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Clinical-Radiomics Nomogram from T1W, T1CE, and T2FS MRI for Improving Diagnosis of Soft-Tissue Sarcoma

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A Correction to this article was published on 30 August 2022

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Abstract

Purpose

To compare values of multiparametric magnetic resonance imaging (MRI) sequences and propose clinical-radiomics nomogram for diagnosis of soft-tissue sarcoma (STS).

Procedures

This study enrolled 148 patients from Dec. 2017 to Feb. 2021. All patients underwent T1-weighted (T1W), contrast-enhanced T1-weighted (T1CE), and T2-weighted fat-suppressed (T2FS) MRI scans. A total of 1967 radiomic features were extracted from the segmented regions of interest (ROIs) in each MRI sequence. Highly diagnostic radiomic features were selected with Mann–Whitney U test, elastic net, and Akaike’s information criterion (AIC) based on MRI images. Logistical regression was used to build Rad scores. Clinical factors were analyzed using the chi-square test or Mann–Whitney U test. The performance of the Rad scores was judged using the area under the receiver operating characteristic area under the curve (ROC AUC), sensitivity, specificity, and accuracy. The nomogram was developed by integrating the Rad score and the most important clinical factor.

Results

By combining the three MRI sequences, the Rad-Com was developed consisting of twelve features selected by with Mann–Whitney U test, elastic net, and AIC: four from T1W, three from TICE, and five from T2FS MRI. The margin (P < 0.05) demonstrated a statistically significant difference between patients with benign and malignant soft-tissue tumors (STT). The nomogram was constructed by integrating the Rad-Com and margin, which yielded favorable diagnostic AUCs of 0.919 (sensitivity (Sen) = 0.784, specificity (Spe) = 0.936) and 0.913 (Sen = 0.923, Spe = 0.792) in the training and validation cohort.

Conclusion

The proposed nomogram may have potential as a noninvasive marker for STS diagnosis.

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Funding

The study was supported by China National Natural Science Foundation (31770147), Project of Pneumoconiosis Prevention and Control of China's coal mines foundation (201909J033) and Natural Science Foundation of Liaoning Province (2021-MS-205).

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Author notes

  1. Zhibin Yue, Xiaoyu Wang and Yan Wang contributed equally to this work.

Authors and Affiliations

  1. School of Intelligent Medicine, China Medical University, Liaoning, 110122, People’s Republic of China

    Zhibin Yue & Yan Wang

  2. Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People’s Republic of China

    Xiaoyu Wang

  3. Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, 110004, People’s Republic of China

    Hongbo Wang

  4. Department of Scientific Research and Academic, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning, 110042, People’s Republic of China

    Wenyan Jiang

Authors
  1. Zhibin Yue
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  4. Hongbo Wang
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Contributions

Zhibin Yue: conceptualization, methodology, software, writing — original draft, visualization. Xiaoyu Wang: data curation, methodology, writing — review and editing, visualization. Yan Wang: visualization, writing — original draft, validation, investigation. Hongbo Wang: investigation, supervision, software, review and editing. Wenyan Jiang: validation, writing — review and editing, supervision, conceptualization, methodology.

Corresponding authors

Correspondence to Hongbo Wang or Wenyan Jiang.

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Yue, Z., Wang, X., Wang, Y. et al. Clinical-Radiomics Nomogram from T1W, T1CE, and T2FS MRI for Improving Diagnosis of Soft-Tissue Sarcoma. Mol Imaging Biol 24, 995–1006 (2022). https://doi.org/10.1007/s11307-022-01751-z

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