KR101862136B1 - Method and system for predicfing risk of percutaneous pulmonary biopsy - Google Patents

Abstract

The present disclosure relates to a method for predicting the risk of percutaneous lung biopsy, comprising the steps of: extracting a risk factor for a percutaneous lung biopsy in a percutaneous lung biopsy procedure; Generating a risk prediction probability model based on the risk factors of the percutaneous lung biopsy extracted from the percutaneous lung biopsy procedure; Extracting a risk factor of a percutaneous lung biopsy for a patient; Inputting a risk factor of an extracted percutaneous lung biopsy of a patient through a user interface; Calculating a risk of a percutaneous lung biopsy of a patient according to a risk factor of a percutaneous lung biopsy of a patient using a risk prediction probability model; And displaying the calculated risk of the percutaneous lung biopsy of the patient through a user interface. The present invention also relates to a method and system for predicting the risk of a percutaneous lung biopsy.

Description

TECHNICAL FIELD The present invention relates to a method and system for predicting the risk of a percutaneous lung biopsy,

Disclosure relates generally to methods and systems for predicting the risk of tissue biopsy, and more particularly to methods and systems for predicting the risk of percutaneous lung biopsy.

Typically, when a nodule or mass in a human body is suspected of being a cancer, the disease is confirmed through tissue sampling called a tissue biopsy and pathological examination of the collected tissue. The surgical biopsy can be divided into two types: a method of collecting tissue through surgery and a minimally invasive percutaneous biopsy which is guided by the latest image guiding equipment. Because of the long hospital stay, pain and complications, and the high risk of death, imaging-guided percutaneous biopsy has become popular in recent years. An image - guided biopsy can be divided into two types: an aspiration biopsy to obtain cells only and a core biopsy to obtain more tissue.

Prior art relating to aspiration biopsy includes a number of patents including U.S. Patent Nos. 3,938,505, 4,314,565, 5,199,441, 5,469,860, 6,872,185, and the like. Prior art relating to core biopsy includes a number of patents including U.S. Patent Nos. 3,477,423, 4,600,014, 5,161,542, 5,236,334, 7,914,463. Aspiration biopsy can be performed when there is a pathologist at the biopsy site, when the likelihood of cancer is very high, if no immunologic or genetic testing is required, if the tumor is accompanied by extensive necrosis, In the case of using a core biopsy, it is necessary to diagnose cancer biopsy more precisely, to obtain genetic information of cancer, to have a benign tumor, or to diagnose a pathologist. . The biopsy needle for biopsy has a core biopsy needle and an aspiration biopsy needle, depending on the biopsy method.

Figure 1 shows an example of a core biopsy needle structure. One example of the core biopsy needle structure described in FIG. 1 is described in U.S. Patent No. 5,236,334, but the terminology and designations have been changed for convenience of explanation. The core biopsy needle consists of an inner stylet (10) and a sheath (20). The inner stylet 10 is composed of a hub 11, a body 12, a notch 13 and a distal end 14. The notch 13 is located between the distal end 14 and the body 12 and is a space in which the cut tissue is stored. The distal end (14) has a pointed shape for the function of passing through the living body. The sheath 20 is composed of a hub 21 and a cannula 22.

Figure 2 shows an example of a method of using a core biopsy needle. One example of a method of using the core biopsy needle described in FIG. 2 is described in U.S. Patent No. 5,161,542, but the terms and symbols have been changed for convenience of explanation. First, insert the inner stylet and sheath into the tissue (30) where harvesting is required (see 40). The notch 13 is then inserted into the tissue 30 using the distal end 14 of the inner stylet (see 41). The cannula 22 of the sheath is then inserted into the tissue 30 (see 42) to cut the tissue entering the notch 13. Thereafter, the core biopsy needle is pulled out from the living body with the cut tissue in the notch 13, and the tissue contained in the notch 13 is used for the examination. Core biopsy Sometimes the needle and the inner stylet are moved directly by the user, but sometimes it is done automatically using a spring or motor structure. A core biopsy needle that can automatically perform core biopsy using a spring or motor structure is called a biopsy gun. Prior art related to biopsy guns are disclosed in Korean Patent Laid-Open Publication No. 2013-0079788, Korean Laid-Open Patent Publication No. 2016-0023924, Korean Laid-Open Patent Publication No. 2013-0126574, Japanese Laid-Open Patent Publication No. 1994-197898, Patent Publication No. 7,841,990 and the like.

Figure 3 shows an example of a needle biopsy needle structure. An example of the aspiration biopsy needle structure described in Fig. 3 is commonly used and is known as a Chiba needle. The chiba needles are composed of a sheath 50 and an inner stylet 60. The sheath 50 is constituted by a hub 51 and a cannula 52. The inner stylet 60 is constituted by a hub 61, a body 62 and a distal end 63. The major difference from the inner stylet 10 in the core biopsy needle is that it does not have a notch 13, which is a space for collecting the tissue and storing it.

Figure 4 shows an example of a method of using an aspiration biopsy needle. First, the inner stylet 60 and the sheath 50 are joined (refer to 81), and then inserted into the tissue 30 to be harvested (see 82). Thereafter, the inner stylet 60 is separated from the sheath 50 (see 82). The syringe 70 is then coupled to the hub 51 of the sheath 50 (see 83). Thereafter, a portion of the tissue 30 is collected using the suction force of the syringe. Various methods other than the injector 70 can be used as a method of using the suction force. A vacuum machine may also be used. Since the inner diameter of the sheath 50 is smaller in order to improve the suction force, the outer diameter of the sheath 50 of the suction biopsy needle is generally smaller than the outer diameter of the core 20 of the core biopsy needle.

FIG. 5 is a view showing a coaxial needle that can be utilized when performing a core biopsy. FIG. The coaxial needle is composed of a sheath 90 and an inner stylet 91. The sheath 90 is composed of a cannula 92 and a hub 93. The inner stylet 91 is composed of a body 94, a hub 95 and a distal end 96. The distal end 96 has a pointed shape to pass through the tissue. The coaxial needle is inserted into a living body through a shape 97 in which a sheath 90 and an inner stylet 91 are combined and is inserted to the vicinity of a tissue requiring harvesting. Then, the inner stylet 91 is pulled out, The core biopsy needle is inserted and used. It is preferable that the inner stylet 91 protrudes much from the sheath 90 in the combined form 97 for the sake of clear division, but the protrusion is less. The coaxial needle is mainly used when a core biopsy is required for the same tissue several times. When the coaxial needle is inserted into the living body, the inner stylet 91 is taken out, and the sheath 90 is used as a path for inserting the core biopsy needle into the living body . This eliminates the need to insert the core biopsy needle every time in the patient. The coaxial needle can also be used as a passageway when inserting an aspiration biopsy needle into a living body.

6 is a view showing an example of a percutaneous lung biopsy.

FIG. 6 shows tissue biopsy needle 100 under image guidance to harvest tissue of abnormal region 111 of lung 110. FIG. In general, before conducting a percutaneous lung biopsy for a patient as shown in FIG. 6, a doctor establishes a procedure for a lung biopsy based on a medical image including an abnormal part of the patient, and sets a path through which a biopsy needle is inserted. The reason for setting the insertion path in advance is to predict the risk of biopsy in advance, such as not to damage the patient's blood vessels or organs during the biopsy needle reaching the abnormal part of the patient. However, in the case of establishing a procedure plan while observing the conventional medical image, only the problem of the path of insertion of the biopsy needle is seen while the doctor watches the medical image. In the case of lung biopsy such as the pneumothorax ) And hemoptysis were difficult to predict.

This disclosure is intended to provide information that can predict the risk of a pneumothorax or hemoptysis in a patient undergoing a percutaneous lung biopsy in establishing a surgical plan for a percutaneous lung biopsy.

This will be described later in the Specification for Enforcement of the Invention.

SUMMARY OF THE INVENTION Herein, a general summary of the present disclosure is provided, which should not be construed as limiting the scope of the present disclosure. of its features).

According to one aspect of the present disclosure, there is provided a method of predicting the risk of a percutaneous lung biopsy, comprising: extracting a risk factor of a percutaneous lung biopsy in a percutaneous lung biopsy procedure; Generating a risk prediction probability model based on the risk factors of the percutaneous lung biopsy extracted from the percutaneous lung biopsy procedure; Extracting a risk factor of a percutaneous lung biopsy for a patient; Inputting a risk factor of an extracted percutaneous lung biopsy of a patient through a user interface; Calculating a risk of a percutaneous lung biopsy of a patient according to a risk factor of a percutaneous lung biopsy of a patient using a risk prediction probability model; And displaying the calculated risk of the percutaneous lung biopsy of the patient through a user interface. [0012] According to another aspect of the present invention, there is provided a method for predicting a risk of a percutaneous lung biopsy.

According to another aspect of the present disclosure, there is provided a system for predicting the risk of a percutaneous lung biopsy, the system comprising: a first extractor for extracting a risk factor of percutaneous lung biopsy in a percutaneous lung biopsy procedure; ; A generating unit for generating a probability model for predicting a risk of a percutaneous lung biopsy based on the risk factors extracted by the first extracting unit; A storage unit for storing a probability model for predicting a risk of a percutaneous lung biopsy; A second extractor for extracting a risk factor of a percutaneous lung biopsy for a patient; An input unit for inputting a risk factor extracted by the second extracting unit through a user interface; A calculation unit for calculating a risk of a percutaneous lung biopsy of a patient according to a risk factor of a percutaneous lung biopsy using a probability model for predicting a risk of a percutaneous lung biopsy stored in a storage unit; And a display unit for displaying the calculated risk of the percutaneous lung biopsy of the patient through a user interface. A system for predicting the risk of a percutaneous lung biopsy is provided.

This will be described later in the Specification for Enforcement of the Invention.

1 is a view showing an example of a core biopsy needle structure,
2 is a view showing an example of a method of using a core biopsy needle,
3 is a view showing an example of a suction biopsy needle structure,
4 is a view showing an example of a method of using a suction biopsy needle,
FIG. 5 is a view showing a coaxial needle that can be utilized when performing a core biopsy,
6 is a view showing an example of a percutaneous lung biopsy,
7 is a flow diagram of a method for predicting the risk of a percutaneous lung biopsy according to the present disclosure,
8 is a diagram illustrating a method for automatically extracting information on a procedure path from a medical image of a patient according to the present disclosure;
9 is an example of a risk factor for a percutaneous lung biopsy entered through a user interface according to the present disclosure and an example of a calculated patient's risk of percutaneous lung biopsy according to the entered patient's percutaneous lung biopsy risk factors Show drawings,
Figure 10 shows an example of a system for predicting the risk of a percutaneous lung biopsy according to the present disclosure;

The present disclosure will now be described in detail with reference to the accompanying drawings.

7 is a flow chart illustrating an example of a method for predicting the risk of a percutaneous lung biopsy according to the present disclosure.

To predict the risk of percutaneous lung biopsy, we first extract risk factors for percutaneous lung biopsy in percutaneous lung biopsy (S1). Risk factors are extracted from information on patients who have had a blood or pneumothorax in a previous percutaneous lung biopsy procedure for more than 4,000 patients. Risk factors extracted from percutaneous lung biopsy can be categorized into patient information, biopsy information, and information on the procedure pathway. Patient information includes gender and age. Biopsy information includes at least one of the location of the lesion, the size of the pulmonary nodule, the presence or absence of emphysema, the location of the lung lobes and the pulmonary nodule. For example, the pulmonary nodule is about whether the pulmonary nodule is a soft type or a solid type. The information on the procedure path includes at least one of the following: the vascular structure on the path of insertion of the biopsy needle, the location of the vessel, the presence of pleural effusion, the presence of blisters, the size of the blister, the distance through the biopsy needle, . In particular, the information on the procedure path can be automatically extracted from the medical image of the patient. The automatic extraction from the medical image will be described with reference to FIG. We then generate a risk prediction probability model based on the risk factors of percutaneous lung biopsy extracted from percutaneous lung biopsy (S2). As the method of generating the risk prediction probability model, various well-known probability model generation methods can be used. In the present disclosure, a Bayesian technique was used. Regarding generating a probability model using Bayesian techniques, it is described in a number of publications such as Korean Patent Laid-Open Publication No. 2011-0121885 and Korean Patent Laid-Open Publication No. 2010-0113382. Also, the risk prediction probability model is continuously modified. For example, in the case of percutaneous lung biopsy, which is the basic data for generating a risk predictive probability model, a risk prediction prediction probability model was created as well as a case of percutaneous lung biopsy performed before the risk predictive probability model was created, The performed percutaneous lung biopsy is also included in a percutaneous lung biopsy procedure to automatically generate a risk prediction model, either automatically or manually, so that the risk prediction model is continuously modified. The risk factors for percutaneous lung biopsy for the patient are then extracted (S3). In other words, we extract risk factors for patients who will perform real percutaneous lung biopsy. The risk factors for percutaneous lung biopsy in patients are extracted from the range of risk factors extracted from percutaneous lung biopsy procedures. However, since the present disclosure predicts the risk of a percutaneous lung biopsy for a patient at the planning stage, the information on the procedure pathway, which is a risk factor in a percutaneous lung biopsy procedure, is replaced by the information on the procedure plan pathway. In other words, the information on the procedure planning pathway can be used to determine the location of the biopsy needle insertion, the location of the vessel, the location of the vessel, the presence of blisters, the size of the blister, Or the like. Then, the risk factors of the extracted percutaneous lung biopsy of the patient are inputted through the user interface (S4). FIG. 9 shows an example of a risk factor of percutaneous lung biopsy entered through the user interface. Then, using the risk prediction probability model obtained in step S2, the risk of the percutaneous lung biopsy of the patient according to the risk factors of the percutaneous lung biopsy of the patient is calculated (S5). Then, the calculated risk of the percutaneous lung biopsy of the patient is displayed through the user interface (S6). FIG. 9 shows an example of a patient's percutaneous lung biopsy risk calculated according to the risk factors of percutaneous pulmonary biopsy entered in the patient. If a percutaneous lung biopsy is performed according to the procedure plan established by the practitioner during the procedure planning stage, the practitioner can change the treatment plan by numerically viewing the risk of the patient's pneumothorax or hemoptysis.

8 is a diagram illustrating a method for automatically extracting information on a procedure path from a medical image of a patient according to the present disclosure;

Automatically extracting the information on the procedure path from the patient's medical image is similar to automatically extracting the information on the procedure plan path from the patient's medical image. Only the path of insertion of a biopsy needle is different only when a real biopsy needle is inserted and when a virtual biopsy needle is inserted. 8 (b), the blood vessel 200 is automatically extracted from the medical image of the patient as shown in FIG. 8 (a). In this case, The position and structure of the blood vessel 200 can be automatically extracted. 8 (c), the path 211 in which the biopsy needle 210 is inserted into the abnormal region 220 is automatically analyzed to determine whether the biopsy needle passing distance 212 or the biopsy needle 210 passes through the left lobe , Whether it passes through the pleura or not.

9 is an example of a risk factor for a percutaneous lung biopsy entered through a user interface according to the present disclosure and an example of a calculated patient's risk of percutaneous lung biopsy according to the entered patient's percutaneous lung biopsy risk factors Fig.

(300) of the patient's percutaneous lung biopsy entered through the user interface and the risk of computed pneumothorax and hemoptysis (310) according to the input patient risk factor (300). The operator can view the risk 310 of the calculated pneumothorax and hemoptysis according to the risk factors 300 of the patient's percutaneous lung biopsy and the risk factors 300 of the inputted patient through one screen 320, .

10 is a diagram showing an example of a system for predicting the risk of a percutaneous lung biopsy according to the present disclosure.

A system 400 for predicting the risk of a percutaneous lung biopsy includes a first extracting unit 410 for extracting a risk factor of a percutaneous lung biopsy in a percutaneous lung biopsy procedure, a second extracting unit 410 for extracting a risk factor extracted from the first extracting unit 410, (420) for generating a risk prediction prediction probability model for predicting the risk of percutaneous lung biopsy, a storage unit (430) for storing a risk prediction probability model of a percutaneous lung biopsy, and a risk factor extracting unit An input unit 450 for inputting the risk factors extracted by the second extracting unit 440 and the second extracting unit 440 through a user interface and a risk prediction probability model for the percutaneous lung biopsy stored in the storage unit 430 A calculation unit 460 for calculating the risk of the percutaneous lung biopsy of the patient according to the risk factors of the percutaneous lung biopsy of the patient and a display unit 47 for displaying the calculated risk of percutaneous lung biopsy through the user interface 0). A system 400 for predicting the risk of a percutaneous lung biopsy may be, for example, a computer system, the input being a mouse or a keyboard, and the display being a monitor. The display unit 470 may display the risk factors extracted from the second extracting unit 440 and the calculated risk of the percutaneous lung biopsy of the patient so that the user can simultaneously view the risk factors.

Various embodiments of the present disclosure will be described below.

(1) A method for predicting the risk of percutaneous pulmonary biopsy, comprising: extracting a risk factor of a percutaneous lung biopsy in a percutaneous lung biopsy; Generating a risk prediction probability model based on the risk factors of the percutaneous lung biopsy extracted from the percutaneous lung biopsy procedure; Extracting a risk factor of a percutaneous lung biopsy for a patient; Inputting a risk factor of an extracted percutaneous lung biopsy of a patient through a user interface; Calculating a risk of a percutaneous lung biopsy of a patient according to a risk factor of a percutaneous lung biopsy of a patient using a risk prediction probability model; And displaying the calculated risk of the percutaneous lung biopsy of the patient through a user interface.

(2) A method for predicting the risk of percutaneous pulmonary biopsy based on risk factors of percutaneous pulmonary biopsy extracted from a percutaneous lung biopsy procedure.

(3) A method for predicting the risk of percutaneous lung biopsy, wherein the risk factor for percutaneous lung biopsy for the patient is at least one of patient information, biopsy information, and information on a procedure plan.

(4) The method of predicting the risk of percutaneous lung biopsy according to claim 1, wherein the patient information is at least one of sex and age, and the biopsy information is at least one of lesion location, pulmonary nodule size, emphysema, lung lobe position and pulmonary nodule.

(5) The information on the procedure plan route includes at least one of the vascular structure on the procedure route, the location of the vessel, the presence of pleural effusion, the presence of blisters, the size of the blister, the distance through the biopsy needle, Gt; a < / RTI > risk of percutaneous lung biopsy.

(6) The method of predicting the risk of a percutaneous lung biopsy, characterized in that the information on the procedure pathway is automatically extracted from the medical image for the patient.

(7) A method for predicting the risk of percutaneous lung biopsy, wherein the risk of percutaneous pulmonary biopsy of the calculated patient is a risk for at least one of pneumothorax and hemoptysis.

(8) In the case of percutaneous lung biopsy in percutaneous lung biopsy, the percutaneous lung biopsy procedure includes the percutaneous pulmonary biopsy of percutaneous lung biopsy using risk prediction model. A method for predicting the risk of percutaneous lung biopsy.

(9) A system for predicting the risk of percutaneous lung biopsy, comprising: a first extractor for extracting a risk factor of a percutaneous lung biopsy in a percutaneous lung biopsy procedure; A generation unit for generating a risk prediction probability model for predicting a risk of a percutaneous lung biopsy based on the risk factors extracted by the first extraction unit; A storage unit for storing a risk prediction probability model of a percutaneous lung biopsy; A second extractor for extracting a risk factor of a percutaneous lung biopsy for a patient; An input unit for inputting a risk factor extracted by the second extracting unit through a user interface; A calculation unit for calculating a risk of a percutaneous lung biopsy of a patient according to a risk factor of a percutaneous lung biopsy using a risk prediction model of a percutaneous lung biopsy stored in a storage unit; And a display unit for displaying the calculated risk of the percutaneous lung biopsy of the patient through a user interface.

(10) A system for predicting the risk of a percutaneous lung biopsy, characterized in that the display simultaneously displays the risk factors extracted from the second extractor and the risk of percutaneous lung biopsy of the calculated patient.

(11) The risk factors extracted from the first extracting unit and the second extracting unit are at least one of sex and age as patient information, and the biopsy information includes at least one of a lesion location, a pulmonary nodule size, emphysema, Wherein the method comprises the steps of:

According to the present disclosure, a practitioner is provided with information on the risk of pneumothorax and hemoptysis according to a percutaneous lung biopsy in the course of performing a surgical procedure for a percutaneous lung biopsy of a patient, so that the operator can conveniently estimate the risk of pneumothorax and hemoptysis .

Biopsy needle: 100
Entered risk factors for percutaneous lung biopsy in patients: 300
Indicated calculated risk of percutaneous lung biopsy in patients: 310

Claims (11)

In a method for predicting the risk of percutaneous lung biopsy,
Extracting a risk factor for a percutaneous lung biopsy in a first embodiment of a system for predicting the risk of a percutaneous lung biopsy in a percutaneous lung biopsy procedure;
Generating a predictive probability model of a system for predicting the risk of a percutaneous lung biopsy; generating a risk prediction probability model based on a risk factor of the percutaneous lung biopsy extracted by the first extracting unit;
Extracting a risk factor of a percutaneous lung biopsy for a second extraction unit of a system for predicting the risk of a percutaneous lung biopsy;
Inputting a risk factor of a patient extracted by a second extracting unit to a system for predicting the risk of a percutaneous lung biopsy;
Calculating a risk of percutaneous pulmonary biopsy; calculating a risk of a percutaneous pulmonary biopsy of a patient according to a risk factor of the percutaneous pulmonary biopsy of the patient using a risk prediction predictive probability model; And
And displaying the risk of a percutaneous lung biopsy of the patient through a user interface, the indication of which is indicative of a system for predicting the risk of a percutaneous lung biopsy,
Wherein the risk factor of a percutaneous lung biopsy for the patient extracted from the second extractor is information on a procedure plan pathway. The method according to claim 1,
A method for predicting the risk of a percutaneous lung biopsy characterized by the generation of a risk prediction probability model based on risk factors of percutaneous lung biopsy extracted from a percutaneous lung biopsy procedure through Bayesian techniques. The method according to claim 1,
Wherein the at least one of the patient information and the biopsy information is added to the risk factors of the percutaneous lung biopsy for the patient extracted from the second extraction unit. The method of claim 3,
Wherein the patient information is at least one of sex and age, and the biopsy information is at least one of a lesion location, a pulmonary nodule size, an emphysema, a lung lobe position, and a pulmonary nodule. The method according to claim 1,
The information on the procedure planning pathway is at least one of the following: the vascular structure on the pathway of the procedure, the location of the vessel, the presence of the pleural passage, the presence of blisters, the size of the blister, the distance of the biopsy needle, the passage of the biopsy needle, A method for predicting the risk of percutaneous lung biopsy. The method according to claim 1,
Wherein the information on the procedure plan path is automatically extracted from the medical image for the patient. The method according to claim 1,
Wherein the risk of a percutaneous lung biopsy of the calculated patient is a risk for at least one of a pneumothorax and a hemoptysis. The method according to claim 1,
In the case of percutaneous lung biopsy, the percutaneous pulmonary biopsy procedure is applied to the percutaneous pulmonary biopsy. The percutaneous pulmonary biopsy is performed by applying a risk prediction model to the percutaneous lung biopsy. Methods for predicting the risk of lung biopsy. A system for predicting the risk of percutaneous lung biopsy,
A first extractor for extracting a risk factor of a percutaneous lung biopsy in a percutaneous lung biopsy procedure;
A generation unit for generating a risk prediction probability model for predicting a risk of a percutaneous lung biopsy based on the risk factors extracted by the first extraction unit;
A storage unit for storing a risk prediction probability model of the percutaneous lung biopsy generated in the generating unit;
A second extractor for extracting a risk factor of a percutaneous lung biopsy for a patient;
An input unit for inputting a risk factor extracted by the second extraction unit to a system for predicting the risk of percutaneous lung biopsy;
A calculation unit for calculating a risk of a percutaneous lung biopsy of the patient according to a risk factor of the patient extracted by the second extraction unit using the risk prediction probability model of the percutaneous lung biopsy stored in the storage unit; And,
And a display unit for displaying the risk of the percutaneous lung biopsy of the patient calculated by the calculating unit,
Wherein the risk factor for a percutaneous lung biopsy for the patient extracted from the second extractor is information on a procedure plan. The method of claim 9,
Wherein the display unit simultaneously displays the risk factors extracted from the second extraction unit and the calculated risk of percutaneous lung biopsy of the patient. The method of claim 9,
The risk factors extracted from the first extracting unit and the second extracting unit include at least one of patient information and biopsy information,
Wherein the patient information is at least one of sex and age, and the biopsy information is at least one of a location of a lesion, a size of a pulmonary nodule, an emphysema, a position of a lung lobe, and a pulmonary nodule.

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