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In this work we present a methodology able to use harmonized PET/CT imaging in dose painting by number (DPBN) approach by means of a robust and accurate treatment planning system. Image processing and treatment planning were performed by using a Matlab-based platform, called CARMEN, in which a full Monte Carlo simulation is included. Linear programming formulation was developed for a voxel-by-voxel robust optimization and a specific direct aperture optimization was designed for an efficient adaptive radiotherapy implementation. DPBN approach with our methodology was tested to reduce the uncertainties associated with both, the absolute value and the relative value of the information in the functional image. For the same H&N case, a single robust treatment was planned for dose prescription maps corresponding to standardized uptake value distributions from two different image reconstruction protocols: One to fulfill EARL accreditation for harmonization of [18F]FDG PET/CT image, and the other one to use the highest available spatial resolution. Also, a robust treatment was planned to fulfill dose prescription maps corresponding to both approaches, the dose painting by contour based on volumes and our voxel-by-voxel DPBN. Adaptive planning was also carried out to check the suitability of our proposal. Different plans showed robustness to cover a range of scenarios for implementation of harmonizing strategies by using the highest available resolution. Also, robustness associated to discretization level of dose prescription according to the use of contours or numbers was achieved. All plans showed excellent quality index histogram and quality factors below 2%. Efficient solution for adaptive radiotherapy based directly on changes in functional image was obtained. We proved that by using voxel-by-voxel DPBN approach it is possible to overcome typical drawbacks linked to PET/CT images, providing to the clinical specialist confidence enough for routinely implementation of functional imaging for personalized radiotherapy. [ABSTRACT FROM AUTHOR]

Introduction: Radiomics extracted from prostate-specific membrane antigen (PSMA)-PET modeled with machine learning (ML) may be used for prediction of disease risk. However, validation of previously proposed approaches is lacking. We aimed to optimize and validate ML models based on 18F-DCFPyL-PET radiomics for the prediction of lymph-node involvement (LNI), extracapsular extension (ECE), and postoperative Gleason score (GS) in primary prostate cancer (PCa) patients. Methods: Patients with intermediate- to high-risk PCa who underwent 18F-DCFPyL-PET/CT before radical prostatectomy with pelvic lymph-node dissection were evaluated. The training dataset included 72 patients, the internal validation dataset 24 patients, and the external validation dataset 27 patients. PSMA-avid intra-prostatic lesions were delineated semi-automatically on PET and 480 radiomics features were extracted. Conventional PET-metrics were derived for comparative analysis. Segmentation, preprocessing, and ML methods were optimized in repeated 5-fold cross-validation (CV) on the training dataset. The trained models were tested on the combined validation dataset. Combat harmonization was applied to external radiomics data. Model performance was assessed using the receiver-operating-characteristics curve (AUC). Results: The CV-AUCs in the training dataset were 0.88, 0.79 and 0.84 for LNI, ECE, and GS, respectively. In the combined validation dataset, the ML models could significantly predict GS with an AUC of 0.78 (p<0.05). However, validation AUCs for LNI and ECE prediction were not significant (0.57 and 0.63, respectively). Conventional PET metrics-based models had comparable AUCs for LNI (0.59, p>0.05) and ECE (0.66, p>0.05), but a lower AUC for GS (0.73, p<0.05). In general, Combat harmonization improved external validation AUCs (-0.03 to +0.18). Conclusion: In internal and external validation, 18F-DCFPyL-PET radiomics-based ML models predicted high postoperative GS but not LNI or ECE in intermediate- to high-risk PCa. Therefore, the clinical benefit seems to be limited. These results underline the need for external and/or multicenter validation of PET radiomics-based ML model analyses to assess their generalizability. [ABSTRACT FROM AUTHOR]

Standardized uptake values (SUVs) are the most widely used quantitative imaging biomarkers in PET. It is important to evaluate the variability and repeatability of measured SUVs. Phantom studies seem to be essential for this purpose; however, repetitive phantom scanning is not recommended due to the decay of radioactivity. In this study, we performed count-based image reconstruction to avoid the influence of decay using two different PET/CT scanners. By adjusting the ratio of 18F-fluorodeoxyglucose solution to tap water, a NEMA IEC body phantom was set for SUVs of 4.0 inside six hot spheres. The PET data were obtained using two scanners (Aquiduo and Celesteion; Toshiba Medical Systems, Tochigi, Japan). We set the start time for image reconstruction when the total radioactivity in the phantom was 2.53 kBq/cc, and employed the counts of the first 2-min acquisition as the standard. To maintain the number of counts for each image, we set the acquisition time for image reconstruction depending on the decay of radioactivity. We obtained 50 images, and calculated the SUVmax and SUVpeak of all six spheres in each image. The average values of the SUVmax were used to calculate the recovery coefficients to compare those measured by the two different scanners. Bland-Altman analyses of the SUVs measured by the two scanners were also performed. The measured SUVs using the two scanners exhibited a 10–30% difference, and the standard deviation (SD) of the measured SUVs was between 0.1–0.2. The Celesteion always exhibited higher values than the Aquiduo. The smaller sphere exhibited a larger SD, and the SUVpeak had a smaller SD than the SUVmax. The Bland-Altman analyses showed poor agreement between the SUVs measured by the two scanners. The recovery coefficient curves obtained from the two scanners were considerably different. The Celesteion exhibited higher recovery coefficients than the Aquiduo, especially at approximately 20-mm-diameter. Additionally, the curves were lower than those calculated from the standard 30-min acquisition images. We propound count-based image reconstruction to evaluate the variability and repeatability of measured SUVs. These results are also applicable for the standardization and harmonization of SUVs in multi-institutional studies. [ABSTRACT FROM AUTHOR]