Feng Gao

Integrated CT Foundation Model and Radiomic Features Define Clinically and Biologically Distinct Colorectal Cancer Subtypes

Du Cai, Haoning Qi, Qiuxia Yang, Yibin Zhao et autres

cn, hk (code pays fourni par la source)

Enhancing representation learning with frequency-augmented feature mixture for robust tuberculosis screening

Abudouresuli Tuersun, Mireayi Tudi, Abudoukeyoumu Abula, Pahatijiang Nijiati et autres

cn (code pays fourni par la source)

CT4CMS: Preoperative Computed Tomography-Based Consensus Molecular Subtyping Prediction in Colorectal Cancer Using Interpretable Deep Learning

Xianrui Zhang, Xiuping Nie, Tan Wu, Du Cai et autres

Abstract Consensus molecular subtyping (CMS) defines the transcriptomic taxonomy of colorectal cancer (CRC) and guides precision therapy. Although current approaches can predict CMS from histopathology, they rely on surgical specimens, limiting their preoperative applicability. In this study, we developed a deep learning …

hk, cn (code pays fourni par la source)

Translating Molecular Subtypes into Cost-Effective Radiogenomic Biomarkers for Prognosis of Colorectal Cancer

Baowen Gai, Xin Duan, Chenghang Li, Chuling Hu et autres

Background: Colorectal cancer (CRC) is currently the third most common cancer worldwide, with high heterogeneity and poor prognosis. Gene expression-based molecular subtypes can effectively dissect tumor heterogeneity, but their clinical translation remains challenging. This study aims to conduct radiogenomic analysis regarding molecular …

cn (code pays fourni par la source)