Contrast-enhanced ultrasound guided core needle biopsy for soft tissue tumors: Accuracy and applicability.

OBJECTIVE: To evaluate the effectiveness of contrast-enhanced ultrasound (CEUS) guided core needle biopsy (CNB) in diagnosing soft tissue tumors (STTs) and to identify the conventional ultrasonography (US) features of STTs that are recommended for CEUS-guided CNB. MATERIALS AND METHODS: A retrospective study was conducted on 123 patients with surgically confirmed STTs. Before surgeries, all subjects underwent CNB under the guidance of US or CEUS. The histopathological results of surgical specimens were considered as the gold standards. A successful biopsy diagnosis was defined as the pathological subtypes obtained by biopsy consistent with the gold standard. The diagnostic yields were compared between the US and CEUS groups, and the diagnostic yields based on various conventional US features of STTs were also compared between the two groups. RESULTS: Sixty-seven cases underwent US-guided CNB and fifty-six cases underwent CEUS-guided CNB. The clinical, biopsy, and conventional US characteristics revealed no significant difference between the two groups. The diagnostic yield of the CEUS group was statistically higher than that of the US group (p = 0.011). In the CEUS group, more STTs with the anechoic areas were identified after CEUS examination (p = 0.031). Furthermore, the diagnostic yields based on the conventional US features of STTs, including deep fascia layer (p = 0.010), a maximum diameter ≥5 cm (p = 0.037), rough margin (p = 0.016), heterogeneous echotexture (p = 0.017), and absence of anechoic area (p = 0.013), were significantly different between the two groups, and the CEUS group exhibited higher diagnostic yields. CONCLUSION: CEUS-guided CNB was found to be an efficient method for STTs diagnosis. It is particularly recommended for STTs with the following conventional US features, including location in deep fascia layer, a maximum diameter ≥5 cm, rough margin, heterogeneous echotexture, or absence of anechoic area.

A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: a combined retrospective and prospective bicentric study.

OBJECTIVE: We aimed at building and testing a multiparametric clinic-ultrasomics nomogram for prediction of malignant extremity soft-tissue tumors (ESTTs). MATERIALS AND METHODS: This 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. RESULTS: The 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. CONCLUSION: The multiparametric clinic-ultrasomics nomogram can accurately predict the malignancy of ESTTs.