ArticleFrontiers in oncology2020
Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 56 citations in OpenAlex.
- Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review.BJS open · 2024Pooled it
- Artificial intelligence in colorectal surgery: an AI-powered systematic review.Techniques in coloproctology · 2023Pooled it
- DeepLNM: A Multimodal Model for Predicting Lymph Node Metastasis in Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Utility of quantitative pathologic analysis of pT1 colorectal carcinomas to improve prediction of lymph node metastasis.Virchows Archiv : an international journal of pathology · 2026Article
- Region-Based Segmentation of Lymph Node Metastases in Whole-Slide Images of Colorectal Cancer: A Pilot Clinical Study.Cancer medicine · 2026Article
- Deep Learning for Pathology: YOLOv8 with EigenCAM for Reliable Colorectal Cancer Diagnostics.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.BMC cancer · 2025Article
- Prediction of 5-year postoperative survival and analysis of key prognostic factors in stage III colorectal cancer patients using novel machine learning algorithms.Frontiers in oncology · 2025Article
- Artificial intelligence (AI) for tumor microenvironment (TME) and tumor budding (TB) identification in colorectal cancer (CRC) patients: A systematic review.Journal of pathology informatics · 2024Review
- Enhancing colorectal cancer histology diagnosis using modified deep neural networks optimizer.Scientific reports · 2024Article
- Article
- Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review.Clinical Medicine Insights. Oncology · 2024Review
- Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?Clinical endoscopy · 2024Review
- Deep learning-based pathology signature could reveal lymph node status and act as a novel prognostic marker across multiple cancer types.British journal of cancer · 2023Article
- Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic study.EClinicalMedicine · 2023Article
- Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework.Scientific reports · 2023Article
- Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning.Communications medicine · 2023Article
- A systematic review on application of deep learning in digestive system image processing.The Visual computer · 2023Review
- Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients.IEEE journal of translational engineering in health and medicine · 2023Article
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHuman evaluation of pathological slides cannot accurately predict lymph node metastasis (LNM), although accurate prediction is essential to determine treatment and follow-up strategies for colon cancer. We aimed to develop accurate histopathological features for LNM in colon cancer.
methodsWe developed a deep convolutional neural network model to distinguish the cancer tissue component of colon cancer using data from the tissue bank of the National Center for Tumor Diseases and the pathology archive at the University Medical Center Mannheim, Germany. This model was applied to whole-slide pathological images of colon cancer patients from The Cancer Genome Atlas (TCGA). The predictive value of the peri-tumoral stroma (PTS) score for LNM was assessed.
resultsA total of 164 patients with stages I, II, and III colon cancer from TCGA were analyzed. The mean PTS score was 0.380 (± SD = 0.285), and significantly higher PTS scores were observed in patients in the LNM-positive group than those in the LNM-negative group (
conclusionWe established the PTS score, a simplified reproducible parameter, for predicting LNM in colon cancer using computer-based analysis that could be used to guide treatment decisions. These findings warrant further confirmation through large-scale prospective clinical trials.
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