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To calculate the diagnostic performance of [sup18/supF]-FDG PET/MR in colorectal cancer (CRC).This study was designed following the PRISMA-DTA guidelines. To be included, published original articles (until December 31, 2021) that met the following criteria were considered eligible: (1) evaluated [sup18/supF]-FDG PET/MR as the diagnostic method to detect CRC; (2) compared [sup18/supF]-FDG PET/MR with histopathology as the reference standard, or clinical/imaging composite follow-up when pathology was not available; (3) provided adequate crude data for meta-analysis. The diagnostic pooled measurements were calculated at patient and lesion levels. Regarding sub-group analysis, diagnostic measurements were calculated in "TNM staging," "T staging," "N staging," "M staging," and "liver metastasis" sub-groups. Additionally, we calculated the pooled performances in "rectal cancer: patient-level" and "rectal cancer: lesion-level" sub-groups. A hierarchical method was used to pool the performances. The bivariate model was conducted to find the summary points. Analyses were performed using STATA 16.A total of 1534 patients from 18 studies were entered. The pooled sensitivities in CRC lesion detection (tumor, lymph nodes, and metastases) were 0.94 (95%CI: 0.89-0.97) and 0.93 (95%CI: 0.82-0.98) at patient-level and lesion-level, respectively. The pooled specificities were 0.89 (95%CI: 0.84-0.93) and 0.95 (95%CI: 0.90-0.98) at patient-level and lesion-level, respectively. In sub-groups, the highest sensitivity (0.97, 95%CI: 0.86-0.99) and specificity (0.99, 95%CI: 0.84-1.00) were calculated for "M staging" and "rectal cancer: lesion-level," respectively. The lowest sensitivity (0.81, 95%CI: 0.65-0.91) and specificity (0.79, 95%CI: 0.52-0.93) were calculated for "N staging" and "T staging," respectively.This meta-analysis showed an overall high diagnostic performance for [sup18/supF]-FDG PET/MR in detecting CRC lesions/metastases. Thus, this modality can play a significant role in several clinical scenarios in CRC staging and restaging. Specifically, one of the main strengths of this modality is ruling out the existence of CRC lesions/metastases. Finally, the overall diagnostic performance was not found to be affected in the post-treatment setting.

To determine the prognostic value of sarcopenia measurements done on staging 2-[18F] FDG PET/CT together with metabolic activity of the tumor in patients with adenocarcinoma esophagogastric cancer with surgical treatment. Patients with early-stage, surgically treated esophageal adenocarcinoma and available pre-treatment 2-[18F] FDG PET/CT were included. The standard uptake value (SUV) and SUV normalized by lean body mass (SUL) were recorded. Skeletal muscle index (SMI) was measured at the L3 level on the CT component of the PET/CT. Sarcopenia was defined as SMI

To assess whether 18F-DCFPyL PET/multiparametric (mp)MR contributes to the diagnosis of clinically significant (cs) prostate cancer (PCa) compared to mpMR in patients with suspicion of PCa, or patients being considered for focal ablative therapies (FT). This ethics review board–approved, prospective study included 55 men with suspicion of PCa and negative systematic biopsies or clinically discordant low-risk PCa (n = 21) or those being considered for FT (n = 34) who received 18F-DCFPyL PET/mpMR. Each modality, PET, mpMR, and PET/MR (using the PROMISE classification), was assessed independently. All suspicious lesions underwent PET/MR-ultrasound fusion biopsies. There were 45/55 patients (81.8%) that had histologically proven PCa and 41/55 (74.5%) were diagnosed with csPCa. Overall, 61/114 lesions (53.5%) identified on any modality were malignant; 49/61 lesions (80.3%) were csPCa. On lesion-level analysis, for detection of csPCa, the sensitivity of PET was higher than that of mpMR and PET/MR (86% vs 67% and 69% [p = 0.027 and 0.041, respectively]), but at a lower specificity (32% vs 85% and 86%, respectively [p

To determine the prognostic value of sarcopenia measurements done on staging 2-[18F] FDG PET/CT together with metabolic activity of the tumor in patients with adenocarcinoma esophagogastric cancer with surgical treatment.Patients with early-stage, surgically treated esophageal adenocarcinoma and available pre-treatment 2-[18F] FDG PET/CT were included. The standard uptake value (SUV) and SUV normalized by lean body mass (SUL) were recorded. Skeletal muscle index (SMI) was measured at the L3 level on the CT component of the PET/CT. Sarcopenia was defined as SMI 34.4cmOf the included 145 patients. 30% were sarcopenic at baseline. On the univariable Cox proportional hazards analysis, ECOG, surgical T and N staging, lymphovascular invasion (LVI) positive lymph nodes, and sarcopenia were significant prognostic factors concerning RFS and OS. On multivariable Cox regression analysis, surgical N staging (p = 0.025) and sarcopenia (p = 0.022) remained significant poor prognostic factors for OS and RFS. Combining the clinical parameters with the imaging-derived nutritional evaluation of the patient but not metabolic parameters of the tumor showed improved predictive ability for OS and RFS.Combining the patients' imaging-derived sarcopenic status with standard clinical data, but not metabolic parameters, offered an overall improved prognostic value concerning OS and RFS.

Artificial intelligence (AI) algorithms based on deep convolutional networks have demonstrated remarkable success for image transformation tasks. State-of-the-art results have been achieved by generative adversarial networks (GANs) and training approaches which do not require paired data. Recently, these techniques have been applied in the medical field for cross-domain image translation. This study investigated deep learning transformation in medical imaging. It was motivated to identify generalizable methods which would satisfy the simultaneous requirements of quality and anatomical accuracy across the entire human body. Specifically, whole-body MR patient data acquired on a PET/MR system were used to generate synthetic CT image volumes. The capacity of these synthetic CT data for use in PET attenuation correction (AC) was evaluated and compared to current MR-based attenuation correction (MR-AC) methods, which typically use multiphase Dixon sequences to segment various tissue types. This work aimed to investigate the technical performance of a GAN system for general MR-to-CT volumetric transformation and to evaluate the performance of the generated images for PET AC. A dataset comprising matched, same-day PET/MR and PET/CT patient scans was used for validation. A combination of training techniques was used to produce synthetic images which were of high-quality and anatomically accurate. Higher correlation was found between the values of mu maps calculated directly from CT data and those derived from the synthetic CT images than those from the default segmented Dixon approach. Over the entire body, the total amounts of reconstructed PET activities were similar between the two MR-AC methods, but the synthetic CT method yielded higher accuracy for quantifying the tracer uptake in specific regions. The findings reported here demonstrate the feasibility of this technique and its potential to improve certain aspects of attenuation correction for PET/MR systems. Moreover, this work may have larger implications for establishing generalized methods for inter-modality, whole-body transformation in medical imaging. Unsupervised deep learning techniques can produce high-quality synthetic images, but additional constraints may be needed to maintain medical integrity in the generated data.

To assess whetherThis ethics review board-approved, prospective study included 55 men with suspicion of PCa and negative systematic biopsies or clinically discordant low-risk PCa (n = 21) or those being considered for FT (n = 34) who receivedThere were 45/55 patients (81.8%) that had histologically proven PCa and 41/55 (74.5%) were diagnosed with csPCa. Overall, 61/114 lesions (53.5%) identified on any modality were malignant; 49/61 lesions (80.3%) were csPCa. On lesion-level analysis, for detection of csPCa, the sensitivity of PET was higher than that of mpMR and PET/MR (86% vs 67% and 69% [p = 0.027 and 0.041, respectively]), but at a lower specificity (32% vs 85% and 86%, respectively [p 0.001]). The performance of MR and PET/MR was comparable. For identification of csPCa in PI-RADS ≥ 3 lesions, the AUC (95% CI) for PET, mpMR, and PET/MR was 0.75 (0.65-0.86), 0.69 (0.56-0.82), and 0.78 (0.67-0.89), respectively. The AUC for PET/MR was significantly larger than that of mpMR (p = 0.04).PSMA PET detects more csPCa than mpMR, but at low specificity. The performance PET/MR is better than mpMR for detection of csPCa in PI-RADS ≥ 3 lesions.NCT03149861.