Cytopathology
Cytopathology techniques
Automation


Last author update: 31 October 2022
Last staff update: 31 October 2022

Copyright: 2022, PathologyOutlines.com, Inc.

PubMed Search: Cytopathology automation

Reid Wilkins, M.D.
Tamar C. Brandler, M.D., M.S.
Page views in 2021: 36
Page views in 2022 to date: 305
Cite this page: Wilkins R, Flaifel A, Brandler TC. Automation. PathologyOutlines.com website. https://www.pathologyoutlines.com/topic/cytopathologyautomation.html. Accessed December 9th, 2022.
Definition / general
  • Automation in cytopathology refers to the process of slide preparation (fixation and staining), image acquisition and image analysis with identification of abnormalities by automated machinery in conjunction with cytologist review
  • Currently primarily utilized in gynecologic cytopathology
Essential features
  • Automation has mainly been implemented in gynecologic cytopathology with the development of automation systems that utilize liquid based cytologic processing
  • Automated screening has comparable sensitivity to manual screening with the added benefit of increased productivity
  • Increased utilization of whole slide imaging and artificial intelligence pose additional benefits and challenges to the development of a completely autonomous digital workflow in gynecologic screening
CPT coding
  • Gynecological
    • 88112 - enriched / concentrated preparation (ThinPrep, SurePath)
    • Automated screening:
Terminology
  • Analytic and quantitative cytology
  • Cytology image analysis techniques
  • Automated screening systems in cytopathology
Overview
  • Specimen collection
    • Spatula / brush
  • Specimen processing
    • Liquid based
      • ThinPrep
        • Methanol based PreservCyt fixative solution
        • Filtration and dispersion separate debris and mucus without adverse effect on cell appearance
        • Controlled pressure deposits cell layer in 20 mm diameter circle
      • SurePath
        • Preservative fluid (ethanol / methanol / isopropanol)
        • Centrifugation separates cells of interest
        • Cell layer placed in 13 mm diameter circle via sedimentation
  • Image acquisition
    • Prepared slides loaded into imaging station
    • Cell spot image scanned and sent to image processor
  • Image analysis (ACM Computing Surveys 2022;54:1)
    • Segmentation approach
      • Thresholding, region, contour, texture, graph, clustering, deep learning
    • Segmentation free
      • Support vector machine (SVM), fuzzy c-means (FCM), convolutional neural network
  • Cytotechnologist review
    • Field of view (FOV) presented
    • Slides without abnormality diagnosed as NILM and signed out
    • Slide with abnormality marked for cytopathology review
  • Slide rescreening
    • Entire slide manually rescreened and signed out
Advantages of automation
  • Increased productivity and cytotechnologist satisfaction
  • Greater efficiency in evaluating slides
  • Decreased fatigue and turnaround time
  • Lower hospital cost (deriving from increased productivity)
  • Reference: Diagn Cytopathol 2021;49:559
Disadvantages of automation
  • Similar sensitivity to manual screening (Cytojournal 2007;4:6)
  • Initial cost and maintenance
  • Additional quality assurance and quality control (continued calibration and validation of machines)
  • Requires additional training of cytopathologists and cytotechnologists
  • Remaining fatigue, inattention and habituation with increased number of slides necessitates implementation of daily slide limitation
  • Reference: Diagn Cytopathol 2021;49:559
Implementation
  • Gynecologic
    • PAPNET (1994, Neuromedical Systems, Inc., Suffern, NY)
      • First truly digital pathology device
      • Neural network technology used to identify abnormal cells in cases already screened as negative by cytotechnologist
      • 128 abnormal cells / clusters identified, photographed and mailed back to laboratory as a digital tape for review by cytotechnologist
      • Additional resources: Laboratory Medicine 1991;22:276
    • AutoPap 300QC (1995, NeoPath Inc., precursor to BD FocalPoint GS imaging system)
      • Originally developed for quality control of screened Pap smear slides to reveal false negatives
      • Cell annotations used to develop algorithms to score overall slides and individual field of view (higher score, more abnormalities)
      • Higher cumulative score, increased likelihood of finding abnormality on slide
      • Additional resources: Acta Cytol 1996;40:45
    • ThinPrep imaging system (2004, HOLOGIC, Marlborough, MA)
      • FDA approved
      • ThinPrep specimen processing
      • ThinPrep image processer consists of imaging station, image processor controller, server and user interface
        • Imaging station: scans entire slides
        • Imaging processor controller: creates images and analyzes data
        • Server: stores slide and imaging data
        • User interface: allows operator to use the machine
      • Image processor selects 22 fields of view (FOV) at 100x magnification for review by cytotechnologist
      • Additional resources: HOLOGIC: ThinPrep Imaging System [Accessed 9 August 2022]
    • BD FocalPoint GS imaging system (2001, Becton, Dickinson and Company, Franklin Lakes, NJ)
      • FDA approved
      • SurePath specimen processing
      • Image processor utilizes slide profiler and review station
        • Slide profiler screens slides and locations based upon likelihood of containing abnormality
        • Review station arranges slides into quintiles based upon likelihood of abnormality (1 = highest risk, 5 = lowest risk)
          • 10 FOVs for each slide are presented
          • Lowest quintile of slides (least likely to have abnormality) can be archived without further review
      • Additional resources: BD: FocalPoint GS Imaging System [Accessed 9 August 2022]
    • Cytoprocessor (2017, DATEXIM, Caen, France)
      • CE certified (approval in Europe)
      • Utilizes any preparation protocol and liquid based cytology (LBC)
      • Detects cells in whole slide images and arranges depending on abnormality
      • Can be integrated into a variety of workflows without requiring additional materials
      • Additional resources: DATEXIM: CYTOPROCESSOR [Accessed 9 August 2022]
Effectiveness
  • MAVARIC trial (Health Technol Assess 2011;15:1)
    • Conducted in England, compared Imager / FocalPoint to manual screening for potential implementation in National Health Service (NHS)
    • Significantly lower sensitivity of automated screening detection of cervical intraepithelial neoplasia grade II (CIN 2) and above (0.92) compared to manual screening
    • Comparable rates of detecting CIN 2 and above, between ThinPrep and SurePath
    • Increased productivity in automated screening arm (60 - 80% higher)
  • Additional studies in Australia, Scotland and U.S.
    • Findings:
      • Significantly lower inadequate / negative reporting rates, higher low grade reporting rates
      • No significant difference in detection of high grade squamous intraepithelial lesion (HSIL)
      • Increased specificity with imager assisted screening compared to manual screening alone (Cytopathology 2013;24:235)
      • No significant difference in positive predictive value of Imager screening versus manual screening of ThinPrep slides
      • Up to 27% increase in productivity using Imager screening versus manual screening of ThinPrep slides (Diagn Cytopathol 2007;35:96)
Implication
  • Daily screening limitations (Diagn Cytopathol 2019;47:20)
    • Automated slides without abnormality count as 0.5 slides
    • Abnormal automated screens requiring rescreening count as 1.5 slides
    • Limits vary by country
      • U.S.: 100 slides/day
      • U.K.: 32 slides/day
      • Italy: 25 - 50 slides/day
      • Australia: 70 slides/day
      • Canada: 80 slides/day
    • American Society of Cytopathology guidelines (Diagn Cytopathol 2013;41:174)
      • Limit Pap screening to < 7 hours/day
      • Average < 70 slides/day
    • Computational cytology
      • Combines whole slide imaging with artificial intelligence
        • Advantages:
          • Potential increase in accuracy of slide interpretation
          • Further increase in productivity
        • Limitations (Acta Cytol 2021;65:301):
          • Requires established digital workflow
          • Regular calibration and maintenance of whole slide imaging and software packaging
          • Cytologic processing artifact present in whole slide imaging impairs digital datasets used for whole slide analysis
            • 3 dimensional clustering necessitates image capture on multiple focal planes (z stacking)
          • Limited datasets for training
          • Added responsibility for cytologist
Diagrams / tables

Contributed by Reid Wilkins, M.D.
Overview of automation workflow

Overview of automation workflow



Images hosted on other servers:

ThinPrep imaging system

BD FocalPoint GS imaging system

Board review style question #1

In modern laboratories, automated analysis of the slide pictured above would most likely utilize which of the following image processors?

  1. AutoPap 300 QC
  2. BD FocalPoint GS imaging system
  3. PAPNET
  4. ThinPrep imaging system
Board review style answer #1
D. ThinPrep imaging system

Comment Here

Reference: Cytopathology automation
Board review style question #2
Review of slides screened as "abnormal" with automated preparation, counts as how many slides toward the daily workload limit?

  1. 0 slides
  2. 0.5 slide
  3. 1.0 slide
  4. 1.5 slides
  5. 2.0 slides
Board review style answer #2
D. 1.5 slides

Comment Here

Reference: Cytopathology automation
Back to top
Image 01 Image 02