Khasawneh, Hala

AbstractBackgroundAnimal models and human data have suggested macrophage‐driven immune dysregulation in diabetic gastroparesis (DG). Translocator protein (TSPO) upregulation has been suggested to indicate activated state of macrophages and ER176 is a high affinity third generation TSPO‐specific radioligand. The aim of this study was to determine feasibility of dynamic 11C‐ER 176 PET to identify macrophage activation in DG.MethodsTwelve patients, all females, were recruited (4 DG, 4 diabetics, and 4 healthy volunteers) for 11C‐ER 176 PET/CT scanning. The standardized uptake value (SUVmax) in the gastric fundus, body, pylorus, and descending part of the duodenum were compared between three groups using Kruskal‐Wallis test to perform the comparisons, and a p‐value of 0.05 was considered statistically significant.Key ResultsAge was comparable among the three groups with a median of 53 years. The uptake was higher in pylorus in diabetics compared to DG and healthy (SUVmax healthy 4.6 ± 0.2, diabetics 8.4 ± 4.1, DG 5.5 ± 1.0, p = 0.04). The uptake was similar in gastric fundus (9.0 ± 1.6, 13.1 ± 8.3, 7.8 ± 1.9 respectively, p = 0.3), body (7.7 ± 1.9, 13 ± 9.2, 7.8 ± 1.9 respectively, p = 0.8), and duodenum (6.2 ± 2.1, 9.5 ± 6.8, 7.0 ± 1.8 respectively, p = 0.6). No correlation was observed between SUVmax uptake and either HbA1C or fasting blood glucose.Conclusions and InferencesFemale diabetic gastroparesis patients did not demonstrate increased TSPO ligand 11C‐ER 176 uptake in the stomach. Possible explanations include lack of specificity of ligand for specific macrophage phenotypes in DG, sex effect, or small sample size. Further studies investigating non‐invasive ways of analyzing immune dysregulation in neurogastrointestinal disorders are warranted.

Rectal cancer presents significant diagnostic and therapeutic challenges, with neoadjuvant therapy playing a pivotal role in improving resectability and patient outcomes. MRI serves as a critical tool in assessing treatment response. However, differentiating viable tumor tissue from therapy-induced changes on MRI remains a complex task. In this comprehensive review, we explore treatment options for rectal cancer based on resectability status, focusing on the role of MRI in guiding therapeutic decisions. We delve into the nuances of MRI-based evaluation of treatment response following neoadjuvant therapy, paying particular attention to emerging techniques like radiomics. Drawing from our insights based on the literature, we provide essential recommendations for post-neoadjuvant therapy management of rectal cancer, all within the context of MRI-based findings.

Purpose This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)–enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability. Methods Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected (“corrected CNN [c-CNN]” segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared. Results Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation–derived volumes [76.6 cm3 (20.2 cm3), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%–87% [P < 0.0001]). Conclusions Convolutional neural network–enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography.