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Summary: Homoharringtonine (HHT), an alkaloid isolated from Cephalotaxus, is an effective anti-leukemia agent and exhibits inhibitory effects in various solid tumors. However, the impacts of HHT treatment on thyroid cancer (TC) remain unclear. Our findings demonstrated that HHT exhibited remarkable anti-TC activity that involved inhibiting cell proliferation, invasion, and migration, as well as inducing apoptosis. Proteomics analysis revealed that the expression of the tissue inhibitor of metalloproteinase 1 (TIMP1) was downregulated in TC cells after HHT treatment. TIMP1 overexpression promoted TC progression and partially reversed the anti-TC effects of HHT, while TIMP1 downregulation inhibited TC progression and enhanced the anti-TC effects of HHT. Furthermore, TIMP1 re-expression attenuated the enhancement of anti-TC effects of HHT induced by TIMP1 knockdown. Mechanistically, HHT exerted anti-TC effects by downregulating TIMP1 expression and then inactivating the FAK/PI3K/AKT signaling pathway. Taken together, our study demonstrated that HHT could inhibit TC progression by inhibiting the TIMP1/FAK/PI3K/AKT signaling pathway.

IntroductionThe utilization of artificial intelligence (AI) augments intraoperative safety and surgical training. The recognition of parathyroid glands (PGs) is difficult for inexperienced surgeons. The aim of this study was to find out whether deep learning could be used to auxiliary identification of PGs on intraoperative videos in patients undergoing thyroid surgery.MethodsIn this retrospective study, 50 patients undergoing thyroid surgery between 2021 and 2023 were randomly assigned (7:3 ratio) to a training cohort (n = 35) and a validation cohort (n = 15). The combined datasets included 98 videos with 9,944 annotated frames. An independent test cohort included 15 videos (1,500 frames) from an additional 15 patients. We developed a deep-learning model Video-Trans-U-HRNet to segment parathyroid glands in surgical videos, comparing it with three advanced medical AI methods on the internal validation cohort. Additionally, we assessed its performance against four surgeons (2 senior surgeons and 2 junior surgeons) on the independent test cohort, calculating precision and recall metrics for the model.ResultsOur model demonstrated superior performance compared to other AI models on the internal validation cohort. The DICE and accuracy achieved by our model were 0.760 and 74.7% respectively, surpassing Video-TransUnet (0.710, 70.1%), Video-SwinUnet (0.754, 73.6%), and TransUnet (0.705, 69.4%). For the external test, our method got 89.5% precision 77.3% recall and 70.8% accuracy. In the statistical analysis, our model demonstrated results comparable to those of senior surgeons (senior surgeon 1: χ2 = 0.989, p = 0.320; senior surgeon 2: χ2 = 1.373, p = 0.241) and outperformed 2 junior surgeons (junior surgeon 1: χ2 = 3.889, p = 0.048; junior surgeon 2: χ2 = 4.763, p = 0.029).DiscussionWe introduce an innovative intraoperative video method for identifying PGs, highlighting the potential advancements of AI in the surgical domain. The segmentation method employed for parathyroid glands in intraoperative videos offer surgeons supplementary guidance in locating real PGs. The method developed may have utility in facilitating training and decreasing the learning curve associated with the use of this technology.

Purpose: To determine whether thyroid-stimulating hormone level ≥ 30 mU/L is necessary for radioiodine (131I) remnant ablation (RRA) in patients with differentiated thyroid cancer (DTC), as well as its influencing factors and pre dictors. Methods: A total of 487 DTC patients were retrospectively enrolled in t his study. They were divided into two groups (TSH < 30 and ≥ 30 mU/L) and further divided into eight subgroups (0–

PurposeThe imaging diagnosis of fracture-related infection is often challenging. The aim of this study was to evaluate the value of 18F-FDG PET/CT for the diagnosis of fracture-related infection (FRI) with internal fixation after orthopedic surgery in lower extremities.MethodsA total of 254 consecutive patients who underwent 18F-FDG PET/CT scans with suspected FRI with internal fixation in lower extremities were retrospectively investigated 18F-FDG PET/CT images were semiquantitatively evaluated with multiple metabolic parameters. Additionally, morphological information of the inguinal draining lymph nodes (DLN) with the highest SUV value was also collected and analyzed.ResultsPatients were divided into two groups according to final diagnosis: the infected (N=197) and the non-infected group (N=57). The differences in the inguinal DLN-related parameters, including the long diameter, short diameter, maximum cross-sectional area, maximum standardized uptake value (SUVmax), metabolic volume (MV) 60%, MV70%, MV80%, total lesional glycolysis (TLG) 60%, TLG70%, TLG80%, and the infection suspected area related parameters, including SUVmax, MV25%, MV30%, MV35%, MV40%, MV50%, and TLG70%, between the two groups were statistically significant. We then compared the highest area under the curves (AUCs) among the morphological parameters of DLN, metabolic parameters of DLN, and metabolic parameters of the suspected infection area. The result demonstrated that SUVmax of the inguinal DLN showed the best diagnostic performance with an AUC of 0.939 (P

Objective To evaluate the diagnostic efficacy of triple‐phase bone scanning and the temporal impact of prosthesis implantation on the diagnostic efficacy of triple‐phase bone scanning for periprosthetic joint infection (PJI). Methods Patients who were admitted to our hospital for joint pain and dysfunction after total joint arthroplasty between 2014 and 2020 were retrospectively included. Triple‐phase bone scanning was performed, and the blood pool images were evaluated to obtain the semi‐quantitative criteria. The patients were then grouped into six groups according to the time interval from index primary arthroplasty to triple‐phase bone scanning. We examined whether there were significant differences in sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy between the groups. Results Overall, 66 patients who underwent total hip arthroplasty (THA) and 74 patients who underwent total knee arthroplasty (TKA) were analyzed. No significant differences were observed between visual analysis and semi‐quantitative measurement in terms of sensitivity, specificity, PPV, NPV, and accuracy. For patients with a time interval from prosthesis implantation to bone scanning of >1 year, visual analysis had a higher PPV (100%) in patients who underwent THA and the use of semi‐quantitative criteria had a higher NPV (85.7%) in patients who underwent TKA. Conclusion The semi‐quantitative criteria showed no advantages in the diagnosis of PJI. In addition, triple‐phase bone scanning demonstrated good clinical diagnostic efficacy when the time interval from prosthesis implantation to bone scanning was >1 year.

PurposeThe aim of this study was to predict standard uptake values (SUVs) from computed tomography (CT) images of patients with lung metastases from differentiated thyroid cancer (DTC-LM).MethodsWe proposed a novel SUVs prediction model using 18-layer Residual Network for generating SUVmax, SUVmean, SUVmin of metastatic pulmonary nodes from CT images of patients with DTC-LM. Nuclear medicine specialists outlined the metastatic pulmonary as primary set. The best model parameters were obtained after five-fold cross-validation on the training and validation set, further evaluated in independent test set. Mean absolute error (MAE), mean squared error (MSE), and mean relative error (MRE) were used to assess the performance of regression task. Specificity, sensitivity, F1 score, positive predictive value, negative predictive value and accuracy were used for classification task. The correlation between predicted and actual SUVs was analyzed.ResultsA total of 3407 nodes from 74 patients with DTC-LM were collected in this study. On the independent test set, the average MAE, MSE and MRE was 0.3843, 1.0133, 0.3491 respectively, and the accuracy was 88.26%. Our proposed model achieved high metric scores (MAE=0.3843, MSE=1.0113, MRE=34.91%) compared with other backbones. The predicted SUVmax (R2 = 0.8987), SUVmean (R2 = 0.8346), SUVmin (R2 = 0.7373) were all significantly correlated with actual SUVs.ConclusionThe novel approach proposed in this study provides new ideas for the application of predicting SUVs for metastatic pulmonary nodes in DTC patients.

Background: The BRAFV600E mutation is a valuable indicator for thyroid cancer diagnosis. This study aimed to develop a deep convolutional neural network (DCNN) model based on ultrasound images to predict the BRAFV600E mutation status of thyroid nodules. Methods: The ultrasound images were obtained from four hospitals between January 2017 and January 2022. We trained and validated the DCNN model based on the primary set from center 1 (979 images, 528 patients). The DCNN network consists of Conv block, Downsample block, Gaussian error linear unit, Global Average Polling, and Full Connected. The predictive performance of this model was evaluated by using areas under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity in four independent test sets from center 1 to center 4 (531 images, 282 patients). Heatmaps were used to visualize the most predictive regions of each image. Specimens obtained through fine‐needle aspiration or surgery were used to detect the BRAFV600E mutation. Results: The DCNN model achieved encouraging predictive performance by fivefold cross‐validation (AUC 0.95) in the primary set. This performance was further confirmed in the independent internal test set (AUC 0.93) and three independent external test sets (AUC 0.84–0.88). The deep learning score revealed significant differences between BRAFV600E‐mutant and BRAFV600E‐wild‐type groups (all test sets p < .001). The heatmaps visualized the most predictive region located inside or alongside the thyroid nodules. Conclusion: A DCNN model with encouraging predictive performance was developed based on ultrasound images to predict the BRAFV600E mutation status of thyroid nodules.

Here, we reported a panel of rare diseases involving bilateral lower extremities with similar imaging patterns on 99mTc-MDP bone scans. Glucose-6-phosphate dehydrogenase deficiency (G-6PD deficiency), Gaucher disease (GD), steroid-induced osteonecrosis, progressive diaphyseal dysplasia (PDD), Erdheim–Chester disease (ECD) and Langerhans cell sarcoma (LCS) were included and imaging characteristics were analyzed. The rare properties of these diseases and mimicking features on 99mTc-MDP bone scans rendered differential diagnosis difficult but necessary. We believe that the rarely known imaging features of the reported diseases will undoubtedly help nuclear medicine physicians make differential diagnoses in clinical practice.

Problem: Ultrasonography is recommended as the first choice for evaluation of thyroid nodules, however, conventional ultrasound features may not be able to adequately predict malignancy. Ultrasound elastography, adjunct to conventional B-mode ultrasound, can effectively improve the diagnostic accuracy of thyroid nodules. However, this technology requires professional elastography equipment and experienced physicians. Aim: in the field of computational medicine, Generative Adversarial Networks (GANs) were proven to be a powerful tool for generating high-quality images. This work therefore utilizes GANs to generate ultrasound elastography images. Methods: this paper proposes a new automated generation method of ultrasound elastography (AUE-net) to generate elastography images from conventional ultrasound images. The AUE-net was based on the U-Net architecture and optimized by attention modules and feature residual blocks, which could improve the adaptability of feature extraction for nodules of different sizes. The additional color loss function was used to balance color distribution. In this network, we first attempted to extract the tissue features of the ultrasound image in the latent space, then converted the attributes by modeling the strain, and finally reconstructed them into the corresponding elastography image. Results: a total of 726 thyroid ultrasound elastography images with corresponding conventional images from 397 patients were obtained between 2019 and 2021 as the dataset (646 in training set and 80 in testing set). The mean rating accuracy of the AUE-net generated elastography images by ultrasound specialists was 84.38%. Compared with that of the existing models in the visual aspect, the presented model generated relatively higher quality elastography images. Conclusion: the AUE-net generated ultrasound elastography images showed natural appearance and retained tissue information. Accordingly, it seems that B-mode ultrasound harbors information that can link to tissue elasticity. This study may pave the way to generate ultrasound elastography images readily without the need for professional equipment.

Purpose: The imaging diagnosis of fracture-related infection is often challenging. The aim of this study was to evaluate the value of 18F-FDG PET/CT for the diagnosis of fracture-related infection (FRI) with internal fixation after orthopedic surgery in lower extremities. Methods: A total of 254 consecutive patients who underwent 18F-FDG PET/CT scans with suspected FRI with internal fixation in lower extremities were retrospectively investigated 18F-FDG PET/CT images were semiquantitatively evaluated with multiple metabolic parameters. Additionally, morphological information of the inguinal draining lymph nodes (DLN) with the highest SUV value was also collected and analyzed. Results: Patients were divided into two groups according to final diagnosis: the infected (N=197) and the non-infected group (N=57). The differences in the inguinal DLN-related parameters, including the long diameter, short diameter, maximum cross-sectional area, maximum standardized uptake value (SUVmax), metabolic volume (MV) 60%, MV70%, MV80%, total lesional glycolysis (TLG) 60%, TLG70%, TLG80%, and the infection suspected area related parameters, including SUVmax, MV25%, MV30%, MV35%, MV40%, MV50%, and TLG70%, between the two groups were statistically significant. We then compared the highest area under the curves (AUCs) among the morphological parameters of DLN, metabolic parameters of DLN, and metabolic parameters of the suspected infection area. The result demonstrated that SUVmax of the inguinal DLN showed the best diagnostic performance with an AUC of 0.939 (P<0.05). Conclusion: Semiquantitative analysis (especially SUVmax) of the inguinal DLN in 18F-FDG PET/CT images could be a promising method for the diagnosis of suspected FRI with internal fixation after orthopedic surgery in lower extremities. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to predict standard uptake values (SUVs) from computed tomography (CT) images of patients with lung metastases from differentiated thyroid cancer (DTC-LM). Methods: We proposed a novel SUVs prediction model using 18-layer Residual Network for generating SUVmax, SUVmean, SUVmin of metastatic pulmonary nodes from CT images of patients with DTC-LM. Nuclear medicine specialists outlined the metastatic pulmonary as primary set. The best model parameters were obtained after five-fold cross-validation on the training and validation set, further evaluated in independent test set. Mean absolute error (MAE), mean squared error (MSE), and mean relative error (MRE) were used to assess the performance of regression task. Specificity, sensitivity, F1 score, positive predictive value, negative predictive value and accuracy were used for classification task. The correlation between predicted and actual SUVs was analyzed. Results: A total of 3407 nodes from74 patientswithDTC-LMwere collected in this study. On the independent test set, the average MAE, MSE and MRE was 0.3843, 1.0133, 0.3491 respectively, and the accuracy was 88.26%. Our proposed model achieved high metric scores (MAE=0.3843, MSE=1.0113, MRE=34.91%) compared with other backbones. The predicted SUVmax (R² = 0.8987), SUVmean (R² = 0.8346), SUVmin (R² = 0.7373) were all significantly correlated with actual SUVs. Conclusion: The novel approach proposed in this study provides new ideas for the application of predicting SUVs for metastatic pulmonary nodes in DTC patients. [ABSTRACT FROM AUTHOR]