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However, existing platformssuch as patient\u2010derived xenografts (PDXs) and patient\u2010derived organoids (PDOs), face major barriers in clinical use due to technical challenges, including limited standardization, high costs, long assay times, scalability constraints, and incomplete recapitulation of the patient tumor microenvironment (TME). Here, we present a scalable, low\u2010cost Organ Chip (OC) platform fabricated entirely from thermoplastics via injection molding. Leveraging a patented channel geometry and surface treatment, the device achieves barrier\u2010free hydrogel confinement through capillary pinning without porous membranes, micropillars, or other barrier structures. This automation\u2010compatible platform supports tissue\u2010specific extracellular matrices and co\u2010culture through versatile perfusion modes, with robust imaging compatibility. We demonstrate its feasibility for drug sensitivity testing using multiple cell lines and patient\u2010derived primary cells, with imaging\u2010based phenotypic profiling for accurate quantification of drug responses, closely aligning with clinical outcomes. Additionally, we integrated a deep learning\u2010based image translation model that predicts fluorescence staining from bright\u2010field images. This approach enables longitudinal, label\u2010free phenotypic analysis with higher sensitivity than conventional endpoint staining. Together, this integrated cancer OC system overcomes key technical challenges and offers a promising framework for functional precision oncology through high\u2010throughput, patient\u2010relevant drug testing.<\/jats:p>","DOI":"10.1002\/advs.202516660","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T15:09:51Z","timestamp":1770131391000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning\u2010Powered Scalable Cancer Organ Chip for Cancer Precision Medicine"],"prefix":"10.1002","volume":"13","author":[{"given":"Yu\u2010Chieh","family":"Yuan","sequence":"first","affiliation":[{"name":"Xellar Biosystems  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beibei","family":"Xu","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Quantitative Engineering Biology Shenzhen Institute of Synthetic Biology Shenzhen 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