Evaluating Nuclear Membrane Irregularity for the Classification of Cervical Squamous Epithelial Cells

Nuclear membrane irregularity in high-grade urothelial carcinoma cells can be measured by using circularity and solidity as morphometric shape definitions in digital image analysis of urinary tract cytology specimens

BACKGROUND

The Paris System for Reporting Urine Cytology defines objective (elevated nuclear/cytoplasmic ratio ≥0.7) and subjective (nuclear membrane irregularity, hyperchromicity, and coarse chromatin) cytomorphologic criteria to identify conventional high-grade urothelial carcinoma (HGUC) cells. Digital image analysis allows quantitative and objective measurement of these subjective criteria. In this study, digital image analysis was used to quantitate nuclear membrane irregularity in HGUC cells.

METHODS

Whole-slide images of HGUC urine specimens were acquired, and HGUC nuclei were manually annotated using the open-source bioimage analysis software QuPath. Custom scripts were used to calculate nuclear morphometrics and perform downstream analysis.

RESULTS

In total, 1395 HGUC cell nuclei were annotated across 24 HGUC specimens (48.1 ± 6.0 nuclei per case) using both pixel-level and smooth annotation approaches. Nuclear membrane irregularity was estimated by calculating nuclear circularity and solidity. Annotating at pixel-level resolution artifactually increases nuclear membrane perimeter, thus smoothing is necessary to better approximate a pathologist's assessment of nuclear membrane irregularity. After smoothing, nuclear circularity and solidity discriminate between HGUC cell nuclei with visually apparent differences in nuclear membrane irregularity.

CONCLUSIONS

Nuclear membrane irregularity defined by The Paris System for Reporting Urine Cytology is inherently subjective. This study identifies nuclear morphometrics that visually correlate with nuclear membrane irregularity. HGUC specimens show intercase variation in nuclear morphometrics, with some nuclei appearing remarkably regular while others show marked irregularity. A small population of irregular nuclei generates most of the intracase variation in nuclear morphometrics. These results highlight nuclear membrane irregularity as an important, but not definitive, cytomorphologic criterion in HGUC diagnosis.

Performance of specific morphologic features in distinguishing low-grade squamous intraepithelial lesions from high-grade squamous intraepithelial lesions in borderline cases: a College of American Pathologists Cytopathology Committee multiobserver study

INTRODUCTION

Distinguishing between low-grade squamous intraepithelial lesions (LSIL) and high-grade squamous intraepithelial lesions (HSIL) can be difficult on certain Papanicolaou (Pap) tests, hindering interobserver concordance. We investigated the variables influencing the interpretation of LSIL versus HSIL in Pap test slides rejected from the College of American Pathologists PAP education program.

MATERIALS AND METHODS

Eleven cytologists, who were unaware of the reference interpretation, examined 21 Pap slides (11 submitted as LSIL and 10 as HSIL) rejected from the PAP education program and recorded the number of LSIL cells, HSIL cells, keratinized dysplastic cells, LSIL clusters with mixed HSIL cells, atypical squamous metaplasia, atypical glandular cells, the presence of inflammation or infectious organisms, and the overall interpretation (LSIL or HSIL). We evaluated the significance of these 11 variables using a nonlinear mixed model analysis.

RESULTS

LSIL had greater concordance (92 of 121 responses; 76.0% concordance) than HSIL (68 of 110 responses; 61.8% concordance; P < 0.001). The only predictors of misclassified cases were the number of atypical squamous metaplastic cells and the number of HSIL cells (P < 0.001). The more of these cells identified, the more likely the reviewers were to classify the slide as HSIL. The reproducibility of the diagnosis was fair (Gwet's agreement coefficient, 0.33).

CONCLUSIONS

Interobserver reproducibility is a challenge for a subset of cases with features intermediate between LSIL and HSIL. Atypical squamous metaplasia and dysplastic nuclei with a nuclear/cytoplasmic ratio greater than one half of the cell volume (HSIL) present on a Pap test influenced the likelihood that a reviewer would interpret the case as HSIL rather than LSIL.

Artificial intelligence driven next-generation renal histomorphometry

PURPOSE OF REVIEW

Successful integration of artificial intelligence into extant clinical workflows is contingent upon a number of factors including clinician comprehension and interpretation of computer vision. This article discusses how image analysis and machine learning have enabled comprehensive characterization of kidney morphology for development of automated diagnostic and prognostic renal pathology applications.

RECENT FINDINGS

The primordial digital pathology informatics work employed classical image analysis and machine learning to prognosticate renal disease. Although this classical approach demonstrated tremendous potential, subsequent advancements in hardware technology rendered artificial neural networks '(ANNs) the method of choice for machine vision in computational pathology'. Offering rapid and reproducible detection, characterization and classification of kidney morphology, ANNs have facilitated the development of diagnostic and prognostic applications. In addition, modern machine learning with ANNs has revealed novel biomarkers in kidney disease, demonstrating the potential for machine vision to elucidate novel pathologic mechanisms beyond extant clinical knowledge.

SUMMARY

Despite the revolutionary developments potentiated by modern machine learning, several challenges remain, including data quality control and curation, image annotation and ontology, integration of multimodal data and interpretation of machine vision or 'opening the black box'. Resolution of these challenges will not only revolutionize diagnostic pathology but also pave the way for precision medicine and integration of artificial intelligence in the process of care.