ArticleJournal of medical imaging (Bellingham, Wash.)2016
Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.
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 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 96 citations in OpenAlex.
- Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review.Journal of digital imaging · 2020Pooled it
- An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images.Journal of medical systems · 2019Pooled it
- Quantifying the Resolution Ceiling of Prototype-Based Few-Shot Segmentation on Sub-Centimetre Pulmonary Nodules: A Training-Free Geometric Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Automated Lesion Segmentation in Medical Imaging via Integration of nnU-Net Optimization and SAM Approach.Biomedical engineering and computational biology · 2026Article
- Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2.Scientific reports · 2025Article
- Proto-Caps: interpretable medical image classification using prototype learning and privileged information.PeerJ. Computer science · 2025Article
- Multi-type classification of lung nodules based on CT radiomics and ensemble learning for diversity weighting.Quantitative imaging in medicine and surgery · 2024Article
- Morphological MRI features as prognostic indicators in brain metastases.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data.The Lancet regional health. Southeast Asia · 2024Article
- Robust explanation supervision for false positive reduction in pulmonary nodule detection.Medical physics · 2024Article
- A Synthesizing Semantic Characteristics Lung Nodules Classification Method Based on 3D Convolutional Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models.Cancers · 2023Article
- A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models.Frontiers in oncology · 2023Article
- Efficient multiscale fully convolutional UNet model for segmentation of 3D lung nodule from CT image.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Local Linearity Analysis of Deep Learning CT Denoising Algorithms.Proceedings of SPIE--the International Society for Optical Engineering · 2022Article
- Longitudinal prediction of lung nodule invasiveness by sequential modelling with common clinical computed tomography (CT) measurements: a prediction accuracy study.Translational lung cancer research · 2022Article
- Lung cancer diagnosis using deep attention-based multiple instance learning and radiomics.Medical physics · 2022Article
- Deep Learning-based Artificial Intelligence Improves Accuracy of Error-prone Lung Nodules.International journal of medical sciences · 2022Article
- Mix Contrast for COVID-19 Mild-to-Critical Prediction.IEEE transactions on bio-medical engineering · 2021Article
- Impact of Lesion Delineation and Intensity Quantisation on the Stability of Texture Features from Lung Nodules on CT: A Reproducible Study.Diagnostics (Basel, Switzerland) · 2021Article
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2 authors at 1 institution in 1 country.
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Abstract
In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 [Formula: see text], which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 ([Formula: see text]), which increases to 0.949 ([Formula: see text]) when diameter and volume features are included and has an accuracy of 88.08 [Formula: see text]. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.
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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.