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Purpose <p>Deauville Score (DS) assessment from [<sup>18</sup>F]FDG PET/CT in multiple myeloma (MM) relies on visual interpretation, limiting reproducibility. This study aimed to develop an automated pipeline for standardised DS computation, evaluate the prognostic value of DS at five anatomical sites, and predict progression-free survival (PFS) by integrating DS with copy number alterations (CNA) and clinical variables.</p> Methods <p>A retrospective cohort of 165 newly diagnosed MM patients with baseline FDG PET/CT, CNA profiling, and blood tests was analysed. An automated pipeline computed DS fully automatically for vertebral bone marrow (BM) and long bones (LB), and semi-automatically for focal (FL), paramedullary (PM), and extramedullary (EM) lesions. DS were subdivided into absent (1), low (2–3), and high (4–5) groups and compared via log-rank test. A penalised Cox model with 12 covariates (five DS, three CNAs, haemoglobin, platelet count, age, sex) was evaluated via nested cross-validation for PFS prediction. For inference on individual prognostic contributions, an unpenalised multivariable Cox model was fitted on the full cohort.</p> Results <p>In univariate analyses, high LB, PM and EM DS were significantly associated with shorter PFS. The penalised Cox model achieved a C-index of 0.710 [95% CI: 0.689–0.732] in predicting the risk of progression. In the multivariable analysis, age, haemoglobin, BM DS, PM DS and amp(1q) were identified as independent prognostic factors.</p> Conclusion <p>Automated DS computation from baseline FDG PET/CT is feasible and, combined with genomic and clinical data, enables a reproducible multimodal approach to prognostic stratification in MM. The pipeline for DS computation is publicly available as an open-source tool (autoDS-PET) at <a href="https://github.com/Sara-Peluso/autoDS-PET">https://github.com/Sara-Peluso/autoDS-PET</a>.</p>

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Automated Deauville Score computation from baseline [¹⁸F]FDG PET/CT predicts progression-free survival in multiple myeloma: a radiogenomic framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月30日

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  • Sara Peluso,
  • Stefano Polizzi,
  • Lisa Pagnini,
  • Martina Tarozzi,
  • Ettore Rocchi,
  • Valentino Dragonetti,
  • Vincenza Solli,
  • Daniele Dall’Olio,
  • Marco Talarico,
  • Carolina Terragna,
  • Claudia Sala,
  • Elena Zamagni,
  • Stefano Fanti,
  • Cristina Nanni,
  • Gastone Castellani

摘要

Purpose

Deauville Score (DS) assessment from [18F]FDG PET/CT in multiple myeloma (MM) relies on visual interpretation, limiting reproducibility. This study aimed to develop an automated pipeline for standardised DS computation, evaluate the prognostic value of DS at five anatomical sites, and predict progression-free survival (PFS) by integrating DS with copy number alterations (CNA) and clinical variables.

Methods

A retrospective cohort of 165 newly diagnosed MM patients with baseline FDG PET/CT, CNA profiling, and blood tests was analysed. An automated pipeline computed DS fully automatically for vertebral bone marrow (BM) and long bones (LB), and semi-automatically for focal (FL), paramedullary (PM), and extramedullary (EM) lesions. DS were subdivided into absent (1), low (2–3), and high (4–5) groups and compared via log-rank test. A penalised Cox model with 12 covariates (five DS, three CNAs, haemoglobin, platelet count, age, sex) was evaluated via nested cross-validation for PFS prediction. For inference on individual prognostic contributions, an unpenalised multivariable Cox model was fitted on the full cohort.

Results

In univariate analyses, high LB, PM and EM DS were significantly associated with shorter PFS. The penalised Cox model achieved a C-index of 0.710 [95% CI: 0.689–0.732] in predicting the risk of progression. In the multivariable analysis, age, haemoglobin, BM DS, PM DS and amp(1q) were identified as independent prognostic factors.

Conclusion

Automated DS computation from baseline FDG PET/CT is feasible and, combined with genomic and clinical data, enables a reproducible multimodal approach to prognostic stratification in MM. The pipeline for DS computation is publicly available as an open-source tool (autoDS-PET) at https://github.com/Sara-Peluso/autoDS-PET.