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This study investigated whether radiomic features extracted from pretreatment [ 18 F]FDG PET could improve the prediction of both histopathologic tumor response and survival in patients with locally advanced cervical cancer (LACC) treated with neoadjuvant chemoradiotherapy followed by surgery compared with conventional PET parameters and histopathologic features. Methods: The medical records of all consecutive patients with LACC referred between July 2010 and July 2016 were reviewed. [ 18 F]FDG PET/CT was performed before neoadjuvant chemoradiotherapy. Radiomic features were extracted from the primary tumor volumes delineated semiautomatically on the PET images and reduced by factor analysis. A receiver-operating-characteristic analysis was performed, and conventional and radiomic features were dichotomized with Liu's method according to pathologic response (pR) and cancer-specific death. According to the study protocol, only areas under the curve of more than 0.70 were selected for further analysis, including logistic regression analysis for response prediction and Cox regression analysis for survival prediction. Results: A total of 195 patients fulfilled the inclusion criteria. At pathologic evaluation after surgery, 131 patients (67.2%) had no or microscopic (≤3 mm) residual tumor (pR0 or pR1, respectively); 64 patients (32.8%) had macroscopic residual tumor (>3 mm, pR2). With a median follow-up of 76.0 mo (95% CI, 70.7-78.7 mo), 31.3% of patients had recurrence or progression and 20.0% died of the disease. Among conventional PET parameters, SUV mean significantly differed between pathologic responders and nonresponders. Among radiomic features, 1 shape and 3 textural features significantly differed between pathologic responders and nonresponders. Three radiomic features significantly differed between presence and absence of recurrence or progression and between presence and absence of cancer-specific death. Areas under the curve were less than 0.70 for all parameters; thus, univariate and multivariate regression analyses were not performed. Conclusion: In a large series of patients with LACC treated with neoadjuvant chemoradiotherapy followed by surgery, PET radiomic features could not predict histopathologic tumor response and survival. It is crucial to further explore the biologic mechanism underlying imaging-derived parameters and plan a large, prospective, multicenter study with standardized protocols for all phases of the process of radiomic analysis to validate radiomics before its use in clinical routine., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [ 18 F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features., Methods: In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [ 18 F]DCFPyL PET-CT. Primary tumors were delineated using 50-70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC., Results: The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p < 0.01), nodal or distant metastasis (AUC 0.86 ± 0.14, p < 0.01), Gleason score (0.81 ± 0.16, p < 0.01), and ECE (0.76 ± 0.12, p < 0.01). The highest AUCs reached using standard PET metrics were lower than those of radiomics-based models. For LNI and metastasis prediction, PVC and a higher delineation threshold improved model stability. Machine learning pre-processing methods had a minor impact on model performance., Conclusion: Machine learning-based analysis of quantitative [ 18 F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice.

The sensitivity of radiomic features to several confounding factors, such as reconstruction settings, makes clinical use challenging. To investigate the impact of harmonized image reconstructions on feature consistency, a multicenter phantom study was performed using 3-dimensionally printed phantom inserts reflecting realistic tumor shapes and heterogeneity uptakes. Methods: Tumors extracted from real PET/CT scans of patients with non-small cell lung cancer served as model for three 3-dimensionally printed inserts. Different heterogeneity pattern were realized by printing separate compartments that could be filled with different activity solutions. The inserts were placed in the National Electrical Manufacturers Association image-quality phantom and scanned various times. First, a list-mode scan was acquired and 5 statistically equal replicates were reconstructed. Second, the phantom was scanned 4 times on the same scanner. Third, the phantom was scanned on 6 PET/CT systems. All images were reconstructed using EANM Research Ltd. (EARL)-compliant and locally clinically preferred reconstructions. EARL-compliant reconstructions were performed without (EARL1) or with (EARL2) point-spread function. Images were analyzed with and without resampling to 2-mm cubic voxels. Images were discretized with a fixed bin width (FBW) of 0.25 and a fixed bin number (FBN) of 64. The intraclass correlation coefficient (ICC) of each scan setup was calculated and compared across reconstruction settings. An ICC above 0.75 was regarded as high. Results: The percentage of features yielding a high ICC was largest for the statistically equal replicates (70%-91% for FBN; 90%-96% for FBW discretization). For scans acquired on the same system, the percentage decreased, but most features still resulted in a high ICC (FBN, 52%-63%; FBW, 75%-85%). The percentage of features yielding a high ICC decreased more in the multicenter setting. In this case, the percentage of features yielding a high ICC was larger for images reconstructed with EARL-compliant reconstructions: for example, 40% for EARL1 and 60% for EARL2 versus 21% for the clinically preferred setting for FBW discretization. When discretized with FBW and resampled to isotropic voxels, this benefit was more pronounced. Conclusion: EARL-compliant reconstructions harmonize a wide range of radiomic features. FBW discretization and a sampling to isotropic voxels enhances the benefits of EARL-compliant reconstructions., (© 2020 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: In longitudinal oncological and brain PET/CT studies, it is important to understand the repeatability of quantitative PET metrics in order to assess change in tracer uptake. The present studies were performed in order to assess precision as function of PET/CT system, reconstruction protocol, analysis method, scan duration (or image noise), and repositioning in the field of view., Methods: Multiple (repeated) scans have been performed using a NEMA image quality (IQ) phantom and a 3D Hoffman brain phantom filled with 18 F solutions on two systems. Studies were performed with and without randomly (< 2 cm) repositioning the phantom and all scans (12 replicates for IQ phantom and 10 replicates for Hoffman brain phantom) were performed at equal count statistics. For the NEMA IQ phantom, we studied the recovery coefficients (RC) of the maximum (SUV max ), peak (SUV peak ), and mean (SUV mean ) uptake in each sphere as a function of experimental conditions (noise level, reconstruction settings, and phantom repositioning). For the 3D Hoffman phantom, the mean activity concentration was determined within several volumes of interest and activity recovery and its precision was studied as function of experimental conditions., Results: The impact of phantom repositioning on RC precision was mainly seen on the Philips Ingenuity PET/CT, especially in the case of smaller spheres (< 17 mm diameter, P < 0.05). This effect was much smaller for the Siemens Biograph system. When exploring SUV max , SUV peak , or SUV mean of the spheres in the NEMA IQ phantom, it was observed that precision depended on phantom repositioning, reconstruction algorithm, and scan duration, with SUV max being most and SUV peak least sensitive to phantom repositioning. For the brain phantom, regional averaged SUVs were only minimally affected by phantom repositioning (< 2 cm)., Conclusion: The precision of quantitative PET metrics depends on the combination of reconstruction protocol, data analysis methods and scan duration (scan statistics). Moreover, precision was also affected by phantom repositioning but its impact depended on the data analysis method in combination with the reconstructed voxel size (tissue fraction effect). This study suggests that for oncological PET studies the use of SUV peak may be preferred over SUV max because SUV peak is less sensitive to patient repositioning/tumor sampling., (© 2017 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)