A [18F]FDG PET based nomogram to predict cancer-associated cachexia and survival outcome: A multi-center study

  • 0Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.

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Summary

This summary is machine-generated.

This study developed a nomogram using [<sup>18</sup>F]FDG PET scans and blood tests to predict cancer-associated cachexia. The tool also helps assess prognosis, offering valuable insights for patient care.

Area Of Science

  • Oncology
  • Nuclear Medicine
  • Biochemistry

Background

  • Cancer cachexia significantly worsens prognosis and survival in cancer patients.
  • Early detection and accurate prognosis prediction for cachexia are critical clinical challenges.

Purpose Of The Study

  • To develop and validate a nomogram integrating [<sup>18</sup>F]fluoro-2-deoxy-D-glucose ([<sup>18</sup>F]FDG) PET findings and clinical biochemistry for predicting cancer-associated cachexia.
  • To assess the nomogram's prognostic value for overall survival in cancer patients.

Main Methods

  • Retrospective analysis of 658 cancer patients from two centers.
  • Logistic regression to identify predictors (age, hemoglobin, liver SUVmax, subcutaneous fat SUVmin).
  • External validation of the nomogram using AUC, calibration, and decision curve analyses; Cox regression and Kaplan-Meier for survival analysis.

Main Results

  • The combined nomogram demonstrated good predictive performance for cachexia (AUC=0.807 in development, 0.726 in validation).
  • Key predictors included age, hemoglobin, maximum standardized uptake value (SUV) of the liver, and minimum SUV of subcutaneous fat.
  • The nomogram effectively categorized overall survival, indicating prognostic value.

Conclusions

  • A nomogram integrating [<sup>18</sup>F]FDG PET data and routine blood tests can effectively predict cancer-associated cachexia.
  • This tool aids in assessing cachexia and its impact on cancer patient prognosis.