colon cancer models by 3D bioprinting. The nanocomposite bioink was composed of aldehyde-modified cellulose nanocrystals (aCNCs), aldehyde-modified hyaluronic acid (aHA), and gelatin. The hydrogels possessed tunable gelation time, mechanical properties, and printability by controlling the ratio between aCNCs and gelatin. In addition, ovarian and colorectal cancer cells embedded in hydrogels showed high survival rates and rapid growth. By the combination of 3D bioprinting, ovarian and colorectal tumor models were constructed in vitro and used for drug screening. The results showed that gemcitabine had therapeutic effects on ovarian tumor cells. However, the ovarian tumor model showed drug resistance for oxaliplatin treatment.

Fully synthetic nanofibrillar hydrogel for the growth and enzyme-free release of patient-derived ovarian tumor organoids

Patient-derived tumor organoids (PTOs) closely recapitulate the histopathology, gene expression, and drug response of patient tumor tissues, thereby making them highly reliable in vitro models in preclinical cancer research. Currently, basement membrane extracts (BME or Matrigel) dominate the in vitro culture of PTO models, although BME hydrogels suffer from batch-to-batch variability, possible contamination of murine host cells, and non-tunable mechanical properties. Here, we design and prepare fully synthetic thermoreversible nanofibrillar hydrogels (TNHs) for the growth and enzyme-free release of ovarian PTOs. PTOs cultured in TNHs exhibit morphological, histological, and gene expression similarities to organoids grown in Matrigel and their corresponding tumor tissues. In addition, the on-demand release of PTOs without loss of cell viability and organoid integrity is achieved upon cooling. The released PTOs show a strong tumorigenic ability by xenografts of PTOs in mice. The results show that the fully synthetic TNHs is a highly promising alternative to BME for the establishment of ovarian PTOs in vitro.

Imaging-Based AI for Predicting Lymphovascular Space Invasion in Cervical Cancer: Systematic Review and Meta-Analysis

Abstract Background The role of artificial intelligence (AI) in enhancing the accuracy of lymphovascular space invasion (LVSI) detection in cervical cancer remains debated. Objective This meta-analysis aimed to evaluate the diagnostic accuracy of imaging-based AI for predicting LVSI in cervical cancer. Methods We conducted a comprehensive literature search across multiple databases, including PubMed, Embase, and Web of Science, identifying studies published up to November 9, 2024. Studies were included if they evaluated the diagnostic performance of imaging-based AI models in detecting LVSI in cervical cancer. We used a bivariate random-effects model to calculate pooled sensitivity and specificity with corresponding 95% confidence intervals. Study heterogeneity was assessed using the I2 statistic. Results Of 403 studies identified, 16 studies (2514 patients) were included. For the interval validation set, the pooled sensitivity, specificity, and area under the curve (AUC) for detecting LVSI were 0.84 (95% CI 0.79-0.87), 0.78 (95% CI 0.75-0.81), and 0.87 (95% CI 0.84-0.90). For the external validation set, the pooled sensitivity, specificity, and AUC for detecting LVSI were 0.79 (95% CI 0.70-0.86), 0.76 (95% CI 0.67-0.83), and 0.84 (95% CI 0.81-0.87). Using the likelihood ratio test for subgroup analysis, deep learning demonstrated significantly higher sensitivity compared to machine learning (P=.01). Moreover, AI models based on positron emission tomography/computed tomography exhibited superior sensitivity relative to those based on magnetic resonance imaging (P=.01). Conclusions Imaging-based AI, particularly deep learning algorithms, demonstrates promising diagnostic performance in predicting LVSI in cervical cancer. However, the limited external validation datasets and the retrospective nature of the research may introduce potential biases. These findings underscore AI’s potential as an auxiliary diagnostic tool, necessitating further large-scale prospective validation.