ArticleJournal of medical imaging (Bellingham, Wash.)2016
LUNGx Challenge for computerized lung nodule classification.
Article in Journal of medical imaging (Bellingham, Wash.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- Vision-language model-based semantic-guided imaging biomarker for lung nodule malignancy prediction.Journal of biomedical informatics · 2025Article
- Establishing predictive models for malignant and inflammatory pulmonary nodules using clinical data and CT imaging features.Quantitative imaging in medicine and surgery · 2025Article
- Deep Learning in Thoracic Oncology: Meta-Analytical Insights into Lung Nodule Early-Detection Technologies.Cancers · 2025Review
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- Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis.Heliyon · 2023Article
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- Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models.Cancers · 2023Article
- AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19.Evolving systems · 2023Article
- Development and performance evaluation of a deep learning lung nodule detection system.BMC medical imaging · 2022Article
- Unsupervised contrastive learning based transformer for lung nodule detection.Physics in medicine and biology · 2022Article
- Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry.Respirology (Carlton, Vic.) · 2022Review
- Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans.Sensors (Basel, Switzerland) · 2022Article
- Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights.Sensors (Basel, Switzerland) · 2022Review
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- Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques.BioMed research international · 2022Article
- ADA-COVID: Adversarial Deep Domain Adaptation-Based Diagnosis of COVID-19 from Lung CT Scans Using Triplet Embeddings.Computational intelligence and neuroscience · 2022Article
- Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.IEEE transactions on medical imaging · 2021Article
- A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification.The British journal of radiology · 2021Article
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11 authors.
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Abstract
The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants' computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. Ten groups applied their own methods to 73 lung nodules (37 benign and 36 malignant) that were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists' AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. The continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.
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