Ping An AskBob Doctor’s Research on Risk Prediction Approach to Tailor Cancer Treatments Published in International Journal JNCI
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The research team used magnetic resonance imaging (MRI) images and clinical data of over 3,000 NPC patients from four hospitals, including SYSUCC. The data was used to train the AI model to estimate the risk of the condition of NPC patients deteriorating in next five years and assign an overall score. Doctors input the original MRI images and clinical factors, then the system generated the prognoses for NPC patients automatically. The system displayed a concordance index of 0.78, which presented a 23% improvement on the conventional Tumor, Node, Metastasis (TNM) Staging System, the globally accepted standard to describe the extent of a primary tumor and the spread of cancer in a patient.
The incorporation of Deep Neural Networks and an ensemble learning model significantly improved the precision of AskBob Doctor's prognoses. The overall scores were used to distinguish low-risk patients from high-risk patients. The system also analyzed the treatment standards and recommended more precise treatment solutions for patients in various risk groups, improving the treatment outcomes of high-risk patients. While high-risk patients are shown to benefit from a combination of concurrent chemoradiotherapy (CCRT) and induction chemotherapy (IC), IC does not benefit low-risk patients significantly more than CCRT alone. Differentiated treatments can shorten waiting times for radiotherapy for those who need it most, remove the financial burden of IC treatment for patients in low-risk groups.
The AI prognostic predictive system is one of the key functions of AskBob Doctor's system. It has been widely adopted in various application scenarios of
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