Mathematical model for predicting therapeutic effect of colorectal cancer liver metastasis

Abstract

The present invention discloses a mathematical model for predicting a therapeutic effect of colorectal cancer liver metastasis. The model is Pcet=exp(Y<^>Cet) / (1+exp(Y<^>Cet)), wherein, Y<^>Cet=1.516-0.165XA-0.726XC-1.140XE-0.944XL-0.477XR+0.821XP. According to the mathematical model for predicting the therapeutic effect of colorectal cancer liver metastasis, after screening a population sensitiveto cetuximab, the objective response rate is increased significantly, and thus the model can be used for the prediction of a treatment mode well.

Description

technical field [0001] The invention relates to the field of tumor diagnosis and treatment, in particular to a mathematical model for mid-effect prediction of colorectal cancer liver metastasis. Background technique [0002] Colorectal cancer is a common malignant tumor worldwide. In my country, its morbidity and mortality are increasing year by year, ranking fifth among all malignant tumors. The liver is the main target organ of hematogenous metastasis of colorectal cancer, and more than half of the patients will develop liver metastasis. Therefore, the diagnosis and treatment of liver metastases is one of the core contents to improve the overall prognosis of colorectal cancer. At present, surgical resection of liver metastases is still the best treatment for colorectal cancer liver metastases, but the vast majority of patients have lost the opportunity for surgery at the time of diagnosis. For these patients, a comprehensive treatment strategy with systemic chemotherapy...

AI Technical Summary

Problems solved by technology

Judging from the current research progress, on the one hand, although many potential predictive targets have been found in previous studies, none of them have been widely used in clinical practice.

In addition to poor prediction performance, the lack of simple detection methods and unified evaluation standards are also important reasons that hinder the transformation of predicted targets into clinically available targets; on the other hand, there are still a considerable number of drug resistance phenomena that cannot be detected by known genetic changes explanation, and researchers generally believe that there are unknown targets that affect the EGFR pathway in more "distant" and more complex ways

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