Feature Identification for Colon Tumor Classification

1. Feature Identification for Colon Tumor Classification UCI Interdisciplinary Computational and Applied Mathematics Program Representative: Anthony Hou Joint Work with Melody Lim, Janine Chua, Natalie Congdon Faculty Advisors: Dr. Fred Park, Dr. Ernie Esser, and Anna Konstorum

2. Problem Statement Tumor spheroids Control Chemical Added

3. Biological Background • Hepatocyte Growth Factor (HGF) has been shown to be increased in colon tumor microenvironment (in vivo) • Increased HGF is correlated with increased growth & dispersiveness Tumor spheroids Control +HGF

4. Experimental Approach • Data obtained from the Laboratory of Dr. Marian Waterman, in the Department of Microbiology at UC Irvine • Cell line used: primary, ‘colon cancer initiating cells’ (CCICs) • Cultured CCICs trypsinized and spun down

5. Experimental Approach (cont.) • Single cells plated in 96 well ultra-low attachment plates with DMEM, supplement, and with or without HGF at various concentrations • CCICs imaged at 10x resolution once aday for 12 days Spheroid grown in media + 50ng/ml HGF, day 8

6. Our Motivational Goal • Having a set of data, biologists can see the qualitative effect when the concentration of HGF is high and when the concentration of HGF is low. • We want to find the feature(s) that can discriminate between a tumor spheroid that has high and low concentrations of HGF. • We hope this discovery can indicate which features are useful in helping biologists measure the amount of HGF in a certain colon tumor spheroid

7. Image Processing/Computer Vision Background • Classification • We humans have an innate ability to learn to identify one object from another

8. Now, how can we automate this process with respect to biological images? Control +HGF

9. Classification Approach • Image Processing • Mathematical features • Shape features: Area, Perimeter/Area, Circularity Ratio, Texture features: Total Variation/Area, Average Intensity, Eccentricity • Why these 6 features? • Given feature: Day • Fisher’s Linear Discriminant (FLD) Classification

10. Processing Data Raw +HGF tumor Binary image with boundary applied Segmented +HGF tumor Boundary of +HGF tumor Thresholdedbinary image

11. Shape Information HGF Binary • Features from Given Shape • Area • Perimeter/Area • Circularity Ratio • Eccentricity

12. Image Information HGF Segmented Features from Given Image • Total Variation • Average Intensity

13. Classification <V1,V2, …Vn> Tumor gets mapped to feature vectors, which get mapped to points in high dimensional space. Now how do we separate the 2 groups?

14. Fisher’s Linear Discriminant • Describe mapping • Fisher’s Linear Discriminant: maximize ratio of inter-class variance to intra-class variance

15. Project Overview • Develop classification scheme for colon tumor spheroids grown in media with and without HGF • Broader goal is to obtain quantitative understanding of HGF action on tumor spheroids. • Feature vectors can be utilized to quantify HGF action on tissue growth in vitro.

16. Results • Ran FLD code on 6 features: Area, Circularity Ratio, Average Intensity, Eccentricity, Perimeter/Area, TV/Area • Train on half the data • Repeated Random Sub-sampling Cross Validation was used on all tests

17. Results • Ran FLD code on 6 features: Area, Circularity Ratio, Average Intensity, Eccentricity, Perimeter/Area, TV/Area • Percent Correct for Control: 91.50% • Percent Correct for +HGF: 90.99%

18. Results: Adding Day • Good results, but our goal is to maximize percentage correct, so included time (day) • Features used: Area, Perimeter/Area, TV/Area, Eccentricity, Average Intensity, Circularity Ratio, Day • Observed some tumors similar in shape and size, so we needed a descriptor to separate those. Caused by larger control tumor from later phase having similar area & perimeter to earlier-stage HGF tumor.

19. Results: Adding Day • Good results, but our goal is to maximize percentage correct, so included time (day) • Features used: Area, Perimeter/Area, TV/Area, Eccentricity, Average Intensity, Circularity Ratio, Day • Observed some tumors similar in shape and size, so we needed a descriptor to separate those. Caused by larger control tumor from later phase having similar area & perimeter to earlier-stage HGF tumor. Percent Correct for Control: 98.88% Percent Correct for +HGF: 100%

20. Next Approach • Excellent results, but curious to see if same results can be obtained using less features • Plot all separately to get an idea of their individual classifying potential

21. Area Control=blue HGF=red Due to area differences between tumors from control and +HGF

22. Circularity Ratio Description • C1 = (Area of a shape)/(Area of circle) where circle has the same perimeter as shape

23. Circularity Ratio Control=blue HGF=red Given data are relatively circular from both groups (control and +HGF)

24. Average Intensity Description • Average Intensity: sum of the image intensities over the shape divided by area • Inversely related to density. • Smaller values indicate less light passing through, suggesting a denser object Control Day 8 (10x) +HGF 10ng/ml Day 11 (10x)

25. Average Intensity Control=blue HGF=red • Control Group is similar in Average Intensity, whereas +HGFs are denser • Not all are very dense, so there are some overlap with controls

26. Eccentricity Description • Measure of elongation of an object

27. Eccentricity Control=blue HGF=red Due to most tumors from both groups being circular except for a few outliers

28. Perimeter to Area Ratio • Why Normalize Perimeter by Area? • We do so because a small, jagged object may have the same area as a large, circular object. Thus, we divide by area, creating a more effective classifier.

29. Perimeter to Area Ratio Control=blue HGF=red This is to be expected because the +HGF tumor spheroids have more dispersion, resulting in greater area, in contrast to the control tumor spheroids.

30. Total Variation to Area Ratio Description • At every point, estimate its gradient (difference in intensities in x and y direction). Use discretization of Total Variation. Also normalized by area. • Texture Control Day 11 (10x) +HGF 10ng/ml Day 12 (10x)

31. Total Variation to Area Ratio Control=blue HGF=red Due to similar densities/intensities in tumors from both groups

32. Intuition Through Trial and Error • Given the individual results, we combined the two strongest features, area and perimeter/area, and plot them both using a scatter plot

33. Area vs. Perimeter/Area Control=blue HGF=red

34. Results • We obtained reasonably accurate results, having only two controls on the +HGF side if we draw an imaginary line to separate the two groups • Ran FLD code on Area and Perimeter/Area

35. Results • We obtained reasonably accurate results, having only two controls on the +HGF side if we draw an imaginary line to separate the two groups • Ran FLD code on Area and Perimeter/Area • Percent Correct for Control: 89.03% • Percent Correct for +HGF: 96.92%

36. Evaluation • Reasonably decent results, but decided to add the feature Day

37. Evaluation • Reasonably decent results, but decided to add the feature Day • Results: Area, Perimeter/Area, Day • Percent Correct for Control: 100% • Percent Correct for +HGF: 100%

38. “Bad” Features • Plotting graphs of “good” features and running FLD showed how strong those features really are. • Our first thoughts: Were the “good” features too strong that the “bad” features couldn’t exhibit their full potential as classifiers? • CR, TV/Area, Average Intensity, Eccentricity

39. Intuition • Decided to run FLD test to see if they perform better as a group by themselves • Results: CR, TV/Area, Average Intensity, Eccentricity

40. Intuition • Results: CR, TV/Area, Average Intensity, Eccentricity • Percent Correct for Control: 75.33% • Percent Correct for HGF: 55.27% • Why?

41. Final Thoughts • Our belief:“bad” features are not necessarily useless. • Data sets vary; some may include tumors with different textures, shapes, area, and so on • Our set of features are extremely versatile • After feature identification, features can be used to further pursue broader goals such as the quantification of a certain chemical’s effect on their tumors

42. Conclusion • Effectiveness of area vector is obviously in accordance with biological hypothesis that HGF increases cellular mitosis rate, resulting in larger tumors. • Effectiveness of perimeter/area vector quantifies contiguous cell spread, supporting hypothesis stating HGF results in a spheroid with greater perimeter/area ratio. • Tried a lot of fancy ways, but turns out the strongest features were the simplest ones that also agreed with biologists’ intuition.

43. Conclusion (cont.) • Including Day Vs. Not Including Day • Day + less features = better results • Less features (without day) = worse results • Use more features (without day) = good results; separation in high dimensions

44. Future Goals • Develop methods to quantify cell spread for cells that are no longer attached to the tumor. • Develop an automated segmentation scheme • Occlusions • Existing strong methods worked, but needed more preprocessing +HGF 10ng/ml Day 13 (10x)

45. Future Experiments • EXPERIMENT IDEA #1: • Run experiment w/ different concentrations of HGF • We want to quantify how HGF acts with respect to increasing concentration • Utilize developed feature vectors to classify images from different concentrations of HGF.

46. Future Experiments • EXPERIMENT IDEA #2: • Stain spheroids for proteins associated with stem and differentiated cell compartments • Stains can be incorporated into new feature vectors to identify whether HGF-induced changes in stem / differentiated cell concentrations are significant enough to improve image classification.

47. Acknowledgements • NSF • Professors Jack Xin, Hongkai Zhao, Sarah Eichorn • Advisors: Dr. Fred Park, Dr. Ernie Esser, and Anna Konstorum • Laboratory of Dr. Marian Waterman • Group: Janine Chua, Melody Lim, Natalie Congdon • MBI

48. References [1] Thomas Brabletz, Andreas Jung, Simone Spaderna, Falk Hlubek, and Thomas Kirchner. Opinion: migrating cancer stem cells - an integrated concept of malignant tumour progression. Nat Rev Cancer, 5(9):744{749, Sep 2005. [2] Caroline Coghlin and Graeme I Murray. Current and emergingconcepts in tumourmetastasis. J Pathol, 222(1):1{15, Sep 2010. [3] A De Luca, M Gallo, D Aldinucci, D Ribatti, L Lamura, A D'Alessio, R De Filippi, A Pinto, and N Normanno. The role of the egfr ligand/receptor system in the secretion of angiogenicfactors in mesenchymal stem cells. J Cell Physiol, Dec 2010.