CN115359912A - Gamma knife treatment non-small cell lung cancer brain metastasis tumor prognosis model - Google Patents
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- G—PHYSICS
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Abstract
The invention belongs to the technical field of tumor prognosis evaluation, and relates to a prognosis model for treating non-small cell lung cancer brain metastasis tumor by using a gamma knife. The invention provides a gamma knife treatment prognosis model for patients, brings the patients into study whether focal neurological symptoms exist, KPS scores and whether the number of metastasis exceeds 4, establishes a nomogram, provides a risk score formula and a model score grading evaluation mode, and shows that the survival time of the RPA grading grade-3 patients is respectively statistically different from grade-1 and grade-2, but the grade-2 patients are not statistically different from grade-1, and an ROC curve shows that the AUC value of the RPA grading grade-1 year follow-up is 0.603. Therefore, the scoring model can well predict the survival condition of the patient with the non-small cell lung cancer brain metastasis tumor treated by the gamma knife, is beneficial to guiding the selection of a clinical treatment scheme and the prognosis evaluation, and has a prediction effect obviously better than that of RPA grading.
Description
Technical Field
The invention belongs to the technical field of tumor prognosis evaluation, and relates to a prognosis model for treating non-small cell lung cancer brain metastasis tumor by using a gamma knife.
Background
Lung cancer is the most common tumor in the world today, with non-small cell lung cancer patients accounting for approximately 80% of all lung cancer patients, with 20-40% of non-small cell lung cancer patients developing craniocerebral metastases during their lifetime. There are studies reporting that 10% of patients with non-small cell lung cancer who have undergone early resection surgery develop craniocerebral metastases in the future.
The presence of cranial brain metastases is often predictive of poor patient prognosis. There are studies reporting median survival of only 1 month in untreated non-small cell lung cancer brain metastasis patients. Currently, brain metastasis therapies include surgery, whole Brain Radiation Therapy (WBRT), gamma knife therapy, and combination therapies. The surgical excision of the brain metastasis tumor is strictly applicable, the patient is required to have good self-state and is an intracranial single-metastasis focus, and the metastasis focus at a deeper position and affecting an important functional area is not suitable for surgical treatment. WBRT has been used for decades to treat brain metastases, which can reduce local and distant recurrence, but does not differ in terms of improvement in overall patient survival from gamma knife treatment. And the number of patients presenting with neurocognitive dysfunction after WBRT treatment is significantly increased. In recent years, gamma knife therapy has become the first treatment method for brain metastasis, the gamma knife therapy is single high-dose irradiation, the irradiation dose of surrounding normal tissues is reduced, the nerve cognitive function of patients is less damaged, and compared with WBRT (white blood cell receptor) therapy, although the progression-free survival time is shorter, the overall survival time is not different, so the gamma knife therapy has become the first treatment method for patients with lung cancer and multiple brain metastases.
Accurate prediction of the prognosis of a patient with lung cancer brain metastasis is crucial to the selection of a proper treatment method. At present, an RPA prediction model is one of the most commonly used prognosis prediction models, but due to the pathological nature of primary tumors and the heterogeneity of treatment equipment, no prognosis prediction model specially aiming at gamma knife treatment of non-small cell lung cancer multiple brain metastasis tumors exists at present.
Disclosure of Invention
The invention utilizes the gamma knife treatment data of the single-center non-small cell lung cancer brain metastasis patient to analyze the factors influencing the prognosis of the gamma knife treatment non-small cell lung cancer brain metastasis patient and establish the prognosis scoring model of the gamma knife treatment non-small cell lung cancer brain metastasis tumor.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
the invention reviews 157 cases collected from the analysis center of the second hospital of Shandong university in 2018-2021 in 6 months, adopts a Gamma knife to treat clinical and follow-up visit data of non-small cell lung cancer brain metastatic tumor patients, and utilizes a Gamma Plan system to collect Gamma knife treatment related parameters of the patients. Cox single-factor and multi-factor regression analysis was performed on factors affecting patient prognosis, and the results showed that the overall survival time of patients was related to the presence or absence of focal neurological symptoms (P = 0.009), KPS score (P = 0.013), and number of metastasis over 4 (P = 0.020). And establishing a nomogram according to the obtained risk factors, and evaluating good consistency between nomogram prediction capability and actual observation by using a calibration curve. Meanwhile, based on relevant risk factors, a corresponding prognosis grading model is established, a K-M survival curve is drawn, and an ROC curve shows that the AUC is 0.737 (the closer to 1, the higher the prediction accuracy) in 1-year follow-up visit. The difference between different grades of the scoring model is compared and researched by applying chi-square test, the result shows that the 3-grade vs 1 grade (P < 0.001 and 2 grade (P = 0.012) and the 2-grade vs 1 grade (P = 0.003)) are statistically different, meanwhile, the model and the model RPA are compared and analyzed, the result shows that the survival time of the RPA grade 3 patients is respectively statistically different from that of the grade 1 (P = 0.026) and that of the grade 2 (P = 0.043), but the survival time of the grade 2 and that of the grade 1 (P = 0.550) are not statistically different, and an ROC curve shows that the AUC value of the RPA grade 1 year follow-up is only 0.603 and is obviously lower than 0.737 of the model, so the scoring model can well predict the survival condition of the gamma knife-treated non-small cell lung cancer brain metastasis patients, and is beneficial to guide the clinical treatment scheme selection and prognosis, and the prediction effect is obviously better than that of the RPA grade.
Bringing the patients into study whether focal nerve functional symptoms exist, KPS scores and whether the number of metastasis exceeds 4, establishing a nomogram, wherein the nomogram comprises a score scale of a first row, the score range is-5, whether focal nerve functional symptoms exist in a second row, no focal nerve functional symptoms are assigned for 0, focal nerve functional symptoms are assigned for 1, and 0 or 1 respectively corresponds to a corresponding score of the first row; the third line is KPS scoring, the KPS scoring is less than 70 points and is assigned 0 points, the KPS scoring is more than or equal to 70 points and is assigned-3 points, and the KPS scoring corresponds to a corresponding scoring of the first line; the fourth row indicates whether the number of the transfer stoves exceeds 4, the number of the transfer stoves is less than or equal to 4 and is assigned 0 point, the number of the transfer stoves is greater than 4 and is assigned 3 points, and the number of the transfer stoves corresponds to a corresponding score of the first row.
The prognostic risk score formula is as follows:
the patient prognostic risk assessment model is as follows: the risk score =0.8882 × focal neurological symptom-0.7192 × KPS score +0.9144 × shift focus score, wherein the risk score has-0.7192 as the optimal cutoff value, and when the risk score of the patient is lower than-0.7192, the patient is a low risk patient, and the risk score is greater than or equal to-0.7192, the patient is a high risk patient.
The assigning method is as follows: the score of 0 is assigned to the patient without focal nerve function symptom, and the score of 1 is assigned to the patient with focal nerve function symptom; assigning 0 score when the KPS score is less than 70, and assigning-3 scores when the KPS score is more than or equal to 70; the number of the transfer stoves is less than or equal to 4 and is assigned 0 minutes, and the number of the transfer stoves is more than 4 and is assigned 3 minutes.
And aiming at the model, a new grading model is further established, and the total score of the patient is graded into 3 grades, the higher the grade is, the higher the risk is, the higher the mortality is, the grades of-3 and-2 are 1 grade, the grades of 0 and 1 are 2 grades, and the grades of 3 and 4 are 3 grades.
To further evaluate the predictive effect between this model and the RPA stratification of the common model, prognostic analysis was performed on the RPA stratification. The results showed statistical differences between the survival of RPA graded grade 3 patients and grade 1 (P = 0.026), grade 2 (P = 0.043), respectively, but no statistical difference between grade 2 and grade 1 (P = 0.550), and the ROC curve showed an AUC value of 0.603 for the RPA graded 1 year follow-up.
Compared with the prior art, the invention has the advantages and positive effects that:
the invention provides a gamma knife treatment prognosis model for a patient, brings into research whether the patient has focal neurological symptoms or not, KPS scores and whether the number of metastasis is more than 4 or not, establishes a nomogram, provides a risk score formula and a model score grading evaluation mode, and shows that the survival time of the RPA grading grade 3 patient is respectively statistically different from that of the RPA grade 1 (P = 0.026) and that of the RPA grade 2 (P = 0.043), but the statistical difference between the RPA grade 2 and the RPA grade 1 (P = 0.550) is not existed, and an ROC curve shows that the AUC value of the RPA grading follow-up in 1 year is 0.603. Therefore, the scoring model can well predict the survival condition of the patient with the non-small cell lung cancer brain metastasis tumor treated by the gamma knife, is helpful for guiding the selection of a clinical treatment scheme and the prognosis evaluation, and has a prediction effect obviously better than that of RPA grading.
Drawings
Fig. 1 is an alignment chart.
FIG. 2 is a Calibration chart.
FIG. 3 is a K-M survival curve for the scoring model.
FIG. 4 is a ROC curve of the scoring model.
FIG. 5 is a RPA classification K-M survival curve.
FIG. 6 is a RPA classification ROC curve.
Detailed Description
In order that the manner in which the above recited objects, features and advantages of the present invention are obtained will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and thus the present invention is not limited to the specific embodiments of the present disclosure.
Example 1
1.1 case data
Retrospective analysis was performed on a total of 157 non-small cell lung cancer brain metastasis patients treated with gamma knife at the analysis center of the second hospital, university of Shandong, from 2018 to 2021, month 6. Clinical and follow-up visit data of the patient are collected through a case system of a second hospital of Shandong university, and parameters related to Gamma knife treatment of the patient are collected through a Gamma Plan system. All patients were pathologically diagnosed by pulmonary surgery or puncture. 92 male patients and 65 female patients; median age 62 (30-83) years, with follow-up dates by 12 months of 2021.
1.2 methods of treatment
The patient goes down local anesthesia in a quiet state, a Leksell three-dimensional positioning head frame is installed, after the installation is finished, a 3.0T magnetic resonance line BRAVO enhanced thin layer is used for scanning, the layer thickness is 2mm, a positioning image is transmitted to a Perfexon Leksell Gamma plan system for three-dimensional reconstruction, and a treatment scheme is made by a fixed neurosurgery doctor, a physical doctor and an imaging doctor together. 157 patients, on the site of focus 517 for co-treatment, are given marginal dose of 12.0-26.0Gy, and average marginal dose of 18.92Gy; the isodose line is 40-60%, and the median isodose line is 50%; the central dose is 24.0-60.0Gy, and the average central dose is 39.49Gy. Partial patient metastasis is close to important organs such as visual conduction paths or brainstems, marginal dose can be properly reduced, and important functional organs are prevented from being damaged. The patient with more metastatic focus can control the whole brain radiation safe dose, and can properly reduce the dose and irradiate for several times. Mannitol and dexamethasone are generally given for treatment after gamma knife treatment, and if no complication occurs, the patient is discharged from hospital after 48 hours of operation.
1.3 survival follow-up
After the gamma knife treatment, the patient rechecks the brain strengthening magnetic resonance every 3 months, and then introduces the Gamma plan system again, and the imaging change of the metastatic focus is evaluated by a neurosurgery doctor, a physicist and an imaging doctor together. If the patient can not complete the reexamination due to special reasons, the telephone visits the survival condition of the patient. The main ending event of the invention is the overall life cycle of the patient after receiving the gamma knife treatment.
1.4 statistical methods
The age, sex, admission symptoms, KPS score, surgery or radiotherapy and chemotherapy, primary tumor control, craniocerebral metastasis and brain metastasis focus number, position, volume size, gamma knife treatment parameters and other related information of the patient are counted. And (5) drawing a Kaplan-Meier survival curve and calculating the median survival time of the patient. And adopting a Cox method to carry out single-factor analysis, wherein the influence factors with P less than 0.1 are included in the Cox regression model to carry out multi-factor regression analysis, and the difference is considered to have statistical significance when the P less than 0.05. The risk factors obtained by multi-factor analysis are established into a nomogram, and the prediction ability of the risk factors on prognosis is evaluated by using a Calibration chart, as shown in fig. 1 and fig. 2. The chi-square test was used to conduct comparative studies on the differences between the different grades of the various prediction models, and P < 0.05 considered that the differences were statistically significant. And (3) drawing an ROC curve of each prediction model, and calculating an AUC value, wherein the AUC value is equal to 0.5 to represent that the model has no prediction capability, the AUC value is equal to 1 to represent that the model has extremely strong prediction capability, and the closer the AUC value is to 1, the better the prediction effect is. The above statistical analyses were performed in SPSS version 20.0 and R (version 3.6.3).
2. As a result, the
2.1 Single and Multi-factor analysis of patient outcomes
By the time of the last follow-up of the present study, a total of 76 out of 157 patients died, with a mortality rate of 48.41%. The median overall survival from the time of receiving gamma knife treatment to the time of patient death was 10 months. The single factor analysis results showed that the overall survival of the patients after gamma knife treatment was related to the presence or absence of focal neurological symptoms (P = 0.032), KPS score (P = 0.023), number of metastases over 4 (P < 0.001= and presence or absence of cerebellar metastases (P = 0.008). The multi-factor analysis results showed that the overall survival of the patients was related to the presence or absence of focal neurological symptoms (P = 0.009), KPS score (P = 0.013), number of metastases over 4 (P = 0.020) (table 1).
TABLE 1 Multi-factor analysis results
2.2 nomogram creation and validation
According to the multi-factor analysis result, whether the patient has focal nerve function symptoms or not, whether KPS scores and the number of metastasis exceed 4 or not are included into the study, a nomogram is established, and the prediction capability of the patient is evaluated by using a Calibration chart, such as fig. 1 and fig. 2. The results show that the nomogram predicted outcome is satisfied with the patient's actual outcome.
The calculation model for the risk score is as follows:
the risk score =0.8882 × focal neurological symptom-0.7192 × KPS score +0.9144 × shift focus score, wherein the risk score has-0.7192 as the optimal cutoff value, and when the risk score of the patient is lower than-0.7192, the patient is a low risk patient, and the risk score is greater than or equal to-0.7192, the patient is a high risk patient.
2.3 New Scoring Classification model establishment
Based on the multi-factor analysis result, in order to clinically predict the prognosis of the gamma knife treatment of the non-small cell lung cancer brain metastasis patient, a corresponding grading model is established. The score of 0 is assigned to the patient without focal nerve function symptom, and the score of 1 is assigned to the patient with focal nerve function symptom; assigning 0 score when the KPS score is less than 70, and assigning-3 scores when the KPS score is more than or equal to 70; the number of the transfer stoves is less than or equal to 4 and is assigned 0 minutes, and the number of the transfer stoves is greater than 4 and is assigned 3 minutes. The patient score was rated 3, -3 and-2 for 1, 0 and 1 for 2, and 3 and 4 for 3, with higher grades giving higher risk and higher mortality (table 2).
TABLE 2 Scoring and scoring results of the scoring and ranking model
2.4
We created a new scoring model with statistical differences between the 3-level vs 1 (P < 0.001, 2-level (P = 0.012)) and also between the 2-level vs 1 (P = 0.003) (table 3).
TABLE 3 model Graded statistical results
| Model classification | Square card | P |
| 3vs1 | 21.849 | ＜0.001 |
| 3vs2 | 6.337 | 0.012 |
| 2vs1 | 8.851 | 0.003 |
The invention also plots a K-M survival curve of the model and plots an ROC curve, and as shown in FIG. 3 and FIG. 4, the AUC value is 0.737.
2.5 to further evaluate the predictive effect between this model and the common model RPA ranking, we performed prognostic analysis on the RPA ranking. As shown in fig. 5 and 6, the results showed statistical differences between the survival of RPA graded grade 3 patients and grade 1 (P = 0.026), grade 2 (P = 0.043), respectively, but no statistical difference between grade 2 and grade 1 (P = 0.550), and the ROC curve showed an AUC value of 0.603 for RPA graded 1 year follow-up.
TABLE 4RPA ranking prognostic assay results
| RPA | Square card | P |
| 3vs1 | 4.933 | 0.026 |
| 3vs2 | 4.079 | 0.043 |
| 2vs1 | 0.357 | 0.550 |
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention.
Claims (2)
1. A model for predicting prognosis of non-small cell lung cancer multiple brain metastases with a gamma knife, characterized in that the model comprises a nomogram comprising a scale of scores in a first row in the range-5~5,
whether the second behavior has focal nerve function symptoms or not, the focal nerve function symptoms are assigned 0 point or not, the focal nerve function symptoms are assigned 1 point or not, and 0 or 1 corresponds to a corresponding score of the first line respectively; the third line is KPS scoring, the KPS scoring is less than 70 points and is assigned with 0 points, the KPS scoring is more than or equal to 70 points and is assigned with-3 points, and the KPS scoring corresponds to a corresponding scoring of the first line; the fourth line indicates whether the number of the transfer stoves exceeds 4, the number of the transfer stoves is less than or equal to 4 and is assigned 0 points, the number of the transfer stoves is greater than 4 and is assigned 3 points, and the number of the transfer stoves corresponds to a corresponding score in the first line;
the prognostic risk score formula is as follows:
risk score =0.8882 x focal neurological symptom-0.7192 x KPS score +0.9144 x shift focus score, wherein the risk score has-0.7192 as a cutoff value, a low risk patient when the patient's risk score is below-0.7192, and a high risk patient when the risk score is greater than or equal to-0.7192.
2. The model for predicting prognosis of Gamma knife therapy of non-small cell lung cancer multiple brain metastases according to claim 1, further comprising a score grading model, wherein the scores corresponding to the three indexes from the second row to the fourth row are added together in the first row to obtain the total score of the patient, and the total score of the patient is graded as 3, graded as-3 and-2, graded as 2 as 0 and 1, graded as 3 and 4, and the higher the grade is, the higher the risk is and the higher the mortality is.
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| CN109493969A (en) * | 2018-09-11 | 2019-03-19 | 中山大学孙逸仙纪念医院 | Assess model and its application of the Paget`s disease with invasive ductal carcinoma patient prognosis |
| CN111863159A (en) * | 2020-06-01 | 2020-10-30 | 中山大学孙逸仙纪念医院 | Nomogram model for predicting curative effect of tumor immunotherapy and establishing method thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109493969A (en) * | 2018-09-11 | 2019-03-19 | 中山大学孙逸仙纪念医院 | Assess model and its application of the Paget`s disease with invasive ductal carcinoma patient prognosis |
| CN111863159A (en) * | 2020-06-01 | 2020-10-30 | 中山大学孙逸仙纪念医院 | Nomogram model for predicting curative effect of tumor immunotherapy and establishing method thereof |
Non-Patent Citations (4)
| Title |
|---|
| JI ZHANG 等: "Computer Tomography Radiomics- Based Nomogram in the Survival Prediction for Brain Metastases From Non-Small Cell Lung Cancer Underwent Whole Brain Radiotherapy" * |
| 俞建臣,汤可,赵亚群,赵莉,高冬晓,高浩: "伽玛刀治疗肺癌脑转移瘤的远期生存分析" * |
| 徐海霞,王雅迪,王宏伟,李心红: "伽玛刀治疗多发脑转移瘤的疗效及影响因素" * |
| 高红祥,张晓智,党亚正,康文星: "伽玛刀治疗非小细胞肺癌脑转移瘤预后指标评价" * |
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