ArticleJournal of translational medicine2023
Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases.
Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Pooled it
- Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Systematic review and meta-analysis of AI in lung cancer metastasis imaging for diagnosis and prognosis.NPJ digital medicine · 2026Article
- Development and validation of machine learning models for predicting cancer-specific survival in colorectal signet ring cell carcinoma.BMC gastroenterology · 2026Article
- Article
- Review
- Multi-omics fusion network for prediction of early recurrence in colorectal liver metastases.NPJ precision oncology · 2026Article
- Integrative multi-omics and radiogenomic profiling decodes NNK-related tumor remodeling and prognostic stratification in pancreatic cancer.International journal of surgery (London, England) · 2026Article
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- Immunometabolism: crosstalk with tumor metabolism and implications for cancer immunotherapy.Molecular cancer · 2025Review
- Dual-Time-Point Radiomics for Prognosis Prediction in Colorectal Liver Metastasis Treated with Neoadjuvant Therapy Before Radical Resection: A Two-Center Study.Annals of surgical oncology · 2025Article
- Development and independent validation of explainable radiomics-based machine learning models for prognosis in colorectal liver metastases.Frontiers in digital health · 2025Article
- Predicting the recurrence risk of liver metastasis from colorectal cancer: a study based on preoperative CT intratumoral and peritumoral radiomics features.Frontiers in oncology · 2025Article
- Gastrointestinal tumor personalized immunotherapy: an integrated analysis from molecular genetics to imaging biomarkers.Therapeutic advances in gastroenterology · 2025Review
- Deep learning-based survival analysis of bladder cancer patients in the Putuo District, Shanghai, China.Frontiers in oncology · 2025Article
- Development of prediction models for liver metastasis in colorectal cancer based on machine learning: a population-level study.Translational cancer research · 2024Article
- CT radiomics and human-machine hybrid system for differentiating mediastinal lymphomas from thymic epithelial tumors.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- CD73 promotes non-small cell lung cancer metastasis by regulating Axl signaling independent of GAS6.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Review
- Construction and evaluation of glucocorticoid dose prediction model based on genetic and clinical characteristics of patients with systemic lupus erythematosus.International journal of immunopathology and pharmacologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFinding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance.
methodsWe trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73
resultsTabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman's ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73
conclusionsOur findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.
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.