Stack or trash? Quality assessment of virtual slides

Automated staining analysis in digital cytopathology and applications

The progress of digital pathology in recent years has been an opportunity for the development of automated image analysis algorithms for quantitative measurements and computer aided diagnosis. With those new methods comes the need for high staining quality and reproducibility, as image analysis tools are typically more sensible to slight stain variations than trained pathologists. This article presents a method for the automated analysis of cytology slides stains specifically adapted to the challenges encountered in digital cytopathology. In particular, the variety of cell types in cytology slides, the 3D distribution of the cellular material, the presence of superposed cells and the need for independent analysis of sub-cellular compartments are addressed. The proposed method is applied to the quantification of staining variations for quality control, resulting from changes in the staining protocol such as reagent immersion time or a reagent change. Another demonstrated application is the selection of staining protocol parameters that maximize the visible details in nucleus. Finally the analysis pipeline is also used to compare different stain normalization algorithms on digital cytology slides. Code available at: https://gitlab.com/vitadx/articles/automated_staining_analysis.

Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies

Immune profiling of formalin-fixed, paraffin-embedded tissues using multiplex immunofluorescence (mIF) staining and image analysis methodology allows for the study of several biomarkers on a single slide. The pathology quality control (PQC) for tumor tissue immune profiling using digital image analysis of core needle biopsies is an important step in any laboratory to avoid wasting time and materials. Although there are currently no established inclusion and exclusion criteria for samples used in this type of assay, a PQC is necessary to achieve accurate and reproducible data. We retrospectively reviewed PQC data from hematoxylin and eosin (H&E) slides and from mIF image analysis samples obtained during 2019. We reviewed a total of 931 reports from core needle biopsy samples; 123 (13.21%) were excluded during the mIF PQC. The most common causes of exclusion were the absence of malignant cells or fewer than 100 malignant cells in the entire section (n = 42, 34.15%), tissue size smaller than 4 × 1 mm (n = 16, 13.01%), fibrotic tissue without inflammatory cells (n = 12, 9.76%), and necrotic tissue (n = 11, 8.94%). Baseline excluded samples had more fibrosis (90 vs 10%) and less necrosis (5 vs 90%) compared with post-treatment excluded samples. The most common excluded organ site of the biopsy was the liver (n = 19, 15.45%), followed by soft tissue (n = 17, 13.82%) and the abdominal region (n = 15, 12.20%). We showed that the PQC is an important step for image analysis and that the absence of malignant cells is the most limiting sample characteristic for mIF image analysis. We also discuss other challenges that pathologists need to consider to report reliable and reproducible image analysis data.

Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies

Inconsistencies in the preparation of histology slides and whole-slide images (WSIs) may lead to challenges with subsequent image analysis and machine learning approaches for interrogating the WSI. These variabilities are especially pronounced in multicenter cohorts, where batch effects (i.e. systematic technical artifacts unrelated to biological variability) may introduce biases to machine learning algorithms. To date, manual quality control (QC) has been the de facto standard for dataset curation, but remains highly subjective and is too laborious in light of the increasing scale of tissue slide digitization efforts. This study aimed to evaluate a computer-aided QC pipeline for facilitating a reproducible QC process of WSI datasets. An open source tool, HistoQC, was employed to identify image artifacts and compute quantitative metrics describing visual attributes of WSIs to the Nephrotic Syndrome Study Network (NEPTUNE) digital pathology repository. A comparison in inter-reader concordance between HistoQC aided and unaided curation was performed to quantify improvements in curation reproducibility. HistoQC metrics were additionally employed to quantify the presence of batch effects within NEPTUNE WSIs. Of the 1814 WSIs (458 H&E, 470 PAS, 438 silver, 448 trichrome) from n = 512 cases considered in this study, approximately 9% (163) were identified as unsuitable for subsequent computational analysis. The concordance in the identification of these WSIs among computational pathologists rose from moderate (Gwet's AC1 range 0.43 to 0.59 across stains) to excellent (Gwet's AC1 range 0.79 to 0.93 across stains) agreement when aided by HistoQC. Furthermore, statistically significant batch effects (p < 0.001) in the NEPTUNE WSI dataset were discovered. Taken together, our findings strongly suggest that quantitative QC is a necessary step in the curation of digital pathology cohorts. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

WHO grade has no prognostic value in the pediatric high-grade glioma included in the HERBY trial

BACKGROUND

The World Health Organization (WHO) adult glioma grading system is questionable in pediatric high-grade gliomas (pHGGs), which are biologically distinct from adult HGGs. We took advantage of the neuropathological review data obtained during one of the largest prospective randomized pHGG trials, namely HERBY (NCT01390948), to address this issue in children with newly diagnosed non-brainstem HGG.

METHODS

HGG diagnosis was confirmed by pre-randomization, real-time central pathology review using WHO 2007 criteria, followed by a consensus review blinded to clinical factors and outcomes. We evaluated association between WHO 2007 grade and other clinical/radiological/biological characteristics and the prognostic value of WHO 2007 grade, midline location, and selected biomarkers (Ki-67 index/Olig2/CD34/EGFR/p53/H3F3A K27M mutation) on overall survival.

RESULTS

Real-time central neuropathological review was feasible in a multicenter study, with a mean time of 2.4 days, and led to the rejection of HGG diagnosis in 20 of 163 cases (12.3%). The different grading criteria and resulting WHO grade were not significantly associated with overall survival in the entire population (n = 118) or in midline and non-midline subgroups. H3F3A K27M mutation was significantly associated with poor outcome. No significant prognostic value was observed for grade, even after regrading H3F3A K27M-mutated midline glioma as grade IV (WHO 2016). Midline location and a high Ki-67 index (≥20%) were associated with poor outcome (P = 0.004 and P = 0.04, respectively). A 10% increase in Ki-67 index was associated with a hazard ratio of 1.53 (95% CI: 1.27-1.83; P < 0.0001).

CONCLUSION

Our findings suggest that WHO grade III versus IV has no prognostic value in pediatric HGG.

Towards better digital pathology workflows: programming libraries for high-speed sharpness assessment of Whole Slide Images

BACKGROUND

Since microscopic slides can now be automatically digitized and integrated in the clinical workflow, quality assessment of Whole Slide Images (WSI) has become a crucial issue. We present a no-reference quality assessment method that has been thoroughly tested since 2010 and is under implementation in multiple sites, both public university-hospitals and private entities. It is part of the FlexMIm R&D project which aims to improve the global workflow of digital pathology. For these uses, we have developed two programming libraries, in Java and Python, which can be integrated in various types of WSI acquisition systems, viewers and image analysis tools.

METHODS

Development and testing have been carried out on a MacBook Pro i7 and on a bi-Xeon 2.7GHz server. Libraries implementing the blur assessment method have been developed in Java, Python, PHP5 and MySQL5. For web applications, JavaScript, Ajax, JSON and Sockets were also used, as well as the Google Maps API. Aperio SVS files were converted into the Google Maps format using VIPS and Openslide libraries.

RESULTS

We designed the Java library as a Service Provider Interface (SPI), extendable by third parties. Analysis is computed in real-time (3 billion pixels per minute). Tests were made on 5000 single images, 200 NDPI WSI, 100 Aperio SVS WSI converted to the Google Maps format.

CONCLUSIONS

Applications based on our method and libraries can be used upstream, as calibration and quality control tool for the WSI acquisition systems, or as tools to reacquire tiles while the WSI is being scanned. They can also be used downstream to reacquire the complete slides that are below the quality threshold for surgical pathology analysis. WSI may also be displayed in a smarter way by sending and displaying the regions of highest quality before other regions. Such quality assessment scores could be integrated as WSI's metadata shared in clinical, research or teaching contexts, for a more efficient medical informatics workflow.