Chinese Academy of Medical Sciences

Xinming Zhao

Qing Zhao

Hongmei Zhang

Hongxia Zhong

A tumor diagnosis system and a construction method thereof, a terminal device and a storage medium are provided, wherein a radiomics signature is obtained by a radiomics signature-generation module in the tumor diagnosis system according to target radiomics information and clinical data information, based on a pre-trained radiomics signature model; a first classification comprehensive diagnosis result is obtained by a benign and malignant comprehensive classification module according to a ambient situation of a lesion outside an intestinal wall, the clinical data information and a first radiomics signature, based on a pre-trained first classification comprehensive diagnosis model; a second classification comprehensive diagnosis result is generated through a malignant focus sub-classification module based on a pre-trained second classification comprehensive diagnosis model, if the first classification comprehensive diagnosis result includes high-risk malignant information; and a final diagnosis result is obtained and displayed in a result displaying module.

Tumor diagnosis system and construction method thereof, terminal device and storage medium

A system for predicting microsatellite instability and a construction method thereof, a terminal device and a medium are provided. Target image information, pathological specimen information and clinical data information of a user to be predicted are acquired by an acquisition module in the system for predicting microsatellite instability; a radiomics signature is generated according to the target image information, and a pathomics signature is generated according to the pathological specimen information, by a signature generation module, based on a pre-trained MSI-H/dMMR multi-omics signature model; and MSI-H/dMMR prediction results are generated according to the radiomics signature, the pathomics signature and the clinical data information, by a prediction generation module, based on a pre-trained MSI-H/dMMR prediction model.

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Beijing, China

Xinming Zhao