Informatics, digital & computational pathology

• Virtual staining (VS) is the conversion of an H&E (or other common standard stain, e.g., PAS) digital whole slide image (WSI) to a digital prediction of a special stain (e.g., trichrome, CK AE1 / AE3 IHC)
  • The prediction relies on the detection of morphological features (if they exist) of the source WSI, which inform the distribution of a special stain
• Typically performed using artificial neural networks (ANN)
• Can obviate the need for application of physical reagents, thereby conserving precious laboratory resources (e.g., reagents, staff time, costs) while improving operational efficiency (e.g., view multiple stains simultaneously via nonautonomous diagnostic decision aid) and standardization of reagents (i.e., assessment quality)
  • It can also serve as a quick initial assessment while waiting for a physical special stain to be processed

• Visual quality tests (subjective); 2 types of visual quality tests exist:
  • Turing test: at random, present pathologists with real and virtual images; Turing test passes if pathologists are unable to distinguish the difference between the real / virtual images
  • Staining quality ratings: judging specific tissue architectures (e.g., submucosa) across select slides, pathologists rate quality of tissue staining on a scale from 1 (low quality) to 5 (high quality), noting type and scope of tissue artifacts introduced through virtual staining
• Computational quality test (Brief Bioinform 2022;23:bbac367):
  • Inception score (IS) provides an objective measurement of staining quality by using machine learning classifiers on the generated tissue stain to measure its ability to characterize certain tissue architectures (e.g., tumor) while comparing images to ensure a diverse set of images were assessed
• Biomarker quantification (Nat Mach Intell 2022;4:401):
  • Marker of accuracy: 1) for paired translation GAN approaches, deconvolving the image into the inferred stains or 2) for segmentation / detection approaches assess agreement with original stains with concordance measurement (e.g., accuracy)
  • Quantification of marker across slide: counting cells that stain positive / negative across local region or across slide and correlating with assessment indices (e.g., Ki67 index; area of positive SOX10 staining, etc.)
• Clinical concordance for diagnostic / staging practices (Mod Pathol 2021;34:808):
  • Noninferiority:
    • Can pathologists grade or stage the disease similarly between the real / virtual stain?
      • Can be assessed through Kappa agreement, Spearman correlation or regression modeling
    • Is the interpretation impacted by artifacts or by perceptions of virtual stain?
      • Results should be interpreted in context of how difficult grading / staging can be for this condition (e.g., inter-rater variation) (see figure 3)
  • Superiority:
    • Does virtual staining, when used in conjunction with the standard assessment, permit improved capacity to grade / stage?
  • Correlation:
    • How well do quantified biomarkers correlate with diagnosis / prognosis?
  • WSI from real and virtual stains should be chosen randomly for at least 2 separate assessment periods with a washout period of at least 2 weeks; if real stain is selected for first period, then virtual stain for case / section should be selected for the second period and vice versa; raters should be blinded
  • Multiple pathologists should assess the same tissue section with agreement between pathologists and different assessment biases noted
• Correlation with other assays:
  • How well does assigned virtual stain stage correlate with other known disease markers (e.g., serology)?
  • Is this association more precise than the traditional stain?
• Survival / risk stratification:
  • How well does virtual stain biomarker quantification or diagnosis / grade / stage assignments associate with objective markers of recurrence / survival / disease progression (e.g., transplant)?
  • Is the association similar or more precise than using the traditional stain?
  • Can be assessed through Kaplan-Meier analysis or Cox proportional hazards (i.e., survival) assessment
• Workflow speed and surveys on usage of digital web application to facilitate virtual stain viewing
• Comparative / cost effectiveness comparison with related automation technologies (e.g., autostaining)

Contributed by Joshua Levy, Ph.D. and Louis Vaickus, M.D., Ph.D.

As opposed to an unpaired image translation task, what different set of procedures are absolutely required to obtain data for a paired image translation task?

1. Application of at least 2 different tissue stains to the same tissue section
2. Segmentation of tissue and partitioning into training / validation sets
3. Tissue restaining and image coregistration of same tissue section
4. Tissue staining of a partially overlapping set of patients

C. Tissue restaining and image coregistration of same tissue section. C is the only true statement. While any of these steps are helpful for data acquisition for paired and unpaired tasks, a paired task can only be performed if there is precise microarchitectural alignment to the same tissue section. While A is also true, it does not explicitly discuss the coregistration procedure. Procedures discussed in D are relevant for an unpaired analysis. Partitioning the dataset, as discussed in B, can be done for both unpaired and paired assessments. See image above for an example of processing required for a paired analysis.

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Reference: Virtual staining

Which assessment method for virtual staining technologies is aligned with clinical application / validity of these technologies?

1. Concordance with stage / serology / survival
2. Cost effectiveness comparison
3. Surveys for attitudes and beliefs surrounding adoptability
4. Virtual assessment time

A. Concordance with stage / serology / survival. While all of these assessment methods are relevant for determining the potential to adopt a virtual staining technology, A demonstrates real world application of this approach. If assessments of virtually stained slides reduces the capacity to grade / stage, the approach should not be adopted or should be further iterated on until it can demonstrate noninferiority to the current assessment method. Other visual assessment methods include the Turing test and inception score, which can help determine visual fidelity. The other mentioned assessment methods are helpful for determining the potential to adopt the technology (i.e., does this technology reduce costs [B] and the time required to stain / assess [D]); surveys discussed in C are opportunities to solicit feedback on changes that can make the technology more user friendly and identify additional barriers for adoption. While virtual staining technologies are used to generate synthetic images of tissue, application of these assessment methods are helpful for demonstrating the utility of these technologies. These assessments should be performed in large scale cohort settings prior to initiating a multicenter randomized controlled trial.

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Reference: Virtual staining