ArticleJournal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer2013
Noninvasive characterization of the histopathologic features of pulmonary nodules of the lung adenocarcinoma spectrum using computer-aided nodule assessment and risk yield (CANARY)--a pilot study.
Article in Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed, 74 citations in OpenAlex.
- Trial
- Detection and quantification of the solid component in pulmonary subsolid nodules by semiautomatic segmentation.European radiology · 2015Trial
- Review
- Radiomics to understand pre-treatment tumor biology for resectable non-small cell lung cancer.Frontiers in oncology · 2026Review
- A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.Current medical imaging · 2026Review
- Long-Term Prognosis of Resected Stage 1 Lung Adenocarcinoma With Radiomics, Regionality, and Histopathologic Correlation.CHEST pulmonary · 2025Article
- A computed tomography-based score indicative of lung cancer aggression (SILA) predicts lung adenocarcinomas with low malignant potential or vascular invasion.Cancer biomarkers : section A of Disease markers · 2025Article
- Radiomic score is prognostic in clinical stage I lung adenocarcinoma ≤2 cm undergoing surgery.Journal of thoracic disease · 2024Article
- Premalignant Progression in the Lung: Knowledge Gaps and Novel Opportunities for Interception of Non-Small Cell Lung Cancer. An Official American Thoracic Society Research Statement.American journal of respiratory and critical care medicine · 2024Review
- Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.Radiology · 2023Review
- What does radiomics do in PD-L1 blockade therapy of NSCLC patients?Thoracic cancer · 2022Review
- Implementation of artificial intelligence in the histological assessment of pulmonary subsolid nodules.Translational lung cancer research · 2021Article
- Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine.Cold Spring Harbor perspectives in medicine · 2021Review
- Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians.Journal of personalized medicine · 2021Review
- Comparison of Comprehensive Morphological and Radiomics Features of Subsolid Pulmonary Nodules to Distinguish Minimally Invasive Adenocarcinomas and Invasive Adenocarcinomas in CT Scan.Frontiers in oncology · 2021Article
- Article
- Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models.Scientific reports · 2020Article
- Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification.Journal of thoracic disease · 2020Review
- Image-assisted video assisted thoracic surgery (iVATS): an important tool in the armamentarium against lung cancer.Journal of thoracic disease · 2020Article
- Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study.European radiology · 2020Article
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14 authors at 1 institution in 1 country.
Funding
Abstract
introductionPulmonary nodules of the adenocarcinoma spectrum are characterized by distinctive morphological and radiologic features and variable prognosis. Noninvasive high-resolution computed tomography-based risk stratification tools are needed to individualize their management.
methodsRadiologic measurements of histopathologic tissue invasion were developed in a training set of 54 pulmonary nodules of the adenocarcinoma spectrum and validated in 86 consecutively resected nodules. Nodules were isolated and characterized by computer-aided analysis, and data were analyzed by Spearman correlation, sensitivity, and specificity and the positive and negative predictive values.
resultsComputer-aided nodule assessment and risk yield (CANARY) can noninvasively characterize pulmonary nodules of the adenocarcinoma spectrum. Unsupervised clustering analysis of high-resolution computed tomography data identified nine unique exemplars representing the basic radiologic building blocks of these lesions. The exemplar distribution within each nodule correlated well with the proportion of histologic tissue invasion, Spearman R = 0.87, p < 0.0001 and 0.89 and p < 0.0001 for the training and the validation set, respectively. Clustering of the exemplars in three-dimensional space corresponding to tissue invasion and lepidic growth was used to develop a CANARY decision algorithm that successfully categorized these pulmonary nodules as "aggressive" (invasive adenocarcinoma) or "indolent" (adenocarcinoma in situ and minimally invasive adenocarcinoma). Sensitivity, specificity, positive predictive value, and negative predictive value of this approach for the detection of aggressive lesions were 95.4, 96.8, 95.4, and 96.8%, respectively, in the training set and 98.7, 63.6, 94.9, and 87.5%, respectively, in the validation set.
conclusionCANARY represents a promising tool to noninvasively risk stratify pulmonary nodules of the adenocarcinoma spectrum.
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