Method and system for predicting expression of biomarker from medical image
The present disclosure relates to a method for predicting biomarker expression from a medical image. The method for predicting biomarker expression includes receiving a medical image, and outputting indices of biomarker expression for the at least one lesion included in the medical image by using a first machine learning model.
1. A method comprising:
obtaining a medical image created by capturing at least one part of a body of a patient without tissue collection; and
using at least one processor:
inputting the medical image to a machine learning model; and
outputting information related to a biomarker for at least one lesion by using the machine learning model,
wherein the at least one lesion is detected in the medical image, and
wherein the information related to the biomarker is associated with a tissue that can be collected from the at least one lesion and is numerical information that is predicted about the biomarker for the at least one lesion.
2. The method according to claim 1 , further comprising:
outputting information associated with the tissue collection for the at least one lesion based on the output information related to the biomarker.
3. The method according to claim 2 , wherein the information associated with the tissue collection includes at least one of a location of the tissue collection for the at least one lesion, a size of the tissue collection for the at least one lesion, or a method of the tissue collection for the at least one lesion.
4. The method according to claim 2 , wherein the at least one lesion included in the medical image includes a plurality of lesions included in the medical image, and
the outputting the information associated with the tissue collection for the at least one lesion comprises:
outputting information on a priority of the tissue collection for the plurality of lesions.
5. The method according to claim 4 , further comprising:
acquiring reference information on the tissue collection associated with the medical image,
wherein the outputting the information on the priority of the tissue collection for the plurality of lesions comprises:
outputting the information on the priority of the tissue collection for the plurality of lesions by using the output information related to the biomarker and the acquired reference information on the tissue collection.
6. The method according to claim 5 , wherein the acquired reference information on the tissue collection includes at least one of an underlying medical condition history of the patient, risk of surgery, a location of the at least one lesion, or a size of the at least one lesion.
7. The method according to claim 1 , further comprising:
acquiring patient information associated with the medical image, wherein the patient information includes at least one of age, gender, previous medical history, treatment history, or family medical history of the patient,
wherein the outputting the information related to the biomarker comprises:
inputting the acquired patient information and the medical image to the machine learning model to output the information related to the biomarker for the at least one lesion.
8. The method according to claim 1 , wherein the biomarker includes at least one of proteins, DNA, RNA, or metabolites in the tissue that is collected from the at least one lesion.
9. The method according to claim 1 , wherein the machine learning model is a model trained by using one or more training medical images and training biomarker information for the at least one lesion included in the one or more training medical images, and
wherein the training biomarker information is information related to the biomarker identified from the tissue collected from the at least one lesion included in the one or more training medical images.
10. The method according to claim 1 , further comprising:
generating an image in which a region for the at least one lesion is displayed on the medical image by extracting the region for the at least one lesion from the medical image,
wherein the outputting the information related to the biomarker comprises:
outputting the information related to the biomarker on the generated image in which the region for the at least one lesion is displayed.
11. The method according to claim 10 , wherein the outputting the information related to the biomarker on the medical image in which the region for the at least one lesion is displayed comprises:
outputting both the information related to the biomarker and an expression index of the biomarker on the medical image in which the region for the at least one lesion is displayed.
12. The method according to claim 1 , wherein the information related to the biomarker includes at least one of a name of a disease associated with the biomarker or a name of the biomarker.
13. The method according to claim 1 , wherein the medical image is an image captured in at least one form of CT(Computed Tomography), MRI(Magnetic Resonance Imaging), PET(Position Emission Tomography), SPECT(Single Photon Emission CT), or DBT(Digital Breast Tomosynthesis).
14. An information processing system comprising:
at least one memory storing one or more instructions; and
at least one processor connected to the at least one memory and configured to execute the one or more instructions to:
obtain a medical image created by capturing at least one part of a body of a patient without tissue collection;
input the medical image to a machine learning model, using the at least one processor; and
output information related to a biomarker for at least one lesion by using the machine learning model,
wherein the at least one lesion is detected in the medical image, using the at least one processor, and
wherein the information related to the biomarker is associated with a tissue that can be collected from the at least one lesion and is numerical information that is predicted about the biomarker for the at least one lesion.
15. The information processing system according to claim 14 , wherein the at least one processor is further configured to:
output information associated with the tissue collection for the at least one lesion based on the output information related to the biomarker.
16. The information processing system according to claim 14 , wherein the machine learning model is a model trained by using one or more training medical images and training biomarker information for the at least one lesion included in the one or more training medical images, and
wherein the training biomarker information is information related to the biomarker identified from the tissue collected from the at least one lesion included in the one or more training medical images.
17. The information processing system according to claim 14 , wherein the at least one processor is further configured to:
generate an image in which a region for the at least one lesion is displayed on the medical image by extracting the region for the at least one lesion from the medical image, and
output the information related to the biomarker on the generated image in which the region for the at least one lesion is displayed.
18. The information processing system according to claim 17 , wherein the at least one processor is further configured to:
output both the information related to the biomarker and an expression index of the biomarker on the medical image in which the region for the at least one lesion is displayed.
19. The information processing system according to claim 14 , wherein the medical image is an image captured in at least one form of CT(Computed Tomography), MRI(Magnetic Resonance Imaging), PET(Position Emission Tomography), SPECT(Single Photon Emission CT), or DBT(Digital Breast Tomosynthesis).
20. The information processing system according to claim 14 , wherein the biomarker includes at least one of proteins, DNA, RNA, or metabolites in the tissue that is collected from the at least one lesion.