ArticleEuropean radiology2023
Preoperative contrast-enhanced CT-based radiomics nomogram for differentiating benign and malignant primary retroperitoneal tumors.
Article in European radiology, 2023. 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, 13 citations in OpenAlex.
- Specific PET Imaging for Precision Management of Lung Cancer: Advances, Clinical Translation and Future Directions.Chemical & biomedical imaging · 2026Review
- Distinguishing nontuberculous mycobacterial lung disease from pulmonary tuberculosis using radiomics machine learning models from CT images.Frontiers in medicine · 2026Article
- End-to-end deep learning model for the diagnosis and segmentation of primary retroperitoneal neoplasm: a multicenter cohort study.EClinicalMedicine · 2025Article
- CT-based quantification of intratumoral heterogeneity for predicting distant metastasis in retroperitoneal sarcoma.Insights into imaging · 2025Article
- CT-based whole lung radiomics nomogram to identify middle-aged and elderly COVID-19 patients at high risk of progressing to critical disease.Journal of applied clinical medical physics · 2025Article
- Malignant retroperitoneal schwannoma in a young adult: rapid recurrence, metastasis, and treatment reflections-a case report.Frontiers in oncology · 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
- A comparison of machine learning methods for radiomics modeling in prediction of occult lymph node metastasis in clinical stage IA lung adenocarcinoma patients.Journal of thoracic disease · 2024Article
- Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet.Scientific reports · 2023Article
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10 authors at 1 institution in 1 country.
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Abstract
objectivesThis study evaluated the ability of a preoperative contrast-enhanced CT (CECT)-based radiomics nomogram to differentiate benign and malignant primary retroperitoneal tumors (PRT).
methodsImages and data from 340 patients with pathologically confirmed PRT were randomly placed into training (n = 239) and validation sets (n = 101). Two radiologists independently analyzed all CT images and made measurements. Key characteristics were identified through least absolute shrinkage selection combined with four machine-learning classifiers (support vector machine, generalized linear model, random forest, and artificial neural network back propagation) to create a radiomics signature. Demographic data and CECT characteristics were analyzed to formulate a clinico-radiological model. Independent clinical variables were merged with the best-performing radiomics signature to develop a radiomics nomogram. The discrimination capacity and clinical value of three models were quantified by the area under the receiver operating characteristics (AUC), accuracy, and decision curve analysis.
resultsThe radiomics nomogram was able to consistently differentiate between benign and malignant PRT in the training and validation datasets, with AUCs of 0.923 and 0.907, respectively. Decision curve analysis manifested that the nomogram achieved higher clinical net benefits than did separate use of the radiomics signature and clinico-radiological model.
conclusionsThe preoperative nomogram is valuable for differentiating between benign and malignant PRT; it can also aid in treatment planning. KEY POINTS: • A noninvasive and accurate preoperative determination of benign and malignant PRT is crucial to identifying suitable treatments and predicting disease prognosis. • Associating the radiomics signature with clinical factors facilitates differentiation of malignant from benign PRT with improved diagnostic efficacy (AUC) and accuracy from 0.772 to 0.907 and from 0.723 to 0.842, respectively, compared with the clinico-radiological model alone. • For some PRT with anatomically special locations and when biopsy is extremely difficult and risky, a radiomics nomogram may provide a promising preoperative alternative for distinguishing benignity and malignancy.
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