{"title":"Ultrasound radiomics-based dynamic nomogram to predict histologic grade in soft tissue sarcoma: a multi-center cohort study.","authors":"Mengjie Wu, Boyang Zhou, Ao Li, Hailing Zha, Xinyue Wang, Hongjin Hua, Tiantian Zhang, Shuping Wei, Wei Zhang, Huixiong Xu","doi":"10.1093/bjr/tqaf227","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To predict histologic grade of soft tissue sarcoma (STS) with preoperative ultrasound images, aiding in the selection of personalized treatment plans and improving long-term prognosis.</p><p><strong>Methods: </strong>In total, 238 patients with histologically proven STS were retrospectively enrolled from April 2016 to December 2023 and divided into the training and internal validation cohorts. 70 patients were prospectively enrolled from three centers between January 2024 and December 2024 as the external validation cohort. Radiomics features were extracted from preoperative grayscale ultrasound images. The dynamic nomogram (DynNom) was developed by using multivariable logistic regression analysis. Predictive performance was evaluated with the receiving operating characteristic curve, calibration curve, Hosmer-Lemeshow test, decision curve analysis (DCA), and clinical impact curve (CIC).</p><p><strong>Results: </strong>The DynNom based on clinical-US characteristics (metastasis status, echogenicity, fascia layer, and vascularity) and radiomics features yielded an optimal AUC of 0.915 (95% CI, 0.873-0.947), 0.87 (95% CI, 0.79-0.93), and 0.90 (95% CI, 0.80-0.96) for predicting the STS histologic grade in the training, internal and external validation cohorts, respectively. The DynNom outperformed the conventional model and radiomics model (P < 0.05). Calibration curves and Hosmer-Lemeshow tests indicated its satisfactory calibration ability. DCA confirmed that the DynNom outperformed other models in overall net benefit, meanwhile CIC suggested that the DynNom had great clinical applicability in predicting histologic grade.</p><p><strong>Conclusions: </strong>The dynamic nomogram is a practical tool that could predict the histologic grade of STS, which might help clinicians to screen histologic high-grade STSs as neoadjuvant treatment candidates.</p><p><strong>Advances in knowledge: </strong>The dynamic nomogram had the potential to accurately predict histologic grade in STS patients before surgery. High-risk patients defined by the dynamic nomogram were potential candidates for preoperative radiotherapy and neoadjuvant chemotherapy.</p>","PeriodicalId":9306,"journal":{"name":"British Journal of Radiology","volume":" ","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/bjr/tqaf227","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: To predict histologic grade of soft tissue sarcoma (STS) with preoperative ultrasound images, aiding in the selection of personalized treatment plans and improving long-term prognosis.
Methods: In total, 238 patients with histologically proven STS were retrospectively enrolled from April 2016 to December 2023 and divided into the training and internal validation cohorts. 70 patients were prospectively enrolled from three centers between January 2024 and December 2024 as the external validation cohort. Radiomics features were extracted from preoperative grayscale ultrasound images. The dynamic nomogram (DynNom) was developed by using multivariable logistic regression analysis. Predictive performance was evaluated with the receiving operating characteristic curve, calibration curve, Hosmer-Lemeshow test, decision curve analysis (DCA), and clinical impact curve (CIC).
Results: The DynNom based on clinical-US characteristics (metastasis status, echogenicity, fascia layer, and vascularity) and radiomics features yielded an optimal AUC of 0.915 (95% CI, 0.873-0.947), 0.87 (95% CI, 0.79-0.93), and 0.90 (95% CI, 0.80-0.96) for predicting the STS histologic grade in the training, internal and external validation cohorts, respectively. The DynNom outperformed the conventional model and radiomics model (P < 0.05). Calibration curves and Hosmer-Lemeshow tests indicated its satisfactory calibration ability. DCA confirmed that the DynNom outperformed other models in overall net benefit, meanwhile CIC suggested that the DynNom had great clinical applicability in predicting histologic grade.
Conclusions: The dynamic nomogram is a practical tool that could predict the histologic grade of STS, which might help clinicians to screen histologic high-grade STSs as neoadjuvant treatment candidates.
Advances in knowledge: The dynamic nomogram had the potential to accurately predict histologic grade in STS patients before surgery. High-risk patients defined by the dynamic nomogram were potential candidates for preoperative radiotherapy and neoadjuvant chemotherapy.
期刊介绍:
BJR is the international research journal of the British Institute of Radiology and is the oldest scientific journal in the field of radiology and related sciences.
Dating back to 1896, BJR’s history is radiology’s history, and the journal has featured some landmark papers such as the first description of Computed Tomography "Computerized transverse axial tomography" by Godfrey Hounsfield in 1973. A valuable historical resource, the complete BJR archive has been digitized from 1896.
Quick Facts:
- 2015 Impact Factor – 1.840
- Receipt to first decision – average of 6 weeks
- Acceptance to online publication – average of 3 weeks
- ISSN: 0007-1285
- eISSN: 1748-880X
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