CN114496239A - Novel lung cancer SBRT (systemic lupus erythematosus) radiation pneumonia risk prediction factor and construction method thereof - Google Patents
Novel lung cancer SBRT (systemic lupus erythematosus) radiation pneumonia risk prediction factor and construction method thereof Download PDFInfo
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Abstract
The invention belongs to the field of disease risk prediction, and relates to a method for constructing and verifying a novel risk prediction factor for lung cancer stereotactic radiotherapy radiation pneumonitis, which comprises the following steps: A. case collection; B. 4DCT analog positioning scanning; C. delineation of the target area and organs at risk; D. designing a radiotherapy plan; E. extracting tumor related parameters; F. calculating the occurrence probability of the concurrent radiation pneumonitis; G. screening a prediction factor; H. and acquiring an intercept point. The invention screens out a novel prediction factor Lung/PTV with highest incidence with grade 2RP in Lung cancer SBRT treatment by using a radiobiological model for the first timeL(ii) a The intercept value of the novel predictor is obtained for the first time. The novel prediction factor can help a clinician distinguish patients with high and low RP (RP) risks of being more than or equal to 2, and the clinician can adopt according to the RP risks of the patientsAnd an appropriate treatment strategy is adopted, the occurrence probability of RP is reduced, and individualized and accurate radiotherapy is realized.
Description
Technical Field
The invention belongs to the field of disease risk prediction factor screening, and relates to a prediction factor for predicting risk of SBRT treatment complicated radiation pneumonitis and a construction method.
Background
The primary lung cancer is one of the most common malignant tumors in China, and data published by the national cancer center in 2019 show that 78.7 thousands of new lung cancer cases in 2015 are found, the incidence rate is 57.26/10 thousands, and the primary lung cancer cases are the first malignant tumors in 2015. Surgical radical resection is the treatment of choice for stage i non-small cell lung cancer (NSCLC) patients, but stereotactic radiation therapy (SBRT) is the primary treatment of choice for patients who cannot tolerate and refuse surgery for age and other reasons, such as poor cardiopulmonary function, severe diabetes, etc. The technique is characterized in that the high dose area is concentrated in the tumor, and simultaneously, the peripheral dose is rapidly reduced to protect the normal tissues. According to literature data, the SBRT technology can obtain a treatment effect which is not inferior to that of operation on early-stage NSCLC patients, and the radiation therapy toxicity is low. Although early NSCLC patients can achieve better treatment effect by using SBRT technology, Radiation Pneumonitis (RP) is one of the common complications in SBRT treatment of lung cancer, and the incidence probability of RP is reported in the literature to be 9.4% -20.3%. The factors that predict RP occurrence at present can be divided into two categories: the dose-related predictor refers to a dose-related factor extracted from a radiation treatment plan, and the tumor-related predictor refers to a factor determined by the characteristics of the tumor itself.
The traditional method for screening the radiation pneumonitis prediction factors utilizes clinical follow-up data to carry out retrospective analysis, a large amount of manpower and material resources are needed, the radiation pneumonitis prediction factors are easily influenced by a plurality of mixed factors, the situation that results are inconsistent among different centers is easily caused, and the radiation pneumonitis prediction factors are difficult to popularize and apply clinically. For example, in recent years, multiple dosimetric predictors, such as V10,V15,V20,V25And mean lung receptor (MLD) have been shown to be closely related to the occurrence of RP, but literature reports that these dosimetry prediction parameters are not uniform and that these factors can only be obtained after the radiotherapy plan is designed. In the actual clinical treatment process, it is important to find factors that can be obtained by simple measurement before the radiotherapy plan is completed to predict the probability of RP occurrence of the patient. If the doctor in charge foresees that the patient has a high risk of RP before the radiotherapy plan is completed, the doctor can adopt other treatment strategies, such as adopting a risk adaptation dose adjustment strategy or adopting a respiratory gating technology to reduce the probability of RP generation of the patient, so as to avoid the trouble caused by repeatedly modifying the radiotherapy treatment. Therefore, in recent years, some easily-accessible tumor-associated predictors have been successively discovered, such as the volume of a solid tumor (GTV), the volume ratio of the GTV to normal lung tissue, the volume ratio of the Planning Target Volume (PTV) to normal lung tissue, and the like. However, because PTV of patients with partial peripheral lung cancer overlaps with the chest wall, the volume of PTV does not truly reflect the dose of normal lung tissueThe actual prediction results of these predictors are less accurate.
Disclosure of Invention
Aiming at the current situation that the probability of the lung cancer SBRT treated RP is not easy to obtain and can be accurately predicted internationally, the invention provides a novel radiation pneumonitis prediction factor which is calculated based on a radiobiological model and used for evaluating the risk of radiation pneumonitis before the radiotherapy plan is finished, and the treatment strategy is adjusted timely according to the risk occurrence probability so as to reduce the probability of the lung cancer treated RP after the lung cancer SBRT treatment and realize the clinical lung cancer SBRT individualized accurate treatment.
In order to solve the technical problems, the invention adopts the following technical scheme:
a method for constructing a novel risk prediction factor for lung cancer SBRT radiation pneumonitis comprises the following steps:
A. case collection: collecting data from NSCLC patients who have been treated with SBRT;
B. 4DCT simulation positioning scanning: scanning under a free respiration state to obtain a set of free respiration images and a set of 4DCT images, and transmitting all CT image sequences obtained by scanning to an Eclipse planning system for target area delineation and plan design;
C. delineation of the target area and organs at risk: delineating a gross tumor target area, an inner target area and a planned target area on the free breathing image under a lung window; the normal organ needs to outline normal lung tissues, chest wall and ribs;
D. radiotherapy plan design: designing radiation treatment plans by using a plurality of dose segmentation schemes respectively;
E. extracting tumor related parameters: measuring a plurality of tumor related parameters by using a planning system;
F. calculating the occurrence probability of the concurrent radiation pneumonitis: respectively deriving normal lung tissue dose-volume histograms of different dose segmentation schemes in a planning system, and calculating probability values of more than or equal to 2 levels of concurrent radiation pneumonitis of each patient by using a prediction model;
the probability values obtained here are calculated from theoretical models. However, the actual steps for calculating the probability value are very complicated and error-prone, and are difficult to popularize and apply clinically. Therefore, the invention carries out correlation analysis on the probability value obtained by calculation and some parameters which are easy to obtain clinically, hopes to obtain some prediction factors which are easy to obtain, and is convenient for clinical application.
G. Screening a prediction factor: respectively calculating correlation coefficients of a plurality of tumor-related parameters and the numerical value of the probability of the occurrence of the radiation pneumonitis with the grade of 2 or more of each patient, the probability of the occurrence of the radiation pneumonitis with the grade of 2 or more of each patient and the R of the plurality of tumor-related parameters when different fitting models are used2Numerical value, said correlation coefficient and said R2The tumor-related parameter with the largest value is a prediction factor with the highest prediction efficiency; when the correlation coefficient and R2When the tumor-related parameters with the largest numerical values are inconsistent, comprehensive evaluation should be performed.
H. Obtaining an intercept point: and analyzing the relation between the probability of the occurrence of the more than or equal to 2-grade complicated radiation pneumonitis of each patient and the predictor with the highest efficiency by utilizing an analysis tool to obtain the intercept value of the predictor with the highest efficiency.
After determining the most potent predictor and the acquisition intercept, the clinician can more quickly predict the risk of developing RP in the patient based on the patient's condition. If the doctor in charge foresees that the risk of the patient generating the RP is larger before the radiotherapy plan is finished, the doctor can adopt other treatment strategies, such as adopting a risk adaptation dose adjustment strategy or adopting technical approaches such as respiratory gating and the like to reduce the probability of the patient generating the RP, so that the inconvenience brought by repeatedly modifying the radiotherapy plan clinically is avoided, the hospitalization waiting treatment time of the patient is saved, the turnover of the patient is accelerated, and the hospitalization cost is reduced.
Preferably, the tumor-related parameter comprises GTV volume, PTV volume, tumor diameter, normal lung tissue, volume of PTV within normal lung tissue-PTVLVolume ratio of normal Lung tissue to PTV-Lung/PTV, normal Lung tissue and PTVLVolume ratio-Lung/PTVLOne or more of the above.
Preferably, in step D, the plurality of dose division schemes comprise 3 × 15Gy and 4 × 12 Gy.
Preferably, in step F, two prediction models including Borst and Wennberg are designed, and the probability value of 2 grade or more concurrent radiation pneumonitis occurring in each patient is calculated.
Preferably, when the Borst model is used for calculating the probability value of the occurrence of the radiation pneumonitis of 2 levels or more in each patient, the derived dose-volume histogram dose is firstly converted into the average lung dose, and then the average lung dose is converted into the EQD equivalent to the conventional 2Gy fractionated radiation by using the linear quadratic model2，EQD2D is total dose, n is segmentation times, alpha/beta is 3Gy, and finally the converted dose is substituted into a Lyman-Kutcher-Burman model to calculate the probability value of more than or equal to 2 levels of concurrent radiation pneumonitis of each patient, and TD is used for calculating the probability value of the occurrence of the concurrent radiation pneumonitis in the process of calculation50And m takes values of 19.6Gy and 0.43, respectively.
Borst model, from the literature (Borst GR, Ishikawa M, Nijkamp J, et al. radiation pulmonary inflammation in tissues treated for cortical expression therapy. radiotherapeutic Oncol. 2009; 91(3): 307-313). The invention is comprehensively applied after being referred to the literature.
Preferably, when the Wennberg model is used for calculating the probability value of the occurrence of the concurrent radiation pneumonitis of 2 grades or more in each patient, the following calculation process is included:
s1, when the dose-volume histogram dose point is less than the converted dose threshold of 5.8 Gy:
s1-1, converting the derived dose-volume histogram dose into an equivalent biological dose EQD equivalent to conventional 2Gy fractionated radiation by utilizing a linear quadratic model2，EQD2D (α/β + D/n)/(α/β +2), in the calculation process of S1-1, D is the total dose, n is the number of divisions, and α/β takes the value of 3 Gy;
s1-2, substituting the dose converted by S1-1 into a Lyman-Kutcher-Burman model to calculate the probability value of the occurrence of the radiation pneumonitis of 2 levels or more of each patient, and in the calculation process of S1-2, TD50M and n take values of 30Gy, 0.4 and 0.71 respectively;
s2, when the dose point of the dose-volume histogram is greater than or equal to the conversion dose threshold value 5.8 Gy:
s2-1, converting the physical dose into an equivalent biological dose equivalent to conventional 2Gy fractionated radiation according to a formula in a universal survival curve model;
s2-2, substituting the dose converted by S2-1 into a Lyman-Kutcher-Burman model to calculate the probability value of the occurrence of the radiation pneumonitis of 2 levels or more of each patient, and in the calculation process of S2-2, TD50M and n take values of 30Gy, 0.4 and 0.71 respectively.
The Wennberg model, from the literature (Wennberg BM, Baumann P, Gagliardi G, et al, NTCP modeling of Long sensitivity after SBRT composing the univocal current and the linear resolution model for fractionation acta environmental. 2011; 50(4): 518-. The invention is comprehensively applied after being referred to the literature.
In practical application, the process of calculating the occurrence probability of RP is obtained by calculation after compiling two mathematical models, i.e. Borst and Wenberg, into program codes by using MATLAB (version 2012).
Preferably, in step G, the SpSS SpPicerman correlation analysis function is used to analyze the correlation coefficient between a plurality of tumor-related parameters and the probability value of the occurrence of the radiation pneumonitis with grade 2 or more in each patient.
Preferably, in step G, the SPSS curve estimation function is used to calculate the probability of the occurrence of grade 2 complicated radiation pneumonitis and the R of a plurality of tumor-related parameters of each patient when a plurality of different fitting models including Lotarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential and Logistic are used2Numerical values.
Preferably, in step H, the relationship between the probability of occurrence of greater than or equal to level 2 concurrent radiation pneumonitis in each patient and the predictor with the highest efficacy is analyzed by using an X-tile analysis tool, and the data of radiation pneumonitis and tumor related parameters of each patient are imported into software and then automatically analyzed by the software to obtain the cutoff value of the predictor with the highest efficacy.
The prediction factor with highest efficiency obtained by the construction method of the novel lung cancer SBRT radiation pneumonitis risk prediction factorThe most potent predictors are normal lung tissue and PTVLVolume ratio-Lung/PTVL。
According to the clinical practice, the dose actually reflecting the exposure of the normal lung tissue is the part of the PTV in the normal lung tissue (PTV)L). Based on this, it is presumed that Lung/PTV is usedLPredicting the occurrence of RP may be more accurate than using tumor-associated predictors such as GTV volume, PTV volume, Lung/PTV, etc. Therefore, a set of mature methods for screening novel lung cancer SBRT (systemic lupus erythematosus) radiation pneumonia risk prediction factors are constructed, and the results show that the correlation coefficient and the R are2The maximum values of the values are all in Lung/PTVLOn this parameter, Lung/PTV was finally confirmedLThe prediction factor with the highest efficacy can provide a theoretical basis for further verifying the accuracy of the clinical application. Determining Lung/PTVLAfter the prediction factor with the highest efficiency is obtained and the intercept value of the prediction factor is obtained, a doctor can obtain more accurate RP occurrence probability through simple measurement of a planning system after sketching a target area, and the prediction factor is expected to be popularized and used in a large-scale clinic.
Compared with the prior art, the implementation of the invention has the following beneficial effects:
the invention provides a novel method for screening a radiation pneumonitis prediction factor based on a radiobiological model, the traditional method for screening the radiation pneumonitis prediction factor utilizes clinical follow-up data to carry out retrospective analysis, a large amount of manpower and material resources are needed, the method is easily influenced by a plurality of mixed factors, the condition that results are inconsistent among different centers is easily caused, and the method is difficult to popularize and apply clinically. The invention is based on the forecasting factor obtained by theoretical analysis, the screening process of the forecasting factor is not influenced by clinical confounding factors, and the novel forecasting factor obtained by screening can provide theoretical basis for further verifying the accuracy of the forecasting factor in clinic. The finally obtained prediction factor is novel and has not been reported before.
The invention screens out a novel prediction factor Lung/PTV with highest incidence with grade 2RP in Lung cancer SBRT treatment by using a radiobiological model for the first timeL(ii) a Obtaining the intercept value of the novel prediction factor for the first time; of the novel predictorThe method has the biggest characteristic of easy acquisition, and can be obtained by simple measurement in a planning system before radiotherapy plan design is finished; the novel prediction factor can help a clinician distinguish patients with high and low occurrence risk of RP (RP) more than or equal to 2, and the clinician can adopt a proper treatment strategy according to the occurrence risk of RP of the patients, so that the occurrence probability of RP is reduced, and individualized accurate radiotherapy is realized.
Drawings
FIG. 1 is a flow chart of the present invention; wherein: DVH, dose-volume histogram; borst, the Borst model; the model Wennberg, Wennberg.
FIG. 2 is a flowchart of RP occurrence probability calculation; wherein: DVH, dose-volume histogram; MLD, mean lung receptor; LQ, linear quartic; EQD2Equivalent to the "equivalent biological dose" of conventional 2Gy fractionated irradiation; LKB, Lyman-Kutcher-Burman; RP, radiation pneumonitis; USC, universal survival curve.
FIG. 3 shows the results of Spearman correlation analysis; wherein: (a) correlation coefficient for 3 × 15Gy dose regimen; (b) correlation coefficient for 3 × 15Gy dose regimen. GTV, GTV volume; PTV, PTV volume; d, tumor diameter; lung, normal Lung tissue volume; PTVL,PTVLThe volume of (a); Lung/PTVLNormal Lung tissue and PTVLThe volume ratio of (A) to (B); Lung/PTV, volume ratio of normal Lung tissue to PTV, Borst, Borst model; the model Wennberg, Wennberg.
FIG. 4 is the R of 10 different fitting models2A value; wherein: (a) the 3 × 15Gy dose protocol uses the Borst model to calculate the RP occurrence probability; (b) the 3 × 15Gy dose protocol uses the Wennberg model to calculate the probability of RP occurrence; (c) the 4 × 12Gy dose protocol uses the Borst model to calculate the RP occurrence probability; (d) the 4X 12Gy dose protocol uses the Wennberg model to calculate the probability of RP occurrence. GTV, GTV volume; PTV, PTV volume; d, tumor diameter; lung, normal Lung tissue volume; PTVL,PTVLThe volume of (a); Lung/PTVLNormal Lung tissue and PTVLThe volume ratio of (A) to (B); Lung/PTV, normal Lung tissue and PTV volume ratio.
FIG. 5 shows the incidence of RP > 2 and Lung/PTV for each patientLScatter diagram of(ii) a Wherein: (a)3 × 15Gy dose regimen; (b)4 × 12Gy dose regimen; RP, radiation pneumonitis; Lung/PTVLNormal Lung tissue and PTVLThe volume ratio of (A) to (B); borst, the Borst model; the model Wennberg, Wennberg.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
The present invention is further described in detail by taking the construction process of a novel RP predictor in SBRT of 32 NSCLC patients as an example, as shown in FIG. 1.
1 materials and methods
1.1 case Collection
32 pathologically confirmed peripheral T1 or T2 staged (American Joint Committee on Cancer, eighth edition) NSCLC patient data were selected, and all patients had been treated with SBRT.
1.24 DCT analog scout scan
After a patient uses a head, neck and shoulder fixing mask or a vacuum bag (Guangzhou Kelai Di medical equipment, Inc.) to fix the body position, a Philips company model Brilliance Big Bore diameter 4DCT special for radiotherapy of a Brilliance Big Bore is adopted to scan under a free respiration state to obtain a set of free respiration images and a set of 4DCT images, and the scanning layer thickness and the layer spacing are set to be 3 mm. After the 4DCT scanning is finished, 10 respiratory phases, Maximum Intensity Projection (MIP) and average intensity projection image (AIP) are automatically reconstructed by a computer. All CT image sequences obtained by scanning are transmitted to an Eclipse planning system (Varian corporation, USA, 10.0 version) through a DICOM 3.0 port for target region delineation and planning design.
1.3 delineation of the target and organs at risk
Delineation of the target area: delineating the GTV on the free breathing image under the lung window; considering the influence of respiratory motion on lung tumors, a doctor in charge respectively delineates the tumors on 10 sets of different respiratory phases on 4DCT under a lung window, and then combines the tumors to generate ITV; PTVs were generated by respectively flaring 5mm in three dimensions from the ITV, taking into account the yaw error. Delineation of organs at risk: the normal lung tissue delineation is carried out under the lung window, including the left lung and the right lung, all the inflated or non-inflated lungs need delineation, and the GTV, the trachea and the bronchial tree need to be deducted; the chest wall delineation is generated by the outward expansion of the affected lung edge by 2cm in the three-dimensional direction, and the extension ranges are 3 cm respectively from the PTV to the head and foot directions; when the ribs are drawn, costal cartilage is required to be included, and only one rib closest to the target area is drawn for dose evaluation.
1.4 radiation treatment planning
The TrueBeam linear accelerator with the mode of averaging (6XFF, 1400MU/min dose rate) is adopted for planning design, partial double arcs are adopted to avoid the healthy lateral lung, and the end angle of the radiation field is based on that the beam does not enter the healthy lateral lung. The small nose angles of the two arcs are set at X ° and (360-X) ° respectively to reduce the meniscus effect of the accumulated multi-leaf grating (MLC). All radiation treatment plans were designed on free breathing images with the computational grid set to 1 mm. An anisotropic algorithm (AAA, version 10.0.28) was used to calculate the dose and perform tissue inhomogeneity correction. Radiation treatment plans were designed using prescribed dose fractions of 3 × 15Gy and 4 × 12Gy, respectively, i.e. total doses of 3 treatments in 45Gy and 4 treatments in 48Gy, respectively. The dose requirements for lung cancer SBRT treatment planning in RTOG 0915 and american society of medical physics (AAPM) TG 101 are limited in terms of target dose, off-target dose gradient, and organ-at-risk dose, respectively, and the radiotherapy plan is designed to ensure that the highest dose in the target is about 120% of the prescribed dose and the highest dose point falls within the GTV.
1.5 extraction of tumor-related parameters
Respectively measuring to obtain GTV volume, PTV volume, tumor diameter, normal lung tissue and PTV by using a planning systemLVolume, Lung/PTV and Lung/PTVLA total of 7 tumor-associated parameters.
1.6 calculating the probability of RP occurrence
DVH of normal lung tissues of two dose segmentation schemes are respectively derived in a planning system, and the occurrence probability of RP larger than or equal to level 2 is calculated by using source codes of two RP calculation models of Borst and Wennberg compiled by MATLAB self. For the Borst model, the derived DVH dose is firstly converted into the MLD dose, and then the MLD dose is converted into the dose equivalent to the conventional 2Gy fractionated radiation by utilizing a Linear Quadrate (LQ) model "Equivalent biological dose "(EQD)2)，EQD2D is total dose, n is segmentation times, alpha/beta is 3Gy, and finally the converted dose is substituted into Lyman-Kutcher-burman (lkb) model to calculate RP occurrence probability, and TD in the calculation process50And m takes values of 19.6Gy and 0.43, respectively. In calculating the probability of occurrence of RP using the Wennberg model: when the DVH dose point is smaller than the conversion dose (d)T) Threshold 5.8Gy, S1, when DVH dose point is less than the conversion dose (d)T) Threshold 5.8 Gy: s1-1, first, the derived DVH dose is converted into an "equivalent biological dose" (EQD) equivalent to conventional 2Gy fractionated radiation by utilizing a linear quaternistic model2)，EQD2D in S1-1 is the total dose, n is the number of divisions, and α/β is 3Gy (α/β + D/n)/(α/β + 2); s1-2, substituting the dose converted by S1-2 into a Lyman-Kutcher-Burman (LKB) model to calculate the RP occurrence probability, and calculating TD in the LKB model calculation process of S1-250M and n take values of 30Gy, 0.4 and 0.71 respectively; s2, when the DVH dosage point is larger than or equal to the conversion dosage (d)T) Threshold 5.8 Gy: s2-1, converting physical dosage into EQD according to formula in Universal Survival Curve (USC) model in literature2The dosage; s2-2, substituting the dose converted by S2-1 into LKB model to calculate RP occurrence probability, and TD in the LKB model calculation process of S2-250M and n take values of 30Gy, 0.4 and 0.71 respectively.
The RP occurrence probability calculation flow is shown in fig. 2.
1.7 screening for predictors
Utilizing the Spearman correlation analysis function of SPSS (version 25) to respectively obtain the incidence probability of two prediction models of more than or equal to 2-level RP and the correlation coefficients of 7 tumor related parameters under different prescription dosage schemes; respectively obtaining the R of the relationship between the RP occurrence probability calculated by two prediction models and 7 tumor related parameters under different prescription dosage schemes by utilizing the regression-curve estimation function of the SPSS when 10 fitting models such as Logirthomic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential and Logistic are used2A value; among the 7 tumor-related parameters, Spearman correlation coefficient and R2The parameter with the largest value is the predictor with the highest prediction performance.
1.8 acquisition intercept point
The relation between the RP occurrence probability and the predicted highest efficiency factor of 32 patients is analyzed by an X-tile analysis tool, and the RP and tumor related parameter data of each patient are imported into software and then automatically analyzed by the software to obtain the intercept value of the predicted highest efficiency factor.
2 results
Distribution of tumor-associated parameters in 2.132 patients
32 patients GTV volume, PTV volume, tumor diameter, Normal Lung tissue, PTVLVolume, Lung/PTV and Lung/PTVLThe distribution range of the 7 tumor-associated parameters is shown in table 1.
TABLE 17 ranges of tumor-associated parameters
In table 1: GTV volume, PTV volume, tumor diameter, normal lung tissue volume, PTVLVolume using
Represents; Lung/PTVLAnd Lung/PTV data using median (P)25％,P75％) And (4) showing. GTV, GTV volume; PTV, PTV volume; d, tumor diameter; lung, normal Lung tissue volume; PTVL,PTVLThe volume of (a); Lung/PTVLNormal Lung tissue and PTVLThe volume ratio of (A) to (B); Lung/PTV, normal Lung tissue and PTV volume ratio.
2.2 calculation of the probability of occurrence of RP
The results of the calculation using two RP occurrence probability calculation models for the two dose regimens are shown in table 2.
TABLE 2 calculation of RP occurrence probability
In table 2: data mining using meso bitsNumber (P)25％,P75％) And (4) showing. Borst, the Borst model; the model Wennberg, Wennberg.
2.3 screening for predictor results
The results of the Spearman correlation analysis showed that the probability of occurrence of RP of not less than grade 2 with GTV volume, PTV volume, tumor diameter and PTVLThe volume is positively correlated with the normal Lung tissue volume, Lung/PTVLAnd Lung/PTV are inversely related. Wherein, the occurrence probability of RP and Lung/PTVLThe absolute value of the correlation coefficient of (A) is the highest and can reach more than 0.955. The Spearman correlation analysis results for the probability of RP occurrence and 7 tumor-related parameters for the two prediction models under the different prescribed dose regimens are shown in fig. 3; the results of functional analysis of curve estimation show that the relationship between the probability of RP occurrence and the 7 tumor-associated parameters is Lung/PTV regardless of which of 10 models, such as Logistrimic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential and Logistic, was used for fittingLAll parameters have the highest R2The value is obtained. Relationship between probability of RP occurrence calculated by two predictive models and 7 tumor-related parameters under different prescribed dose regimes R when 10 fitting models are used2The values are shown in FIG. 4. Determining Lung/PTV based on the Spearman correlation analysis and the curve estimation function analysisLThe parameter is the predictor of highest predicted performance.
2.4 determination of New predictor intercept
FIG. 5 is a scatter plot of RP occurrence probability ≥ 2 and the predicted highest-efficiency factor, wherein the X-tile analysis tool is used to analyze the relationship between the RP occurrence probability and the predicted highest-efficiency factor for 32 patients, and for the 3 × 15Gy dosage regimen, when Lung/PTVLWhen the numerical value of (c) is more than or equal to 65.1, the RP occurrence probability is more than or equal to 2<12.17 percent; while for the 3X 15Gy dose regimen, when Lung/PTVLWhen the numerical value of (c) is more than or equal to 65.1, the RP occurrence probability is more than or equal to 2<10.21％。
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the scope of the present invention, therefore, the present invention is not limited by the appended claims.
Claims (10)
1. A method for constructing a novel risk prediction factor for lung cancer SBRT radiation pneumonitis is characterized by comprising the following steps:
A. case collection: collecting data from NSCLC patients who have been treated with SBRT;
B. 4DCT simulation positioning scanning: scanning under a free respiration state to obtain a set of free respiration images and a set of 4DCT images, and transmitting all CT image sequences obtained by scanning to an Eclipse planning system for target area delineation and plan design;
C. delineation of the target area and organs at risk: delineating a gross tumor target area, an inner target area and a planned target area on the free breathing image under a lung window; the normal organ needs to outline normal lung tissues, chest wall and ribs;
D. radiotherapy plan design: designing radiation treatment plans by using a plurality of dose segmentation schemes respectively;
E. extracting tumor related parameters: measuring a plurality of tumor related parameters by using a planning system;
F. calculating the occurrence probability of the concurrent radiation pneumonitis: respectively deriving normal lung tissue dose-volume histograms of different dose segmentation schemes in a planning system, and calculating probability values of more than or equal to 2 levels of concurrent radiation pneumonitis of each patient by using a prediction model;
G. screening a prediction factor: respectively calculating correlation coefficients of a plurality of tumor-related parameters and the numerical value of the probability of the occurrence of the radiation pneumonitis with the grade of 2 or more of each patient, the probability of the occurrence of the radiation pneumonitis with the grade of 2 or more of each patient and the R of the plurality of tumor-related parameters when different fitting models are used2Numerical value, said correlation coefficient and said R2The tumor-related parameter with the largest value is a prediction factor with the highest prediction efficiency;
H. obtaining an intercept point: and analyzing the relation between the probability of the occurrence of the more than or equal to 2-grade complicated radiation pneumonitis of each patient and the predictor with the highest efficiency by utilizing an analysis tool to obtain the intercept value of the predictor with the highest efficiency.
2. According to claimThe method for constructing the novel lung cancer SBRT radiation pneumonitis risk predictor of claim 1, wherein the tumor-related parameters include GTV volume, PTV volume, tumor diameter, normal lung tissue, PTV volume in normal lung tissue-PTVLVolume ratio of normal Lung tissue to PTV-Lung/PTV, normal Lung tissue and PTVLVolume ratio-Lung/PTVLOne or more of the above.
3. The method for constructing a novel predictor factor for the risk of lung cancer SBRT-induced pneumonia according to claim 1, wherein in step D, the plurality of dose division schemes include 3 x 15Gy and 4 x 12 Gy.
4. The method for constructing the novel risk predictor of lung cancer SBRT-type radiation pneumonitis according to claim 1, wherein in step F, two prediction models including Borst and Wennberg are designed, and the probability value of the occurrence of the concurrent radiation pneumonitis of more than or equal to grade 2 of each patient is calculated.
5. The method for constructing the novel risk predictor of lung cancer SBRT-type radiation pneumonitis according to claim 4, wherein when the Borst model is used for calculating the probability value of occurrence of more than or equal to 2-grade complicated radiation pneumonitis of each patient, the derived dose-volume histogram dose is firstly converted into the average lung dose, and then the linear resolution model is used for converting the average lung dose into the EQD equivalent to the conventional 2Gy fractionated radiation2，EQD2D is total dose, n is segmentation times, alpha/beta is 3Gy, and finally the converted dose is substituted into a Lyman-Kutcher-Burman model to calculate the probability value of more than or equal to 2 levels of concurrent radiation pneumonitis of each patient, and TD is used for calculating the probability value of the occurrence of the concurrent radiation pneumonitis in the process of calculation50And m takes values of 19.6Gy and 0.43, respectively.
6. The method for constructing the novel risk predictor of lung cancer SBRT-type radiation pneumonitis according to claim 4, which comprises the following calculation processes when calculating the probability value of the occurrence of the concurrent radiation pneumonitis of more than or equal to grade 2 of each patient by using a Wennberg model:
s1, when the dose-volume histogram dose point is less than the converted dose threshold 5.8 Gy:
s1-1, converting the derived dose-volume histogram dose into an equivalent biological dose EQD equivalent to conventional 2Gy fractionated radiation by utilizing a linear quadratic model2，EQD2D (α/β + D/n)/(α/β +2), in the calculation process of S1-1, D is the total dose, n is the number of divisions, and α/β takes the value of 3 Gy;
s1-2, substituting the dose converted by S1-1 into a Lyman-Kutcher-Burman model to calculate the probability value of the occurrence of the radiation pneumonitis of 2 levels or more of each patient, and in the calculation process of S1-2, TD50M and n take values of 30Gy, 0.4 and 0.71 respectively;
s2, when the dose point of the dose-volume histogram is greater than or equal to the conversion dose threshold value 5.8 Gy:
s2-1, converting the physical dose into an equivalent biological dose equivalent to conventional 2Gy fractionated radiation according to a formula in a universal survival curve model;
s2-2, substituting the dose converted by S2-1 into a Lyman-Kutcher-Burman model to calculate the probability value of the occurrence of the radiation pneumonitis of 2 levels or more of each patient, and in the calculation process of S2-2, TD50M and n take values of 30Gy, 0.4 and 0.71 respectively.
7. The method for constructing a novel risk predictor of lung cancer SBRT-type radiation pneumonitis according to claim 1, wherein in step G, the correlation coefficient between a plurality of tumor-related parameters and the probability value of the occurrence of more than or equal to grade 2 concurrent radiation pneumonitis of each patient is analyzed by using the SpSS Sppicolman correlation analysis function.
8. The method for constructing a novel risk predictor of lung cancer SBRT-type radiation pneumonitis according to claim 1, wherein in step G, the SPSS curve estimation function is used to calculate the probability of the occurrence of more than or equal to grade 2 concurrent radiation pneumonitis and the multiple tumor-related parameters of each patient in the clinical application including Logirthomic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential and LogisticR at different fitting models2Numerical values.
9. The method for constructing the novel lung cancer SBRT radiation pneumonitis risk predictor according to claim 1, wherein in step H, the X-tile analysis tool is used for analyzing the relationship between the probability of occurrence of more than or equal to 2-grade concurrent radiation pneumonitis of each patient and the predictor with highest efficacy, and the software automatically analyzes the data of the radiation pneumonitis and the tumor related parameters of each patient after the data are imported into the software to obtain the cutoff value of the predictor with highest efficacy.
10. The lung cancer SBRT radiation pneumonitis risk novel predictor obtained by the construction method of the lung cancer SBRT radiation pneumonitis risk novel predictor according to claim 1, wherein the predictor with highest efficiency is normal lung tissue and PTVLVolume ratio-Lung/PTVL。
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