ArticleNPJ breast cancer2021
Unmasking the immune microecology of ductal carcinoma in situ with deep learning.
Article in NPJ breast cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
26 citing papers in PubMed, 32 citations in OpenAlex.
- Integrative machine learning reveals a TLS signature and CCL5-CCR1 axis-associated immune remodeling in breast cancer.Cellular oncology (Dordrecht, Netherlands) · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology.NPJ digital medicine · 2025Article
- A morphometric signature to identify ductal carcinoma in situ with a low risk of progression.NPJ precision oncology · 2025Article
- Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization.Cancer biology & medicine · 2025Review
- Deep learning-based risk stratification of ductal carcinomaFrontiers in oncology · 2025Article
- The Microenvironment in DCIS and Its Role in Disease Progression.Advances in experimental medicine and biology · 2025Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Article
- Artificial intelligence-based morphometric signature to identify ductal carcinoma in situ with low risk of progression to invasive breast cancer.Research square · 2023Article
- Growth Dynamics of Ductal Carcinoma in Situ Recapitulate Normal Breast Development.bioRxiv : the preprint server for biology · 2023Article
- Artificial Intelligence in Histologic Diagnosis of Ductal Carcinoma In Situ.Mayo Clinic proceedings. Digital health · 2023Review
- Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma.Cancer research · 2023Article
- Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.NPJ breast cancer · 2023Review
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Comprehensive multiplexed immune profiling of the ductal carcinoma in situ immune microenvironment regarding subsequent ipsilateral invasive breast cancer risk.British journal of cancer · 2022Article
- Spatial interplay of tissue hypoxia and T-cell regulation in ductal carcinoma in situ.NPJ breast cancer · 2022Article
- Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage IChinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2022Article
- Ductal Carcinoma in Situ: State-of-the-Art Review.Radiology · 2022Review
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 3 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.