Challenging the significance of SUV-based parameters in a large-scale retrospective study on lung lesions

Although many well-known factors affect the maximum standardized uptake value (SUVmax), it remains the most requested and used parameter, especially among clinicians, despite other parameters, such as the standardized uptake value corrected for lean body mass and the metabolic tumor volume, being proven to be less sensitive to the same factors, more robust, and eventually more informative. This study intends to provide robust evidence regarding the diagnostic and prognostic value of SUVmax in a large cohort of subjects with suspected malignant lung nodules imaged by [¹⁸F]FDG PET/CT.

We performed a retrospective analysis of patients with suspected/confirmed primary lung tumours undergoing [18F]FDG PET/CT. The sample size was 567 patients. Demographics, imaging, surgical, histological, and follow-up data were collected. SUVmax was analysed according to histology, stage, scanner, and outcome. The impact on measured values of different reconstruction protocols was assessed. All potential predictors of patients' outcome were assessed.

91% cases were primary lung tumours. Lung benign nodules or metastases accounted for 5% and 4% of cases. Most patients presented with adenocarcinoma (70%) and stage I disease (51%); 144 patients relapsed and 55 died. SUVmax failed to effectively differentiate benign lesions from primary tumours or metastases. Stage I patients presented lower SUVmax. SUVmax significantly correlated with patient weight, injected [¹⁸F]FDG activity, and lesion size and differed between reconstructions' protocols. Survival analyses revealed no independent prognostic significance for SUVmax in progression-free after adjusting for other variables. SUVmax correlated with overall survival, disease stage and tumour histotype.

Our study confirms that SUVmax, though widely employed, present relevant limitations in discriminating between benign lesion and lung cancer, in classifying cancer histotypes, and in predicting patient outcomes independently. Known influencing factors significantly impact on numerical values, thus SUV values should be regarded with caution in clinical practice.