Goal: PET is a relatively noisy process compared to other imaging modalities, and sparsity of acquisition data leads to noise in the images. Recent work has focused on machine learning techniques to improve PET images, and this study investigates a deep learning approach to improve the quality of reconstructed image volumes through denoising by a 3D convolution neural network. Potential improvements were evaluated within a clinical context by physician performance in a reading task.Methods: A wide range of controlled noise levels was emulated from a set of chest PET data in patients with lung cancer, and a convolutional neural network was trained to denoise the reconstructed images using the full-count reconstructions as the ground truth. The benefits, over conventional Gaussian smoothing, were quantified across all noise levels by observer performance in an image ranking and lesion detection task.Results: The CNN-denoised images were generally ranked by the physicians equal to or better than the Gaussian-smoothed images for all count levels, with the largest effects observed in the lowest-count image sets. For the CNN-denoised images, overall lesion contrast recovery was 60% and 90% at the 1 and 20 million count levels, respectively. Notwithstanding the reduced lesion contrast recovery in noisy data, the CNN-denoised images also yielded better lesion detectability in low count levels. For example, at 1 million true counts, the average true positive detection rate was around 40% for the CNN-denoised images and 30% for the smoothed images.Conclusion: Significant improvements were found for CNN-denoising for very noisy images, and to some degree for all noise levels. The technique presented here offered however limited benefit for detection performance for images at the count levels routinely encountered in the clinic.

Purpose: The aim of the study is to evaluate the prognostic value of a joint evaluation of PET and CT radiomics combined with standard clinical parameters in patients with HL. Methods: Overall, 88 patients (42 female and 46 male) with a median age of 43.3 (range 21–85 years) were included. Textural analysis of the PET/CT images was performed using freely available software (LIFE X). 65 radiomic features (RF) were evaluated. Univariate and multivariate models were used to determine the value of clinical characteristics and FDG PET/CT radiomics in outcome prediction. In addition, a binary logistic regression model was used to determine potential predictors for radiotherapy treatment and odds ratios (OR), with 95% confidence intervals (CI) reported. Features relevant to survival outcomes were assessed using Cox proportional hazards to calculate hazard ratios with 95% CI. Results: albumin (p = 0.034) + ALP (p = 0.028) + CT radiomic feature GLRLM GLNU mean (p = 0.012) (Area under the curve (AUC): 95% CI (86.9; 100.0)—Brier score: 3.9, 95% CI (0.1; 7.8) remained significant independent predictors for PFS outcome. PET-SHAPE Sphericity (p = 0.033); CT grey-level zone length matrix with high gray-level zone emphasis (GLZLM SZHGE mean (p = 0.028)); PARAMS XSpatial Resampling (p = 0.0091) as well as hemoglobin results (p = 0.016) remained as independent factors in the final model for a binary outcome as predictors of the need for radiotherapy (AUC = 0.79). Conclusion: We evaluated the value of baseline clinical parameters as well as combined PET and CT radiomics in HL patients for survival and the prediction of the need for radiotherapy treatment. We found that different combinations of all three factors/features were independently predictive of the here evaluated endpoints.

For primary staging of unfavorable intermediate or high-risk prostate cancer, 18F-DCFPyL PET helped to detect more nodal, distant metastases than did conventional imaging (bone scan, CT, and option... Background Data regarding 2-(3-{1-carboxy-5-[(6-[18F]fluoro-pyridine 3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid (18F-DCFPyL) PET in primary staging of prostate cancer (PCa) are limited. P...

Purpose: The aims of this study were to perform a comprehensive review and meta-analyses and to report pooled diagnostic results on CXCR4-targeted PET, particularly considering detection, visualization, and prognostication. Patients and Methods: This study followed PRISMA-DTA. A systematic search was conducted on major medical literature databases up to March 1, 2024. The search strategy was designed to include CXCR4 PET studies in hematologic malignancies. A random-effects model combined sensitivity values derived from 2-by-2 contingency tables. Pooled means for SUV_max were computed. Analyses were performed by R software. Results: The initial search resulted in a total of 1428 studies. Ultimately, 18 were eligible for systematic review and meta-analytic calculations. Twelve studies (320 patients) included B-cell lymphoma. The pooled detection rate of CXCR4 PET was 99.4% (95% confidence interval [CI]: 88.3%–100%). Marginal zone lymphoma was investigated in 5 studies (209 patients), with a pooled sensitivity of 97.6% (95% CI: 79.7%–99.8%). In studies on central nervous system lymphoma, CXCR4 PET demonstrated 100% accuracy at both patient and lesion levels. Also, it demonstrated a significantly higher tumor-to-background ratio than ¹⁸F-FDG PET. For multiple myeloma, 5 studies (116 patients) showed a patient-level pooled sensitivity of 77.8% (95% CI: 64.4%–87.2%), whereas ¹⁸F-FDG PET had 65.0% (95% CI: 55.2%–73.7%). The pooled SUV_max for CXCR4 PET was 13.6 (95% CI: 9.3–17.8) versus 9.0 (95% CI: 6.3–11.7) for ¹⁸F-FDG PET. Additionally, CXCR4 PET-derived parameters were significant predictors of survival in multiple myeloma. Conclusions: CXCR4 PET can be a helpful imaging tool for evaluating hematologic malignancies, particularly in B-cell lymphoma and multiple myeloma patients. In specific clinical scenarios, it appears to be superior compared with the current standard-of-care imaging.

We investigated, whether ¹⁸[18F]-FDG PET/CT-derived radiomics combined with sarcopenia measurements improves survival prognostication among patients with advanced, metastatic gastroesophageal cancer. In our study, 128 consecutive patients with advanced, metastatic esophageal and gastroesophageal cancer (n = 128; 26 females; 102 males; mean age 63.5 ± 11.7 years; age range: 29–91 years) undergoing ¹⁸[18F]-FDG PET/CT for staging between November 2008 and December 2019 were included. Segmentation of the primary tumor and radiomics analysis derived from PET and CT images was performed semi-automatically with a commonly used open-source software platform (LIFEX, Version 6.30, lifexsoft.org). Patients’ nutritional status was determined by measuring the skeletal muscle index (SMI) at the level of L3 on the CT component. Univariable and multivariable analyses were performed to establish a survival prediction model including radiomics, clinical data, and SMI score. Univariable Cox proportional hazards model revealed ECOG (<0.001) and bone metastasis (p = 0.028) to be significant clinical parameters for overall survival (OS) and progression free survival (PFS). Age (p = 0.017) was an additional prognostic factor for OS. Multivariable analysis showed improved prognostication for overall and progression free survival when adding sarcopenic status, PET and CT radiomics to the model with clinical parameters only. PET and CT radiomics derived from hybrid ¹⁸[18F]-FDG PET/CT combined with sarcopenia measurements and clinical parameters may improve survival prediction among patients with advanced, metastatic gastroesophageal cancer.