A multidimensional data-driven approach to surgical plan optimization and postoperative residual tumor prediction in ovarian cancer

BackgroundsOvarian cancer represents a deadly gynecological malignancy, with surgical treatment being a key component of its management. We sought to integrate clinical characteristics and ascites immune microenvironment features into a deep learning m…

Decoding ascitic immunological niches with multi-modal machine learning reveals prognostic and chemoresistant determinants in ovarian cancer

BackgroundMalignant ascites in high-grade serous ovarian cancer (HGSOC) represent a fluid extension of the tumor microenvironment, embedding immune programs that may inform prognosis and treatment response. We investigated whether ascitic T-cell phenot…

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