ArticleScientific reports2018
Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Region-Based Segmentation of Lymph Node Metastases in Whole-Slide Images of Colorectal Cancer: A Pilot Clinical Study.Cancer medicine · 2026Article
- Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging, and Classification.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Review
- Keep it accurate and robust: An enhanced nuclei analysis framework.Computational and structural biotechnology journal · 2024Article
- Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach.British journal of cancer · 2024Article
- Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY.Nature communications · 2024Article
- DeepBLS: Deep Feature-Based Broad Learning System for Tissue Phenotyping in Colorectal Cancer WSIs.Journal of digital imaging · 2023Article
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- GSN-HVNET: A Lightweight, Multi-Task Deep Learning Framework for Nuclei Segmentation and Classification.Bioengineering (Basel, Switzerland) · 2023Article
- Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer.PloS one · 2023Article
- A graph neural network framework for mapping histological topology in oral mucosal tissue.BMC bioinformatics · 2022Article
- Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations.NPJ digital medicine · 2022Article
- Breast tumor microenvironment structures are associated with genomic features and clinical outcome.Nature genetics · 2022Article
- Rheumatoid Arthritis Synovial Inflammation Quantification Using Computer Vision.ACR open rheumatology · 2022Article
- Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- TSHVNet: Simultaneous Nuclear Instance Segmentation and Classification in Histopathological Images Based on Multiattention Mechanisms.BioMed research international · 2022Article
- Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification.Journal of imaging · 2021Article
- Bringing Open Data to Whole Slide Imaging.Digital Pathology : 15th European congress, ECDP 2019, Warwick, UK, April 10-13, 2019 : proceedings. European Congress on Digital Pathology (15th : 2019 : Warwick, England) · 2019Article
- The use of digital pathology and image analysis in clinical trials.The journal of pathology. Clinical research · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Distant metastasis is the major cause of death in colorectal cancer (CRC). Patients at high risk of developing distant metastasis could benefit from appropriate adjuvant and follow-up treatments if stratified accurately at an early stage of the disease. Studies have increasingly recognized the role of diverse cellular components within the tumor microenvironment in the development and progression of CRC tumors. In this paper, we show that automated analysis of digitized images from locally advanced colorectal cancer tissue slides can provide estimate of risk of distant metastasis on the basis of novel tissue phenotypic signatures of the tumor microenvironment. Specifically, we determine what cell types are found in the vicinity of other cell types, and in what numbers, rather than concentrating exclusively on the cancerous cells. We then extract novel tissue phenotypic signatures using statistical measurements about tissue composition. Such signatures can underpin clinical decisions about the advisability of various types of adjuvant therapy.
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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.