Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature.

BACKGROUND: Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning. PURPOSE: To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy. MATERIAL AND METHODS: A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs). A total of 1037 radiomics features were extracted from each US image. The most useful predictive radiomics features were selected by the maximum relevance and minimum redundancy method, least absolute shrinkage, and selection operator algorithm in the training cohort. A US-based radiomics signature was built based on these selected radiomics features. In addition, a conventional radiologic model based on the US features from the interpretation of two experienced radiologists was developed by a multivariate logistic regression algorithm. The diagnostic performances of the selected radiomics features, the US-based radiomics signature, and the conventional radiologic model for differentiating ESTTs were evaluated and compared in the validation cohort. RESULTS: In the validation cohort, the area under the curve (AUC), sensitivity, and specificity of the US-based radiomics signature for predicting ESTTs malignancy were 0.866, 84.2%, and 81.8%, respectively. The US-based radiomics signature had better diagnostic predictability for predicting ESTT malignancy than the best single radiomics feature and the conventional radiologic model (AUC = 0.866 vs. 0.719 vs. 0.681 for the validation cohort, all P <0.05). CONCLUSION: The US-based radiomics signature could provide a potential imaging biomarker to accurately predict ESTT malignancy.

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.

A low-grade malignant soft tissue tumor with S100 and CD34 co-expression showing novel CDC42SE2-BRAF fusion with distinct features.

Recently, a novel group of spindle cell tumors defined by S100 and CD34 co-expression harboring recurrent fusions involving RET, RAF1, BRAF, and NTRK1/2 gene has been identified. Morphologically, they are characterized by monomorphic neoplasm cells, "patternless" growth pattern, stromal, and perivascular hyalinization, lacked necrosis. We reported a 52-year-old Chinese female patient with a S100 and CD34 co-expression sarcoma presenting in the right proximal forearm. The forearm mass initially emerged 19 months ago when it was misdiagnosed as a solitary fibrous tumor and was surgically removed without further treatment. Microscopically, the primary and the recurred tumors share the same features, resembling the morphology of the recently characterized group. Nevertheless, some distinct features, such as predominantly epithelioid tumor cells and focally staghorn vessels, were also present in our case. Genomic profiling with clinical next-generation sequencing was performed and revealed CDC42SE2-BRAF gene fusion, MET amplification, and CDKN2A/B deletion. Both FISH and nested RT-PCR were performed to confirm the gene fusion. The patient was treated with crizotinib for two cycles but showed no obvious benefit. The presented case adds to the spectrum of the novel, characterized solid tumors, and provides suggestions for emerging therapeutic strategies for precision medicine involving targeted kinase inhibitors.

GSTO1 regards as a meritorious regulator in cutaneous malignant melanoma cells.

BACKGROUND: Glutathione S-transferase omega 1 (GSTO1), as a member of the glutathione S-transferase (GST) family genes, has been discovered to be up-regulated in several cancer cell lines which exhibited strong aggressiveness. However, the function of GSTO1 on cutaneous malignant melanoma (CMM) has not been illuminated. METHODS: Outcome of expression level and prognosis of GSTO1 were obtained from Oncomine and TCGA database. The specific effects of GSTO1 on the characteristics and regulatory mechanism of CMM cells were demonstrated by cell counting kit-8, colony formation, flow cytometry, and transwell assays in vitro. Western blot was employed to analyze the expression of proliferating cell nuclear antigen (PCNA), p53 and epithelial-to-mesenchymal (EMT) related proteins. RESULTS: We observed that GSTO1 was up-regulated in CMM samples when compared with the corresponding controls. Moreover, patients in CMM with high expression of GSTO1 were more likely to have a poor prognosis. Through in vitro experiments, silenced GSTO1 resulted in inhibition of CMM cells growth and aggressiveness, increased cell apoptosis, and blocked cell cycle. Finally, the expression of PCNA, p53 and EMT-related proteins were changed due to reduction of GSTO1. CONCLUSIONS: To sum up, our outcomes exhibited that weakening GSTO1 reduced the proliferation and mobility of CMM cells, increased the apoptosis ability of CMM cells, and arrested cell cycle at G1 phase, which can be achieved by affecting the expression of PCNA, p53 and the EMT process. This discovery provided a new perspective for elucidating the mechanism of CMM, and offered theoretical support for searching clinical therapeutic targets in the future.

A practical risk stratification system based on ultrasonography and clinical characteristics for predicting the malignancy of soft tissue masses.

OBJECTIVE: To establish a practical risk stratification system (RSS) based on ultrasonography (US) and clinical characteristics for predicting soft tissue masses (STMs) malignancy. METHODS: This retrospective multicenter study included patients with STMs who underwent US and pathological examinations between April 2018 and April 2023. Chi-square tests and multivariable logistic regression analyses were performed to assess the association of US and clinical characteristics with the malignancy of STMs in the training set. The RSS was constructed based on the scores of risk factors and validated externally. RESULTS: The training and validation sets included 1027 STMs (mean age, 50.90 ± 16.64, 442 benign and 585 malignant) and 120 STMs (mean age, 51.93 ± 17.90, 69 benign and 51 malignant), respectively. The RSS was constructed based on three clinical characteristics (age, duration, and history of malignancy) and six US characteristics (size, shape, margin, echogenicity, bone invasion, and vascularity). STMs were assigned to six categories in the RSS, including no abnormal findings, benign, probably benign (fitted probabilities [FP] for malignancy: 0.001-0.008), low suspicion (FP: 0.008-0.365), moderate suspicion (FP: 0.189-0.911), and high suspicion (FP: 0.798-0.999) for malignancy. The RSS displayed good diagnostic performance in the training and validation sets with area under the receiver operating characteristic curve (AUC) values of 0.883 and 0.849, respectively. CONCLUSION: The practical RSS based on US and clinical characteristics could be useful for predicting STM malignancy, thereby providing the benefit of timely treatment strategy management to STM patients. CRITICAL RELEVANCE STATEMENT: With the help of the RSS, better communication between radiologists and clinicians can be realized, thus facilitating tumor management. KEY POINTS: There is no recognized grading system for STM management. A stratification system based on US and clinical features was built. The system realized great communication between radiologists and clinicians in tumor management.

Added value of contrast-enhanced ultrasound to conventional ultrasound for characterization of indeterminate soft-tissue tumors.

OBJECTIVE: To assess the added value of contrast-enhanced ultrasound (CEUS) to conventional ultrasound in differentiating benign soft-tissue tumors from malignant ones. METHODS: 197 soft-tissue tumors underwent ultrasound examination with confirmed histopathology were retrospectively evaluated. The radiologists classified all the tumors as benign, malignant, or indeterminate according to ultrasound features. The indeterminate tumors underwent CEUS were reviewed afterwards for malignancy identification by using individual and combined CEUS features. RESULTS: Ultrasound analysis classified 62 soft-tissue tumors as benign, 111 tumors as indeterminate and 24 tumors as malignant. There 104 indeterminate tumors were subject to CEUS. Three CEUS features including enlargement of enhancement area, infiltrative enhancement boundary, and intratumoral arrival time difference were significantly associated with the tumor nature in both univariable and multivariable analysis for the indeterminate tumors (all p < 0.05). When at least one out of the three discriminant CEUS features were present, the best sensitivity of 100% for malignancy identification was obtained with the specificity of 66.7% and the AUC of 0.833. When at least two of the three discriminant CEUS features were present, the best area under the receiver operating characteristic curve (AUC) of 0.924 for malignancy identification was obtained. The combination of at least two discriminant CEUS features showed much better diagnostic performance than the optimal combination of ultrasound features in terms of AUC (0.924 vs 0.608, p < 0.0001), sensitivity (94.0% vs 42.0%, p < 0.0001), and specificity (90.7% vs 79.6%, p = 0.210) for the indeterminate tumors. CONCLUSION: The combination CEUS features of enlargement of enhancement area, infiltrative enhancement boundary and intratumoral arrival time difference are valuable to improve the discriminating performance for indeterminate soft-tissue tumors on conventional ultrasound. ADVANCES IN KNOWLEDGE: The combination of peritumoral and arrival-time CEUS features can improve the discriminating performance for indeterminate soft-tissue tumors on conventional ultrasound.

Diagnostic performance of conventional ultrasound and quantitative and qualitative real-time shear wave elastography in musculoskeletal soft tissue tumors.

BACKGROUND: To explore the feasibility to identify malignant musculoskeletal soft tissue tumors using real-time shear wave elastography (rtSWE). METHODS: One hundred fifteen musculoskeletal soft tissue tumors in 92 consecutive patients were examined using both conventional ultrasonography (US) and rtSWE. For each patient, the rtSWE parameters including maximum elasticity (Emax), mean elasticity (Emean), minimum elasticity (Emin), standard deviation of the elasticity (Esd), and rtSWE image pattern were obtained. Eighty-one histopathologically confirmed tumors from 73 patients were subjected to analysis. RESULTS: The 81 lesions included in the study were histopathologically classified as malignant (n = 21) or benign (n = 60). The statistically significant differences between benign and malignant lesions were found in conventional US characters including size, depth, margin, echogenicity, mass texture, and power Doppler signal. Meanwhile, the significant differences were also found in quantitative rtSWE findings including Emax, Emean, Emin, and Esd values and in qualitative rtSWE parameter named rtSWE image pattern. Multivariate analysis showed that infiltrative margin (OR, 4.470), and size (OR, 1.046) were independent predictors for malignancy in US findings, while Esd value (OR, 9.047) was independent predictors for malignancy in quantitative rtSWE parameters. Areas under the ROC curve (Azs) for US features, Esd value, and rtSWE image pattern were 0.851, 0.795, and 0.792, respectively. CONCLUSIONS: Conventional US and quantitative and qualitative rtSWE parameters are useful for malignancy prediction of musculoskeletal soft tissue tumors. rtSWE can be used to supplement conventional US to diagnose musculoskeletal soft tissue tumors.