METHOD AND SYSTEM FOR ANALYZING BIOLOGICAL SPECIMENS BY SPECTRAL IMAGING

• US 20140286561A1
• Filed: 03/19/2014
• Published: 09/25/2014
• Est. Priority Date: 10/05/2012
• Status: Active Grant

• Current Assignee: Cireca Theranostics, LLC
• Original Assignee: Cireca Theranostics, LLC
• Inventors: REMISZEWSKI, Stanley H., Thompson, Clay M., Diem, Max, Egin, Aysegul
• Granted Patent: US 9,798,918 B2
• Time in Patent Office: Days
• US Class Current: 382/133
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/231 Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts
• CPC Class Codes: G06V 30/1916 Validation; Performance evaluation
• CPC Class Codes: G06V 30/248 involving plural approaches, e.g. verification by template match; Resolving confusion among similar patterns, e.g. "O" versus "Q" G06V30/242 takes precedence

Abstract

The methods, devices, and systems may allow a practitioner to obtain information regarding a biological sample, including analytical data, a medical diagnosis, and/or a prognosis or predictive analysis. The method, devices, and systems may provide a grade or level of development for identified diseases. In addition, the methods, devices and systems may generate a confidence value for the predictive classifications generated, which may, for example be generated in a format to show such confidence value or other feature in a graphical representation (e.g., a color code). Further, the methods, devices and system may aid in the identification and discovery of new classes and tissue sub-types.

Claims

1. A method for classifying a biological sample, the method comprising:

receiving, at a system, an image of a biological sample;

generating a true image of the biological sample by applying annotation information to the image and associated spectral data, wherein the annotation information is associated with a specific disease or condition;

generating a prediction image by applying one or more algorithms to the image and associated spectral data, wherein the prediction image includes a predictive classification of diseases or conditions identified from the biological sample; and

transmitting the prediction image with the predictive classification to be presented on a display.

2. The method of claim 1, wherein the predictive classification classifies the biological sample into classes, and

wherein the classes are used for one or more of diagnostic decisions, prognostic decisions, therapeutic decisions, predictive decisions, and companion diagnostic.

3. The method of claim 2, wherein the classes include one or more sub-classifications.

4. The method of claim 2, wherein the classes distinguish heterogeneity of the biological sample.

5. The method of claim 1, further comprising:

calculating a confidence level for the predictive classification of the diseases or conditions identified from the biological sample;

generating a confidence prediction image by applying the confidence level to the prediction image; and

transmitting the confidence prediction image to be presented on a display.

6. The method of claim 1, further comprising:

determining a differentiation value of the true image, wherein the differentiation value provides a quantitative measure for the grade or level of development of the disease or condition.

7. The method of claim 6, wherein when the differentiation value is low, the particular disease is not developed;

and

wherein when the differentiation value is high, the particular disease is more developed.

8. The method of claim 1, further comprising:

performing a difference analysis between the true image and the prediction image; and

identifying a region of interest in the prediction image based on the difference analysis.

9. The method of claim 8, wherein the region of interest is one or more selected from a group consisting of a poorly differentiated region with a high confidence level, and a well differentiated region with a low confidence level.

10. The method of claim 8, wherein the region of interest comprises one or more selected from a group consisting of a true positive region of interest, a true negative region of interest, a false positive region of interest, and a false negative region of interest.

11. The method of claim 8, further comprising:

assigning the region of interest to a new class of cancer.

12. The method of claim 11, further comprising:

applying annotation information to the region of interest, wherein the annotation information is associated with a specific disease or condition.

13. The method of claim 8, wherein the difference analysis comprises one or more selected from a group consisting of comparing textures in the true image and prediction image, comparing the true image and the prediction image, comparing spectral variations, identifying spatial locality differences, comparing immuno-histochemical (IHC) markers, comparing molecular markers, comparing histopathology, and comparing meta data.

14. The method of claim 5, further comprising:

applying a heuristic to an image set, wherein the image set comprises one or more selected from a group consisting of the true image, the predictive image, and the confidence prediction image;

identifying a sample area region of interest of the biological sample based on the heuristic;

aiding in a harvesting a portion of the biological sample, wherein the portion corresponds to the sample area region of interest; and

performing a molecular test on the portion of the biological sample.

15. The method of claim 14, wherein the heuristic comprises one or more selected from a group consisting of identifying areas of the biological sample with low confidence, identifying areas of the biological sample with multiple attributes, identifying areas of the biological sample classified by a certain tissue type, identifying areas of the biological sample classified with a particular class or sub-class of cancer, and processing clinical data associated with the biological sample.

16. The method of claim 14, further comprising:

associating the sample area region of interest with a spatial location from the prediction image.

18. The system of claim 14, wherein the predictive classification classifies the biological sample into classes, and

wherein the classes are used for one or more of diagnostic decisions, prognostic decisions, therapeutic decisions, predictive decisions, and companion diagnostic.

19. The system of claim 14, wherein the classes include one or more sub-classifications.

20. The system of claim 18, wherein the classes distinguish heterogeneity of the biological sample.

17. A system for classifying a biological sample, the system comprising:

a memory in communication with a processor, wherein the memory and the processor are cooperatively configured to;

receive an image of a biological sample;

generate a true image of the biological sample by applying annotation information to the image and spectral data, wherein the annotation information is associated with a specific disease or condition;

generate a prediction image by applying one or more algorithms to the image and spectral data, wherein the prediction image includes a predictive classification of diseases or conditions identified from the biological sample; and

transmit the prediction image with the predictive classification to be presented on a display.

21. The system of claim 17, wherein the processor is further configured to:

calculate a confidence level for the predictive classification of the diseases or conditions identified from the biological sample;

generate a confidence prediction image by applying the confidence level to the prediction image; and

transmit the confidence prediction image to be presented on a display.

22. The system of claim 17, wherein the processor is further configured determine a differentiation value of the true image, wherein the differentiation value provides a quantitative measure for the grade or level of development of the disease or condition.

23. The system of claim 22, wherein, when the differentiation value is low, the particular disease is not developed;

and

wherein, when the differentiation value is high, the particular disease is more developed.

24. The system of claim 17, wherein the processor is further configured to:

perform a difference analysis between the true image and the prediction image; and

identify a region of interest in the prediction image based on the difference analysis.

25. The system of claim 24, wherein the region of interest is one or more of a poorly differentiated region with a high confidence level and a well differentiated region with a low confidence level.

26. The system of claim 24, wherein the region of interest comprises one or more selected from a group consisting of a true positive region of interest, a true negative region of interest, a false positive region of interest, and a false negative region of interest.

27. The system of claim 24, wherein the processor is further configured to assign the region of interest to a new class of cancer.

28. The system of claim 27, wherein the processor is further configured to apply annotation information to the region of interest, wherein the annotation information is associated with a specific disease or condition.

29. The system of claim 24, wherein the difference analysis comprises one or more selected from a group consisting of comparing textures in the true image and prediction image, comparing the true image and the prediction image, comparing spectral variations, identifying spatial locality differences, comparing IHC markers, comparing molecular markers, comparing histopathology, and comparing meta data.

30. The system of claim 21, wherein the processor is further configured to:

apply a heuristic to an image set, wherein the image set comprises one or more selected from a group consisting of the true image, the predictive image, and the confidence prediction image;

identify a sample area region of interest of the biological sample based on the heuristic;

aid in a harvesting a portion of the biological sample, wherein the portion corresponds to the sample area region of interest; and

perform a molecular test on the portion of the biological sample.

31. The system of claim 30, wherein the heuristic comprises one or more selected from a group consisting of identifying areas of the biological sample with low confidence, identifying areas of the biological sample with multiple attributes, identifying areas of the biological sample classified by a certain tissue type, identifying areas of the biological sample classified with a particular class or sub-class of cancer, and processing clinical data associated with the biological sample.

32. The system of claim 30, wherein the processor is further configured to associate the sample area region of interest to a spatial location from the prediction image.

33. A system for classifying a biological sample, the system comprising:

a receiving module for receiving an image of a biological sample;

a generating module for generating a true image of the biological sample by applying annotation information to the image and spectral data, wherein the annotation information is associated with a specific disease or condition;

wherein the generating module is configured to generate a prediction image by applying one or more algorithms to the image and spectral data, wherein the prediction image includes a predictive classification of diseases or conditions identified from the biological sample; and

a transmitting module for transmitting the prediction image with the predictive classification to be presented on a display.

34. The system of claim 33, further comprising:

a heuristic module for applying a heuristic to an image set, wherein the image set comprises one or more selected from a group consisting of the true image, the predictive image, and the confidence prediction image;

an identifying module for identifying a sample area region of interest of the biological sample based on the heuristic;

a harvest module for aiding in a harvesting a portion of the biological sample, wherein the portion corresponds to the sample area region of interest; and

a testing module for performing a molecular test on the portion of the biological sample.

35. A computer program product, comprising:

a computer-readable medium comprising;

at least one instruction for causing a computer to receive an image of a biological sample;

at least one instruction for causing the computer to generate a true image of the biological sample by applying annotation information to the image and spectral data, wherein the annotation information is associated with a specific disease or condition;

at least one instruction for causing the computer to generate a prediction image by applying one or more algorithms to the image and spectral data, wherein the prediction image includes a predictive classification of diseases or conditions identified from the biological sample; and

at least one instruction for causing the computer to transmit the prediction image with the predictive classification to be presented on a display.