ArticlePloS one2017
Application of unsupervised analysis techniques to lung cancer patient data.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis 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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Enhancing stratification for survival analyses across standardized data sources.BMC medical informatics and decision making · 2026Article
- Temporal Trends and Patient Stratification in Lung Cancer: A Comprehensive Clustering Analysis from Timis County, Romania.Cancers · 2025Article
- Review and recommendations for using artificial intelligence in intracoronary optical coherence tomography analysis.European heart journal. Digital health · 2025Review
- A novel analytical framework for risk stratification of real-world data using machine learning: A small cell lung cancer study.Clinical and translational science · 2022Article
- An Interpretable Two-Phase Modeling Approach for Lung Cancer Survivability Prediction.Sensors (Basel, Switzerland) · 2022Article
- Artificial intelligence and machine learning for medical imaging: A technology review.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- Machine Learning in Oncology: Methods, Applications, and Challenges.JCO clinical cancer informatics · 2020Article
- Multi-objective optimization of tumor response to drug release from vasculature-bound nanoparticles.Scientific reports · 2020Article
- Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database.NPJ digital medicine · 2020Article
- Classify multicategory outcome in patients with lung adenocarcinoma using clinical, transcriptomic and clinico-transcriptomic data: machine learning versus multinomial models.American journal of cancer research · 2020Article
- Machine learning in oncology: a review.Ecancermedicalscience · 2020Review
- Lung Cancer Survival Prediction via Machine Learning Regression, Classification, and Statistical Techniques.Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology · 2018Article
- Prediction of Protein-Protein Interactions from Amino Acid Sequences Based on Continuous and Discrete Wavelet Transform Features.Molecules (Basel, Switzerland) · 2018Article
- Multiple Machine Learnings Revealed Similar Predictive Accuracy for Prognosis of PNETs from the Surveillance, Epidemiology, and End Result Database.Journal of Cancer · 2018Article
- Prediction of lung cancer patient survival via supervised machine learning classification techniques.International journal of medical informatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
This study applies unsupervised machine learning techniques for classification and clustering to a collection of descriptive variables from 10,442 lung cancer patient records in the Surveillance, Epidemiology, and End Results (SEER) program database. The goal is to automatically classify lung cancer patients into groups based on clinically measurable disease-specific variables in order to estimate survival. Variables selected as inputs for machine learning include Number of Primaries, Age, Grade, Tumor Size, Stage, and TNM, which are numeric or can readily be converted to numeric type. Minimal up-front processing of the data enables exploring the out-of-the-box capabilities of established unsupervised learning techniques, with little human intervention through the entire process. The output of the techniques is used to predict survival time, with the efficacy of the prediction representing a proxy for the usefulness of the classification. A basic single variable linear regression against each unsupervised output is applied, and the associated Root Mean Squared Error (RMSE) value is calculated as a metric to compare between the outputs. The results show that self-ordering maps exhibit the best performance, while k-Means performs the best of the simpler classification techniques. Predicting against the full data set, it is found that their respective RMSE values (15.591 for self-ordering maps and 16.193 for k-Means) are comparable to supervised regression techniques, such as Gradient Boosting Machine (RMSE of 15.048). We conclude that unsupervised data analysis techniques may be of use to classify patients by defining the classes as effective proxies for survival prediction.
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.