Value of Intratumoral Metabolic Heterogeneity and Quantitative 18F-FDG PET/CT Parameters to Predict Prognosis in Patients With HPV-Positive Primary Oropharyngeal Squamous Cell Carcinoma

Abstract

Objective To evaluate the impact of intra-tumoral metabolic heterogeneity and quantitative FDG-PET/CT imaging parameters for predicting patient outcomes in primary oropharyngeal squamous cell cancer (OPSCC). METHOD AND MATERIALS We retrospective investigated 105 patients with HPV-positive OPSCC. Maximum standardized uptake value (SUVmax,), and metabolic tumor volume (MTV)were measured for the primary tumors and when available for the metastatic sites. Primary tumor intra-tumoral metabolic heterogeneity was calculated as the area under a cumulative SUV-volume Histograms curve (AUC-CSH). The median follow-up time was 35.4 months (range 3-92 months). Outcome endpoint was event free survival (EFS). Kaplan–Meier survival plots and Cox regression analyses were performed. RESULTS Of the 105 patients included, 19 patients relapsed and 11 deceased during the study period. AUC-CSH indexes were associated with EFS using PET gradient-based (p=0.034) and 50%-Threshold (p=0.02) segmentation methods, on multivariate analysis. Kaplan–Meier survival analysis using optimum cutoff of 16.7 SUVmax and 12.7 ml total MTV were significant predictors of EFS. Combining SUVmax and AUC-CSH index in three subgroups, patients with higher intra-tumoral heterogeneity and higher SUVmax were associated with worse outcome (log-rank p=0.026). Similarly, patients with higher intra-tumoral heterogeneity tumors and higher MTV had worse prognosis (log-rank, p= 0.022). CONCLUSION Intra-tumoral metabolic heterogeneity using FDG-PET was a prognostic factor for EFS in patients with primary HPV (+) OPSCC. The combined predictive effect of FDG avidity, metabolic tumor burden and intra-tumoral heterogeneity provided prognostic survival information in these patients.

Practical no-gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

“…These methods were chosen due to their wide use in segmenting FDG-PET images of patients with head-and-neck cancer. 30,33,[49][50][51][52][53] The wide usage of these methods is due to their ease of deployment and their availability in clinical workstations, such as MIM. Our study concluded that the 40% SUV SUV max thresholding method was the most precise of the considered segmentation methods.…”

“…Similarly, the maximum measured MTV values were between 90 and 170 cc. Using this information, the search ranges for the upper and lower value of the GBD were set to [2.4, 3.0] cc and [90, 170] cc, respectively, and the search ranges for the bias and standard deviation of the noise terms were set to [−10; 10] cc and [1,10] cc, respectively.…”

“…A particularly important application is quantitative imaging biomarkers (QIBs). Various QIBs are being explored for clinical usage, including tumor standardized uptake value (SUV), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) measured from 18 F-Fluoro-2-deoxyglucose positron emission tomography (18F-FDG PET), [2][3][4][5][6][7][8][9][10] apparent diffusion coefficient measured with diffusion magnetic resonance imaging, 11,[12][13] and dopamine transporter using single-photon emission computed tomography. 14 A major challenge in QIB development is that the quantitative metrics should be measured precisely.…”

Volumetric 18F‐FDG‐PET parameters as predictors of locoregional failure in low‐risk HPV‐related oropharyngeal cancer after definitive chemoradiation therapy

“…As such, the prognostic value of SUV max in head and neck cancer is controversial. [16][17][18][19][20][21][22] Recently, volumetric PET parameters, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), have been heavily studied. 23 MTV is the volume of tumor that shows FDG avidity and TLG is a product of MTV and mean SUV.…”

No-gold-standard evaluation of image-acquisition methods using patient data

“…the measurement and use of numerical/statistical features from medical images to facilitate clinical decision making 1,2 , is finding applications in many diagnostic and therapeutic procedures. For example, the absorbed organ doses measured using single-photon emission computed tomography (SPECT) for dosimetry 3 , imaging biomarkers to predict and monitor cancer response such as metabolic tumor volume and total lesion glycolysis measured from positron emission tomography (PET) 4–7 , tumor size measured with magnetic resonance imaging (MRI) 8 , apparent diffusion constant measured with diffusion MRI 9–12 , myocardial blood flow measured using PET to diagnose cardiac diseases 13,14 , and bone-mineral density estimated using dual x-ray absorptiometry for diagnosing osteoporosis 15 .…”