ArticleQuantitative imaging in medicine and surgery2021
Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 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
- Distinguishing Organizing Pneumonia from Malignancy During Cancer Surveillance: Predominant Diagnostic Value of Imaging over Systemic Inflammatory Biomarkers.Current oncology (Toronto, Ont.) · 2026Article
- Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review.European radiology · 2026Article
- Single-session agreement of ChatGPT and Gemini treatment recommendations with multidisciplinary tumor board decisions in thoracic oncology.BMC cancer · 2026Observational
- Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground-glass nodules.Translational lung cancer research · 2026Article
- Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground-Glass Nodules Based on AI-Extracted Radiomic Features.Thoracic cancer · 2025Article
- Image-Based Deep Learning Model for Predicting Lymph Node Metastasis in Lung Adenocarcinoma With CT ≤ 2 cm.Thoracic cancer · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- The feasibility and cost-effectiveness of implementing mobile low-dose computed tomography with an AI-based diagnostic system in underserved populations.BMC cancer · 2025Article
- Preoperative markers for identifying CT ≤2 cm solid nodules of lung adenocarcinoma based on image deep learning.Thoracic cancer · 2024Article
- A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence.Journal of cancer research and clinical oncology · 2023Article
- Lung nodule in patients with extrathoracic primary cancer-not always what it seems.Journal of thoracic disease · 2023Article
- An uncertainty-aware self-training framework with consistency regularization for the multilabel classification of common computed tomography signs in lung nodules.Quantitative imaging in medicine and surgery · 2023Article
- Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.Cancers · 2023Review
- Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection with quantification.Quantitative imaging in medicine and surgery · 2023Article
- Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review.NPJ digital medicine · 2022Article
- Article
- Deep learning-based pulmonary tuberculosis automated detection on chest radiography: large-scale independent testing.Quantitative imaging in medicine and surgery · 2022Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) products have been widely used for the clinical detection of primary lung tumors. However, their performance and accuracy in risk prediction for metastases or benign lesions remain underexplored. This study evaluated the accuracy of an AI-driven commercial computer-aided detection (CAD) product (InferRead CT Lung Research, ICLR) in malignancy risk prediction using a real-world database.
methodsThis retrospective study assessed 486 consecutive resected lung lesions, including 320 adenocarcinomas, 40 other malignancies, 55 metastases, and 71 benign lesions, from September 2015 to November 2018. The malignancy risk probability of each lesion was obtained using the ICLR software based on a 3D convolutional neural network (CNN) with DenseNet architecture as a backbone (without clinical data). Two resident doctors independently graded each lesion using patient clinical history. One doctor (R1) has 3 years of chest radiology experience, and the other doctor (R2) has 3 years of general radiology experience. Cochran's Q test was used to assess the performances of the AI compared to the radiologists.
resultsThe accuracy of malignancy-risk prediction using the ICLR for adenocarcinomas, other malignancies, metastases, and benign lesions was 93.4% (299/320), 95.0% (38/40), 50.9% (28/55), and 40.8% (29/71), respectively. The accuracy was significantly higher in adenocarcinomas and other malignancies compared to metastases and benign lesions (all P<0.05). The overall accuracy of risk prediction for R1 was 93.6% (455/486) and 87.4% for R2 (425/486), both of which were higher than the 81.1% accuracy obtained with the ICLR (394/486) (R1
conclusionsThe accuracy of the ICLR for risk prediction is very high for primary lung cancers but poor for metastases and benign lesions.
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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.