ArticleJournal of translational medicine2022
Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- A scoping review of TSR analysis in colorectal cancer: implications for automated solutions.Oncology reviews · 2025Pooled it
- Development and evaluation of the mrTE scoring system for MRI-detected tumor deposits and extramural venous invasion in rectal cancer.Abdominal radiology (New York) · 2025Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Deep-learning enabled combined measurement of tumour cell density and tumour infiltrating lymphocyte density as a prognostic biomarker in colorectal cancer.BJC reports · 2025Article
- Single-cell spatial immune profiling for precision immunotherapy in Lynch syndrome.Journal of the National Cancer Center · 2025Article
- Correction: Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer.Journal of translational medicine · 2025Article
- Facilitation of Tumor Stroma-Targeted Therapy: Model Difficulty and Co-Culture Organoid Method.Pharmaceuticals (Basel, Switzerland) · 2025Review
- The Glasgow Microenvironment Score: an exemplar of contemporary biomarker evolution in colorectal cancer.The journal of pathology. Clinical research · 2024Review
- Article
- Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.Cancer medicine · 2024Observational
- Interaction of human gut microbiota and local immune system in progression of colorectal adenoma (MIMICA-1): a protocol for a prospective, observational cohort study.Frontiers in oncology · 2024Article
- Association of artificial intelligence-based immunoscore with the efficacy of chemoimmunotherapy in patients with advanced non-squamous non-small cell lung cancer: a multicentre retrospective study.Frontiers in immunology · 2024Article
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
- Tumor-Stroma Ratio in Colorectal Cancer-Comparison between Human Estimation and Automated Assessment.Cancers · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
19 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer.
methodsA total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor stroma proportion and the infiltration of immune cells in the stroma region. We further analyzed the correlation between the score and CD3
resultPatients were classified into 4-level score groups (score 1-4). A high Deep-immune score was associated with a high level of CD3
conclusionThe proposed Deep-immune score quantified by artificial intelligence can reflect the immune status of patients with colorectal cancer and is associate with favorable survival. This digital pathology-based finding might advocate change in risk stratification and consequent precision medicine.
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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.