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Objectives <p>This study aimed to identify quantitative MRI features through radiomic analysis and to develop predictive models for determining the histological grade of myxoid liposarcoma (MLS).</p> Materials and methods <p>This retrospective single-center study included 57 patients with histologically confirmed MLS (30 low-grade, 27 high-grade). Tumors were segmented and 107 radiomic features were extracted from&#xa0;T1-weighted imaging (WI), T2-WI, short tau inversion recovery (STIR), apparent diffusion coefficient (ADC) maps, and contrast-enhanced (CE) images with and without fat saturation (FS). Features showing statistical significance (<i>p</i> &lt; 0.05) were selected and used to develop predictive models, whose performance was assessed using cross-validation and reported as area under the curve (AUC).</p> Results <p>Mean age was 51.6 ± 14.7&#xa0;years (32 men, 25 women).&#xa0;Radiomic analysis identified three significant features for T1-WI and STIR and 19 for T2-WI. For CE-T1-WI, CE-T1-FS-WI, and CE-3D, four, six, and three features were significant, respectively. Models based on T2-WI and CE-3D achieved the highest performance (AUC up to 0.88). Additional models trained exclusively on institutional T1-WI and T2-WI showed reduced performance on external validation, although AUCs improved when applied to patients scanned with the same vendor.</p> Conclusion <p>Radiomic analysis of pre-treatment MRI shows promising results in predicting histological grade of MLS. This study is novel in addressing grading rather than diagnosis alone, a distinction with clear clinical relevance for treatment planning and prognostic assessment. In particular, models based on T2-WI may complement conventional imaging and histopathology by providing whole-tumor quantitative grading, while multicentric validation is required for clinical application.</p>

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MRI-based radiomic analysis for grading myxoid liposarcoma: a multisequence retrospective study

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Silvia Ruggeri,
  • Giuliana Roselli,
  • Roberto Scanferla,
  • Sebastiano Paolucci,
  • Annarita Palomba,
  • Daniela Greto,
  • Mauro Loi,
  • Francesco Muratori,
  • Guido Scoccianti,
  • Marco Bartolini,
  • Linda Calistri,
  • Lorenzo Livi,
  • Domenico Andrea Campanacci,
  • Vittorio Miele

摘要

Objectives

This study aimed to identify quantitative MRI features through radiomic analysis and to develop predictive models for determining the histological grade of myxoid liposarcoma (MLS).

Materials and methods

This retrospective single-center study included 57 patients with histologically confirmed MLS (30 low-grade, 27 high-grade). Tumors were segmented and 107 radiomic features were extracted from T1-weighted imaging (WI), T2-WI, short tau inversion recovery (STIR), apparent diffusion coefficient (ADC) maps, and contrast-enhanced (CE) images with and without fat saturation (FS). Features showing statistical significance (p < 0.05) were selected and used to develop predictive models, whose performance was assessed using cross-validation and reported as area under the curve (AUC).

Results

Mean age was 51.6 ± 14.7 years (32 men, 25 women). Radiomic analysis identified three significant features for T1-WI and STIR and 19 for T2-WI. For CE-T1-WI, CE-T1-FS-WI, and CE-3D, four, six, and three features were significant, respectively. Models based on T2-WI and CE-3D achieved the highest performance (AUC up to 0.88). Additional models trained exclusively on institutional T1-WI and T2-WI showed reduced performance on external validation, although AUCs improved when applied to patients scanned with the same vendor.

Conclusion

Radiomic analysis of pre-treatment MRI shows promising results in predicting histological grade of MLS. This study is novel in addressing grading rather than diagnosis alone, a distinction with clear clinical relevance for treatment planning and prognostic assessment. In particular, models based on T2-WI may complement conventional imaging and histopathology by providing whole-tumor quantitative grading, while multicentric validation is required for clinical application.