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Identification of Fatty Acids in Bacillus cereus
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The value of different

Hoda Anwar1, Thomas J Vogl2, Mahasen A Abougabal3

  • 1Nuclear Medicine Unit, Kasr Al-Ainy Center of Clinical Oncology and Nuclear Medicine, Faculty of Medicine-Cairo University, Cairo, Egypt. hoda.nagui@gmail.com.

Annals of Nuclear Medicine
|September 17, 2018
PubMed
Summary

Baseline 18F-FDG PET scan parameters, particularly metabolic tumor volume (MTV), can predict recurrence in patients with stage I non-small cell lung cancer (NSCLC) after surgery. This helps identify high-risk patients who may benefit from further treatment.

Keywords:
18F-FDG PET/CTMTVNSCLCRecurrenceSUVmaxTLG

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Area of Science:

  • Oncology
  • Nuclear Medicine
  • Radiomics

Background:

  • Postoperative chemotherapy for completely resected stage I non-small cell lung cancer (NSCLC) remains controversial.
  • Identifying patients at high risk for recurrence is crucial for guiding adjuvant treatment decisions.

Purpose of the Study:

  • To evaluate baseline 18F-FDG PET parameters for predicting recurrence in stage I NSCLC patients post-surgery.
  • To determine if these parameters can identify patients who would benefit from further treatment.

Main Methods:

  • Retrospective analysis of 49 stage I NSCLC patients who underwent baseline 18F-FDG PET/CT and complete resection.
  • Measurement of SUVmax, Metabolic Tumor Volume (MTV), and Total Lesion Glycolysis (TLG) at baseline.
  • Recurrence prediction using ROC curve analysis and Kaplan-Meier survival analysis based on calculated cut-off values.

Main Results:

  • Metabolic tumor volume (MTV) demonstrated the highest predictive performance (AUC=0.825) for recurrence.
  • Volumetric parameters (MTV and TLG) showed better predictive performance than SUVmax.
  • All studied PET parameters (SUVmax, MTV, TLG) were statistically significant in predicting recurrence.

Conclusions:

  • Baseline 18F-FDG PET-derived parameters are predictive of recurrence in surgically treated stage I NSCLC.
  • MTV emerged as the most significant parameter for predicting recurrence in this patient cohort.