Trial reportSensors (Basel, Switzerland)2018
A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer.
Trial report in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses 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
32 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- eNose technologies in the detection of cancer: a systematic review and meta-analysis.The oncologist · 2026Pooled it
- Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis.JAMA network open · 2022Pooled it
- Accuracy of the Electronic Nose Breath Tests in Clinical Application: A Systematic Review and Meta-Analysis.Biosensors · 2021Pooled it
- Pooled it
- The investigation of the volatile metabolites of lung cancer from the microenvironment of malignant pleural effusion.Scientific reports · 2021Trial
- Revolutionizing lung cancer screening: the rise of artificial intelligence integrating circulating tumor markers.World journal of surgical oncology · 2026Review
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Review
- Online monitoring and stable isotope tracing of cancer associated volatiles in murine model captures tumor associated markers in vivo.Analytica chimica acta · 2025Article
- The electronic nose in lung cancer diagnostics: a systematic review and meta-analysis.ERJ open research · 2025Article
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- Collection methods of exhaled volatile organic compounds for lung cancer screening and diagnosis: a systematic review.Journal of thoracic disease · 2024Review
- Advances in early detection of non-small cell lung cancer: A comprehensive review.Cancer medicine · 2024Review
- Modeling Epidemiology Data with Machine Learning Technique to Detect Risk Factors for Gastric Cancer.Journal of gastrointestinal cancer · 2024Article
- Developing technologies and areas of interest in lung cancer screening adjuncts.Journal of thoracic disease · 2024Review
- AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis.Cancers · 2024Review
- Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data.Respiratory research · 2024Article
- Pancreatic Cancer and Detection Methods.Biomedicines · 2023Review
- Predicting metastasis in gastric cancer patients: machine learning-based approaches.Scientific reports · 2023Article
- The Use of Breath Analysis in the Management of Lung Cancer: Is It Ready for Primetime?Current oncology (Toronto, Ont.) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79⁻1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80⁻0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy.
Indexed as
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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.