An assessment of Ultrasound screening for soft tissue lumps referred from primary care

Charnock, Mark; Kotnis, Nikhil; Fernando, M. A.; Wilkinson, V.

doi:10.1016/j.crad.2018.07.102

Cited by 8 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ultrasound scan is the first-line investigation of choice 14 , as it can reliably exclude benign differential diagnosis such as cysts, lipomas, hernias of growths from vascular origin 15,16 . For larger, deeper or suspicious lesions on ultrasound, magnetic resonance imagining can provide confirmation of a soft tissue neoplasia, while providing information on the compromise of neighbouring structures 17 .…”

Section: Discussionmentioning

confidence: 99%

Sarcomas in the groin and inguinal canal: A 16-year single-centre experience

Berner

Yeats

Crowley

et al. 2021

Journal of Plastic, Reconstructive & Aesthetic Surgery

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Sarcomas in the groin and inguinal canal: A 16-year single-centre experience

Berner

Yeats

Crowley

et al. 2021

Journal of Plastic, Reconstructive & Aesthetic Surgery

View full text Add to dashboard Cite

“…Musculoskeletal ultrasound (US) is usually the first-line diagnostic imaging examination for relatively superficial and accessible ESTTs in clinical practice, before these patients are referred to the orthopedic surgery department or musculoskeletal tumors center (7,8). Due to the complex histological subtypes in ESTTs, there are many overlapping US imaging features between benign and malignant ESTTs.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Hu,

Cui

et al. 2024

Acta Radiol

View full text Add to dashboard Cite

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.

show abstract