Automated lymphocyte counting in tissue microarrays using the Nuance/Vectra/inForm imaging system...
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Automated lymphocyte counting in tissue microarrays using the Nuance/Vectra/inForm
imaging system
Ian Hagemann, MD, PhDCliff Hoyt, MS
Mike Feldman, MD, PhD
Tumor-infiltrating lymphocytes (TILs) in ovarian cancer
• Ovarian cancer may be recognized and attacked by the immune system
• Tumor may contain a lymphocytic infiltrate
• TILs exhibit oligoclonal expansion, recognize tumor antigens, circulate in vivo, and display tumor-specific cytolytic activity in vitro
• Clinical results have been seen with interferon or adoptive T cell immunotherapy
Zhang L , NEJM 2003
The question
• How many lymphocytes are present in this tumor?– Intraepithelial– Stromal
• Alternate phrasing: how densely is this tumor infiltrated by lymphocytes?
The problem
• Ambiguous histology– Limited tissue– Hematoxylin only
• Variable surface area of core, tumor, and stroma
• Human factors– Difficult to count numerous
events– Boredom
Vectra system (CRI, Inc.)
• Multispectral brightfield and fluorescent slide imaging (Nuance)
• Pattern recognition-based, partially automated scanning (Vectra)
• Automated tissue and cell segmentation (inForm)
Imaging a TMA using VectraInput: Stained TMA slideOutput: Hundreds of multispectral image files indexed by grid location.
Input
Training regionsfor tissue segmenter
Output of tissue segmenter Output of tissue and cell segmenter
Review classified images
• Some histospots will have been classified incorrectly– Core fell off or folded over– Unsuitable tissue– Tumor interpreted as stroma, or vice versa– Lymphocyte over- or undercounting
• Task: visually review each core for appropriate segmentation– Despite sophisticated segmentation algorithms,
this step (performed by a human) appears to be essential
Tales of woe
Segmentation algorithms fail
on some fraction of histospots
Total histospots evaluated 618Pre-algorithmic failures
Spot fell offUnsuitable tissue (e.g., colon or fat
only)
3777
Tissue segmentation failuresTumor interpreted as stroma
Stroma interpreted as tumor2649
Cell segmentation failuresOverdetection of lymphocytesUnderdetection of lymphocytes
93
Spots successfully segmented 436
Manual and automated TIL scores are significantly correlated
r=0.54 (95% CI, 0.47–0.61) r=0.68 (95% CI, 0.61–0.74) p<0.0001 p<0.0001
Simulated perfect concordance between manual and automated TIL counts
Observations and conclusions
• Automated event scoring provides a consistent approach to tedious, poorly reproducible tasks.
• Histology scoring tasks can probably never be completely automated.
• Automated lymphocyte counts are significantly correlated with manual counts.
• Gold-standard performance for this task is undefined (and probably impossible to define)
Future directions
• Improved machine learning and classification algorithms will shrink the group of segmentation failures (never to zero)
• Greater leveraging of multispectral technology may allow a qualitative leap forward in the depth of tissue annotation (e.g., “tumor mask” staining by cytokeratin)
• An integrated TMA-aware workflow would reduce manual steps (cut and paste) and increase throughput
• Quantitative direct feature counting can inform semi-quantitative analyses (e.g., where to set cutoffs?)
Acknowledgments
UPENNMike Feldman, MD, PhD
Tim Baradet, PhDGeorge Coukos, MD, PhDAndrea Hagemann, MD
CRi, Inc.Cliff Hoyt, MS
Craig Lassy, PhD