Automation in cytology.

This presentation in mainly focused of understanding of automation and its utility in cytopathology. It will be very usefull for postgraduate in pathology, cytopathologist and cytotechnicians.

1. AUTOMATION IN CYTOLOGY PRESENTER: DR Manan Shah

2. Automation Defined as the technology by which a process or procedure is performed without human assistance. Automation in a clinical laboratory is defined as a process by which analytical instruments perform many tests with the least involvement of an analyst.

3. Introduction and History Despite the success of manual screening, some faults do exist, prompting the development of viable automated systems. Automation in cytology started and was focused for a long time on PAP smears. Later the same principles were applied to other areas of cytopathology like organ cytology , body fluids and so forth.

4. Historical Background in Pap smears The first attempts to automate the screening of cervical smears dates back to the early 1950s. Mellors and coworkers, among them Papanicolaou, developed a scanning device, the “ Cytoanalyzer , which could gather data on nuclear size and nuclear optical density of a large number of cells. Lacking computerization, the process was slow.

5. The “Better” Pap Smear In May 1996, the ThinPrep ® Pap test was approved by the FDA. Clinical trials confirmed increased sensitivity compared to the conventional smears. Although adding cost, a number of studies suggested that a reduction of ASCUS and unsatisfactory Pap tests have saved lots of money spent on unnecessary recall visits and negative colposcopic examinations and biopsies.

6. Need for automation The most challenging cases in cytology are those representing failure to detect abnormalities existing at the time of screening Mainly difficulties in reporting occur due to overlapping of cells and nuclei and obscuring factors . Automated instrumentation may improve sensitivity, reduce unsatisfactory specimens and provide for reasonable bottom lines.

7. Expectations from the Auto analyser The system should be able to comment on smear adequacy Scanning should be rapid and reliable and reproducible. The system should select suspicious cells (or slides) and present them to the cytologist for final classification Sensitivity of the automated device (plus the cytologist) should equal or exceed the sensitivity of the conventional method.

8. Different types of Automation tools Semi automated Fully automated

9. Artificial Neural Network An artificial neural network (ANN) is a statistical classifier that can be trained to recognize and distinguish patterns .

10. Feed forward networks Unidirectional flow of information . 2. Good at extracting patterns Generalisation and prediction . 3. Parallel processing of data. 4. Not exact models, but good at demonstrating principles Recurrent networks Multidirectional flow of information. 2. Memory / sense of time 3. Complex temporal dynamics. 4. Various training methods 5. Often better biological models .

11. Goals of automation 1. Improving the accuracy of test results. 2. Shortening the length of time needed to perform the tests. 3. Obtaining a slide that is representative of the original sample collected from the patient.

12. A utomation in Cervical C ytology

13. Specimen collection and preparation device The FDA has approved 2 automated systems: 1.Thin-Prep Processor 2.AutoCyte Prep / Surepath - now part of TriPath Imaging, Both systems use fluid-based collection devices for the collection of the specimens.

14. Thin prep Utilizes the controlled membrane transfer technology Vial is spun gently to breakup the mucus, blood, debris and large cell cluster, mixes the sample Series of negative pressure draws the fluid through the thin prep membrane Epithelial cells and organism are trapped and blood, mucus and debris pass thro it.

15. Thin prep Draws thin evenly layered diagnostic material Cellular material is transferred to glass slide using computer controlled mechanical positioning and positive air pressure Slide with thin evenly layered circle of epithelial cell ( 20mm ) is made Slide is ejected into a cell fixative bath ready to staining and evaluation .

16. Surepath technique Layering of cell sample on to a liquid density gradient- vortexing and centrifugation Vortex –breaks up large cell aggregates, mucus and blood Density gradient centrifugation separation of cellular elements from obscuring inflammation and debris Filtrate is placed in a chamber and applied glass slide by gravity sedimentation Even layered circle of cells on slide ( 13mm ) Automatically stained by surepath processer.

17. Advantages Decrease in number of inadequate smears and interpretation time Randomised representative sample of cells-more accurate diagnosis Back ground environment absent (!!!!!) Improves sensitivity and specificity Infective organisms, benign cellular changes, endocervical atypia and carcinoma have similar features Increase relative sensitivity of ASC-US , ASC-H, and LSIL

18. Pitfalls of LBC Smear patterns altered because of randomization of cells. Abnormal cells are dispersed. Scanty LBC preparations can be difficult to screen and interpret. Blood mucous inflammation and malignant diathesis are very difficult to interpret

19. Pitfalls of LBC Epithelial cells appear mostly as single cells and are slightly smaller than they appear in conventional smears especially endocervical cells and immature metaplastic cells. LBC is more expensive than conventional test.

20. Ancillary Testing One of the most compelling reasons for using LBC over conventional cytology is the ability to perform ancillary tests on the remaining cells in the LBC medium. The first ancillary test taken from LBC to be evaluated and proven to be useful in multiple studies was testing for HPV. The sensitivity for CIN II or III of HPV testing of residual LBC from specimens interpreted as ASC-US reported in these studies varied from 89%96 to 92%. Testing for chlamydia and gonorrhea also possible - from the sample taken from thin layer preparations.

21. A utomated D evice for Screening

22. 1. Manual screening adjunctive device It speeds up the manual screening process. Maps out specific fields on slides that the cytotechnologist needs to review as opposed to the technologist screening the entire slide. These are computerized microscopes which can electronically and physically dot abnormal cells or even mechanically drive the stages to the coordinates of previously identified abnormal cells.

23. Pathfinder The Pathfinder is considered an adjunctive screening device because slides are manually screened by cytotechnologists . It consists of monitor , a keyboard, and a storage device attached to the microscope. During the screening process, the Pathfinder maps area of each smear that has been screened by cytotechnologist , calculates the average percentage of fields overlapped , records the time that the cytotechnologist spent evaluating the smear . It is no longer manufactured or marketed.

24. Review scope

25. 2. The Papnet System This automated screening device is designed to detect rare abnormal cells when present in a conventionally prepared slide . It uses the principle of neural network processing ,

26. 3. The AUTOPAP 300 System The AutoPap automates the screening of conventionally prepared cervical smears. The system uses the principle of image analysis algorithms and field of view (FOV) computers to classify cell images. In the primary-screening mode, the instrument screens the slides and ranks them into 2 categories: Archived or no further review required Review required

27. 4. Autocryte interactive system The AutoCyte is undergoing FDA clearance to be approved for screening of monolayer cervical smears . It uses the same principle of algorithmic classifiers as does the AutoPap , presents a computer evaluation derived from the population histogram analysis , and allows the technologist to view specific fields on the slide.

28. Automated screening system in gynec cytology-Outline Focal point slide profiler Thin Prep imaging system Focal point GS system

29. 1. Focal point slide profiler Smears or SurePath slides 8 slides/tray, 36 trays Capacity : 288 slides per 24 hours High speed video microscope 3 cameras operate on different focal planes : dynamic focussing . Strobe light used to acquire 25 images/sec 4x magnification: map of entire slide and 1000 fields captured at 20x magnification Image analysis performed using preset algorithms Score assigned to each slide (range: 0 to 1)

30. Focal point slide profiler- Sensitivity for conventional smear 25,125 cases ASCUS LSIL HSIL Current practice 79% 86% 93% Focal point 86% 92% 97% Significant Significant Not significant

31. Summary for focal point slide profiler At least as accurate as manual screening . False-negatives do occur . Modest productivity enhancement (15-20% saving in screening time).

32. 2. Location guided imaging with the thin prep Imaging system For thin prep slides only Image processor is computer based system run on window NT 25 slides / cartrige , 10 cartriges and Capacity 300 slides/day Measures integrated optical density of nuclei Identifies 22 fields on each slides that are most likely to harbour abnormal cells If all 22 field are judged normal –Negative without further review If any field are judged abnormal-Full slide screening

33. Sensitivity 9550 CASES ASCUS LSIL HSIL Manual screening 76% 80% 74% Image assisted 82% 79% 80% Significant Non significant Non significant

34. Specificity 9550 CASES ASCUS LSIL HSIL Manual Screening 97.6% 99.0% 99.4% Image assisted 97.8% 99.1% 99.2% Non significant Non significant Non significant

35. Thin prep Imaging system-Summary At least as accurate as manual screening As with focal point ,false negative do occur More significant productive enhancement (25-50%) than the focal point Many favourable post approval studies 70% of thin prep slides in US are evaluated using TIS

36. 3. Focal point GS imaging system Similar in design concept to thin prep imaging system FDA approval granted in 2008 Slides imaged by FP slide profiler Field of vision examined for all adequately scanned slides 10 FOV presented in order of decreasing score. All to be examined

37. Summary of focal point GS imaging system Improved sensitivity Less false negatives More significant productivity enhancement

38. Computer vision Techniques

39. Computer vision Techniques Automated systems for cytology are static image analysis systems which comprise a cell scanner (Digital camera) which “ see” images by measuring the light intensity and colour properties being received by their electronic sensor elements . If stained cytology samples is placed in an apparatus which has lenses and a digital light sensor (camera) one can “train” the computer to react to chromatin clumping as well as some of the other criteria we use, such as nuclear size, form etc.

40. Computer vision Techniques The optical images caught by the camera are converted into digital images inside the camera and stored on a magnetic disc. The computer is programmed to analyse and classify the images. The  computer selects  images/ smears which are most likely to contain abnormal cells  and presents them to the cytotechnologist for further triage under the microscope. 

41. Computer version techniques Pattern recognition Segmentation Image pre-processing Feature extraction Feature pre-processing Feature selection and discrimination measures Classification Evaluation of classifier performance 2. Texture analysis

42. Segmentation Extraction of: 1. The background 2. The heaps- Separation of the isolated cells and the heaps. 3. The position of the nuclei 4. The boundary of the nuclei

43. Recent advances

44. Automation in Lung lesions

45. The lung cell evaluation device ( LuCED ) Early Detection of Lung Cancer in Sputum Based on 3D Morphology. It produces 3D volumetric cell representations in isometric, sub-micron resolution based on computed tomography. VisionGate , Inc. in collaboration with the University of Washington, is developing LuCED test to score sputum samples processed by the Cell-CT for evidence of cell dysplasia or cancer.

46. The LuCED test comprises a series of steps starting with cell preparation including fixation and staining with hematoxylin . Based on cellular prevalence counts, its estimated that LuCED sensitivity exceeds 90% as specificity approaches 100% for patients with cancer cells in sputum. Cell analysis in 3D provides an unobstructed and unambiguous representation of normal and cancer cell morphology.

47. Automation in urine cytology

48. Automated Urine Microscopy Analyzer Automated instruments have reduced the need for labour intensive manual microscopy . There are 3 systems currently available to automate manual microscopy. An image-based analysis system that uses a video camera and strobe lamp (stops fluid motion) to detect and sort particles based on predetermined particle dimensions. The other type is based on principle of flow cytometry , it classifies particles based on fluorescent intensity, electrical impedance, and forward angle light scatter A next-generation automated image-based urinalysis system, the Iris iQ200 Elite recently received US FDA clearance.

49. Images are stored and can be viewed on the workstation screen, thereby eliminating the need for manual microscopy in most cases. Only urine samples containing crystals and/or yeast that would require review images for confirmation.

50. Conclusion OF ARTICLE The results from the automated analyzers for erythrocytes, leukocytes and epithelial cells were similar to the result of microscopic examination . However, in order to avoid any error or uncertainty, some images ( particularly: dysmorphic cells, bacteria , yeasts , casts and crystals) have to be analyzed by manual microscopic examination by trained staff. Therefore, the software programs which are used in automatic urine sediment analysers need further development to recognize urinary shaped elements more accurately. Automated systems are important in terms of time saving and standardization .

51. Automation in Molecular Cytopathology Growing field Time-consuming Need for standardization Increase Efficiency Automation FISH Laser Micro dissection (mutation analysis)

52. Diagnostic Urine (multi-target) Mesothelioma (9p21) Biliary tract (9p21) Translocations Few others… BRAF - Melanoma, pap. thyroid carcinoma, NSCLC C-kit – GIST Predictive Breast (HER-2) Lung (EML4-ALK) Lung (MET) Lung (EGFR)

53. Laser Capture Microdissection (LCM) Laser Capture Microdissection (LCM) - technique for isolating pure cell populations from a heterogeneous tissue section or cytological preparation through direct visualization of the cells . Molecular profiling of diseased and disease-free tissue, permitting correlation of cellular molecular signatures with specific cell populations . DNA , RNA, or protein analysis may be performed with the microdissected tissue by any method with adequate sensitivity . Automated LCM platforms combine graphical user interfaces and annotation software for visualization of the tissue of interest in addition to robotically controlled microdissection .

54. Laser Microdissection Laser Pressure Catapulting(PALM®)

55. The principal components of LCM technology are Visualization of the cells of interest through microscopy, Transfer of laser energy to a thermolabile polymer with formation of a polymer—cell composite, and Removal of the cells of interest from the heterogeneous tissue section . Automated LCM is compatible with a variety of tissue types , cellular staining methods, and tissue preservation protocols allowing micro-dissection of fresh or archival specimens in a high-throughput manner.  

56. Short term implication Decision on which system of automation. Calculation of minimum workload required for cost effective implementation of the machine. Reduction in workforce. Calculation of the minimum and maximum workload for the screener. QC/QA procedure for the machine. Revision of the patient information leaflet and report format.

57. Long term implication Room for new technologies . Risk for litigation- human versus machine errors . Who takes medico-legal responsibility for machine error ? Effect on workload of machine on the volume and pattern.

58. Conclusions Automation in cytology has taken a long time to be realized, but it is now a reality . The technology is exciting and, if given time to develop in tandem with standard good laboratory practice, i.e., parallel studies in routine settings, then the most effective components of these systems will prevail. Cooperation among pathologists, clinicians, and manufacturers will ensure that the technology performs as expected and contributes to affordable and reliable patient care.

59. History in other organs

60. REFERENCES Koss’s Diagnostic cytology-Volume II Henry’s clinical diagnosis and management by laboratory methods . The Automation Trend in Cytology Laboratory Medicine. Volume 31(4) April 2000 Cervical Cytology Automation: the U.S. Experience . 17th International Congress of Cytology, Edinburgh, Scotland -2010 Textbook on adaptive multi-scale and texture analysis with applications to automated cytology. Image segmentation applied to cytology .

61.  Thank you 