ArticleLa Radiologia medica2023
A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: a combined retrospective and prospective bicentric study.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 6 citations in OpenAlex.
- Elastosonography in the Differential Diagnosis of Musculoskeletal Soft Tissue Tumors: A Systematic Review.Journal of clinical medicine · 2026Review
- Ultrasound-based radiogenomics: status, applications, and future direction.Ultrasonography (Seoul, Korea) · 2025Article
- A practical risk stratification system based on ultrasonography and clinical characteristics for predicting the malignancy of soft tissue masses.Insights into imaging · 2024Article
- Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound.BMC medical imaging · 2024Article
- Article
- A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.Frontiers in endocrinology · 2024Article
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12 authors at 4 institutions in 1 country.
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Abstract
objectiveWe aimed at building and testing a multiparametric clinic-ultrasomics nomogram for prediction of malignant extremity soft-tissue tumors (ESTTs). MATERIALS AND
methodsThis combined retrospective and prospective bicentric study assessed the performance of the multiparametric clinic-ultrasomics nomogram to predict the malignancy of ESTTs, when compared with a conventional clinic-radiologic nomogram. A dataset of grayscale ultrasound (US), color Doppler flow imaging (CDFI), and elastography images for 209 ESTTs were retrospectively enrolled from one hospital, and divided into the training and validation cohorts. A multiparametric ultrasomics signature was built based on multimodal ultrasomic features extracted from the grayscale US, CDFI, and elastography images of ESTTs in the training cohort. Another conventional radiologic score was built based on multimodal US features as interpreted by two experienced radiologists. Two nomograms that integrated clinical risk factors and the multiparameter ultrasomics signature or conventional radiologic score were respectively developed. Performance of the two nomograms was validated in the retrospective validation cohort, and tested in a prospective dataset of 51 ESTTs from the second hospital.
resultsThe multiparametric ultrasomics signature was built based on seven grayscale ultrasomic features, three CDFI ultrasomic features, and one elastography ultrasomic feature. The conventional radiologic score was built based on five multimodal US characteristics. Predictive performance of the multiparametric clinic-ultrasomics nomogram was superior to that of the conventional clinic-radiologic nomogram in the training (area under the receiver operating characteristic curve [AUC] 0.970 vs. 0.890, p = 0.006), validation (AUC: 0.946 vs. 0.828, p = 0.047) and test (AUC: 0.934 vs. 0.842, p = 0.040) cohorts, respectively. Decision curve analysis of combined training, validation and test cohorts revealed that the multiparametric clinic-ultrasomics nomogram had a higher overall net benefit than the conventional clinic-radiologic model.
conclusionThe multiparametric clinic-ultrasomics nomogram can accurately predict the malignancy of ESTTs.
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