ArticleJournal of applied clinical medical physics2021
Differentiation of pulmonary sclerosing pneumocytoma from solid malignant pulmonary nodules by radiomic analysis on multiphasic CT.
Article in Journal of applied clinical medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 18 citations in OpenAlex.
- Noninvasive differentiation of benign and malignant solid pulmonary nodules using multiparameter dual-layer spectral CT radiomics.Insights into imaging · 2026Article
- Artificial intelligence-based density proportion analysis in predicting the invasiveness of neoplastic ground-glass nodules.Translational lung cancer research · 2026Article
- Pulmonary sclerosing pneumocytoma with rapid progression: a case description of serial images.Quantitative imaging in medicine and surgery · 2025Article
- Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors.European radiology · 2023Article
- Energy spectrum CT index-based machine learning model predicts the effect of intravenous thrombolysis in lower limbs.Journal of applied clinical medical physics · 2023Article
- Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study.Frontiers in oncology · 2023Article
- Feature selection methods and predictive models in CT lung cancer radiomics.Journal of applied clinical medical physics · 2023Review
- Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics.Insights into imaging · 2021Article
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Authors and funding
6 authors at 1 institution in 1 country.
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Abstract
purposeTo investigate the diagnostic value and feasibility of radiomics-based texture analysis in differentiating pulmonary sclerosing pneumocytoma (PSP) from solid malignant pulmonary nodules (SMPN) on single- and three-phase computed tomography (CT) images. MATERIALS AND
methodsA total of 25 PSP patients and 35 SMPN patients with pathologically confirmed results were retrospectively included in this study. For each patient, the tumor regions were manually labeled in images acquired at the noncontrast phase (NCP), arterial phase (AP), and venous phase (VP). The least absolute shrinkage and selection operator (LASSO) method was used to select the most useful predictive features extracted from the CT images. The predictive models that discriminate PSP from SMPN based on single-phase CT images (NCP, AP, and VP) or three-phase CT images (Combined model) were developed and validated through fivefold cross-validation using a logistic regression classifier. Model performance was evaluated using receiver operating characteristic (ROC) analysis. The predictive performance was also compared between the Combined model and human readers.
resultsFour, five, and five features were selected from NCP, AP, and VP CT images for the development of radiomic models, respectively. The NCP, AP, and VP models exhibited areas under the curve (AUCs) of 0.748 (95% confidence interval [CI], 0.620-0.852), 0.749 (95% CI, 0.620-0.852), and 0.790 (95% CI, 0.665-0.884) in the validation dataset, respectively. The Combined model based on three-phase CT images outperformed the NCP, AP, and VP models (all p < 0.05), yielding an AUC of 0.882 (95% CI, 0.773-0.951) in the validation dataset. The Combined model displayed noninferior performance compared to two senior radiologists; however, it outperformed two junior radiologists (p = 0.004 and 0.001, respectively).
conclusionThe Combined model based on radiomic features extracted from three-phase CT images achieved radiologist-level performance and could be used as promising noninvasive tool to differentiate PSP from SMPN.
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