ArticleJournal of clinical oncology : official journal of the American Society of Clinical Oncology2024
Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.
Article in Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2024. 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 19 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Image-Based Analyses for Morphology and Staging of Colon Cancer: A Comprehensive Review.BioMed research international · 2025Pooled it
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Targeting Tex10 Overcomes Oxaliplatin Resistance by Competitively Disrupting the Non-Canonical BAF Complex in Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Hypoxia-induced lnc-IRP drives oxaliplatin resistance in colorectal cancer through IRP2 sequestration and iron metabolism reprogramming.Journal of translational medicine · 2026Article
- Intratumoral PKM2 expression predicts benefit from adjuvant FOLFOX in stage II-III colorectal cancer.Journal of gastrointestinal oncology · 2026Article
- Copy number alteration fingerprint predicts the clinical response of oxaliplatin-based chemotherapy in metastatic colorectal cancer.NPJ precision oncology · 2026Article
- Artificial intelligence applications in oxaliplatin-based chemotherapy for colon cancer: advancing prognosis, toxicity prediction, and dose personalization.Frontiers in pharmacology · 2026Review
- Development and validation of an AI-augmented deep learning model for survival prediction in de novo metastatic colorectal cancer.Discover oncology · 2025Article
- Review
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- Prognostic role of tertiary lymphatic structures and their modulation by adjuvant FOLFOX in stage III colon cancer: a retrospective cohort study.Journal of gastrointestinal oncology · 2025Article
- Multi-Pathway Study for Oxaliplatin Resistance Reduction.Current issues in molecular biology · 2025Review
- A Machine Learning-Based Hypoxia-Related Gene Signatures to Facilitate Prediction of Cetuximab Response in Patients with Colorectal Cancer.International journal of medical sciences · 2025Article
- Establishment and validation of a prognostic risk early-warning model for retinoblastoma based on XGBoost.American journal of cancer research · 2025Article
- Chronic Inflammatory Comprehensive Signature Predicts Oxaliplatin and 5-Fluorouracil Benefit in Early Colorectal Cancer.Drug design, development and therapy · 2025Article
- Signaling pathways involved in colorectal cancer: pathogenesis and targeted therapy.Signal transduction and targeted therapy · 2024Review
- Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.World journal of gastroenterology · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
21 authors.
Funding
Abstract
purposeA combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects.
methodsWe trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS- (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the
resultsAmong 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS-. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15];
conclusionThe COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted.
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