ArticlePloS one2018
A multi-parameterized artificial neural network for lung cancer risk prediction.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses 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
32 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prediction models for early detection and diagnosis of lung cancer in people who have never smoked: a systematic review and critical appraisal.Cancer causes & control : CCC · 2026Pooled it
- Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Research on Risk Transfer Pathways for Lung Cancer Among Middle-Aged and Older Individuals Using Deep Reinforcement Learning: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Article
- Imaging patterns and recommendations for diagnosis, staging, and management of lung cancer.BJR open · 2025Review
- Prospects and challenges of deep learning in gynecologic malignancies.Frontiers in oncology · 2025Review
- Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction.Frontiers in public health · 2025Article
- Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients.Cancer medicine · 2024Article
- Opportunities and challenges of 5G network technology toward precision medicine.Clinical and translational science · 2023Review
- Artificial Neural Networks in Lung Cancer Research: A Narrative Review.Journal of clinical medicine · 2023Review
- The leading global health challenges in the artificial intelligence era.Frontiers in public health · 2023Review
- Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach.Journal of multidisciplinary healthcare · 2023Review
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- An overview of artificial intelligence in oncology.Future science OA · 2022Review
- Statistical biopsy: An emerging screening approach for early detection of cancers.Frontiers in artificial intelligence · 2022Article
- Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms.Frontiers in public health · 2022Article
- Predicting the risk of cancer in adults using supervised machine learning: a scoping review.BMJ open · 2021Article
- Urinary Biomarkers for Early Diagnosis of Lung Cancer.Journal of clinical medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The objective of this study is to train and validate a multi-parameterized artificial neural network (ANN) based on personal health information to predict lung cancer risk with high sensitivity and specificity. The 1997-2015 National Health Interview Survey adult data was used to train and validate our ANN, with inputs: gender, age, BMI, diabetes, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. We identified 648 cancer and 488,418 non-cancer cases. For the training set the sensitivity was 79.8% (95% CI, 75.9%-83.6%), specificity was 79.9% (79.8%-80.1%), and AUC was 0.86 (0.85-0.88). For the validation set sensitivity was 75.3% (68.9%-81.6%), specificity was 80.6% (80.3%-80.8%), and AUC was 0.86 (0.84-0.89). Our results indicate that the use of an ANN based on personal health information gives high specificity and modest sensitivity for lung cancer detection, offering a cost-effective and non-invasive clinical tool for risk stratification.
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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.