ArticleDiagnostics (Basel, Switzerland)2020
Value of Shape and Texture Features from
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.BMC medical informatics and decision making · 2026Pooled it
- Predictive radiomics based ensemble machine learning approach in CT lung nodule diagnosis.Journal of the Egyptian National Cancer Institute · 2025Article
- Article
- Outcomes and Clinical Practices in Follow-Up of Nonmalignant Pulmonary Nodules After Bronchoscopic Biopsy.CHEST pulmonary · 2024Article
- Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study.BMC pulmonary medicine · 2024Article
- An 18F-FDG-PET/CT-based radiomics signature for estimating malignance probability of solitary pulmonary nodule.The clinical respiratory journal · 2024Article
- Classification of Pulmonary Nodules in 2-[Nuclear medicine and molecular imaging · 2024Article
- PET/CT radiomics and deep learning in the diagnosis of benign and malignant pulmonary nodules: progress and challenges.Frontiers in oncology · 2024Review
- PretreatmentCancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Qualitative and Semiquantitative Parameters ofCancers · 2023Article
- Classification of solid pulmonary nodules using a machine-learning nomogram based onAnnals of translational medicine · 2022Article
- Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans.Sensors (Basel, Switzerland) · 2022Article
- Diagnostic Performance of Machine Learning Models Based onMolecular imaging and radionuclide therapy · 2022Article
- Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers.Diagnostics (Basel, Switzerland) · 2022Review
- The Role and Potential ofDiagnostics (Basel, Switzerland) · 2022Review
- Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results.Cancers · 2022Review
- Establishment of a malignancy and benignancy prediction model of sub-centimeter pulmonary ground-glass nodules based on the inflammation-cancer transformation theory.Frontiers in medicine · 2022Article
- Cost-Sensitive Uncertainty Hypergraph Learning for Identification of Lymph Node Involvement With CT Imaging.Frontiers in medicine · 2022Article
- Computational approaches to detect small lesions inJournal of applied clinical medical physics · 2021Article
- Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans.Communications biology · 2021Article
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Authors and funding
9 authors.
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Abstract
In this paper, we investigate the role of shape and texture features from 18F-FDG PET/CT to discriminate between benign and malignant solitary pulmonary nodules. To this end, we retrospectively evaluated cross-sectional data from 111 patients (64 males, 47 females, age = 67.5 ± 11.0) all with histologically confirmed benign (n=39) or malignant (n=72) solitary pulmonary nodules. Eighteen three-dimensional imaging features, including conventional, texture, and shape features from PET and CT were tested for significant differences (Wilcoxon-Mann-Withney) between the benign and malignant groups. Prediction models based on different feature sets and three classification strategies (Classification Tree, k-Nearest Neighbours, and Naïve Bayes) were also evaluated to assess the potential benefit of shape and texture features compared with conventional imaging features alone. Eight features from CT and 15 from PET were significantly different between the benign and malignant groups. Adding shape and texture features increased the performance of both the CT-based and PET-based prediction models with overall accuracy gain being 3.4-11.2 pp and 2.2-10.2 pp, respectively. In conclusion, we found that shape and texture features from 18F-FDG PET/CT can lead to a better discrimination between benign and malignant lung nodules by increasing the accuracy of the prediction models by an appreciable margin.
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