CN109493969B - Model for evaluating prognosis of patients with Paget's disease complicated with invasive ductal carcinoma and application of model - Google Patents
Model for evaluating prognosis of patients with Paget's disease complicated with invasive ductal carcinoma and application of model Download PDFInfo
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Abstract
The invention discloses a model for evaluating the prognosis of a patient with Paget's disease complicated with invasive ductal carcinoma of breast, which comprises a nomogram for evaluating the specific survival probability of the patient after 5 years and a prognosis risk scoring formula of the patient with Paget's disease complicated with invasive ductal carcinoma of breast. The nomogram is a unique survival prediction model which is established for PD-IDC people for the first time, and the PD-IDC model can more accurately predict the survival prognosis of special people; in the present invention, it was demonstrated that its predicted potency is superior to the other two (AJCC staging and NPI staging); secondly, the PD-IDC nomogram is concise, simple, popular and easy to understand, is convenient for clinicians and patients to operate per se, and predicts the specific survival probability of the breast cancer of 5 years and 10 years; meanwhile, the patient score calculated according to the risk scoring formula in the model can clearly distinguish high-risk and low-risk groups, so that a clinician is assisted to prepare an efficient treatment scheme.
Description
Technical Field
The invention relates to the technical field of prognosis evaluation of patients with invasive ductal carcinoma, in particular to a model for evaluating prognosis of patients with breast Paget's disease with invasive ductal carcinoma and application thereof.
Background
Mammary Paget Disease (PD) was first reported by Velpeau in 1856, and James Paget subsequently described a group of syndromes including erythema, eczema, ulceration, bleeding and itching in the areola papillae region in 1874 and named PD. PD is relatively rare, accounting for 1-3% of all breast malignancies. In up to 82% to 93% of PD patients, PD is accompanied by potentially Invasive Ductal Carcinoma (IDC) or Ductal Carcinoma In Situ (DCIS). Diagnosis of breast PD is characterized by histopathological infiltration of balloon-like tumorous Paget's cells. The pathogenesis of PD remains controversial to date. A more widely accepted theory is the migration theory, that is, Paget's cells are ductal carcinoma cells migrating from the ductal mammary gland to the epidermis of the papilla. Another theory of transformation is that Paget's cells are thought to transform into cancerous glandular cells in situ in the papillary epidermis. Both theories suggest that the disease progression, survival prognosis for PD with IDC or DCIS may be different from IDC or DCIS alone.
The literature suggests that the presence of PD in IDC patients may be associated with poor prognosis and reduced survival. Other studies summarize the clinical pathology of patients with PD accompanied by IDC (PD-IDC) by comparison with IDC alone. Although the breast cancer NCCN guidelines suggest that treatment strategies should be tailored to the stage and pathological characteristics of IDC lesions for PD-IDC patients, PD-IDC is actually a disease with its own characteristics, and a specific method is actually needed to estimate the survival rate of PD-IDC patients to help optimize treatment decisions.
At present, no special survival prognosis judgment method exists for the specific population. According to the NCCN guidelines for breast cancer, if prognosis is determined simply from IDC associated with PD-IDC patients, the prior art approaches include AJCC breast cancer staging and the Nottingham Index (NPI).
First, AJCC staging combines three assessments, including tumor size (T stage), number of lymph node metastases (N stage), and the presence or absence of distant metastases (M stage). AJCC (TNM) staging, which is used as a basic prognostic evaluation tool with the most extensive clinical application, has well-established prognostic prediction value on breast cancer. However, AJCC staging was not tailored to the PD-IDC specific type of breast cancer population.
Another prognostic evaluation index for breast cancer is NPI, which comprehensively considers tumor size, lymph node metastasis count, and histological grade of tumor by modeling. The calculation formula is as follows: NPI ═ (S x 0.2.2) + N + G, S is the tumor maximum diameter (cm), N is the lymph node metastasis rating, and G is the histological rating. Calculating an NPI score, and classifying the breast cancer patients into low-risk and middle-risk high-risk groups according to the score, wherein the specific points are shown in official websites: (http:// www.farmacologiaclinica.info/scales/nottingham-prognotic-index /). Similarly, NPI is not a predictive model established for the PD-IDC population. Neither AJCC nor NPI take into account some of the clinical pathological features of PD-IDC as opposed to IDC alone. Therefore, predicting the prognosis of PD-IDC populations is not accurate enough.
Disclosure of Invention
Based on the above problems, the present invention aims to overcome the disadvantages of the prior art and provide a model capable of more accurately predicting the prognosis of PD-IDC patients.
In order to achieve the purpose, the technical scheme adopted by the invention comprises the following aspects:
in a first aspect, the present invention provides a nomogram for use in assessing the prognosis of a patient with Paget's disease with invasive ductal carcinoma in the breast, said nomogram comprising a scale of scores in a first row, wherein the scores range from 0 to 10; selecting 1 for the skin symptoms of the patient in the second behavior, and selecting 0, 0 or 1 for the corresponding score of the first behavior if the skin symptoms are accompanied; the third row represents the patient's age, and the different age groups correspond to a corresponding score in the first row; the fourth line is the NPI classification of the patients, wherein low-risk selection 1, medium-risk selection 2 and high-risk selection 3; the fifth row is the tumor position of the patient, wherein 1 is selected for central tumors and 2 is selected for non-central tumors, and the values correspond to the corresponding scores of the first row respectively; the sixth behavior is the total score of the patient, and the scores of the 4 indexes from the second row to the fifth row corresponding to the first row are added to obtain the total score of the patient; and the specific survival probability of the patient 5 years later is obtained by correspondingly projecting the total score of the patient in the sixth row to the seventh row. The positions of the second line and the fifth line can be replaced with each other as long as the total score of the patient can be calculated based on the score scale of the first line.
Preferably, the nomogram further comprises a specific chance of survival 10 years after the patient in the eighth row, and projecting the total patient score in the sixth row onto the eighth row correspondingly yields a specific chance of survival 10 years after the patient. The seventh row may also be the patient's specific survival probability 3 years, 4 years later, as desired; the eighth row may also be the specific survival rate of the patient 8, 9, 11 years later, at which time the range of the survival rate and the position of the ruler relative to the sixth row may be adjusted accordingly.
In a second aspect, the invention provides a model for assessing the prognosis of a patient with Paget's disease associated with invasive ductal carcinoma in the breast, the model comprising the nomogram described above.
Preferably, the model further comprises a prognostic risk score formula for patients with Paget's disease-associated invasive ductal carcinoma, said formula being: risk score 0.5071 × skin symptoms +0.0166 × age +1.1364 × NPI stratification +0.2247 × tumor location, wherein the risk score has 4.28 as the optimal cutoff value, and a patient is a low-risk patient when the risk score is lower than 4.28, and a patient is a high-risk patient when the risk score is greater than or equal to 4.28. Therefore, the patient can be divided into a high-risk group and a low-risk group through the model, and doctors are assisted to make efficient treatment schemes.
More preferably, the skin condition is a skin condition of a patient with Paget's disease with invasive ductal carcinoma, 1 is selected if the patient has a skin condition, and 0 is selected if the patient does not have a skin condition; the NPI classification is the NPI classification of patients with Paget's disease complicated with invasive ductal carcinoma, wherein low-risk selection 1, medium-risk selection 2 and high-risk selection 3 are adopted; the tumor position is the tumor position of a patient suffering from Paget's disease complicated with invasive ductal carcinoma, wherein 0 is selected as a central tumor, and 1 is selected as a non-central tumor.
In a third aspect, the invention provides the use of the model described above for assessing the prognosis of a patient with Paget's disease associated with invasive ductal carcinoma in the breast.
In a fourth aspect, the present invention provides a model construction method for assessing the prognosis of a patient with Paget's breast disease with invasive ductal carcinoma, comprising the steps of:
(1) selecting patients diagnosed with Paget's disease complicated with invasive ductal carcinoma of breast, excluding stage IV breast cancer, excluding patients with incomplete clinical pathological information record, excluding inflammatory breast cancer, and excluding patients with follow-up visit time less than 6 months after diagnosis;
(2) defining T4b and T4c patients among the patients screened in step (1) as tumors with "skin symptoms" according to NCCN guidelines;
grouping according to the position of the tumor of the patient, wherein the tumor is positioned around, below and within 1 cm of the areola complex as the centrality, and the other parts are not centrality;
and using the NPI formula (see background) to generate an NPI classification for the patient;
(3) screening indexes influencing the specific survival of the breast cancer by adopting single factor analysis, and finally selecting age, whether skin symptoms, tumor parts, NPI classification, ER state, operation, chemotherapy and radiotherapy are accompanied to carry out multi-factor analysis;
next, multi-factor analysis using the Cox proportional hazards model showed that significant factors affecting breast cancer-specific survival included the patient's skin symptoms, NPI classification, tumor site, and age, and thus age, skin symptoms, tumor site, and NPI classification were selected to establish the model.
In conclusion, the beneficial effects of the invention are as follows:
firstly, a (PDIDC) nomogram disclosed by the invention is a unique survival prediction model which is firstly established for PD-IDC population, and is established based on the whole breast cancer population no matter AJCC staging or NPI grading, wherein most of the population is pure IDC population; therefore, the PDIDC model can theoretically more accurately predict the survival prognosis of the special population; also, in the present invention, it was confirmed that its predicted potency is significantly better than the other two (AJCC staging and NPI staging);
secondly, the PDIDC nomogram is concise, popular and easy to understand, and is convenient for clinicians and patients to operate per se, so that the specific survival probability of the breast cancer of 5 years and 10 years is predicted; meanwhile, the risk score of the patient calculated according to the risk score formula in the model can clearly distinguish high-risk and low-risk groups, so that a clinician is assisted to prepare an efficient treatment scheme.
Drawings
FIG. 1 is a nomogram for predicting PD-IDC population survival time in accordance with the present invention;
FIG. 2 is a graph of time-to-survival probability of PD-IDC patients (K-M method);
FIG. 3 is a calibration curve of the patient's 5-year probability of survival predicted using the model of example 1 versus the patient's true 5-year probability of survival;
FIG. 4 is a calibration curve of the patient's 10-year probability of survival predicted using the model of example 1 versus the patient's true 10-year probability of survival;
FIG. 5 is a ROC curve of PD-IDC nomograms and AJCC staging, NPI grading for predicting BSCC in patients from example 1.
Detailed Description
The invention relates to the field of clinical medicine, and discloses a model which is established based on large clinical pathological feature data of a breast Paget's invasive ductal carcinoma population and can be used for evaluating and predicting survival prognosis of the population individuals, and the model is applied in a nomogram form; the method can make more accurate judgment and prediction on the prognosis of special population with breast Paget's invasive ductal carcinoma, and provides a powerful reference basis for clinical treatment decision. The inventor of the application develops a nomogram based on clinical pathological factor characteristics for predicting survival prognosis of a specific population aiming at the population PD-IDC, and models and verifies are carried out in the population PD-IDC.
To better illustrate the objects, aspects and advantages of the present invention, the present invention will be further described with reference to the accompanying drawings and specific embodiments.
Example 1
One embodiment of the present invention for constructing a prognostic model for evaluating patients with breast Paget's disease complicated with invasive ductal carcinoma, comprises the following steps:
(1) modeling crowd screening:
a total of 2502 patients diagnosed with PD-ICD from 1988 to 2015 were included in the study cohort. Median follow-up time was 77 months, with an average follow-up time of 92.9 months. Before the study expiration date, 477 PD-IDC patients died due to breast cancer-related causes and 344 patients died due to other causes. The discharge standard of part of people is as follows: the patients with stage IV breast cancer, incomplete clinical pathological information record, inflammatory breast cancer and follow-up visit time less than 6 months after diagnosis are excluded.
(2) Establishing indexes: in the study cohort, 10.31% had T4 tumors, 8 of which (3.1%) were T4a, 206 (79.8%) were T4b, and 44 (17.1%) were T4 c. Then, according to the definition of T4 in the NCCN guideline, the inventors defined T4b patients (ulcers and/or ipsilateral macroscopic satellite nodules and/or skin edema that did not meet the criteria for inflammatory cancer) and T4c patients (T4a and T4b) as tumors with "skin symptoms". Furthermore, the inventors grouped the location of tumors, with tumors located around, below, and within 1 cm of the areola complex as "central" and other sites as "non-central". To combine tumor size, histological grade and number of positive lymph nodes into one factor, the inventors used the NPI formula to generate NPI classifications for the study population.
(3) Firstly, screening indexes influencing Breast Cancer Specific Survival (BCSS) by adopting single factor analysis; finally selecting age, whether skin symptoms are accompanied, tumor site, NPI classification, ER state, operation, chemotherapy and radiotherapy for multi-factor analysis;
multifactorial analysis using the Cox proportional hazards model showed that NPI classification (medium and low risk, HR 2.17, 95% CI 1.51-3.14, P0.000; high and low risk, HR 7.26, 95% CI 4.96-10.63, P0.000), skin symptoms (with and without, HR 1.76, 95% CI 1.34-2.32, P0.000), tumor sites (noncentral location, HR 1.2595% CI 1.07-1.56, P0.042), age (HR 1.01, 95% CI 1.01-1.03, P0.001) and surgery (unknown mastectomy, HR 1.74, 95% CI 1.43-2.13, P0.000) were significant factors affecting the ss. Thus, the present example selects age, skin condition, tumor site and NPI classification to establish a predictive nomogram.
(4) Constructing an alignment chart:
as shown in fig. 1, the nomogram for predicting the survival time of PD-IDC population established in this example is named PDIDC nomogram. The nomogram can be used to predict 5-year and 10-year breast cancer specific survival in PD-IDC populations. A first action score scale of the nomogram; whether the patient with the second behavior is accompanied by skin symptoms, selecting ' with ' skin symptoms ', selecting ' without ' skin symptoms, and respectively corresponding to a corresponding score of the first row; the third row represents the patient's age, with different age groups corresponding to a corresponding score for the first row; the fourth row represents the NPI grading of the patients, and the low risk, the medium risk and the high risk respectively correspond to the corresponding scores of the first row; the fifth row is the patient's tumor location, and the "central" and "non-central" tumors correspond to the respective scores of the first row, respectively; and (3) adding the scores corresponding to the 4 indexes of the second row to the fifth row in the first row to obtain a total score of the patient in the sixth row, and correspondingly projecting the total score to the seventh row and the eighth row to obtain the specific survival probability of the breast cancer of the patient in 5 years and 10 years respectively.
For example, if a 30 year old (1 point) PD-IDC patient is received; its tumor is not accompanied by skin symptoms, the second row is "none" (score 0); the NPI is classified as high risk, and the fourth row selects '3' (10 points); for noncentral tumors, the fifth row selects "noncentral" (score 1); the total score of the patient is 12, which corresponds to 75% survival for 5 years and 65% survival for 10 years.
(5) And (3) risk scoring:
in addition, the risk score of each PD-IDC patient can be obtained by calculating the risk score of the patient according to the parameters screened in the step (3), and the risk score can be divided into a low-risk group and a high-risk group according to the risk score. The calculation formula is as follows:
the Risk score (Risk score) is 0.5071 × skin symptoms (no 0, 1) +0.0166 × age +1.1364 × NPI classification (low Risk 1, medium Risk 2, high Risk 3) +0.2247 × tumor location (central 1, non-central 2), wherein 4.28 is used as the optimal cut-off value to distinguish high-Risk and low-Risk patients, and if the patient score is greater than or equal to 4.28, the patient is high-Risk patient, and if the patient Risk score is less than 4.28, the patient is low-Risk patient. The Kaplan-Meier method time-survival curve is drawn for the actual survival of the high-risk and low-risk patients distinguished by the models, and a difference graph (figure 2) of the actual survival of the two groups of people is obtained. Log-rank test is carried out on the survival of the two groups of patients, and the significant difference (P <0.0001) between the survival of the high-risk patients and the survival of the low-risk patients is confirmed.
As shown in FIG. 2, the total survival probability of PD-IDC low-risk patients is 77.8%, and the total survival probability of high-risk patients is 31.1%, which are significantly different. Thus, the model of the present invention can also differentiate patients into high-risk and low-risk groups, thereby guiding the formulation of treatment regimens.
One embodiment of the model for assessing the prognosis of a patient with Paget's invasive ductal carcinoma in the breast of the present invention comprises the nomogram in step (4) and the formula for calculating the risk score in step (5) as described above.
Example 2 calibration curves and C-index were used to verify the prediction accuracy of the model in example 1
The index C ranges from 0 to 1, the prompt model with the index C being greater than 0.5 has better discrimination, and the closer to 1, the better the discrimination of the prompt model is. Based on the patient population data screened in example 1, for the in-model validation of example 1, the inventors used the boottrap method, in which 500 random samples were replaced 1000 times from the original dataset, and the C-index was recalculated and corrected with 1000 iterations. After verification, the average C index was 0.739 (95% CI, 0.692-0.746), suggesting that the model of example 1 is well differentiated. The inventors then plotted a calibration curve for predicted and observed 5-and 10-year survival using 1000 boottraps.
FIG. 3 shows the predicted 5-year survival probability and the true 5-year survival probability of the patient, with the predicted 5-year survival probability on the abscissa and the true 5-year survival probability on the ordinate, the closer the curve is to 45 degrees, the higher the prediction accuracy of the model is. FIG. 4 shows the predicted 10-year survival probability and the true 10-year survival probability of the patient, with the predicted 10-year survival probability on the abscissa and the true 10-year survival probability on the ordinate, showing that the accuracy of the model for predicting the 5-year and 10-year breast cancer specific survival probability of PD-IDC patients is high.
Example 3 comparison of PD-IDC nomograms and AJCC staging, NPI fractionation of example 1
Based on the patient population data screened in example 1, the inventors compared the PD-IDC nomogram of example 1 with AUC (area under subject working curve) between AJCC staging, NPI staging, which predicts 5-year and 10-year BCSS, respectively. In addition, the C-index was calculated to compare the performance of the three prediction methods.
The predicted results show that the C index of the PD-IDC nomograms is 0.791 (95% CI, 0.783-0.818), the AJCC stage is 0.718 (95% CI, 0.695-0.741), and the NPI classification is 0.691 (95% CI, 0.650-0.735). The C index of PD-IDC is significantly better than AJCC staging (P0.000) and NPI staging (P0.000).
In FIG. 5, the solid light gray line is the ROC curve of PD-IDC alignment, the solid black line is the ROC curve of AJCC stage, and the dotted line is the ROC curve of NPI stage. The upper left is the ROC of 5-year BCSS predicted by PD-IDC nomograms, and the lower left is the ROC of 5-year BCSS predicted by AJCC stages and NPI; the top right is the ROC for PD-IDC nomogram prediction of 5-year BCSS, and the bottom right is the ROC for AJCC staging and NPI prediction of 5-year BCSS. As can be seen in fig. 5, AUC for PD-IDC was higher than NPI stratification and AJCC stratification in predicting BCSS both 5 and 10 years, demonstrating that PD-IDC nomograms can more accurately predict prognosis for PD-IDC patients.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the protection scope of the present invention, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (5)
1. A model for assessing the prognosis of a patient with breast Paget's disease with invasive ductal carcinoma, wherein the model comprises a nomogram;
the alignment chart comprises a score scale in a first row, wherein the score range is 0-10; selecting 1 for the skin symptoms of the patient in the second behavior, and selecting 0, 0 or 1 for the corresponding score of the first behavior if the skin symptoms are accompanied; the third row represents the patient's age, and the different age groups correspond to a corresponding score in the first row; the fourth line is the NPI classification of the patients, wherein low-risk selection 1, medium-risk selection 2 and high-risk selection 3; the fifth row is the tumor position of the patient, wherein 1 is selected for central tumors and 2 is selected for non-central tumors, and the values correspond to the corresponding scores of the first row respectively; the sixth behavior is the total score of the patient, and the scores of the 4 indexes from the second row to the fifth row corresponding to the first row are added to obtain the total score of the patient; the specific survival probability of the patient 5 years later in the seventh behavior is obtained by correspondingly projecting the total score of the patient in the sixth behavior to the seventh behavior;
the nomogram also comprises the specific survival probability of the patients in the eighth row after 10 years, and the specific survival probability of the patients in 10 years is obtained by correspondingly projecting the total score of the patients in the sixth row to the eighth row.
2. The model of claim 1, further comprising a prognostic risk scoring formula for patients with Paget's disease-associated invasive ductal carcinoma, said formula being: risk score 0.5071 × skin symptoms +0.0166 × age +1.1364 × NPI stratification +0.2247 × tumor location, wherein the risk score has 4.28 as the optimal cutoff value, and a patient is a low-risk patient when the risk score is lower than 4.28, and a patient is a high-risk patient when the risk score is greater than or equal to 4.28.
3. The model of claim 2, wherein the skin condition is a skin condition of a patient with Paget's disease with invasive ductal carcinoma, and wherein 1 is selected if the patient has a skin condition and 0 is selected if the patient has no skin condition; the NPI classification is the NPI classification of patients with Paget's disease complicated with invasive ductal carcinoma, wherein low-risk selection 1, medium-risk selection 2 and high-risk selection 3 are adopted; the tumor position is the tumor position of a patient suffering from Paget's disease complicated with invasive ductal carcinoma, wherein 0 is selected as a central tumor, and 1 is selected as a non-central tumor.
4. A method of using the model of any one of claims 1 to 3 for assessing the prognosis of a patient with Paget's disease associated invasive ductal carcinoma in the breast.
5. A model construction method for assessing prognosis of patients with breast Paget's disease complicated with invasive ductal carcinoma, comprising the steps of:
(1) selecting patients diagnosed with Paget's disease complicated with invasive ductal carcinoma of breast, excluding stage IV breast cancer, excluding patients with incomplete clinical pathological information record, excluding inflammatory breast cancer, and excluding patients with follow-up visit time less than 6 months after diagnosis;
(2) defining T4b and T4c patients among the patients screened in step (1) as tumors with "skin symptoms" according to NCCN guidelines;
grouping according to the position of the tumor of the patient, wherein the tumor is positioned around, below and within 1 cm of the areola complex as the centrality, and the other parts are not centrality;
and generating an NPI classification for the patient using the NPI formula;
(3) screening indexes influencing the specific survival of the breast cancer by adopting single factor analysis, and finally selecting age, whether skin symptoms, tumor parts, NPI classification, ER state, operation, chemotherapy and radiotherapy are accompanied to carry out multi-factor analysis;
then, a multi-factor analysis using a Cox proportional hazards model shows that significant factors affecting breast cancer specific survival include skin symptoms, NPI classification, tumor site and age of the patient, whereby age, skin symptoms, tumor site and NPI classification are selected to establish the model of any one of claims 1-3;
the NPI formula is: NPI ═ N + G (S x 0.2.2) + S, S is the tumor maximum diameter cm, N is the lymph node metastasis rating, and G is the histological rating.
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