ArticleBMC medical informatics and decision making2024
A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Development and validation of a machine learning model for predicting objective response to PD-1 inhibitors in lung cancer patients.Translational lung cancer research · 2026Article
- A Weighted Neural Network Model Based on Laboratory Tests for Identifying Lymph Node Metastases in Esophageal Squamous Cell Carcinomas.Biosensors · 2026Article
- Machine Learning-Derived Risk Groups and Clinical Implementation of Survival Prediction in Lung Cancer: Evidence from a Kazakh National Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- Real-world evidence of baseline soluble CD25 as a prognostic biomarker and indicator of differential EGFR-TKI benefit in stage IV lung adenocarcinoma.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundLung cancer is characterized by high morbidity and mortality due to the lack of practical early diagnostic and prognostic tools. The present study uses machine learning algorithms to construct a clinical predictive model for non-small cell lung cancer (NSCLC) patients.
methodsLaboratory indices of the NSCLC patients at their initial visit were collected for quality control and exploratory analysis. By comparing the levels of the above indices between the survival and death groups, the statistically significant indices were selected for subsequent machine learning modeling. Ten machine learning algorithms were then employed to develop the predictive models with survival and recurrence as outcomes, respectively. Moreover, regression models were constructed using the random survival forest algorithm by incorporating the survival time dimension. Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application.
results682 patients were enrolled according to the inclusion and exclusion criteria. The preliminary comparison results revealed that except for fast blood glucose, CD
conclusionThe neural network model exhibited ideal predictive performance in the survival status of NSCLC patients, and the decision tree model constructed based on selected important variables was conducive to rapid bedside prognosis assessment and decision-making.
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.