[title missed]

Corresponding author: zhou yong, 332090635@qq.com

DOI: 10.12201/bmr.202601.00080

•  

  Abstract: Pancreatic cancer is a digestive tract tumor with extremely high malignancy. Its early symptoms are not obvious, and the diagnosis is often delayed, resulting in generally poor prognosis for patients. In recent years, the combination of radiomics and deep learning has brought new progress to the precise diagnosis and treatment of pancreatic cancer. Radiomics technology uses high - throughput methods to extract a large number of quantitative features from medical images and constructs prediction models based on these features, showing significant value in the early detection, differential diagnosis, grading and staging, and prognosis evaluation of pancreatic cancer. Deep learning can automatically learn the deep and complex features of images, greatly improving the automation level and accuracy of tumor segmentation, recognition, and classification. Currently, most studies combine the advantages of both to build multimodal fusion models to more accurately predict gene phenotypes, treatment responses, and patient survival outcomes, which are expected to promote the substantial application of clinical decision - support systems in the future.

  Key words: Pancreatic cancer; Radiomics; Deep learning; Artificial intelligence; Precision medicine

  Submit time: 27 January 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
• html

• 图表

• 孟. Advances in deep learning-based Artificial Intelligence techniques in gastrointestinal stromal tumors.. 2024. doi: 10.12201/bmr.202411.00057

  ZHAO Qing, JING Jiangxin. Advances in the application of ultrasound-based radiomics in predicting molecular typing of breast cancer. 2024. doi: 10.12201/bmr.202410.00040

  Zhaoqing, Jing Jiangxin. Progress and status of ultrasound imaging in breast cancer. 2024. doi: 10.12201/bmr.202412.00053

  李明峻, Zhai Ning. Advances and prospects of artificial intelligence in precision phenotyping of heart failure. 2025. doi: 10.12201/bmr.202512.00030

  SHU Qijin. Experience of Shu Qijin in Treating Pancreatic Cancer from Stagnant Toxin due to Spleen Deficiency. 2025. doi: 10.12201/bmr.202507.00004

  LuoYanhong, GuoYarong. Construction of a prediction model for postoperative survival of pancreatic cancer based on SMOTE-ENN combined with XGBoost algorithm. 2025. doi: 10.12201/bmr.202506.00058

  ZhengYanli, Han Fuhai, LI Shuyu, SU Wenxing. Application Status and Prospect of Artificial Intelligence Large Models in Medicine. 2023. doi: 10.12201/bmr.202312.00027

  Shi Shenghong. . 2024. doi: 10.12201/bmr.202411.00062

  zhang chen, chen hui, cao feng, wang yueqi, ke ren. Research progress of automatic segmentation of left atrial CTA image based on deep learning. 2025. doi: 10.12201/bmr.202503.00042

  WEI Yuguo, LIU Liqiu, XU Zhuliang, WANG Guoyu. Application value of biparametric magnetic resonance radiomics combined with prostate-specific antigen density in Gleason grade group of prostate cancer. 2024. doi: 10.12201/bmr.202406.00029

• ID	Submit time	Number	Download
  1	2026-01-15	10.12201/bmr.202601.00080V1	Download

• Public  Anonymous  To author only Submit Comments