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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1433196
This article is part of the Research Topic Radiomics and artificial intelligence in oncology imaging View all articles

Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study

Provisionally accepted
Bo Wang Bo Wang 1Hongwei Guo Hongwei Guo 2Meng Zhang Meng Zhang 1Yonghua Huang Yonghua Huang 3Lisha Duan Lisha Duan 4Chencui Huang Chencui Huang 5Jun Xu Jun Xu 6*Hexiang Wang Hexiang Wang 1*
  • 1 Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China
  • 2 Qingdao University, Qingdao, China
  • 3 Puyang Oil Field General Hospital, Puyang, Henan, China
  • 4 Third Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China
  • 5 Other, Beijing, China
  • 6 Department of Radiology, Peking University Third Hospital, Beijing, China

The final, formatted version of the article will be published soon.

    Abstract Background: Accurate identification of pathologic grade before operation is helpful for guiding clinical treatment decisions and improving the prognosis for soft tissue sarcoma (STS). Purpose: To construct and assess a magnetic resonance imaging (MRI)-based radiomics nomogram incorporating intratumoral habitats (subregions of clusters of voxels containing similar features) and peritumoral features for the preoperative prediction of the pathological grade of STS. Methods: The MRI data of 145 patients with STS (74 low-grade and 71 high-grade) from 4 hospitals were retrospectively collected, including enhanced T1-weighted and fat-suppressed-T2-weighted sequences. The patients were divided into training cohort (n = 102) and validation cohort (n = 43). K-means clustering was used to divide intratumoral voxels into three habitats according to signal intensity. A number of radiomics features were extracted from tumor-related regions to construct radiomics prediction signatures for seven subgroups. Logistic regression analysis identified peritumoral edema as an independent risk factor. A nomogram was created by merging the best radiomics signature with the peritumoral edema. We evaluated the performance and clinical value of the model using area under the curve (AUC), calibration curves, and decision curve analysis. Results: A multi-layer perceptron classifier model based on intratumoral habitats and peritumoral features combined gave the best radiomics signature, with an AUC of 0.856 for the validation cohort. The AUC of the nomogram in the validation cohort was 0.868, which was superior to the radiomics signature and the clinical model established by peritumoral edema.. The calibration curves and decision curve analyses revealed good calibration and a high clinical application value for this nomogram. Conclusion: The MRI-based nomogram is accurate and effective for predicting preoperative grading in patients with STS.

    Keywords: Soft Tissue Sarcoma, habitats, nomogram, Radiomics, Grade

    Received: 16 May 2024; Accepted: 22 Nov 2024.

    Copyright: © 2024 Wang, Guo, Zhang, Huang, Duan, Huang, Xu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Jun Xu, Department of Radiology, Peking University Third Hospital, Beijing, China
    Hexiang Wang, Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, China

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