This application claims priority to Chinese application number 201810480047.0, filed May 18, 2018, with a title of METHOD AND SYSTEM FOR DETERMINING IRRADIATION DOSE. The above-mentioned patent application is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present invention relates to the field of medical radiotherapy, and in particular to a method and system for determining an irradiation dose.

BACKGROUND

Radiotherapy, surgical treatment, and medical chemotherapy are called three major means for oncotherapy. Statistics show that about 65% of patients need to be subjected to radiotherapy during oncotherapy. Some types of cancers (such as early nasopharyngeal carcinoma, lung cancer, breast cancer, and prostate cancer) can be cured by radiotherapy, and the contribution of radiotherapy to tumor curing is 40%.

The general procedure for radiotherapy is as follows:

Step1, as shown inFIG. 1, is simulated locating, i.e., medically imaging a patient under a condition of a fixed posture.

Step2, as shown inFIG. 2, is treatment plan design for radiotherapy, i.e., transmitting the image of the previous step to a treatment planning system (TPS) software; simulating an incident angle, a shape, and energy of X ray on this software, and calculating an irradiation dose through the software; simulating the distribution of expose doses of a tumor and organs at risk surrounding it in the patient after radiotherapy implementation; and performing statistical quantitative evaluation in a dose-volume histogram form.

Step3, plan implementation, i.e., submitting the above radiotherapy plan to a medical linear accelerator for implementation after it is confirmed by both a radiotherapy doctor and a radiological physicist, in which as shown inFIG. 3, before the implementation, firstly the patient is fixed again on a medical linear accelerator treatment table by means of the same fixing device according to the posture used in “Step1, simulated locating”, i.e., “positioning”; then activating a imaging system integrated with the medical linear accelerator, i.e., the CBCT imaging system pointed by a white arrow inFIG. 3, which executes fast imaging of the treatment site of the patient in a time period of about 2 minutes under the circumstance that the patient does not leave the treatment table; subsequently conducting image registration between the image of fast imaging and the patient image used in “Step2, treatment plan design for radiotherapy”, and moving the accelerator treatment table utilizing the posture changes revealed by the registration result, such that the patient's posture is restored to the same posture as that used in “Step1, simulated locating” and “Step2, treatment plan design for radiotherapy”, as shown inFIG. 4; and finally, calling the radiotherapy plan of the patient to implement radiotherapy for the patient.

For an existing radiotherapy planning method, as described in “Step2, treatment plan design for radiotherapy” mentioned above, the designing process of the existing radiotherapy plan is formulated under the guidance of anatomical imaging and functional imaging. The anatomical imaging (e.g., CT and MRI) reflects anatomical structures and locations of tumors and normal tissues in the patient, and the functional imaging (e.g., f-MRI, PET, SPECT, etc.) reflects partial biological features in the patient's tumor. By using the anatomical imaging and functional imaging as references, the irradiation field parameters (including shape and intensity of the ray) are adjusted during the pan designing process, so as to simulatedly calculate the dose distribution in the patient and thus complete the plan designing.

As described in “Step2, treatment plan design for radiotherapy” mentioned above, the formulation process of the existing radiotherapy plan makes reference to the anatomical imaging (e.g., CT and MRI) and the functional imaging (e.g., f-MRI, PET, SPECT, etc.), the limitation of which is reflected in that:

When the radiotherapy plan is formulated under the guidance of the anatomical imaging, the anatomical imaging can only reflect the positions and structures of the tumor and surrounding normal tissues, and cannot reflect biological function information (such as the glucose metabolic level, the hypoxia level, and angiogenesis conditions in the tumor, the magnitude of complications risk in normal tissues, and the like); while for the biological features of the tumor or normal tissues in the human body, on one hand they largely determine the success or failure of radiotherapy (for example, for a hypoxic region in the tumor, the existence of the hypoxic region has direct correlations with distant metastases of the tumor and poor prognosis, and a large number of studies have confirmed that tumor cells in the hypoxic region are resistant to radioactive rays, so that if no high-dose irradiation is conducted, the tumor may not be effectively killed; also for example, as shown inFIG. 5, no volume reduction occurs as seen from the CT anatomical structure of the tumor region indicated by the white arrow, but the CT perfusion functional imaging shows that the blood flow volume is reduced, which means that the tumor-reactive cells have been significantly reduced, and the actual treatment is effective; and also for example, if no targeted protection is performed on a normally functioning lung which is prone to radiation pneumonitis, the patient may have fatal radiation pneumonitis after subjected to the radiotherapy), and on the other hand, there are significant differences among individuals, and there may be huge differences in biological features between two patients with the same type of tumor (for example, after a same irradiation dose of radiotherapy is implemented, the tumor in a radiation-sensitive patient is regressed significantly, while the tumor in a radiation-resistant patient do not change or even proliferate, so that it is essential and necessary to let factors capable of reflecting biological function information to participate in the setting process of the radiotherapy plan.

When the radiotherapy plan is formulated under the guidance of the functional imaging, although the functional imaging reflects specific biological function information (such as glucose metabolism conditions, hypoxia conditions, and angiogenesis conditions in the tumor, and angiogenesis conditions in the tumor, the magnitude of complications risk in normal tissues, and the like), it has many limitations, including:

(1) The location and anatomical information cannot be accurately reflected. The f-MRI, PET and SPECT images, while reflecting the biological functions of some regions, cannot accurately reflect the accurate anatomical location of the region, and the functional region can be directed to the corresponding anatomical position only through image fusion of these images with an anatomical image (i.e., overlaying of the two kinds of images), and as shown inFIGS. 6 to 8, the highlighted region inFIG. 8is a region with a specific biological function, such as a tumor region having glucose hypermetabolism or a hypoxic tumor region; but such a “directed to” is often less accurate, as its accuracy is completely dependent on the image fusion algorithm, and images of different modalities often have problems such as distortion and inconsistent scale, and thus no truly one-to-one correspondence can be achieved.

(2) The cost of functional imaging is very expensive, ranging from more than 1,000 yuan per time (SPECT) to nearly 10,000 yuan per time (PET, a non-medical insurance reimbursement item, completely at people's own expense), so that it is difficult to carry out among most patients and cannot benefit more patients.

(3) Functional imaging such as PET and SPECT require injecting radioisotopes into the patient's body, which brings additional radiation to the patient, and in turn brings the limitation that the total number of functional imaging scans subjected by the same patient during the treatment course should not be too many (usually the patient only subjected to the functional imaging scan once before the radiotherapy begins, and at most subjected to the functional imaging scan once again after the radiotherapy, that is the patient is subjected to the functional imaging scans for 2 times before and after in total.

(4) One type of functional imaging can only reveal one biological function feature. For example, 18F-FETNIM PET can reveal the hypoxic region in the tumor, and thus promote that it is appropriate to increase the irradiation dose in this region during the treatment planning process, but 18F-FETNIM PET can only reflect such a feature of “tumor hypoxia” and cannot explain other biological function features such as angiogenesis, glucose metabolism in the tumor, and currently there is no tracer agent that can display multiple functional features at the same time after injected into the human body.

In view of the above existed problems, a method for identifying where is tumor and where is non-tumor by extracting local radiomics features is studied abroad. For the region which is identified as a tumor, a higher irradiation dose is given in the radiotherapy course, and for the region which is identified as non-tumor, a relatively lower prophylactic irradiation dose is given. However, since the biological features at different locations in tumor region are inhomogeneous, if homogeneous irradiation doses are given to the tumor region and the non-tumor region, the tumor cells cannot be completely killed in the tumor region. Therefore, being unable to give respective irradiation doses to tumors with different biological features in the tumor region may affect the therapeutic effect of radiotherapy.

SUMMARY

The object of the present invention is to provide a method and system for determining an irradiation dose, so as to solve the problem in the prior art that irradiation doses cannot be given correspondingly to tumors respecting to different biological features both in the tumor region and in the organs at risk, which affects the therapeutic effect and side-effects of radiotherapy.

A method for determining an irradiation dose, including:

determining a pixel having a biological feature in a region of interest in a radiotherapy simulated locating image by using a retrospective label, where the radiotherapy simulated locating image includes computed tomography imaging, nuclear magnetic resonance imaging, and positron emission tomography imaging, and the biological feature includes the glucose metabolism conditions, hypoxic regions, oxygen-enriched regions, angiogenesis conditions in the tumor, and the magnitude of complications risk in normal tissues;

extracting the local radiomics feature based on the pixel having the biological feature, where the local radiomics feature includes a grayscale histogram intensity, a tumor shape feature, a textural feature, a Laplacian of Gaussian (LoG) filtering feature, and a wavelet feature;

acquiring the local radiomics feature to be measured;

identifying a positive region having the local radiomics feature to be measured based on the local radiomics feature;

performing three-dimensional reconstruction for the peripheral boundary of the positive region to determine a three-dimensional image, where the three-dimensional image is a three-dimensional image displaying the biological features; and

determining irradiation doses at different locations in different regions based on the three-dimensional image.

Alternatively, the extracting the local radiomics feature based on the pixel having the biological feature specifically includes:

extracting local radiomics features from pixels having the biological features pixel by pixel through a per-pixel method by adopting a grayscale histogram feature extraction method, a textural feature extraction method, a LoG filtering feature extraction method, and a wavelet feature extraction method.

Alternatively, the identifying a positive region having the local radiomics feature to be measured based on the local radiomics feature specifically includes:

screening the local radiomics features to determine an optimal feature subset;

establishing a supervised machine learning model based on the optimal feature subset; and

identifying a positive region having the local radiomics features to be measured based on the supervised machine learning model.

Alternatively, the screening the local radiomics features to determine an optimal feature subset specifically includes:

screening the local radiomics features through a feature selection method so as to determine an optimal feature subset, where the feature selection method includes Minimum Redundancy-Maximum Relevance.

Alternatively, the determining irradiation doses at different locations in different regions based on the three-dimensional image specifically includes:

determining an irradiation dose parameter based on the three-dimensional image, where the irradiation dose parameter includes an incident angle, an intensity, and a shape; and

determining irradiation doses at different locations in different regions based on the irradiation dose parameter.

A system for determining an irradiation dose, including:

a pixel determining module for determining a pixel having a biological feature in a region of interest in a radiotherapy simulated locating image by using a retrospective label, where the radiotherapy simulated locating image includes computed tomography imaging, nuclear magnetic resonance imaging, and positron emission tomography imaging, and the biological feature includes the glucose metabolism conditions, hypoxic regions, oxygen-enriched regions, angiogenesis conditions in the tumor, and the magnitude of complications risk in normal tissues;

a local radiomics feature extraction module for extracting a local radiomics feature based on the pixel having the biological feature, where the local radiomics feature includes a grayscale histogram intensity, a tumor shape feature, a textural feature, a LoG filtering feature, and a wavelet feature;

an acquisition module for acquiring the local radiomics features to be measured;

a positive-region identification module for identifying a positive region having the local radiomics features to be measured based on the local radiomics features;

a three-dimensional reconstruction module for performing three-dimensional reconstruction for the peripheral boundary of the positive region to determine a three-dimensional image, where the three-dimensional image is a three-dimensional image displaying the biological features; and

an irradiation dose determination module for determining irradiation doses at different locations in different regions based on the three-dimensional image.

Alternatively, the local radiomics feature extraction module specifically includes:

a local radiomics feature extraction unit for extracting local radiomics features from pixels having the biological features pixel by pixel through a per-pixel method by adopting a grayscale histogram feature extraction method, a textural feature extraction method, a LoG filtering feature extraction method, and a wavelet feature extraction method.

Alternatively, the positive-region identification module specifically includes:

a screening unit, for screening the local radiomics features to determine an optimal feature subset;

a model establishing module for establishing a supervised machine learning model based on the optimal feature subset, and

a positive-region identification unit for identifying a positive region having the local radiomics features to be measured based on the supervised machine learning model.

Alternatively, the screening unit specifically includes:

a screening subunit for screening the local radiomics features through a feature selection method so as to determine an optimal feature subset, where the feature selection method includes Minimum Redundancy-Maximum Relevance.

Alternatively, the irradiation dose determination module specifically includes:

an irradiation dose parameter determination unit for determining an irradiation dose parameter based on the three-dimensional image, where the irradiation dose parameter includes an incident angle, an intensity, and a shape; and

an irradiation dose determination unit for determining irradiation doses at different locations in different regions based on the irradiation dose parameter.

According to a specific embodiment provided by the present invention, the present invention discloses the following technical effects: the present invention provides a method and system for determining an irradiation dose, including: determining a pixel of a biological feature; extracting a local radiomics feature according to the pixel; determining the positive region having the local radiomics feature to be measured based on the local radiomics feature; and in turn establishing a three-dimensional image capable of displaying the biological features at different locations in different regions, so that respective irradiation doses at different locations in different regions can be determined based on the three-dimensional image. Therefore, even when the tumors are in the same the tumor region, different irradiation doses can be given to different tumors based on differences in the biological features in the tumor region, so that the irradiation dose can be given based on the actual tumor condition, thereby improving the therapeutic effect of radiotherapy.

