Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

Purpose: Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images. Methods: Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center). Results: In terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21–14.81%) and FL-PL (CI:11.82–13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32–12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34–26.10%). Furthermore, the Mann–Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R2 = 0.94), FL-SQ (R2 = 0.93), and FL-PL (R2 = 0.92), while CB model achieved a far lower coefficient of determination (R2 = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed. Conclusion: Deep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers

A cycle-consistent adversarial network for brain PET partial volume correction without prior anatomical information

Purpose: Partial volume effect (PVE) is a consequence of the limited spatial resolution of PET scanners. PVE can cause the intensity values of a particular voxel to be underestimated or overestimated due to the effect of surrounding tracer uptake. We propose a novel partial volume correction (PVC) technique to overcome the adverse effects of PVE on PET images. Methods: Two hundred and twelve clinical brain PET scans, including 50 18F-Fluorodeoxyglucose (18F-FDG), 50 18F-Flortaucipir, 36 18F-Flutemetamol, and 76 18F-FluoroDOPA, and their corresponding T1-weighted MR images were enrolled in this study. The Iterative Yang technique was used for PVC as a reference or surrogate of the ground truth for evaluation. A cycle-consistent adversarial network (CycleGAN) was trained to directly map non-PVC PET images to PVC PET images. Quantitative analysis using various metrics, including structural similarity index (SSIM), root mean squared error (RMSE), and peak signal-to-noise ratio (PSNR), was performed. Furthermore, voxel-wise and region-wise-based correlations of activity concentration between the predicted and reference images were evaluated through joint histogram and Bland and Altman analysis. In addition, radiomic analysis was performed by calculating 20 radiomic features within 83 brain regions. Finally, a voxel-wise two-sample t-test was used to compare the predicted PVC PET images with reference PVC images for each radiotracer. Results: The Bland and Altman analysis showed the largest and smallest variance for 18F-FDG (95% CI: − 0.29, + 0.33 SUV, mean = 0.02 SUV) and 18F-Flutemetamol (95% CI: − 0.26, + 0.24 SUV, mean = − 0.01 SUV), respectively. The PSNR was lowest (29.64 ± 1.13 dB) for 18F-FDG and highest (36.01 ± 3.26 dB) for 18F-Flutemetamol. The smallest and largest SSIM were achieved for 18F-FDG (0.93 ± 0.01) and 18F-Flutemetamol (0.97 ± 0.01), respectively. The average relative error for the kurtosis radiomic feature was 3.32%, 9.39%, 4.17%, and 4.55%, while it was 4.74%, 8.80%, 7.27%, and 6.81% for NGLDM_contrast feature for 18F-Flutemetamol, 18F-FluoroDOPA, 18F-FDG, and 18F-Flortaucipir, respectively. Conclusion: An end-to-end CycleGAN PVC method was developed and evaluated. Our model generates PVC images from the original non-PVC PET images without requiring additional anatomical information, such as MRI or CT. Our model eliminates the need for accurate registration or segmentation or PET scanner system response characterization. In addition, no assumptions regarding anatomical structure size, homogeneity, boundary, or background level are required. © 2023, The Author(s)

Assessment of oxidative stress in acute myeloid leukemia

&quot;nBackground: Many chemotherapeutic regimens used in the treatment of cancer generate free radicals that may be a part of their beneficial effects. The aim of this study was to assess the oxidative status in patients undergoing chemotherapy for acute myeloid leukemia (AML).&quot;n &quot;nMethods: Thirty-eight patients with AML (17 female and 21 male patients) with a mean age 34.05&amp;plusmn;12.49 years were included in the study. All the patients received cytarabine and daunorubicin as their standard induction therapy. Serum levels of malondialdehyde (MDA), total antioxidant capacity (TAC), and also the erythrocyte superoxide dismutase and glutathione peroxidase activities were measured before and 14 days after chemotherapy.&quot;n &quot;nResults: Plasma malondialdehyde concentrations increased significantly (from a former 2.68&amp;plusmn;0.89 nmol/ml to 3.14&amp;plusmn;1.29 nmol/ml) 14 days post chemotherapy (p=0.04). Moreover, the total plasma antioxidant capacity changed from 1.09&amp;plusmn;0.15 mmol/L to 1.02&amp;plusmn;0.14 mmol/L (p=0.005). Erythrocyte superoxide dismutase and glutathione peroxidase activity decreased over time from 1157.24&amp;plusmn;543.61 U/gHb to 984.01&amp;plusmn;419.09 U/gHb (p=0.04) and 46.96&amp;plusmn;13.70 U/gHb to 41.40&amp;plusmn;6.44 U/gHb (p=0.02), respectively.&quot;n &quot;nConclusion: In this study, an increase in malondialdehyde levels and a decrease in the levels of antioxidant enzymes and total antioxidant capacity were observed. It seems that chemotherapy by cytarabine and daunorubicin generates enormous amounts of free radicals in patients undergoing the treatment for AML. Use of antioxidant supplementation during chemotherapy i is discouraged as it may interfere with the generation of free radicals that may be a part of the therapeutic efficacy of these drugs

Fatigue and Its Related Factors Among Iranian Cancer Survivors

Introduction: Cancer-related fatigue (CRF) is one of the major problems experienced by cancer patients. Identifying the prevalence and factors associated with CRF may be effective in designing appropriate interventions to reduce this problem. This study aimed to examine the prevalence of CRF and its related factors among Iranian cancer survivors. Methods: The samples of this descriptive cross-sectional study included 131 cancer survivors referred to outpatient clinic of Shahid Gazi Hospital affiliated to Tabriz University of Medical Sciences. Brief fatigue inventory (BFI) questionnaire was used for data collection. The data were analyzed using SPSS software version 13, descriptive statistics, and regression analysis. Results: The mean (SD) fatigue score was 6.41 (1.68) and 89 of survivors reported that they had suffered from CRF. The factors affecting CRF included blood pressure, diabetes mellitus, anemia, serum levels of blood urea nitrogen (BUN), marital status, type of cancer, and physical activity. Conclusion: High level of CRF in cancer survivors requires special attention and designing effective interventions through considering the identified factors associated with CRF

Association of clinicopathologic characteristics and outcomes with EZH2 expression in patients with breast cancer in East Azerbaijan, Iran

Farnaz Boostani,1 Roya Dolatkhah,1 Ashraf Fakhrjou,2 Faris Farassati,3 Zohreh Sanaat1 1Hematology and Oncology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; 2Tabriz University of Medical Sciences, Tabriz, Iran; 3Midwest Biomedical Research Foundation, Kansas City, MO, USA Background: Recently, it was found that the overexpression and mutation status of EZH2 affect cancer progression and patient outcome in several human tumors. We aimed to evaluate the clinicopathologic significance of EZH2 in patients with breast cancer.Methods: This was an analytical descriptive study of surgical specimens of primary breast tumors. Specimens were analyzed immunohistochemically for EZH2, estrogen receptor, progesterone receptor, Ki-67, P53, and human epidermal growth factor receptor 2 (HER2) expressions. Regression analysis was performed to calculate hazard ratios (HRs) and 95% CIs. Kaplan&ndash;Meier and Cox regression models were used to estimate the overall survival (OS) and disease-free survival (DFS).Results: We included 100 patients with breast cancer (mean age 51.05&plusmn;9.54 years). The multivariate regression analysis showed that HER2-positive patients had approximately twice the levels of EZH2 expression compared with HER2-negative patients (HR 2.16, 95% CI 0.48&ndash;11.49). The likelihood of EZH2 expression was significantly higher in patients with lymph node involvement than in those without (HR 8.44, 95% CI 3.06&ndash;23.33; P&le;0.05). EZH2 expression did not have any significant effect on the OS, although the mean OS in high EZH2 expression was shorter than for those with low EZH2 expression (655 vs 787 days; log-rank P=0.336). The mean DFS was 487 days for patients with high EZH2 expression compared with 908 days for those with low EZH2 expression (log-rank P=0.188).Conclusion: There was no association found between EZH2 expression and OS and DFS in our patients. Further studies involving larger sample sizes, and conducted in different populations, are needed to validate this hypothesis. Keywords: breast cancer, tumor markers, enhancer of zeste homolog 2 protein, survival analysi

Reduction of scan duration and radiation dose in cerebral CT perfusion imaging of acute stroke using a recurrent neural network

Objective. Cerebral CT perfusion (CTP) imaging is most commonly used to diagnose acute ischaemic stroke and support treatment decisions. Shortening CTP scan duration is desirable to reduce the accumulated radiation dose and the risk of patient head movement. In this study, we present a novel application of a stochastic adversarial video prediction approach to reduce CTP imaging acquisition time. Approach. A variational autoencoder and generative adversarial network (VAE-GAN) were implemented in a recurrent framework in three scenarios: to predict the last 8 (24 s), 13 (31.5 s) and 18 (39 s) image frames of the CTP acquisition from the first 25 (36 s), 20 (28.5 s) and 15 (21 s) acquired frames, respectively. The model was trained using 65 stroke cases and tested on 10 unseen cases. Predicted frames were assessed against ground-truth in terms of image quality and haemodynamic maps, bolus shape characteristics and volumetric analysis of lesions. Main results. In all three prediction scenarios, the mean percentage error between the area, full-width-at-half-maximum and maximum enhancement of the predicted and ground-truth bolus curve was less than 4 ± 4%. The best peak signal-to-noise ratio and structural similarity of predicted haemodynamic maps was obtained for cerebral blood volume followed (in order) by cerebral blood flow, mean transit time and time to peak. For the 3 prediction scenarios, average volumetric error of the lesion was overestimated by 7%-15%, 11%-28% and 7%-22% for the infarct, penumbra and hypo-perfused regions, respectively, and the corresponding spatial agreement for these regions was 67%-76%, 76%-86% and 83%-92%. Significance. This study suggests that a recurrent VAE-GAN could potentially be used to predict a portion of CTP frames from truncated acquisitions, preserving the majority of clinical content in the images, and potentially reducing the scan duration and radiation dose simultaneously by 65% and 54.5%, respectively

Age pattern of the occurrence of breast cancer in the northwest of Iran

Breast cancer represents 27% of the cancers and 19% of the cancer deaths in female population. The aim of this study was to document the age pattern of the incidence of breast cancer in Iranian female population in the northwest region of the country. The study subjects were 1764 patients with breast cancer diagnosed/registered in the six university clinics between 1988 and 2008 in the northwest of Iran. The highest occurrence rates were observed for the birth year cohorts 1940-1949 (for 59-69 years old), 1950-1959 (for 49-58 years old), and 1960-1969 (for 39-48 years old). Among these three cohorts, the highest rate was observed in 1950-1959 birth year cohort (284.38 per 100,000 female populations, 95% CI: 260-310). This rate was significantly higher compared with the similar rates of other birth cohorts. There was no statistically significant difference between various years in terms of the average age at the diagnosis of breast cancer in our study setting. Despite the previous research reports, we found no significant difference between the mean ages at diagnosis of breast cancer from 1988 to 2008 in Iranian female population

Age pattern of the occurrence of breast cancer in the northwest of Iran

Breast cancer represents 27% of the cancers and 19% of the cancer deaths in female population. The aim of this study was to document the age pattern of the incidence of breast cancer in Iranian female population in the northwest region of the country. The study subjects were 1764 patients with breast cancer diagnosed/registered in the six university clinics between 1988 and 2008 in the northwest of Iran. The highest occurrence rates were observed for the birth year cohorts 1940-1949 (for 59-69 years old), 1950-1959 (for 49-58 years old), and 1960-1969 (for 39-48 years old). Among these three cohorts, the highest rate was observed in 1950-1959 birth year cohort (284.38 per 100,000 female populations, 95% CI: 260-310). This rate was significantly higher compared with the similar rates of other birth cohorts. There was no statistically significant difference between various years in terms of the average age at the diagnosis of breast cancer in our study setting. Despite the previous research reports, we found no significant difference between the mean ages at diagnosis of breast cancer from 1988 to 2008 in Iranian female population