Macrovasculature and Positron Emission Tomography (PET) Standardized Uptake Value in Patients with Lung Cancer

Overview

Journal Med Phys

Specialty Biophysics

Date 2021 Aug 12

PMID 34382221

Citations 1

Authors

Jiantao Pu

Joseph K Leader

Dongning Zhang

Cameron A Beeche

Jacob Sechrist

Arjun Pennathur

Liza C Villaruz

David Wilson

Affiliations

Soon will be listed here.

Abstract

Purpose: To investigate the relationship between macrovasculature features and the standardized uptake value (SUV) of positron emission tomography (PET), which is a surrogate for the metabolic activity of a lung tumor.

Methods: We retrospectively analyzed a cohort of 90 lung cancer patients who had both chest CT and PET-CT examinations before receiving cancer treatment. The SUVs in the medical reports were used. We quantified three macrovasculature features depicted on CT images (i.e., vessel number, vessel volume, and vessel tortuosity) and several tumor features (i.e., volume, maximum diameter, mean diameter, surface area, and density). Tumor size (e.g., volume) was used as a covariate to adjust for possible confounding factors. Backward stepwise multiple regression analysis was performed to develop a model for predicting PET SUV from the relevant image features. The Bonferroni correction was used for multiple comparisons.

Results: PET SUV was positively correlated with vessel volume (R = 0.44, p < 0.001) and vessel number (R = 0.44, p < 0.001) but not with vessel tortuosity (R = 0.124, p > 0.05). After adjusting for tumor size, PET SUV was significantly correlated with vessel tortuosity (R = 0.299, p = 0.004) and vessel number (R = 0.224, p = 0.035), but only marginally correlated with vessel volume (R = 0.187, p = 0.079). The multiple regression model showed a performance with an R-Squared of 0.391 and an adjusted R-Squared of 0.355 (p < 0.001).

Conclusions: Our investigations demonstrate the potential relationship between macrovasculature and PET SUV and suggest the possibility of inferring the metabolic activity of a lung tumor from chest CT images.

Citing Articles

Nanoparticle trends and hotspots in lung cancer diagnosis from 2006-2023: a bibliometric analysis.

Yilun W, Yaojing Z, Hongcan S Front Oncol. 2025; 14:1453021.

PMID: 39759141 PMC: 11695240. DOI: 10.3389/fonc.2024.1453021.

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