Classification of Biological Samples Using Spectroscopic Analysis

• US 20120016818A1
• Filed: 10/30/2009
• Published: 01/19/2012
• Est. Priority Date: 10/31/2008
• Status: Abandoned Application

• Current Assignee: University of Sydney
• Original Assignee: University of Sydney
• Inventors: Lay, Peter, Hackett, Mark, Carter, Elizabeth, Hunt, Nicholas, Grau, Geoges, Gottlieb, David
• Application Number:
  US13/124,208
• Publication Number:
  US 20120016818A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G01N 21/65 Raman scattering
• CPC Class Codes: G01N 2201/129 Using chemometrical methods
• CPC Class Codes: G06V 20/69 Microscopic objects, e.g. biological cells or cellular parts
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: Y02A 50/30 Against vector-borne diseases, e.g. mosquito-borne, fly-borne, tick-borne or waterborne diseases whose impact is exacerbated by climate change
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A method and system is described for rapidly classifying a sample of a biological fluid, comprising obtaining a spectrum of the biological fluid in response to excitation of the sample in a specified frequency range, and applying a multivariate classifier to one or more spectral regions of the spectrum to classify the biological sample into one class in a set of classes, the classes comprising at least two disease states having similar clinical symptoms. Methods and systems for developing the classifiers are also described. In one example the classification uses a vibrational spectrometer (5) to provide spectra from serum. The multivariate classifier may run on processor (9) to distinguish between disease states having similar clinical symptoms, such as malaria and cerebral malaria.

Claims

1. A method of classifying a sample of a biological fluid comprising:

(a) obtaining a spectrum of the biological fluid in response to excitation of the sample in a specified frequency range; and

(b) applying a multivariate classifier to one or more spectral regions of the spectrum to classify the biological sample into one class in a set of classes, the classes comprising at least two disease states having similar clinical symptoms.

2. A method according to claim 1 wherein the disease states are selected from the group consisting of:

bacterial meningitis;

cerebral malaria;

severe malaria anaemia;

mild malaria anaemia; and

healthy.

3. A method according to claim 1 wherein the disease states comprise viral meningitis and bacterial meningitis.

4. A method according to claim 1 wherein the disease states comprise:

graft-versus-host-disease (GVHD) and

healthy.

5. A method according to claim 4 wherein the GVHD disease state is early-stage GVHD prior to the presentation of clinical symptoms.

6. A method according to claim 2 wherein the disease states comprise:

Parkinson'"'"'s disease; and

healthy.

7. A method according to claim 1 wherein the biological fluid comprises serum.

8. A method according to claim 1 wherein the biological fluid comprises plasma.

9. A method according to claim 1 wherein the specified frequency range is an infrared frequency range.

10. A method according to claim 1 wherein the step of obtaining a spectrum utilises at least one of Fourier Transform Infrared spectroscopy (FTIR) and Raman spectroscopy.

11. A method according to claim 1 wherein the spectral regions include at least one of:

a fingerprint spectral region between 550 and 1490 cm<sup>−

1</sup>;

a C═

O stretching spectral region between 1700 and 1760 cm<sup>−

1</sup>;

an amide spectral region between 1490 and 1700 cm<sup>−

1</sup>; and

a C—

H stretching spectral region between 2800 and 3100 cm<sup>−

1</sup>.

12. A method according to claim 1 wherein the multivariate classifier comprises a hierarchical classification and the method comprises:

applying a first classifier to the spectrum to classify the sample into one class in a first set of classes; and

, if the one class represents a plurality of sub-classes

applying a second classifier to the spectrum to classify the sample into one of the sub-classes.

13. A method according to claim 12 wherein the first classifier classifies the sample into a sick class or a healthy class and the second classifier classifies samples from the sick class into i) a cerebral malaria class, ii) a bacterial meningitis class or iii) a severe malaria anaemia class.

50. A computer program product comprising machine-readable instructions recorded on a machine-readable recording medium, for controlling the operation of a data processing apparatus on which the instructions execute to perform a method according to claim 1.

51. A computer program comprising machine-readable instructions for controlling the operation of a data processing apparatus on which the instructions execute to perform a method according to claim 1.

14. A method of classifying a biological sample comprising:

(a) obtaining a spectrum of the biological sample in response to excitation of the sample in a specified frequency range; and

(b) applying a multivariate classifier to the spectrum to classify the biological sample into one class in a set of classes, the classes comprising at least one disease caused by a pathogen.

15. A method of classifying a sample of a biological fluid comprising:

(a) obtaining a spectrum of the biological fluid in response to excitation of the sample in a specified frequency range; and

(b) applying a multivariate classifier to a plurality of spectral regions of the spectrum, wherein the classifier assigns a score for the biological fluid in each of the spectral regions;

(c) classifying the biological fluid into one class in a set of classes dependent on the assigned scores, the classes comprising at least one disease state selected from the group consisting of bacterial meningitis, cerebral malaria, mild malaria anaemia, severe malaria anaemia and healthy.

16. A method according to claim 15 wherein the biological fluid is serum.

17. A method according to claim 15 wherein the spectral regions include at least one of:

a fingerprint spectral region between 550 and 1490 cm<sup>−

1</sup> or part thereof;

a C═

O stretching spectral region between 1700 and 1760 cm<sup>−

1</sup> or part thereof;

an amide spectral region between 1490 and 1700 cm<sup>−

1</sup> or part thereof; and

a C—

H stretching spectral region between 2800 and 3100 cm<sup>−

1</sup> or part thereof.

18. A method of classifying a sample of a biological fluid comprising:

(a) obtaining a spectrum of the biological fluid in response to excitation of the sample in a specified frequency range; and

(b) applying a multivariate classifier to a plurality of spectral regions of the spectrum, wherein the classifier assigns a score for the biological fluid in each of the spectral regions;

(c) classifying the biological fluid into one class in a set of classes dependent on the assigned scores, the classes comprising i) healthy and ii) early-stage GVHD prior to the presentation of clinical symptoms.

19. A method of classifying a sample of a biological fluid comprising:

(a) obtaining a spectrum of the biological fluid in response to excitation of the sample in at least one specified frequency range; and

(b) applying a multivariate classifier to the at least one frequency range, wherein the classifier assigns one or more scores for the biological fluid in the at least one frequency range;

(c) classifying the biological fluid into one class in a set of classes dependent on the assigned scores, the classes comprising i) healthy and ii) meningitis prior to the onset of clinical symptoms.

20. A method of classifying a sample of a biological fluid comprising:

(a) obtaining a spectrum of the biological fluid in response to excitation of the sample in a specified frequency range; and

(b) applying a multivariate classifier to a plurality of spectral regions of the spectrum, wherein the classifier assigns one or more scores for the biological fluid in each of the spectral regions;

(c) classifying the biological fluid into one class in a set of classes dependent on the assigned scores, the classes comprising i) healthy and ii) Parkinson'"'"'s disease.

21. A method for rapidly diagnosing a malarial state of a patient, comprising:

(a) obtaining a blood sample from the patient;

(b) measuring a vibrational spectrum of serum from the blood sample;

(c) applying a multivariate classifier to a plurality of spectral regions of the vibrational spectrum, wherein the classifier assigns a score for the patient in each of the spectral regions;

(d) classifying the patient into one class in a set of malarial classes dependent on the assigned scores, the set of malarial classes comprising cerebral malaria, mild malaria anaemia, severe malaria anaemia and healthy.

22. A method of classifying a sample of a biological fluid to assess progression of a disease, the method comprising:

(a) obtaining a spectrum of the biological fluid in response to excitation of the sample in at least one specified frequency range; and

(b) applying a multivariate classifier to the at least one frequency range, wherein the classifier assigns one or more scores for the biological fluid in the at least one frequency range;

(c) classifying the biological fluid into one class in a set of classes dependent on the assigned scores, the classes comprising different stages of the disease.

23. A method according to claim 22 wherein the disease is meningitis.

24. A method according to claim 22 wherein the classes comprise a plurality of different diseases and, for at least one of the diseases, a plurality of classes indicative of different stages of the at least one disease.

25. A method according to claim 22 wherein the plurality of diseases includes cerebral malaria, severe malaria and bacterial meningitis.

26. A method of determining a multivariate classifier for classifying samples of a biological fluid, comprising:

(a) obtaining a spectrum in a specified frequency range of each of a plurality of training biological fluid samples in response to excitation of the training fluid samples ;

(b) associating a clinical characterisation with each of the spectra, wherein the clinical characterisation is drawn from a set comprising at least two disease states having similar clinical symptoms;

(c) performing a multivariate statistical analysis of a plurality of spectral regions of the spectra to identify distinguishing features of the spectra;

(d) defining a multivariable classifier that partitions the spectra into a plurality of classes dependent on the distinguishing features; and

(e) assessing whether the partitioning of the spectra by the multivariate classifier correlates to the respective clinical characterisations associated with the spectra.

27. A method according to claim 26 wherein step (d) comprises defining a hierarchical classifier having a first classifier that partitions the spectra into a first set of classes and a second classifier that partitions at least one class from the first set into a second set of classes.

28. A method according to claim 26 further comprising applying the multivariate classifier to at least one spectrum obtained from a clinical sample to classify the clinical sample.

29. A method according to any one of claim 26 wherein the set of clinical characterisations comprises at least two of:

bacterial meningitis;

cerebral malaria;

severe malaria anaemia;

mild malaria anaemia; and

healthy.

30. A method according to claim 26 wherein the set of clinical characterisations comprises viral meningitis and bacterial meningitis.

31. A method according to claim 26 wherein the set of clinical characterisations comprises GVHD and healthy.

32. A method according to claim 26 wherein the set of clinical characterisations comprises Parkinson'"'"'s disease and healthy.

33. A method according to claim 26 wherein the set of clinical characterisations comprises different stages of a disease.

34. A method according to claim 33 wherein the disease is meningitis.

35. A method according to claim 26 wherein the set of clinical characterisations comprises a plurality of diseases and, for at least one of the diseases, a plurality of different stages of the at least one disease.

36. A method according to claim 35 wherein the plurality of diseases includes cerebral malaria, severe malaria and bacterial meningitis.

37. A method of determining a multivariate classifier for classifying biological samples, comprising:

(a) obtaining a spectrum of each of a plurality of training biological samples in response to excitation of the training samples in a specified frequency range;

(b) associating a clinical characterisation with each of the spectra, wherein the clinical characterisation is drawn from a set comprising at least one disease caused by a pathogen;

(c) performing a multivariate statistical analysis of the spectra to identify distinguishing features of the spectra;

(d) defining a multivariable classifier that partitions the spectra into a plurality of classes dependent on the distinguishing features; and

(e) assessing whether the partitioning of the spectra by the multivariate classifier correlates to the respective clinical characterisations associated with the spectra.

38. A method of determining a multivariate classifier for classifying biological samples dependent on at least one disease, comprising:

(a) obtaining a spectrum of each of a plurality of training biological samples in response to excitation of the training samples in a specified frequency range, the training samples including samples from subjects having the at least one disease;

(b) associating a clinical characterisation with each of the spectra;

(c) performing a multivariate statistical analysis of the spectra to remove variations in the plurality of training samples due to natural variations in the samples and identify distinguishing features dependent on the at least one disease;

(d) defining a multivariable classifier that partitions the spectra into a plurality of classes dependent on the distinguishing features; and

(e) assessing whether the partitioning of the spectra by the multivariate classifier correlates to the respective clinical characterisations associated with the spectra.

39. A method of determining a multivariate classifier for classifying samples of serum, comprising:

(a) obtaining a spectrum of each of a plurality of training serum samples in response to excitation of the training samples in an infrared specified frequency range, the training samples including samples from subjects having at least one disease state selected from the group consisting of acute bacterial meningitis, cerebral malaria, severe malaria anaemia, mild malaria anaemia and healthy;

(b) associating a clinical characterisation with each of the spectra;

(c) performing a multivariate analysis of the spectra to remove variations in the plurality of training samples due to natural variations in the samples and identify distinguishing features dependent on the at least one disease;

(d) defining a multivariable classifier that partitions the spectra into a plurality of classes dependent on the distinguishing features; and

(e) assessing whether the partitioning of the spectra by the multivariate classifier correlates to the respective clinical characterisations associated with the spectra.

40. A system for classifying a sample of a biological fluid comprising:

a spectrometer that provides a spectrum of the biological fluid in a specified frequency range; and

a processor having a multivariate classifier that in use is applied to one or more spectral regions of the spectrum to classify the biological sample into one class in a set of classes, the classes comprising at least two disease states having similar clinical symptoms.

41. A system according to claim 40 wherein the disease states are selected from the group consisting of:

bacterial meningitis;

cerebral malaria;

severe malaria anaemia;

mild malaria anaemia; and

healthy.

42. A system according to claim 40 wherein the disease states comprise viral meningitis and bacterial meningitis.

43. A system according to claim 40 wherein the disease states comprise:

graft-versus-host-disease (GVHD) and

healthy.

44. A system according to claim 40 wherein the disease states comprise:

Parkinson'"'"'s disease; and

healthy.

45. A system according to claim 40 wherein the spectrometer utilises Fourier Transform Infrared (FTIR) spectroscopy.

46. A system according to claim 40 wherein the spectrometer utilises Raman spectroscopy.

47. A system according to claim 40 wherein the classifier is applied to spectral regions including at least one of:

a fingerprint spectral region between 550 and 1490 cm<sup>−

1</sup>;

a C═

O stretching spectral region between 1700 and 1760 cm<sup>−

1</sup>;

an amide spectral region between 1490 and 1700 cm<sup>−

1</sup>; and

a C—

H stretching spectral region between 2800 and 3100 cm<sup>−

1</sup>.

48. A system according to claim 40 wherein the multivariate classifier comprises a hierarchical classification that:

applies a first classifier to the spectrum to classify the sample into one class in a first set of classes; and

, if the one class represents a plurality of sub-classes

applies a second classifier to the spectrum to classify the sample into one of the sub-classes.

49. A system according to claim 48 wherein the first classifier classifies the sample into a sick class or a healthy class and the second classifier classifies samples from the sick class into i) a cerebral malaria class, ii) a bacterial meningitis class or iii) a severe malaria anaemia class.