A survival prediction system and prediction method for t3-larc patients before treatment

Abstract

The invention discloses a survival prediction system for T3‑LARC patients before treatment, which includes a risk factor acquisition module, a risk factor preprocessing module, a risk factor comprehensive analysis module, an operator selection module, a survival model generation module and a survival rate display In addition, the present invention also provides a prediction method for the survival prediction system of T3‑LARC patients before treatment. The present invention can provide clinical doctors with individualized risk factor analysis of patients before treatment, predict the recurrence rate and mortality of patients in N years after operation, and provide references for clinicians to formulate individualized treatment and follow-up plans, which has important significance Clinical significance, can greatly improve the prognosis of patients, prolong survival and improve quality of life.

Description

technical field [0001] The invention relates to the technical field of survival prediction, in particular to a survival prediction system and method for T3-LARC patients before treatment. Background technique [0002] Patients with T3 stage locally advanced rectal cancer (T3-LARC) are the most common type of first-episode rectal cancer with the greatest difference in prognosis. Individualized survival prediction for patients at the initial stage of treatment is helpful for early formulation of treatment decisions. Improving overall prognosis helps a lot. Previous domestic and foreign related studies only analyzed the prognostic effect of one or two specific imaging factors, and there was no selectivity for the staging of patients, so it was impossible to obtain imaging individuals specifically for the specific population of T3-LARC. Model software that comprehensively evaluates and predicts survival risk. At present, only a few studies have used postoperative pathological ...

AI Technical Summary

Problems solved by technology

In summary, the current technical background has non-specificity in patient selection (not targeting T3-LARC), incompleteness of factor analysis (selective study of certain 1-2 factors) and lag in prediction (based on surgical The prediction of post-pathology is already at the end of treatment, and the first-diagnosis imaging can realize the prediction before treatment) may be due to the small sample size and unreasonable design of previous studies, which are only satisfied with exploring a certain angle of a certain factor Significance, or only satisfied with the prediction of postoperative pathology, but cannot explore the probability prediction model of multi-factor combination before treatment from a comprehensive perspective

[0003] In addition, the previous literatures only studied the impact of a certain MRI sign on prognosis, but did not comprehensively analyze and obtain a predictive model

Images