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<p>Immune checkpoint inhibitors (ICIs) have improved outcomes for patients with solid tumors, but reliable predictors of overall survival (OS) are limited. This retrospective study of 146 advanced solid tumor patients treated with ICIs aims to provide a nomogram to predict 1-year (1y) OS integrating body composition (BC) parameters with standard clinicopathological (CP) features. A two-stage approach was implemented: first random survival forest models were trained and tested to evaluate the prognostic value of (a) CP features alone, (b) BC metrics alone or as newly introduced BC scores, and (c) their combination. The best predictive performance (average cumulative AUC of 0.73 in test set) was achieved by combining 12 CP features with the BC score comprising intramuscolar adipose tissue content, visceral fat area index, and the visceral-to-subcutaneous fat area index ratio. Finally, a nomogram was developed with this feature set, offering a tool for personalized risk stratification and treatment planning (mean absolute error in calibration curve of 0.03 and overall AUC of 0.76). Integrating BC parameters with CP features substantially enhances 1y OS prediction in patients receiving ICIs.</p>

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Clinically interpretable nomogram combining body composition and clinicopathological features for one year survival prediction in advanced solid tumors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月12日

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  • Giulia Bruschi,
  • Francesco Paoloni,
  • Federica Pecci,
  • Elisabetta Tola,
  • Valeria Cognigni,
  • Tommaso Galassi,
  • Alessandra Borgheresi,
  • Luca Cantini,
  • Luca Santamaria,
  • Mariangela Gualtieri,
  • Valentina Lunerti,
  • Natalia Chiodi,
  • Veronica Agostinelli,
  • Marzia Di Pietro Paolo,
  • Agnese Sbrollini,
  • Andrea Agostini,
  • Giulia Mentrasti,
  • Salvatore Ficarra,
  • Giulia Mazzaschi,
  • Alessandro Parisi,
  • Riccardo Giampieri,
  • Kamal S. Saini,
  • Sebastiano Buti,
  • Marcello Tiseo,
  • Arianna Vignini,
  • Andrea Giovagnoni,
  • Laura Burattini,
  • Rossana Berardi

摘要

Immune checkpoint inhibitors (ICIs) have improved outcomes for patients with solid tumors, but reliable predictors of overall survival (OS) are limited. This retrospective study of 146 advanced solid tumor patients treated with ICIs aims to provide a nomogram to predict 1-year (1y) OS integrating body composition (BC) parameters with standard clinicopathological (CP) features. A two-stage approach was implemented: first random survival forest models were trained and tested to evaluate the prognostic value of (a) CP features alone, (b) BC metrics alone or as newly introduced BC scores, and (c) their combination. The best predictive performance (average cumulative AUC of 0.73 in test set) was achieved by combining 12 CP features with the BC score comprising intramuscolar adipose tissue content, visceral fat area index, and the visceral-to-subcutaneous fat area index ratio. Finally, a nomogram was developed with this feature set, offering a tool for personalized risk stratification and treatment planning (mean absolute error in calibration curve of 0.03 and overall AUC of 0.76). Integrating BC parameters with CP features substantially enhances 1y OS prediction in patients receiving ICIs.