ABSTRACT
Objective: To evaluate the performance of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET/CT) radiomic features to predict overall survival (OS) in patients with locally advanced uterine cervical carcinoma. Methods: Longitudinal and retrospective study that evaluated 50 patients with cervical epidermoid carcinoma (clinical stage IB2 to IVA according to FIGO). Segmentation of the 18F-FDG PET/CT tumors was performed using the LIFEx software, generating the radiomic features. We used the Mann-Whitney test to select radiomic features associated with the clinical outcome (death), excluding the features highly correlated with each other with Spearman correlation. Subsequently, ROC curves and a Kaplan-Meier analysis were performed. A p-value < 0.05 were considered significant. Results: The median follow-up was 23.5 months and longer than 24 months in all surviving patients. Independent predictors for OS were found-SUVpeak with an AUC of 0.74, sensitivity of 77.8%, and specificity of 72.7% (p = 0.006); and the textural feature gray-level run-length matrix GLRLM_LRLGE, with AUC of 0.74, sensitivity of 72.2%, and specificity of 81.8% (p = 0.005). When we used the derived cut-off points from these ROC curves (12.76 for SUVpeak and 0.001 for GLRLM_LRLGE) in a Kaplan-Meier analysis, we can see two different groups (one with an overall survival probability of approximately 90% and the other with 30%). These biomarkers are independent of FIGO staging. Conclusion: By radiomic 18F-FDG PET/CT data analysis, SUVpeak and GLRLM_LRLGE textural feature presented the best performance to predict OS in patients with cervical cancer undergoing chemo-radiotherapy and brachytherapy.
ABSTRACT
PURPOSE: Methodologies for optimization of SPECT image acquisition can be challenging due to imaging throughput, physiological bias, and patient comfort constraints. We evaluated a vendor-independent method for simulating lower count image acquisitions. METHODS: We developed an algorithm that recombines the ECG-gated raw data into reduced counting acquisitions. We then tested the algorithm to simulate reduction of counting statistics from phantom SPECT image acquisition, which was synchronized with an ECG simulator. The datasets were reconstructed with a resolution recovery algorithm and the summed stress score (SSS) was assessed by three readers (two experts and one automatic). RESULTS: The algorithm generated varying counting levels, simulating multiple examinations at the same time. The error between the expected and the simulated countings ranged from approximately 5% to 10% for the ungated simulations and 0% for the gated simulations. CONCLUSIONS: The vendor-independent algorithm successfully generated lower counting statistics datasets from single-gated SPECT raw data. This method can be readily implemented for optimal SPECT research aiming to lower the injected activity and/ or to shorten the acquisition time.
