Observational studyCancer medicine2024
Predicting Disease Progression in Inoperable Localized NSCLC Patients Using ctDNA Machine Learning Model.
Observational study in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04014465 (Monitoring Efficacy of Radiotherapy Based on Next Generation Sequencing Liquid Biopsy Technique in Lung Cancer and Esophageal Cancer), which is not on this map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Monitoring Efficacy of Radiotherapy Based on Next Generation Sequencing Liquid Biopsy Technique in Lung Cancer and Esophageal Cancer: a Prospective Study
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research trends and hotspots of circulating tumor DNA in colorectal cancer: a bibliometric study.Frontiers in oncology · 2025Pooled it
- Identification of candidate variants in plasma associated with early versus late disease progression under anti-PD-1 therapy in metastatic NSCLC.Translational lung cancer research · 2026Article
- Review
- A machine learning framework combining cfDNA fragmentomics and serum biomarkers for early ovarian cancer detection.Cell communication and signaling : CCS · 2026Article
- Review
- neomerDB: a comprehensive database of neomer biomarkers in cancer.Database : the journal of biological databases and curation · 2026Article
- Circulating tumor DNA in lung cancer immunotherapy: prognostic marker today, predictive tool tomorrow?Frontiers in immunology · 2026Review
- Comprehensive Liquid Biopsy Approaches for the Clinical Management of Lung Cancer Using Multiple Biological Matrices.International journal of molecular sciences · 2025Review
- Review
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.
Funding
Abstract
introductionThere is an urgent clinical need to accurately predict the risk for disease progression in post-treatment NSCLC patients, yet current ctDNA mutation profiling approaches are limited by low sensitivity. We represent a non-invasive liquid biopsy assay utilizing cfDNA neomer profiling for predicting disease progression in 44 inoperable localized NSCLC patients.
methodsA total of 97 plasma samples were collected at various time points during or post-treatments (TP1: 39, TP2: 33, TP3: 25). cfDNA neomer profiling, generated based on target sequencing data, was used to fit survival support vector machine models for each time point. Leave-one-out cross-validation (LOOCV) was performed to evaluate the models' predictive performances.
resultsOur cfDNA neomer profiling assay showed excellent performance in detecting patients with a high risk for disease progression. At TP1, the high-risk patients detected by our model showed an increased risk of 3.62 times (hazard ratio [HR] = 3.62, p = 0.0026) for disease progression, compared to 3.91 times (HR = 3.91, p = 0.0022) and 4.00 times (HR = 4.00, p = 0.019) for TP2 and TP3. These neomer profiling determined HRs were higher than the ctDNA mutation-based results (HR = 2.08, p = 0.074; HR = 1.49, p = 0.61) at TP1 and TP3. At TP1, the predictive model reached 40% sensitivity at 92.9% specificity, outperforming the mutation-based method (40% sensitivity at 78.6% specificity), while the combination results reached a higher sensitivity (60%). Finally, the longitudinal analysis showed that the combination of neomer and ctDNA mutation-based results could predict disease progression with an excellent sensitivity of 88.9% at 80% specificity.
conclusionIn conclusion, we developed a cfDNA neomer profiling assay for predicting disease progression in inoperable NSCLC patients. This assay showed increased predicting power during and post-treatment compared to the ctDNA mutation-based method, thus illustrating a great clinical potential to guide treatment decisions in inoperable NSCLC patients.
trial registrationClinicalTrials.gov: NCT04014465.
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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 Socratic graph.