OligoM-Cancer: A multidimensional information platform for deep phenotyping of heterogenous oligometastatic cancer
文献类型： 外文期刊
第一作者： Wu, Rongrong
作者： Wu, Rongrong;Zong, Hui;Feng, Weizhe;Zhang, Ke;Li, Jiakun;Wu, Erman;Tang, Tong;Zhan, Chaoying;Liu, Xingyun;Zhou, Yi;Zhang, Chi;Zhang, Yingbo;He, Mengqiao;Ren, Shumin;Shen, Bairong;Wu, Rongrong;Zong, Hui;Feng, Weizhe;Zhang, Ke;Li, Jiakun;Wu, Erman;Tang, Tong;Zhan, Chaoying;Liu, Xingyun;Zhou, Yi;Zhang, Chi;Zhang, Yingbo;He, Mengqiao;Ren, Shumin;Shen, Bairong;Tang, Tong;Liu, Xingyun;Zhang, Chi;Zhang, Yingbo
作者机构：
关键词： Oligometastatic cancer; Metastasis; Knowledge platform; Disease heterogeneity; Personalized treatment
期刊名称：COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL （ 影响因子：4.1； 五年影响因子：5.2 ）
ISSN： 2001-0370
年卷期： 2024 年 24 卷
页码：
收录情况： SCI
摘要： Patients with oligometastatic cancer (OMC) exhibit better response to local therapeutic interventions and a more treatable tendency than those with polymetastatic cancers. However, studies on OMC are limited and lack effective integration for systematic comparison and personalized application, and the diagnosis and precise treatment of OMC remain controversial. The application of large language models in medicine remains challenging because of the requirement of high-quality medical data. Moreover, these models must be enhanced using precise domain-specific knowledge. Therefore, we developed the OligoM-Cancer platform (http://oligo. sysbio.org.cn), pioneering knowledge curation that depicts various aspects of oligometastases spectrum, including markers, diagnosis, prognosis, and therapy choices. A user-friendly website was developed using HTML, FLASK, MySQL, Bootstrap, Echarts, and JavaScript. This platform encompasses comprehensive knowledge and evidence of phenotypes and their associated factors. With 4059 items of literature retrieved, OligoMCancer includes 1345 valid publications and 393 OMC-associated factors. Additionally, the included clinical assistance tools enhance the interpretability and credibility of clinical translational practice. OligoM-Cancer facilitates knowledge-guided modeling for deep phenotyping of OMC and potentially assists large language models in supporting specialised oligometastasis applications, thereby enhancing their generalization and reliability.
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