Automated Nuclear Segmentation in Head and Neck Squamous Cell Carcinoma Pathology Reveals Relationships between Cytometric Features and ESTIMATE Stromal and Immune Scores

Blocker, Stephanie J.; Cook, J.P.; Everitt, Jeffrey I.; Austin, Wyatt M; Watts, Tammara L.; Mowery, Yvonne M.

doi:10.1016/j.ajpath.2022.06.003

Cited by 3 publications

(2 citation statements)

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“…To generate quantitative cytometric feature maps, automated nuclear segmentation was performed on digitized H&E images using the StarDist plugin for FIJI as described previously (25,26). 30 features broadly divided into four categories were measured for each nucleus: Topology, Delaunay triangulation distance, nuclear stain Haralick features, and stain intensity features.…”

Section: Cytometric Feature Mappingmentioning

confidence: 99%

“…Measured features were plotted as a function of ADC or T2* for linear regression analysis. Where multiple slides were available per animal, the measured cytometric-to-MRH relationships were averaged on a per-animal basis prior to group analysis, as described previously (18,25,26). This ensured that the number of available slides for each subject did not impact the group analysis.…”

Section: Image Analysismentioning

confidence: 99%

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MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma

Blocker,

Mowery,

Everitt

et al. 2024

Front. Oncol.

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PurposeTo identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS).Materials and methodsIn a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast in vivo MRI, three-dimensional (3D) multi-contrast high-resolution ex vivo MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant.ResultsThree features correlated with ex vivo apparent diffusion coefficient (ADC), and no features correlated with in vivo ADC. Six features demonstrated significant linear relationships with ex vivo T2*, and fifteen features correlated significantly with in vivo T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics.ConclusionRegistered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.

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Section: Cytometric Feature Mappingmentioning

confidence: 99%

Section: Image Analysismentioning

confidence: 99%