Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood. To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of individual DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2–3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an automated morphological and microenvironmental analysis of DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.

Consistent localization of cerebellar cortex in a standard coordinate system is important for functional studies and detection of anatomical alterations in studies of morphometry. To date, no pediatric cerebellar atlas is available. The probabilistic Cape Town Pediatric Cerebellar Atlas (CAPCA18) was constructed in the age-appropriate National Institute of Health Pediatric Database asymmetric template space using manual tracings of 16 cerebellar compartments in 18 healthy children (9-13 years) from Cape Town, South Africa. The individual atlases of the training subjects were also used to implement multi atlas label fusion using multi atlas majority voting (MAMV) and multi atlas generative model (MAGM) approaches. Segmentation accuracy in 14 test subjects was compared for each method to 'gold standard' manual tracings. Spatial overlap between manual tracings and CAPCA18 automated segmentation was 73% or higher for all lobules in both hemispheres, except VIIb and X. Automated segmentation using MAGM yielded the best segmentation accuracy over all lobules (mean Dice Similarity Coefficient 0.76; range 0.55-0.91; mean Hausdorff distance 0.9 mm; range 0.8-2.7 mm). In all lobules, spatial overlap of CAPCA18 segmentations with manual tracings was similar or higher than those obtained with SUIT (spatially unbiased infra-tentorial template), providing additional evidence of the benefits of an age appropriate atlas. MAGM segmentation accuracy was comparable to values reported recently by Park et al. (Neuroimage 2014;95(1):217) in adults (across all lobules mean DSC=0.73, range 0.40-0.89). CAPCA18 and the associated multi-subject atlases of the training subjects yield improved segmentation of cerebellar structures in children.

Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial distribution pattern surrounding ductal carcinoma in situ (DCIS) and its association with progression is not well understood.To characterize the tissue microecology of DCIS, we designed and tested a new deep learning pipeline, UNMaSk (UNet-IM-Net-SCCNN), for the automated detection and simultaneous segmentation of DCIS ducts. This new method achieved the highest sensitivity and recall over cutting-edge deep learning networks in three patient cohorts, as well as the highest concordance with DCIS identification based on CK5 staining.Following automated DCIS detection, spatial tessellation centred at each DCIS duct created the boundary in which local ecology can be studied. Single cell identification and classification was performed with an existing deep learning method to map the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, TILs co-localise significantly less with DCIS ducts in pure DCIS compared with adjacent DCIS, suggesting a more inflamed tissue ecology local to adjacent DCIS cases.Our experiments demonstrate that technological developments in deep convolutional neural networks and digital pathology can enable us to automate the identification of DCIS as well as to quantify the spatial relationship with TILs, providing a new way to study immune response and identify new markers of progression, thereby improving clinical management.

Background/Objectives: Imaging-based spatial transcriptomic platforms measure gene expression in single cells within intact tissue, but cell type annotation and spatial analysis are usually performed with separate tools. We developed Xen-Spat, an analysis pipeline that carries one set of cell type labels through complementary measures of cellular colocalization in the tumor microenvironment. Methods: Cells were quality-filtered, annotated in two tiers within each sample and mapped to uniform cell type labels. Four spatial modules were applied: the Morisita index for regional colocalization, niche analysis with assignment of tumor niches by the Dice coefficient, Getis-Ord Gi* hotspot detection with quantification of hotspot overlap, and distance-based enrichment with pseudobulk differential expression. Results: In six HER2+ breast cancer biopsies profiled by 10x Xenium, one niche per sample was assigned to the tumor compartment (Dice coefficient 0.60 to 0.83, compared with 0.22 or lower for other niches). Macrophages were enriched within 25 µm of tumor cells in all six samples; after excluding genes affected by segmentation spillover, 50 genes differed between macrophages near tumor cells and at the margin. In an independent public Xenium dataset, the pipeline reproduced a reported difference in MMP11 expression between tissue regions. Conclusions: Xen-Spat provides a reproducible workflow for quantifying cellular colocalization in imaging-based spatial transcriptomic data.