Semi-quantitative analysis of pre-treatment morphological and intratumoral characteristics using 18F-fluorodeoxyglucose positron-emission tomography as predictors of treatment outcome in nasal and paranasal squamous cell carcinoma

Abstract

Background: To investigate the utility of quantitative morphological and intratumoral characteristics obtained by 18 F-fluorodeoxyglucose positron-emission tomography/computed tomography (FDG-PET/CT) for the prediction of treatment outcome in patients with nasal or paranasal cavity squamous cell carcinoma (SCC). Methods: Twenty-four patients with nasal or paranasal cavity SCC who received curative non-surgical therapy (a combination of super-selective arterial cisplatin infusion and radiotherapy) were retrospectively analyzed. From pre-treatment FDG-PET data, a total of 13 parameters of quantitative morphological characteristics (tumor volume, surface area and sphericity), intratumoral characteristics (the maximum and mean standard uptake value, three intratumoral histogram and four textural parameters) and total lesion glycolysis (TLG) were respectively calculated. Information regarding the treatment outcome was determined from the histological diagnosis or clinical follow-up. Each of the 13 quantitative parameters as well as T-and N-stage was assessed for its relation to treatment outcome of local control or failure. Results: In univariate analysis, significant differences in surface area and sphericity between the local control and failure groups were observed. The receiver operating characteristic (ROC) curve analysis showed that sphericity had the highest accuracy of 0.88. In the multivariate analysis, sphericity was revealed as an independent predictor of the local control or failure. Conclusions: The quantitative parameters of sphericity are useful to predict the treatment outcome in patients with nasal or paranasal SCC.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

“…We collected information from 60 articles that satisfy our selection criteria and evaluated head and neck cancer [ 28 , 82 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , ...…”

“…Over the last years, there has been an increasing interest in extracting quantitative information from PET images using image analysis [ 26 , 27 ]. Thus, semi-quantitative parameters such as the standard uptake value (SUV), the metabolic tumor volume (MTV) or the total lesion glycolysis (TLG) [ 28 ] obtained from 18 F-fluorodeoxyglucose PET (FDG-PET) images, have been demonstrated to provide relatively objective information useful for the diagnosis, earlier evaluation and monitoring of treatment response [ 24 , 25 , 28 ]. These parameters are now fully incorporated into clinical guidelines and commonly measured at most hospitals in developed countries [ 29 ].…”

Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study

“…Quantitative morphological shape characteristics of tumors (i.e., sphericity) were recently reported to be useful for the prediction of treatment outcomes in head and neck SCC as an advanced morphological parameter compared to TNM stage and tumor size [5,23]. Intratumoral heterogeneity was also indicated as a prognostic factor in head and neck SCC patients [26,27]; in these reports, tumors with high degree of intratumoral heterogeneity demonstrated a tendency for poor prognoses.…”

“…For the analysis of morphological characteristics, we calculated the tumor volume and tumor surface area from the morphological shape of all selected tumor voxels with the delineated tumor ROI on Fs-T2WI. The sphericity of each tumor was calculated by a previously reported method [23]. For the intra-tumoral characteristics analysis, we calculated the relative Fs-T2WI mean signal intensity (=mean signal on tumor ROI/ the medial pterygoid muscle ROI), the coefficient of variance (CV) in the tumor ROI and the textural parameters within the ROI.…”

“…In using MR imaging, most abovementioned tumor characteristics can be obtained completely non-invasively without the use of contrast agent and radiation exposure. Several of these MR derived parameters have been reported as independent predictors for treatment outcomes [4,8,20,21,22,23]. If we can efficiently integrate these data, prediction of the treatment outcome with high diagnostic accuracy may be obtained.…”