Prognostic Value of Tumor Dissemination (Dmax) Derived from Basal 18F-FDG Positron Emission Tomography/Computed Tomography in Patients with Advanced Non-Small-Cell Lung Cancer
<b>Objectives</b>: The aim of the present study was to test whether a parameter reflecting tumor dissemination (Dmax), derived from basal 18F-FDG PET/CT, may predict clinical outcome in patients with advanced non-small-cell lung cancer (NSCLC). <b>Methods</b>: A total of 78 patients (55 men, 23 women) with stage III and IV NSCLC who had undergone whole-body 18F-FDG PET/CT scan at diagnosis were included in this study. Imaging parameters of primary lung tumors along with total MTV (MTV<sub>TOT</sub>) and whole-body TLG (TLG<sub>WB</sub>) of all malignant lesions were determined. Moreover, the largest distance between two 18F-FDG avid lesions (Dmax) in each patient was measured. Univariate and multivariate analyses of clinical and imaging variables were performed followed by overall survival (OS) curves. <b>Results</b>: A total of 441 lesions were analyzed, including 78 primary tumors, 174 metastatic lymph nodes, and 189 distant metastases. In primary tumors, the average values of SUVmax, SUVmean, MTV, and TLG were 11.80 ± 5.73, 5.37 ± 2.09, 60.61 ± 102.57 mL, and 340.36 ± 558.40 g, respectively. The mean value of Dmax was 29.98 ± 20.98 cm, whereas the average values of MTV<sub>TOT</sub> and TLG<sub>WB</sub> were 155.90 ± 176.94 mL and 851.08 ± 1032.17 g, respectively. In the univariate analysis, OS was predicted by MTV<sub>TOT</sub> (<i>p</i> = 0.0145), TLG<sub>WB</sub> (<i>p</i> = 0.0518), Dmax (<i>p</i> = 0.0031), and stage (<i>p</i> = 0.0130), whereas in the multivariate analysis, only Dmax was retained in the model (χ<sup>2</sup> = 7.3130, <i>p</i> = 0.0068). In particular, a high Dmax value indicates a worse prognosis. Moreover, the combination of Dmax with MTV<sub>TOT</sub> was able to improve the prognostic stratification of patients with advanced stages of NSCLC. <b>Conclusions</b>: Dmax, by reflecting tumor dissemination throughout the body, can predict overall survival in NSCLC patients.
Authors
- Sara Pellegrino (ORCID: https://orcid.org/0000-0002-2325-3583)
- Rocco Morra (ORCID: https://orcid.org/0000-0002-2516-076X)
- Silvana Del Vecchio (ORCID: https://orcid.org/0000-0002-5466-1213)
- Roberto Bianco (ORCID: https://orcid.org/0000-0003-3311-260X)
- Rosa Fonti (ORCID: https://orcid.org/0000-0002-3385-3013)
- Alberto Servetto (ORCID: https://orcid.org/0000-0003-3624-224X)
- Erica Di Donna
Institutions
- University of Naples Federico II (IT)
Publication Details
- Journal
- Biomedicines
- Published
- 2025-02-15
- DOI
- https://doi.org/10.3390/biomedicines13020477
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.16
Funders
- Università degli Studi di Napoli Federico II
- European Commission
- Associazione Italiana per la Ricerca sul Cancro