Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study

Ann Nucl Med. 2024 Aug;38(8):647-658. doi: 10.1007/s12149-024-01936-2. Epub 2024 May 5.

Abstract

Objective: To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment.

Methods: We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively.

Results: In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively).

Conclusions: Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.

Keywords: 18F-FDG PET; Deep learning; Epidermal growth factor receptor; Lung adenocarcinoma; Prognosis.

Publication types

  • Validation Study

MeSH terms

  • Adenocarcinoma of Lung* / diagnostic imaging
  • Adenocarcinoma of Lung* / drug therapy
  • Adenocarcinoma of Lung* / genetics
  • Adenocarcinoma of Lung* / pathology
  • Adult
  • Aged
  • Aged, 80 and over
  • Deep Learning*
  • ErbB Receptors* / genetics
  • Female
  • Fluorodeoxyglucose F18*
  • Humans
  • Lung Neoplasms* / diagnostic imaging
  • Lung Neoplasms* / drug therapy
  • Lung Neoplasms* / genetics
  • Lung Neoplasms* / pathology
  • Lung Neoplasms* / therapy
  • Male
  • Middle Aged
  • Molecular Targeted Therapy
  • Mutation*
  • Positron-Emission Tomography
  • Prognosis
  • Protein Kinase Inhibitors / therapeutic use
  • Retrospective Studies

Substances

  • ErbB Receptors
  • Fluorodeoxyglucose F18
  • EGFR protein, human
  • Protein Kinase Inhibitors