KR102404649B1 - Estimation method and appratus for polmonary function according to radiation therapy - Google Patents

Abstract

A method for predicting lung function according to radiation therapy according to an embodiment of the present invention comprises the steps of: receiving and storing patient data on changes in lung function before and after radiation therapy; setting a predictive factor affecting lung function according to radiation treatment by substituting the data into a linear complex model; estimating a pulmonary function reduction predictive model based on the predictive factors; and predicting changes in the decrease in lung function of the patient to be treated by applying the dose-volume information of the patient to be treated to the pulmonary function reduction prediction model.

Description

Pulmonary function prediction method and apparatus according to radiation therapy

The present invention relates to an apparatus and method for predicting lung function according to radiation therapy, and more particularly, by establishing a pulmonary function reduction prediction model based on patients' information about changes in lung function before and after radiation treatment, a radiation treatment plan for a target patient It relates to a method and an apparatus for predicting lung function in stages.

Radiation therapy is a method of delaying, inhibiting, or even annihilating the growth of malignant tissue by damaging or destroying a target tissue using high-energy waves such as X-rays and gamma rays, or high-energy particles such as electron beams and proton beams. Radiation therapy is used not only for cancer, but also for the treatment of benign tumors, medical diseases, and some skin diseases. Recently, a radiosurgery method in which a large amount of radiation is irradiated and treated without an incisional operation has been developed.

Recently, it has become so common that more than 60% of cancer patients receive radiation therapy. Radiation therapy is not only used to treat a tumor by itself, but it is also used in combination with other surgical procedures to treat a local area that cannot be removed by surgery or to reduce the size of the tumor because it is large and invasive and difficult to operate. It can be used to make surgery easier or to destroy malignant cells left after surgery.

As such, before irradiating radiation to the patient's tumor tissue, a precise radiation treatment plan is required, such as determining the location and dose of radiation in consideration of the size and location of the tumor existing in the patient's current body tissue.

Conventionally, after setting the location of the tumor using tomographic images, the optimal radiation treatment plan was derived by adjusting the location of radiation and the amount of radiation based on the experience and physical knowledge of the medical staff.

On the other hand, the decrease in lung function induced by radiation therapy is one of the side effects that can inhibit the effect of radiation therapy in cancer patients, especially lung cancer patients. In particular, in patients with very low residual lung function, further decrease in lung function due to radiation therapy can be fatal, and it can make radiation therapy impossible. Therefore, it is absolutely necessary to quantitatively predict the decrease in lung function that can be caused by radiation therapy in the planning stage of radiation therapy.

The problem to be solved by the present invention is to provide a method and apparatus for predicting lung function according to radiation therapy, which can quantitatively predict how much the dose to be irradiated to the lungs will decrease lung function at the time of planning for radiation therapy.

The problem to be solved by the present invention is not limited to the problem(s) mentioned above, and another problem(s) not mentioned will be clearly understood by those skilled in the art from the following description.

A method for predicting lung function according to radiation therapy according to an embodiment of the present invention comprises the steps of: receiving and storing patient data on changes in lung function before and after radiation therapy; setting a predictive factor affecting lung function according to radiation treatment by substituting the data into a linear complex model; estimating a pulmonary function reduction predictive model based on the predictive factors; and applying the dose-volume information of the patient to be treated to the predictive model for reducing lung function, predicting changes in the decrease in lung function of the patient to be treated.

Preferably, the step of setting the predictive factors affecting the lung function according to the radiation treatment based on the data includes: the basic lung function, the lung resection range, the dose-volume information, and the elapsed time after radiation treatment as the predictive factors set

Preferably, the predicting of the decrease in lung function is represented by a forced expiratory volume in 1 second (FEV1), which is a volume forcibly discharging the lung function of the patient to be treated for 1 second.

Preferably, the lung function reduction prediction model is lung function prediction FEV1=beta_1*iFEV1 (basal lung function)+beta_2*(pulmonary resection range)+beta_3*(dose-volume information)+beta_4*(time elapsed after radiation treatment) ) + alpha.

Preferably, in the lung function reduction prediction model, 1 is applied in the case of lobectomy and 2 is applied in the case of pneumonectomy in the range of the lung resection.

Preferably, the lung function reduction prediction model is a lung function prediction method according to radiation therapy, characterized in that it has a different coefficient according to the dose-volume information irradiated to the lungs.

The apparatus for predicting lung function according to radiation therapy according to an embodiment of the present invention includes a storage unit that stores patient data on changes in lung function before and after radiation therapy, and substituting the data into a linear complex model to predict lung function according to radiation therapy. Decrease the lung function of the patient to be treated by setting the predictive factors affecting the function, estimating the predictive model for reduced lung function based on the predictor, and applying the dose-volume information of the patient to be treated to the predictive model for the reduced lung function. A control unit for predicting a change is included.

According to the method and apparatus for predicting lung function according to radiation therapy according to an embodiment of the present invention, it is possible to quantitatively predict how much the dose to be irradiated to the lungs will deteriorate the lung function at the time of the radiation treatment plan.

In addition, by using this prediction formula, it is possible to perform customized radiation therapy that can maximize the efficiency of radiation therapy by identifying the minimum tolerable lung dose for each patient.

Effects of the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a block diagram schematically illustrating a configuration of an apparatus for predicting lung function according to radiation therapy according to an embodiment of the present invention.
2 is a flowchart illustrating a method for predicting lung function according to radiation therapy according to an embodiment of the present invention.
3 is a graph showing changes in lung function before and after radiation treatment in patients according to an embodiment of the present invention.
4 is a diagram illustrating predictive factors affecting lung function set according to an embodiment of the present invention.
5 is a graph showing the correlation between the predicted value according to the lung function prediction model according to an embodiment of the present invention and the lung function actually measured for each patient.

Since the present invention can have various changes and can have various embodiments, specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention. In describing each figure, like reference numerals have been used for like elements.

Terms such as first, second, A, and B may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component. and/or includes a combination of a plurality of related listed items or any of a plurality of related listed items.

When a component is referred to as being “connected” or “connected” to another component, it may be directly connected or connected to the other component, but it is understood that other components may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram schematically illustrating the configuration of an apparatus for predicting lung function according to radiation therapy according to an embodiment of the present invention.

Referring to FIG. 1 , an apparatus for predicting lung function according to radiation therapy according to an embodiment of the present invention includes a storage unit 110 , an output unit 120 , and a control unit 130 , and an input unit 140 and a communication unit ( 150) may be further included.

The storage unit 110 performs a function of temporarily or permanently storing data related to the operation of the lung function prediction apparatus 100 according to radiation treatment. Here, the storage unit 110 may use a known storage medium, for example, any one or more of known storage media such as ROM, PROM, EPROM, EEPROM, RAM, etc. may be used.

In particular, the storage unit 110 may store a program or application for analyzing data of patients on changes in lung function before and after radiation treatment. In addition, the storage unit 110 may store various algorithms (or equations) related to building a lung function reduction prediction model. In this case, the control unit 130 may obtain a necessary algorithm by calling the storage unit 110 .

The output unit 120 is configured to display various information related to the operation of the apparatus 100 for predicting lung function according to radiation treatment under the control of the control unit 130 . In particular, the output unit 120 is a predictor set by the control unit 130 by analyzing the data of patients on changes in lung function before and after radiation treatment, and the correlation between the predictive factor and the amount of pulmonary function deterioration. Various information such as a function reduction prediction model can be displayed. The output unit 120 may be implemented as, for example, a liquid crystal display (LCD), a light emitting diode (LED), an organic light emitting diode (OLED), a projector, or other display devices capable of present, past, or future display. . The output unit 120 may display, for example, an interface page for providing information or an information provision result page.

The control unit 130 is a component that controls the operation of various components of the apparatus 100 for predicting lung function according to radiation treatment, and may include at least one computing device, wherein the computing device is a general-purpose central computing device ( CPU), a programmable device device (CPLD, FPGA) that is suitably implemented for a specific purpose, an application specific integrated circuit (ASIC), or a microcontroller chip.

The input unit 140 includes an input means through which a user can manipulate data, such as input and selection of data. The input means may include a general keypad, a mouse, or the like, and when the output unit 120 is provided as a touch screen capable of a touch input, it may be provided integrally with the output unit 120 .

The communication unit 150 provides hardware and software necessary for the lung function prediction apparatus 100 according to radiation therapy to transmit and receive signals such as control signals or data signals through wired/wireless connection with other network devices such as an external device (not shown). It may be a device comprising

The communication unit 150 may receive data from patients on changes in lung function before and after radiation treatment from an external device.

The communication unit 150 transmits the data received from the external device to the control unit 130 . The communication technology used by the communication unit 150 may vary depending on the type of communication network or other circumstances.

All of the above-described components are not necessarily required, and some may be omitted.

Meanwhile, the device 100 may be a communication terminal such as a computer, a notebook computer, a netbook, or a PDA, and may be a smart device such as a smart phone, a smart note, a tablet PC, or a smart TV. Also, the device 1100 may be implemented as a single arithmetic unit, or may be embodied in the form of an aggregate device in which two or more arithmetic units are connected to each other. For example, the device 100 may be implemented as a single server or may be implemented in a form in which two or more servers are connected.

Hereinafter, a method of quantitatively predicting a change in lung function according to radiation treatment by the apparatus 100 for predicting lung function according to radiation treatment will be described with reference to FIGS. 2 to 5 .

2 is a flowchart illustrating a method for predicting lung function according to radiation treatment according to an embodiment of the present invention, and FIG. 3 is a graph showing changes in lung function before and after radiation treatment in patients according to an embodiment of the present invention. , FIG. 4 is a diagram illustrating predictive factors affecting lung function set according to an embodiment of the present invention.

In order to quantitatively predict changes in lung function according to radiation treatment by the apparatus 100 for predicting lung function according to radiation treatment, first, referring to FIG. 2 , data from patients on changes in lung function before and after radiation treatment are provided Save (S110).

Patients' data on changes in lung function before and after radiotherapy include sex, age, stage of lung cancer, extent of surgery, presence or absence of underlying lung disease, comorbidities, staging information, underlying lung function (aerobic volume before treatment), patient It may include any one or more of radiation dose information performed for radiation therapy, pulmonary function (aerobic volume) after radiation dose treatment, radiation dose treatment period, and radiation irradiation information. Personal information, diagnosis and examination information, and stage information may be information derived from medical records of patients who have been previously performed.

FIG. 3 is an example of patient data according to an embodiment of the present invention, and is a graph showing the 1 second aerobic volume (FEV1) according to the time before and after radiation treatment of the patients.

Referring to FIG. 3, 1 second aerobic volume according to time before and after radiotherapy (PORT) for about 51 patients is shown. In FIG. 3 , the horizontal axis of the graph indicates time (months) and the vertical axis indicates aerobic volume (liter) in 1 second. PORT indicates the point at which radiotherapy begins. Through the graph of FIG. 3 , it can be confirmed how the 1 second aerobic volume (liter) changes according to the lapse of time before and after radiation treatment of patients.

Returning to FIG. 2 again, based on the data of the patients stored in step S110, a predictive factor affecting the lung function according to the radiation treatment is set (S120).

To this end, the lung function prediction device 100 is a dose-representing radiation dose irradiated to the lungs from the acquired data of patients - to set a predictive factor having a correlation with volume information, repeatedly measured patient data (data) Upon input, the data is analyzed to obtain information on independent variables and dependent variables, and substituted into the linear complex model.

In an embodiment of the present invention, a 95% confidence interval was constructed for each variable factor, but the present invention is not limited thereto.

Referring to FIG. 4 , according to an embodiment of the present invention, predictive factors affecting the decrease in lung function before and after radiotherapy are baseline lung function, lung resection range, radiotherapy (dose-volume information), and elapsed time after radiotherapy. includes Here, lung function is quantitatively expressed by FEV1 forced expiratory volume in 1 second and forced expiratory volume in 1 second.

The lung function prediction apparatus 100 calculates a significance probability (p-value) for verifying the hypothesis from a statistical point of view. The significance probability (p-value) is a numerical value for testing a hypothesis from a statistical point of view.

Table 1 shows the results of statistical testing after applying the obtained data to the linear composite model to correlate with dose-volume information indicating the amount of radiation irradiated to the lungs.

[Table 1]

In Table 1, looking at each parameter (dose-volume parameters), the mean lung dose (MLD) is the average dose irradiated to the lungs, and the V-x dose is the ratio of the irradiated volume to the total volume of the lung by x Gy or more. For example, V20 means the ratio (%) of the volume of the lung irradiated with more than 20 Gy. Also, in the type of surgery, lobectomy means lobectomy to remove the lung lobe as a unit, and pneumonectomy means total lung resection to remove all of one lung. For total pulmonary resection, substitute 2. The elapsed time after radiation treatment is expressed in months.

Based on the predictive factors, the lung function reduction predictive model is built (S130).

The device compares the significance probability with a predefined set value, and selects only the variable whose significance probability is less than the set value as a significant variable. In an embodiment of the present invention, the set value is 0.05, but the p-value may be set differently depending on the situation.

In Table 1, only parameters with a P value less than 0.05 are recognized as significant parameters.

Table 2 shows the pulmonary function reduction predictive model estimated by configuring only significant parameters in Table 1.

[Table 2]

In Table 2, MLD (Mean lung dose) means the average dose irradiated to the lungs, V-x (dose) represents the ratio of the irradiated volume to the total lung volume by x (Gy) or more, and FEV1 is the aerobic volume in 1 second (liter), and iFEV1 is the lung function before irradiation, and is expressed in 1 second aerobic volume (liter) of the patient before irradiation. T is the duration of radiation dose treatment in months.

The lung function reduction prediction model has different coefficients according to the dose-volume information irradiated to the lungs.

The lung function reduction prediction model is expressed as FEV1= beta_1*iFEV1(basal lung function)+beta_2*(pulmonary resection range)+beta_3*(dose-volume information)+beta_4*(elapsed time after radiation treatment)+alpha can

When the lung function reduction prediction model is the average dose irradiated to the lungs, the coefficient beta_1 of the pulmonary function reduction prediction formula is 0.769, the coefficient beta_2 is -0.411, the coefficient beta_3 is -0.028, the coefficient beta_4 is -0.003, and the alpha is 1.288 to be.

In the case of the ratio of the irradiated volume to the total volume of the lungs of 15 Gy or more, the coefficient beta_1 of the predictive formula for decreased lung function is 0.760, the coefficient beta_2 is -0.476, the coefficient beta_3 is -1.165, the coefficient beta_4 is -0.003, and the alpha is 1.370 to be.

In the case of the ratio of the volume irradiated over 20 Gy to the total volume of the lung, the coefficient beta_1 is 0.760, the coefficient beta_2 is -0.410, the coefficient beta_3 is -1.086, the coefficient beta_4 is -0.003, and the alpha is 1.194.

In the case of the ratio of the volume to be irradiated more than 30 Gy from the total volume of the lung, the coefficient beta_1 is 0.779, the coefficient beta_2 is -0.416, the coefficient beta_3 is -1.856, the coefficient beta_4 is -0.003, and the alpha is 1.176.

In the case of the ratio of the volume to be irradiated over 35 Gy from the total volume of the lung, the coefficient beta_1 is 0.781, the coefficient beta_2 is -0.405, the coefficient beta_3 is -2.070, the coefficient beta_4 is -0.003, and the alpha is 1.135.

In the case of the ratio of the volume to be irradiated over 40 Gy from the total volume of the lung, the coefficient beta_1 is 0.788, the coefficient beta_2 is -0.390, the coefficient beta_3 is -2.356, the coefficient beta_4 is -0.003, and the alpha is 1.086.

Returning back to FIG. 2 , when the prediction model for decreased lung function is estimated, dose-volume information of the patient to be treated is applied to the estimated decrease in lung function prediction model to predict the change in lung function of the patient to be treated ( S140 ).

In an embodiment of the present invention, if the dose and volume information of the patient to be treated is applied to the pulmonary function reduction prediction formula provided as shown in Table 2, it is possible to quantitatively predict the lung function after radiotherapy.

Specifically, in the lung function reduction prediction model shown in Table 2, if the irradiated lung volume is applied to the desired dose to be predicted from the baseline FEV1, type of surgery, radiation dose treatment period, and total lung volume of the patient to be treated, the patient FEV1 of is predicted.

Using the lung function prediction technology according to an embodiment of the present invention, it is possible to quantitatively predict how much the dose to be irradiated to the lungs will deteriorate the lung function at the time of the radiation treatment plan. Using this predictive formula, it is possible to determine how much lung dose can be tolerated at the minimum for each patient, and then perform customized radiation therapy that can maximize the efficiency of radiation therapy.

5 is a graph showing the correlation between the predicted value according to the lung function prediction model according to an embodiment of the present invention and the lung function actually measured for each patient.

In each graph of FIG. 5 , the horizontal axis represents the predicted lung function (FEV1) for the patient, and the vertical axis represents the measured lung function (FEV1) of the patient. 5 (a) to 5 (f) show the correlation between the actually measured lung function and the predicted lung function for each patient at MLD, V15, V20, V30, V35, and V40, respectively.

Referring to FIGS. 5A to 5F , it can be confirmed that the predicted value according to the lung function prediction model according to an embodiment of the present invention and the lung function actually measured for each patient have a high correlation.

Meanwhile, the embodiments of the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the present invention, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - Examples of program instructions such as magneto-optical and ROM, RAM, and flash memory can be executed by a computer using an interpreter as well as machine code such as generated by a compiler. contains high-level language codes. The hardware device described above may be configured to operate as one or more software modules to perform the operations of the embodiments of the present invention, and vice versa.

So far, the present invention has been looked at with respect to preferred embodiments thereof. Those of ordinary skill in the art to which the present invention pertains will understand that the present invention can be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments are to be considered in an illustrative rather than a restrictive sense. The scope of the present invention is indicated in the claims rather than the foregoing description, and all differences within the scope equivalent thereto should be construed as being included in the present invention.

100: device
110: storage
120: output unit
130: control unit
140: communication department

Claims (12)

receiving and storing patient data on changes in lung function before and after radiation therapy;
setting a predictive factor affecting lung function according to radiation treatment by substituting the data into a linear complex model;
estimating a pulmonary function reduction predictive model based on the predictive factors; and
Predicting the change in the decrease in lung function of the patient to be treated by applying the dose-volume information of the patient to be treated to the prediction model for the decrease in lung function
Including, the lung function reduction prediction model is lung function prediction FEV1=beta_1*iFEV1 (basal lung function)+beta_2*(pulmonary resection range)+beta_3*(dose-volume information)+beta_4*(time elapsed after radiation treatment) ) + alpha pulmonary function prediction method according to radiation therapy, characterized in that. According to claim 1,
The step of setting a predictive factor affecting lung function according to radiation treatment based on the data is,
Pulmonary function prediction method according to radiation therapy, characterized in that the baseline lung function, lung resection range, dose-volume information, and elapsed time after radiation therapy are set as the predictive factors. According to claim 1,
The step of predicting the change in the decrease in lung function is
A method for predicting lung function according to radiation therapy, characterized in that it is expressed as a forced expiratory volume in 1 second (FEV1), which is a volume that is forcibly discharged in 1 second of the predicted treatment target patient's lung function. delete According to claim 1,
The lung function reduction prediction model is a lung function prediction method according to radiation therapy, characterized in that 1 is applied in the case of lobectomy and 2 is applied in the case of pneumonectomy within the range of the lung resection. According to claim 1,
The lung function reduction prediction model is dose irradiated to the lungs - Pulmonary function prediction method according to radiation therapy, characterized in that it has a different coefficient according to the volume information. a storage unit for storing patients' data on changes in lung function before and after radiation therapy; and
By substituting the data into a linear composite model to set predictive factors affecting lung function according to radiation therapy, and to estimate a pulmonary function reduction predictive model based on the predictive factors, A control unit that predicts changes in the decrease in lung function of the patient to be treated by applying dose-volume information
including,
The lung function reduction prediction model is lung function prediction FEV1 = beta_1*iFEV1 (basal lung function) + beta_2* (pulmonary resection range) + beta_3* (dose-volume information) + beta_4* (time elapsed after radiation treatment) + alpha Pulmonary function prediction device according to radiation therapy, characterized in that. 8. The method of claim 7,
The control unit is a lung function prediction device according to radiation therapy, characterized in that it sets the baseline lung function, the lung resection range, dose-volume information, and the elapsed time after radiation treatment as the predictive factors. 8. The method of claim 7,
The control unit predicts lung function according to radiation therapy, characterized in that it represents the predicted pulmonary function of the patient to be treated as FEV1 (forced expiratory volume in 1 second), which is a volume forcibly discharged in 1 second. delete 8. The method of claim 7,
The lung function reduction prediction model is a lung function prediction device according to radiotherapy, characterized in that 1 is applied in the case of lobectomy and 2 is applied in the case of pneumonectomy in the range of the lung resection. 8. The method of claim 7,
The lung function reduction prediction model is a lung function prediction device according to radiation therapy, characterized in that it has different coefficients according to the dose-volume information irradiated to the lungs.

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