ArticleBiomedical engineering online2016
Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy.
Article in Biomedical engineering online, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it, 243 citations in OpenAlex.
- An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images.Journal of medical systems · 2019Pooled it
- Pulmonary nodules: classification, biomarkers, signaling pathways, diseases, early diagnosis and management.Signal transduction and targeted therapy · 2026Review
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- TCMNet: dynamic tanh enhanced 3D transformer with cascaded multiplicative fusion for pulmonary nodule detection.Scientific reports · 2026Article
- Hybrid CNN-transformer model with BM3D and YOLOv8 for early detection of lung cancer in low-dose CT scans.Scientific reports · 2026Article
- Artificial Intelligence in Pancreatobiliary Endoscopy: Current Advances, Opportunities, and Challenges.Journal of clinical medicine · 2025Review
- The Right to Explanation in AI: In a Lonely Place.Journal of medical Internet research · 2025Article
- Diagnostic performance of a fully automated AI algorithm for lesion detection and PI-RADS classification in patients with suspected prostate cancer.La Radiologia medica · 2025Article
- Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease.Cancers · 2025Review
- Cholangioscopy-based convoluted neuronal network vs. confocal laser endomicroscopy in identification of neoplastic biliary strictures.Endoscopy international open · 2024Article
- Review
- Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging.Cureus · 2024Review
- Recommended approaches for screening and early detection of lung cancer in the Middle East and Africa (MEA) region: a consensus statement.Journal of thoracic disease · 2024Review
- Article
- Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network.Journal of imaging informatics in medicine · 2024Article
- Article
- Challenges and Innovations in Breast Cancer Screening in India: A Review of Epidemiological Trends and Diagnostic Strategies.International journal of breast cancer · 2024Review
- Use of fractals in determining the malignancy degree of lung nodules.Frontiers in medical technology · 2024Article
- Efficacy of exponentiation method with a convolutional neural network for classifying lung nodules on CT images by malignancy level.European radiology · 2023Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings.
methodsThe purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives.
resultsThe database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant.
conclusionsFrom our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.
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