CT-based radiomics deep learning to predict lymph node metastasis in tumors

To address the aforementioned challenge, the Tsukuba team has created a predictive model by integrating radiomics features extracted from CT and MRI images using artificial intelligence deep-learning techniques. This model has demonstrated an 89% success rate in predicting lymph node metastasis, a rate that rises to 91% when the model is validated with data from an external hospital. Furthermore, the performance of the model remains consistent, irrespective of the tumor size being larger or smaller than 2 cm.

In conclusion, the model can help predict lymph node metastasis. Moreover, the model equips surgeons with a crucial tool for selecting the most appropriate surgical procedures and treatment strategies, potentially transforming patient outcomes in the challenging medical field.

References: Wenchao Gu o, Yingli Chen o, Haibin Zhu o, Haidi Chen, Zongcheng Yang, Shaocong Mo, et al. Open AccessPublished:October 19, 2023DOI:https://doi.org/10.1016/j.eclinm.2023.102269