ArticleScientific reports2018
Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Phase I/II Study of AXL-Specific Antibody-Drug Conjugate Enapotamab Vedotin in Patients with Advanced Solid Tumors.Cancer research communications · 2025Trial
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Interpretable dynamic quantitative vascular morphometry features using SHAP for anti-angiogenic therapy response prediction.Science advances · 2026Article
- QVT score, a radiomic biomarker of vascular complexity, enables prognostication and monitoring of NSCLC immunotherapy.Journal for immunotherapy of cancer · 2026Article
- Differentiation of intraductal papillary mucinous neoplasms and pancreatic ductal adenocarcinoma using arterial-phase CT radiomics combined with clinical features.Frontiers in oncology · 2026Article
- Towards equitable AI in oncology.Nature reviews. Clinical oncology · 2024Review
- A tumor vasculature-based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors.Science advances · 2022Article
- Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022Article
- Overview of radiomics in prostate imaging and future directions.The British journal of radiology · 2022Review
- Predicting cancer outcomes with radiomics and artificial intelligence in radiology.Nature reviews. Clinical oncology · 2022Review
- Immunotherapy in non-small cell lung cancer: Past, present, and future directions.Frontiers in oncology · 2022Review
- Article
- Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.European journal of cancer (Oxford, England : 1990) · 2021Article
- Applications of artificial intelligence in oncologicAnnals of translational medicine · 2021Review
- Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework.BMC research notes · 2021Article
- Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling.Journal of the Royal Society, Interface · 2021Article
- [Research Advances and Obstacles of CT-based Radiomics in Diagnosis and Treatment of Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2020Review
- Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma.European radiology · 2020Article
- Offbeat approaches to cancer research.Nature · 2019Article
- Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery.Neuro-oncology advancesArticle
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
Abstract
Adenocarcinomas and active granulomas can both have a spiculated appearance on computed tomography (CT) and both are often fluorodeoxyglucose (FDG) avid on positron emission tomography (PET) scan, making them difficult to distinguish. Consequently, patients with benign granulomas are often subjected to invasive surgical biopsies or resections. In this study, quantitative vessel tortuosity (QVT), a novel CT imaging biomarker to distinguish between benign granulomas and adenocarcinomas on routine non-contrast lung CT scans is introduced. Our study comprised of CT scans of 290 patients from two different institutions, one cohort for training (N = 145) and the other (N = 145) for independent validation. In conjunction with a machine learning classifier, the top informative and stable QVT features yielded an area under receiver operating characteristic curve (ROC AUC) of 0.85 in the independent validation set. On the same cohort, the corresponding AUCs for two human experts including a radiologist and a pulmonologist were found to be 0.61 and 0.60, respectively. QVT features also outperformed well known shape and textural radiomic features which had a maximum AUC of 0.73 (p-value = 0.002), as well as features learned using a convolutional neural network AUC = 0.76 (p-value = 0.028). Our results suggest that QVT features could potentially serve as a non-invasive imaging biomarker to distinguish granulomas from adenocarcinomas on non-contrast CT scans.
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