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

The tumor microenvironment (TME) plays an important role in the progression of head and neck squamous cell carcinoma (HNSCC). Currently, pathologic assessment of TME is nonstandardized and subject to observer bias. Genome-wide transcriptomic approaches to understanding the TME, while less subject to bias, are expensive and not currently a part of the standard of care for HNSCC. To identify pathology-based biomarkers that correlate with genomic and transcriptomic signatures of TME in HNSCC, cytometric feature maps were generated in a publicly available data set from a cohort of patients with HNSCC, including whole-slide tissue images and genomic and transcriptomic phenotyping (N = 49). Cytometric feature maps were generated based on whole-slide nuclear detection, using a deep-learning algorithm trained for StarDist nuclear segmentation. Cytometric features in each patient were compared to transcriptomic measurements, including Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) scores and stemness scores. With correction for multiple comparisons, one feature (nuclear circularity) demonstrated a significant linear correlation with ESTIMATE stromal score. Two features (nuclear maximum and minimum diameter) correlated significantly with ESTIMATE immune score. Three features (nuclear solidity, nuclear minimum diameter, and nuclear circularity) correlated significantly with transcriptomic stemness score. This study provides preliminary evidence that observer-independent, automated tissue-slide analysis can provide insights into the HNSCC TME which correlate with genomic and transcriptomic assessments.

OpenSFDI: an open-source guide for constructing a spatial frequency domain imaging system.

Significance: Spatial frequency domain imaging (SFDI) is a diffuse optical measurement technique that can quantify tissue optical absorption (µa) and reduced scattering (µs') on a pixel-by-pixel basis. Measurements of µa at different wavelengths enable the extraction of molar concentrations of tissue chromophores over a wide field, providing a noncontact and label-free means to assess tissue viability, oxygenation, microarchitecture, and molecular content. We present here openSFDI: an open-source guide for building a low-cost, small-footprint, three-wavelength SFDI system capable of quantifying µa and µs' as well as oxyhemoglobin and deoxyhemoglobin concentrations in biological tissue. The companion website provides a complete parts list along with detailed instructions for assembling the openSFDI system.

Aim: We describe the design of openSFDI and report on the accuracy and precision of optical property extractions for three different systems fabricated according to the instructions on the openSFDI website.

Approach: Accuracy was assessed by measuring nine tissue-simulating optical phantoms with a physiologically relevant range of µa and µs' with the openSFDI systems and a commercial SFDI device. Precision was assessed by repeatedly measuring the same phantom over 1 h.

Results: The openSFDI systems had an error of 0 ± 6 % in µa and -2 ± 3 % in µs', compared to a commercial SFDI system. Bland-Altman analysis revealed the limits of agreement between the two systems to be ± 0.004 mm - 1 for µa and -0.06 to 0.1 mm - 1 for µs'. The openSFDI system had low drift with an average standard deviation of 0.0007 mm - 1 and 0.05 mm - 1 in µa and µs', respectively.

Conclusion: The openSFDI provides a customizable hardware platform for research groups seeking to utilize SFDI for quantitative diffuse optical imaging.