The lesion detection rate of Ga-68 DOTATATE PET/MR in multiple endocrine neoplasia type 1.

INTRODUCTION: The purpose of the study was to determine the usefulness of Ga-68 DOTATATE PET/MR in the identification of tumours in individuals with multiple endocrine neoplasia type 1 (MEN1). METHODS: In this retrospective investigation, five individuals who had tested positive for a hereditary MEN1 variant underwent Ga-68 DOTATATE PET/MR between May 2020 and January 2023. Several types of tumours associated with MEN1 were studied. MEN1-related tumours included pituitary, parathyroid, gastroenteropancreatic, and adrenal. The rates of lesion identification between MRI, Ga-68 DOTATATE PET, and Ga-68 DOTATATE PET/MRI were examined. The maximum and mean standard uptake values (SUVmax and SUVmean) were evaluated in carefully delineated volumes of interest (VOI) for the relevant tumours. RESULTS: Of the 24 primary lesions, 14 were identified by Ga-68 DOTATATE PET, 18 by MRI, and 20 by Ga-68 DOTATATE PET/MRI. Two pituitary tumours were detected by all three techniques. All parathyroid tumours that were not detected by Ga-68 DOTATATE PET and MRI were found by Tc-99m MIBI SPECT/CT or/and EUS. Ga-68 DOTATATE PET/MR detected more gastroenteropancreatic lesions. All adrenal tumours not identified by Ga-68 DOTATATE PET were found by MRI or CT. The median SUVmax for lesions identified on Ga-68 DOTATATE PET/MRI was 18.4 (range, 3.8-85.2), and the median SUVmean was 12.0 (range, 2.3-49.8). CONCLUSION: The combination of Ga-68 DOTATATE PET and MRI demonstrated a higher detection rate and may be more useful in the work-up of MEN1 providing a panoramic view of MEN1-related lesions. To increase the identification of MEN1-associated neuroendocrine lesions in the parathyroid gland, approaches other than Ga-68 DOTATATE PET/MRI should be used.

Outcome prediction of SSTR-RADS-3A and SSTR-RADS-3B lesions in patients with neuroendocrine tumors based on 68Ga-DOTATATE PET/MR.

PURPOSE: Somatostatin receptor (SSTR)-targeted PET imaging has emerged as a common approach to evaluating those patients with well-differentiated neuroendocrine tumors (NETs). The SSTR reporting and data system (SSTR-RADS) version 1.0 provides a means of categorizing lesions from 1 to 5 according to the likelihood of NET involvement, with SSTR-RADS-3A (soft-tissue) and SSTR-RADS-3B (bone) lesions being those suggestive of but without definitive NET involvement. The goal of the present study was to assess the ability of 68Ga-DOTATATE PET/MR imaging data to predict outcomes for indeterminate SSTR-RADS-3A and 3B lesions. METHODS: NET patients with indeterminate SSTR-RADS-3A or SSTR-RADS-3B lesions who underwent 68Ga-DOTATATE PET/MR imaging from April 2020 through August 2023 were retrospectively evaluated. All patients underwent follow-up through December 2023 (median, 17 months; (3-31 months)), with imaging follow-up or biopsy findings ultimately being used to classify lesions as malignant or benign. Lesion maximum standardized uptake value (SUVmax) along with minimum and mean apparent diffusion coefficient (ADCmin and ADCmean) values were measured and assessed for correlations with outcomes on follow-up. RESULTS: In total, 33 indeterminate SSTR-RADS-3 lesions from 22 patients (19 SSTR-RADS-3A and 14 SSTR-RADS-3B) were identified based upon baseline 68Ga-DOTATATE PET/MR findings. Over the course of follow-up, 16 of these lesions (48.5%) were found to exhibit true NET positivity, including 9 SSTR-RADS-3A and 7 SSTR-RADS-3B lesions. For SSTR-RADS-3A lymph nodes, a diameter larger than 0.7 cm and an ADCmin of 779 × 10-6mm2/s or lower were identified as being more likely to be associated with metastatic lesions. Significant differences in ADCmin and ADCmean were identified when comparing metastatic and non-metastatic SSTR-RADS-3B bone lesions (P < 0.05), with these parameters offering a high predictive ability (AUC = 0.94, AUC = 0.86). CONCLUSION: Both diameter and ADCmin can aid in the accurate identification of the nature of lesions associated with SSTR-RADS-3A lymph nodes, whereas ADCmin and ADCmean values can inform the accurate interpretation of SSTR-RADS-3B bone lesions.

Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area.

Correlative species distribution models (SDMs) are important tools to estimate species' geographic distribution across space and time, but their reliability heavily relies on the availability and quality of occurrence data. Estimations can be biased when occurrences do not fully represent the environmental requirement of a species. We tested to what extent species' physiological knowledge might influence SDM estimations. Focusing on the Japanese sea cucumber Apostichopus japonicus within the coastal ocean of East Asia, we compiled a comprehensive dataset of occurrence records. We then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs: a naïve model that solely depends on environmental correlates, and a physiologically informed model that further incorporates physiological information as priors. We further tested the models' sensitivity to calibration area choices by fitting them with different buffered areas around known presences. Compared with naïve models, the physiologically informed models successfully captured the negative influence of high temperature on A. japonicus and were less sensitive to the choice of calibration area. The naïve models resulted in more optimistic prediction of the changes of potential distributions under climate change (i.e., larger range expansion and less contraction) than the physiologically informed models. Our findings highlight benefits from incorporating physiological information into correlative SDMs, namely mitigating the uncertainties associated with the choice of calibration area. Given these promising features, we encourage future SDM studies to consider species physiological information where available. Supplementary Information: The online version contains supplementary material available at 10.1007/s42995-024-00226-0.

An efficient approach to detect and track winter flush growth of litchi tree based on UAV remote sensing and semantic segmentation.

The immature winter flush affects the flower bud differentiation, flowering and fruit of litchi, and then seriously reduces the yield of litchi. However, at present, the area estimation and growth process monitoring of winter flush still rely on manual judgment and operation, so it is impossible to accurately and effectively control flush. An efficient approach is proposed in this paper to detect the litchi flush from the unmanned aerial vehicle (UAV) remoting images of litchi crown and track winter flush growth of litchi tree. The proposed model is constructed based on U-Net network, of which the encoder is replaced by MobeilNetV3 backbone network to reduce model parameters and computation. Moreover, Convolutional Block Attention Module (CBAM) is integrated and convolutional layer is added to enhance feature extraction ability, and transfer learning is adopted to solve the problem of small data volume. As a result, the Mean Pixel Accuracy (MPA) and Mean Intersection over Union (MIoU) on the flush dataset are increased from 90.95% and 83.3% to 93.4% and 85%, respectively. Moreover, the size of the proposed model is reduced by 15% from the original model. In addition, the segmentation model is applied to the tracking of winter flushes on the canopy of litchi trees and investigating the two growth processes of litchi flushes (late-autumn shoots growing into flushes and flushes growing into mature leaves). It is revealed that the growth processes of flushes in a particular branch region can be quantitatively analysed based on the UAV images and the proposed semantic segmentation model. The results also demonstrate that a sudden drop in temperature can promote the rapid transformation of late-autumn shoots into flushes. The method proposed in this paper provide a new technique for accurate management of litchi flush and a possibility for the area estimation and growth process monitoring of winter flush, which can assist in the control operation and yield prediction of litchi orchards.