Prognosis prediction model for patients with metastatic prostate cancer at first diagnosis and establishment method and application thereof
A technology of prostate cancer and prediction model, which is applied in the fields of medical automatic diagnosis, medical informatics, informatics, etc., to achieve the effect of convenient use, good prediction accuracy and discrimination.
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Embodiment 1
[0063] Example 1 Establishment of CFS and OS Prognostic Model and Visual Presentation of Patients with Newly Diagnosed Metastatic Prostate Cancer
[0064] The establishment of the CFS and OS prognosis model of newly diagnosed metastatic prostate cancer patients of the present invention comprises the following steps:
[0065] (1) A total of 449 patients who were diagnosed with newly diagnosed metastatic prostate cancer in West China Hospital of Sichuan University between 2011 and 2016 were collected. All patients received standard maximal androgen deprivation therapy after diagnosis of prostate cancer, that is, chemical or surgical castration combined with bicalutamide or flutamide antiandrogen therapy. After a median follow-up of 50 months, 286 patients entered the mCRPC stage and 164 patients died. The median CFS and OS were 26.4 months (95% CI: 21.8-31.0 months) and 57.0 months (95% CI: 48.9-65.1 months), respectively.
[0066] (2) Collect clinical and pathological data of...
Embodiment 2
[0074] Example 2 Verification of CFS and OS Prognostic Models in Patients with Newly Diagnosed Metastatic Prostate Cancer
[0075] The model obtained in Example 1 was verified using 135 patients in the verification group.
[0076] The verification of the model is carried out by three methods: C-index index, consistency curve analysis and decision curve analysis. The specific steps are as follows:
[0077] (1) The C-index index can reflect the prediction accuracy of the model in predicting CFS and OS or the ability to distinguish the prognosis.
[0078] C-index was calculated by R software. The C-index of the prognostic model for predicting CFS and OS were 0.762 and 0.723, respectively, showing good prediction accuracy and discrimination of the model.
[0079] (2) The consistency curve is used to reflect the predicted probability of mCRPC or death and the actual proportion of mCRPC or death of patients in the validation group predicted by the model at the 12th month, 24th mo...
Embodiment 3
[0082] Example 3 Establishment of CFS and OS Prognostic Grading Tool for Patients with Newly Diagnosed Metastatic Prostate Cancer
[0083] In order to use the prognostic model more conveniently in clinical practice, and to more intuitively divide patients with newly diagnosed metastatic prostate cancer into different risk groups according to different prognosis conditions, the present invention adds a separate Prognostic risk grading system for CFS and OS.
[0084] The establishment of a prognostic grading tool for CFS and OS in patients with newly diagnosed metastatic prostate cancer includes the following steps:
[0085] (1) According to the Beta value of each influencing variable in predicting CFS and OS in the multivariate COX proportional hazard model shown in Table 1, the weight of these variables in predicting CFS and OS is scored, and each patient is based on The state of 6 variables including prostate cancer Gleason score, presence of IDC-P, pre-treatment ALP level, ...
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