ArticleFrontiers in oncology2022
Accurate Prediction of Metachronous Liver Metastasis in Stage I-III Colorectal Cancer Patients Using Deep Learning With Digital Pathological Images.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- [Development and validation of a multicenter nomogram predicting the risk of liver metastasis after curative resection of colorectal cancer].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026Article
- Article
- Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification.World journal of clinical oncology · 2025Review
- 5-hydroxymethylcytosine features of portal venous blood predict metachronous liver metastases of colorectal cancer and reveal phosphodiesterase 4 as a therapeutic target.Clinical and translational medicine · 2025Article
- Development of prediction models for liver metastasis in colorectal cancer based on machine learning: a population-level study.Translational cancer research · 2024Article
- Article
- Predictive modeling algorithms for liver metastasis in colorectal cancer: A systematic review of the current literature.Annals of hepato-biliary-pancreatic surgery · 2024Review
- A nomogram prediction model based on clinicopathological combined radiological features for metachronous liver metastasis of colorectal cancer.Journal of Cancer · 2024Article
- Rapid detection of liver metastasis risk in colorectal cancer patients through blood test indicators.Frontiers in oncology · 2024Article
- Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Radiomics based on preoperative rectal cancer MRI to predict the metachronous liver metastasis.Abdominal radiology (New York) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Metachronous liver metastasis (LM) significantly impacts the prognosis of stage I-III colorectal cancer (CRC) patients. An effective biomarker to predict LM after surgery is urgently needed. We aimed to develop deep learning-based models to assist in predicting LM in stage I-III CRC patients using digital pathological images. Methods: Six-hundred eleven patients were retrospectively included in the study and randomly divided into training (428 patients) and validation (183 patients) cohorts according to the 7:3 ratio. Digital HE images from training cohort patients were used to construct the LM risk score based on a 50-layer residual convolutional neural network (ResNet-50). An LM prediction model was established by multivariable Cox analysis and confirmed in the validation cohort. The performance of the integrated nomogram was assessed with respect to its calibration, discrimination, and clinical application value. Results: Patients were divided into low- and high-LM risk score groups according to the cutoff value and significant differences were observed in the LM of the different risk score groups in the training and validation cohorts ( Conclusions: The LM risk score based on ResNet-50 and digital HE images was significantly associated with LM. The integrated nomogram could identify stage I-III CRC patients at high risk of LM after primary colectomy, so it may serve as a potential tool to choose the appropriate treatment to improve the prognosis of stage I-III CRC patients.
Indexed as
Identifiers
What OpenQuestion holds
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.