Tumor drug resistance presents a growing challenge in medical practice, particularly during anti-cancer therapies, where the emergence of drug-resistant cancer cells significantly complicates clinical treatment. In recent years, three-dimensional (3D) tumor culture technology, which more effectively simulates the in vivo physiological environment, has gained increasing attention in tumor drug resistance research and clinical applications. By mimicking the in vivo cellular microenvironment, 3D tumor culture technology not only recapitulates cell-cell interactions but also more faithfully reproduces the biological effects of therapeutic agents. Consequently, 3D tumor culture technology is emerging as a crucial tool in biomedical and clinical research. We summarize the benefits of 3D culture models and organoid technology, explore their application in the realm of drug resistance, drug screening, and personalized therapy, and discuss their potential application prospects and challenges in clinical transformation, with the aim of providing insights for optimizing cancer treatment strategies and advancing precision therapy.

Background: Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy. Materials and methods: PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented. Results: Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the meta-analysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76–0.89), 0.83 (0.79–0.86), and 0.84 (0.81–0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75–0.93), 0.89 (0.83–0.93), and 0.93 (0.90–0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the low-risk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39–4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91–3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature. Conclusions: Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research.