ArticleTranslational lung cancer research2019
Differentiation of non-small cell lung cancer and histoplasmosis pulmonary nodules: insights from radiomics model performance compared with clinician observers.
Article in Translational lung cancer research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 27 citations in OpenAlex.
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- Impact of Field-of-view Zooming and Segmentation Batches on Radiomics Features Reproducibility and Machine Learning Performance in Thyroid Scintigraphy.Clinical nuclear medicine · 2025Article
- An Enhanced CT-based Radiomics Model for Predicting the Anaplastic Lymphoma Kinase Mutation Status in Lung Adenocarcinoma.Current medical imaging · 2025Article
- Building radiomics models based on ACR TI-RADS combining clinical features for discriminating benign and malignant thyroid nodules.Frontiers in endocrinology · 2025Article
- A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test.Translational lung cancer research · 2023Article
- Radiomic biomarkers from chest computed tomography are assistive in immunotherapy response prediction for non-small cell lung cancer.Translational lung cancer research · 2023Article
- Predicting Ki-67 labeling index level in early-stage lung adenocarcinomas manifesting as ground-glass opacity nodules using intra-nodular and peri-nodular radiomic features.Cancer medicine · 2022Article
- A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- Differentiation between immune checkpoint inhibitor-related and radiation pneumonitis in lung cancer by CT radiomics and machine learning.Medical physics · 2022Article
- A review on advances inInsights into imaging · 2022Review
- Acute pulmonary histoplasmosis related to occupational roofing: A case report of two brothers.Current medical mycology · 2021Article
- Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept.Iranian journal of medical sciences · 2021Article
- Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.Quantitative imaging in medicine and surgery · 2021Review
- Revising Conventional Wisdom About Histoplasmosis in the United States.Open forum infectious diseases · 2021Review
- Radiomics Score Combined with ACR TI-RADS in Discriminating Benign and Malignant Thyroid Nodules Based on Ultrasound Images: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2021Article
- Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison-Cardiac MRI Radiomics in Pulmonary Hypertension.Journal of clinical medicine · 2021Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHistoplasmosis pulmonary nodules often present in computed tomography (CT) imaging with characteristics suspicious for lung cancer. This presents a work-up decision issue for clinicians in regions where histoplasmosis is an endemic fungal infection, when a nodule suspicious for lung cancer is detected. We hypothesize the application of radiomic features extracted from pulmonary nodules and perinodular parenchyma could accurately distinguish between suspicious histoplasmosis lung nodules and non-small cell lung cancer (NSCLC).
methodsA retrospective clinical cohort of pulmonary nodules with a confirmed diagnosis of histoplasmosis or NSCLC was collected from the University of Iowa Hospitals and Clincs. Radiomic features were extracted describing characteristics of the nodule and perinodular parenchyma regions and used to build a machine learning tool. These cases were assessed by four expert clinicians who gave a blinded risk prediction for NSCLC. Tool and observer performance were assessed by calculating the area under the curve for the receiver operating characteristic (AUC-ROC) and interclass correlation coefficient (ICC).
resultsA cohort of 71 subjects with confirmed histopathology (40 NSCLC, 31 histoplasmosis) were case-matched based on age, sex, and smoking history. Superior performance (AUC-ROC =0.89) was demonstrated using leave-one-subject out validation in the tool that incorporated radiomics from the nodule and perinodular parenchyma region extended to 100% nodule diameter. Observers had perfect intra-repeatability (ICC =1.0) and demonstrated fair inter-reader variability (ICC =0.52).
conclusionsRadiomics have potential utility in the challenging task of differentiation between lung cancer and histoplasmosis. Expert clinician readers have high intra-repeatability but demonstrated inter-reader variability which could provide context for a supplemental radiomics-based tool.
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