ArticleCancers2020
The Use of Artificial Intelligence in the Differentiation of Malignant and Benign Lung Nodules on Computed Tomograms Proven by Surgical Pathology.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation.Health technology assessment (Winchester, England) · 2025Pooled it
- Application of artificial intelligence in laryngeal lesions: a systematic review and meta-analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Pooled it
- Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.European radiology · 2024Pooled it
- Lung cancer detection in CT scans using extracted nodule features and precise multistage segmentation with deep learning based ensemble model.Scientific reports · 2026Article
- Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review.European radiology · 2026Article
- Advantages of integrating artificial intelligence and spectral CT for lung nodule classification and prognostic judgment: a narrative review.Journal of thoracic disease · 2025Review
- [Application Value of an AI-based Imaging Feature Parameter Model for Predicting the Malignancy of Part-solid Pulmonary Nodule].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Article
- Review
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.BMC medical imaging · 2025Article
- Artificial intelligence in automated detection of lung nodules: a narrative review.International journal of physiology, pathophysiology and pharmacology · 2025Review
- Differentiation of early-stage tumors from benign lesions manifesting as pure ground-glass nodule: a clinical prediction study based on AI-derived quantitative parameters.Frontiers in oncology · 2025Article
- Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels.Scientific reports · 2024Article
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT.La Radiologia medica · 2024Article
- A Bi-LSTM and multihead attention-based model incorporating radiomics signatures and radiological features for differentiating the main subtypes of lung adenocarcinoma.Quantitative imaging in medicine and surgery · 2023Article
- Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening.Cancer medicine · 2023Article
- Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.Cancers · 2023Review
- Machine Learning System for Lung Neoplasms Distinguished Based on Scleral Data.Diagnostics (Basel, Switzerland) · 2023Article
- Comparability of Pulmonary Nodule Size Measurements among Different Scanners and Protocols: Should Diameter Be Favorized over Volume?Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
The purpose of this work was to evaluate the performance of an existing commercially available artificial intelligence (AI) software system in differentiating malignant and benign lung nodules. The AI tool consisted of a vessel-suppression function and a deep-learning-based computer-aided-detection (VS-CAD) analyzer. Fifty patients (32 females, mean age 52 years) with 75 lung nodules (47 malignant and 28 benign) underwent low-dose computed tomography (LDCT) followed by surgical excision and the pathological analysis of their 75 nodules within a 3 month time frame. All 50 cases were then processed by the AI software to generate corresponding VS images and CAD outcomes. All 75 pathologically proven lung nodules were well delineated by vessel-suppressed images. Three (6.4%) of the 47 lung cancer cases, and 11 (39.3%) of the 28 benign nodules were ignored and not detected by the AI without showing a CAD analysis summary. The AI system/radiologists produced a sensitivity and specificity (shown in %) of 93.6/89.4 and 39.3/82.1 in distinguishing malignant from benign nodules, respectively. AI sensitivity was higher than that of radiologists, though not statistically significant (
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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.