Exploring the correlation of radiomic features of ultrasound images and FNCLCC Grading of soft tissue sarcoma

Background Presurgical evaluation of the histopathological grade of soft tissue sarcoma (STS) is important for enacting treatment strategies. In this study, we plan to investigate the correlation of high-output ultrasound (US) radiomic features and the histopathological grade of STS. Methods Patients with STS were retrospectively enrolled. The radiomic features were extracted from the US images of the STS lesions. The lesions were graded according to the Fédération Nationale des Centers de Lutte Contre le Cancer (FNCLCC) histopathological grading system. The correlation of the radiomic features and the FNCLCC grades was evaluated. We used the features correlated with the histopathological grades to build a model for predicting high-grade STS (Grade II and III). Results A total of 79 patients with STS were enrolled. And 15 radiomic features were found correlated with the FNCLCC grades of STSs, with the correlation coefficient ranging from 0.22 to 0.38. And 8 features showed significant difference among the three grades. The model for predicting high-grade STS based on the 8 radiomic features had an AUC value of 0.80, a sensitivity of 0.73, and a specificity of 0.78. Conclusion The US radiomic features were correlated with the FNCLCC grade of STS. The radiomic analysis of US imaging could be potentially helpful for identifying the FNCLCC grades of STS pre-surgically.

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Publication Details

Journal
Frontiers in Imaging
Published
2025-03-05
DOI
https://doi.org/10.3389/fimag.2025.1436275
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Exploring the correlation of radiomic features of ultrasound images and FNCLCC Grading of soft tissue sarcoma

Nan Zhuang, Licong Dong, Yusen Zhang, Chenyang Zhao et al.
Frontiers in Imaging
Radiomics and Machine Learning in Medical Imaging
article

Exploring the correlation of radiomic features of ultrasound images and FNCLCC Grading of soft tissue sarcoma

Nan Zhuang, Licong Dong, Yusen Zhang, Chenyang Zhao, Desheng Sun, Zhao Yi, Wangjie Wu, Haiqin Xie, Yaqi Shen, Guangyin Yu, Heng Lv, Lu Xie, Yun Tian, Xingen Wang
article en

Abstract

Background Presurgical evaluation of the histopathological grade of soft tissue sarcoma (STS) is important for enacting treatment strategies. In this study, we plan to investigate the correlation of high-output ultrasound (US) radiomic features and the histopathological grade of STS. Methods Patients with STS were retrospectively enrolled. The radiomic features were extracted from the US images of the STS lesions. The lesions were graded according to the Fédération Nationale des Centers de Lutte Contre le Cancer (FNCLCC) histopathological grading system. The correlation of the radiomic features and the FNCLCC grades was evaluated. We used the features correlated with the histopathological grades to build a model for predicting high-grade STS (Grade II and III). Results A total of 79 patients with STS were enrolled. And 15 radiomic features were found correlated with the FNCLCC grades of STSs, with the correlation coefficient ranging from 0.22 to 0.38. And 8 features showed significant difference among the three grades. The model for predicting high-grade STS based on the 8 radiomic features had an AUC value of 0.80, a sensitivity of 0.73, and a specificity of 0.78. Conclusion The US radiomic features were correlated with the FNCLCC grade of STS. The radiomic analysis of US imaging could be potentially helpful for identifying the FNCLCC grades of STS pre-surgically.

Frontiers in ImagingVol. 4
Peking University Shenzhen Hospital (CN)
Openalex Percentile: Top 94%
Radiomics and Machine Learning in Medical Imaging
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