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Background The optimal dose of adjuvant radioiodine( 131 I) therapy for differentiated thyroid cancer (DTC) remains controversial. This study aimed to determine the efficacy and prognostic impact of two doses of adjuvant 131 I therapy (3.7 GBq and 5.55 GBq) in DTC patients with unexplained TSH-stimulated Tg(sTg) elevation. Methods Data for eligible patients with DTC who received adjuvant 131 I therapy at our institution between January 2015 and December 2016 were retrospectively reviewed. The results of dynamic risk assessment of persistent and recurrent disease (PRD) and recurrence-free survival (RFS) were compared between the 3.7 GBq and 5.55 GBq 131 I groups using the chi-squared test, Fisher’s exact test, log-rank test, and a Cox proportional hazards model. Results In total, 224 patients with DTC were enrolled. Six months after adjuvant 131 I therapy, 132 patients(58.9%) had an acceptable response and 92 (41.1%) had an unacceptable response. After a median follow-up duration of 6.7 years (range, 6.0–7.9), 12 patients (33.33%) had persistent disease and 24 (66.7%) had recurrent disease. One patient died during follow-up. The 5-year RFS rate after 131 I treatment was 91.7%. At 6 months after treatment, there was no significant between-group difference in efficacy or the incidence of PRD or RFS ( P  > 0.05). Univariate analysis revealed significant associations of 131 I whole-body scan combined with 131 I-WBS/SPECT results after 131 I treatment, and number of 131 I treatments with the incidence of PRD ( P  = 0). In multivariate analysis, the number of surgeries (hazard ratio [HR] 3.147, 95% confidence interval [CI] 1.360–7.282, P  = 0.007), number of 131 I treatments (HR 0.046, 95% CI 0.020–0.108, P  = 0.001), and efficacy at 6 months after 131 I treatment (HR 0.287, 95% CI 0.113–0.732, P  = 0.009) were significantly associated with RFS. Conclusions The efficacy of adjuvant 131 I therapy and the prognosis in DTC patients with unexplained sTg elevation was unaffected by whether the dose is 3.7 GBq or 5.55 GBq. Prospective, large-scale, long-term and RCTs clinical studies are needed to confirm these findings.

Hepatocellular cancer (HCC) remained a life-threatening carcinoma. Agents for HCC imaging and therapy were expected to possess different intratumoral retention time. To construct an agent with different intratumoral retention time when applied for tumor imaging or therapy remained great values. A lasialoglycoprotein receptor (ASGPR) targeted lactobionic acid derivative (LABO) was constructed for fluorescent imaging and photodynamic therapy of HCC. 18 F labeled LABO ( 18 F-LABO) was developed for PET imaging of HCC. LABO and 18 F-LABO showed similar molecular structure. LABO exhibited characteristic of viscosity and concentration-induced intratumoral in-situ self-assembly to expand the intratumoral retention. LABO was non-fluorescent at free stage, but emitted NIR fluorescence and generated irradiation-induced ROS after self-assembly for fluorescent imaging and photodynamic therapy. ASGPR specificity of LABO and 18 F-LABO was confirmed using HepG2 cell. Biodistribution and fluorescent imaging confirmed the different tumor retention time of LABO and 18 F-LABO when used for photodynamic therapy and PET imaging. PET imaging and photodynamic therapy were performed on HepG2 tumor bearing mice, which revealed that 18 F-LABO/LABO could specifically accumulated in the HepG2 tumor for tumor location/inhibition. LABO/ 18 F-LABO with excellent HCC specificity but different intratumoral behaviors showed great values for the PET/NIR imaging and photodynamic therapy for HCC. Graphical Abstract

Background Intraoperative preservation of parathyroid glands (PGs) remained a significant challenge in thyroidectomy. Recently, deep learning has demonstrated considerable potential in medical applications. We proposed a novel intraoperative method for PG identification. Methods We developed a localization subnet based on YOLOX and a novel semantic segmentation model termed Trans-U-HRNet, collectively termed PG-AI. The dataset included 976 images from 121 patients undergoing open thyroidectomy, with images from 101 patients randomly split 8:2 for training and internal validation. PG detection was quantified using PG-AI, and its performance was visually compared with near-infrared autofluorescence (NIRAF) imaging and assessments by surgeons with varying experience levels. Results PG-AI achieved an accuracy of 91.1% and a recall rate of 86.5% on the internal validation set. The recognition rates of PG-AI were 88.7% and 85.0% on the internal and external validation sets, respectively, in visualization. PG-AI showed 72.1% agreement with NIRAF imaging, and the combined approaches successfully identified all PGs. In external validation, PG-AI significantly outperformed junior surgeons in recognition rate ( p  = 0.004). Conclusion PG-AI generated accurate segmentation masks of PGs in real-time intraoperative images, providing reliable visual guidance to surgeons during identification.

Background Multiple myeloma (MM) is a heterogeneous malignancy with prognosis significantly affected by high-risk cytogenetic abnormalities (HRCAs). Traditional detection using fluorescence in situ hybridisation is invasive and limited in capturing disease heterogeneity. We aimed to develop and validate radiomics model based on pretreatment [18F] fluoro-deoxyglucose (FDG) positron emission tomography/computed tomographic (18F-FDG PET/CT) imaging to non-invasively predict HRCAs in newly diagnosed MM patients. Results Among the 42 candidate models, the Decision Tree classifier utilizing PET active lesions features demonstrated optimal performance in the validation cohort, exhibiting excellent predictive ability (Area Under the Curve (AUC) = 0.89), significantly outperforming the PET metrics model (AUC = 0.84) and clinical model (AUC = 0.74). SHapley Additive exPlanations analysis identified the PET-derived feature as the most important contributor to the model’s predictive capacity. The model stratified patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly worse PFS and OS (median PFS: high-risk 24.5 months vs. low-risk 29 months; p  = 0.0360; median OS: high-risk 33.5 months vs. low-risk 50 months; p  = 0.0023). Conclusion As a non-invasive imaging biomarker, PET/CT radiomics holds potential for predicting high-risk cytogenetic status and facilitating patient prognosis stratification Further large-scale, multi-center prospective validations are essential to confirm its utility for personalized therapeutic decision-making in MM.

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.

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. Deep convolutional neural network (DCNN) has shown tremendous potential for medical imaging diagnosis. In this study, In this study developed a DCNN model based on ultrasound images to predict the BRAFV600E mutation in thyroid nodules. The DCNN model achieved encouraging predictive performance in the test sets from four hospitals (AUC 0.84–0.93). This model might provide a non‐invasive and convenient method for predicting the BRAFV600E mutation to assist clinicians to select more appropriate management for patients with thyroid nodules or thyroid cancer.

The management of aggressive and progressive metastatic papillary thyroid cancer (PTC) is very difficult. An inverse relationship between radioiodine and F-18 fluorodeoxyglucose (FDG) uptake (''flip-flop'' phenomenon) is described for invasive PTC during dedifferentiation. However, no satisfactory biologic explanation for this phenomenon. Hypoxia is an important microenvironmental factor that promotes cancer progression and glycolysis. The Hippo-YAP is a highly conserved tumor suppressor pathway and contributes to cancer metabolic reprogramming. Thus, we investigated the underlying molecular mechanisms of glucose/iodine metabolic reprogramming in PTC, focusing on the tumor hypoxia microenvironment and Hippo-YAP signaling. Immunohistochemistry staining was conducted to evaluate the expressions of hypoxia-inducible factor 1α (HIF-1α), yes-associated protein (YAP), glucose transporters 1 (GLUT1) and sodium iodine symporter (NIS) in matched PTC and the adjacent noncancerous tissues. PTC cell lines were cultured under normoxic (20% O ) and hypoxic (1% O ) conditions and the glycolysis level and NIS expression were measured. Further, we characterized the molecular mechanism of glucose/iodine metabolic reprogramming in PTC cell. Finally, we validated the results in vivo by establishing subcutaneous xenografts in nude mice. The expression levels of HIF1-α, YAP and GLUT1 were upregulated in PTC tissues and YAP expression was positively associated with HIF-1α, GLUT1 and TNM stages. Meanwhile, the expression of NIS was negatively correlated with YAP. Further, in vitro studies indicated that hypoxia-induced YAP activation was critical for accelerating glycolysis and reducing NIS expression in PTC cells. Inhibition of YAP had the opposite effects in vitro and tumorigenicity in vivo. Hypoxia inhibited the Hippo signaling pathway resulting in the inactivation of YAP phosphorylation, further promoting the nuclear localization of YAP in PTC cells. The mechanism is that hypoxic stress promoted YAP binding to HIF-1α in the nucleus and maintained HIF-1α protein stability. The YAP/HIF-1α complex bound and directly activated the GLUT1 transcription to accelerate glycolysis. Meanwhile, HIF-1α/YAP signaling might indirectly reduce the expression of NIS by promoting the output of MAPK signaling. In vivo studies confirmed the YAP-mediated reprogramming of glucose/iodine metabolism promoted PTC progression. Collectively, our data revealed a novel regulatory mechanism of the glucose/iodine metabolic program rewritten by HIF-1α/YAP signaling in PTC. Inhibition of HIF-1α/YAP signaling alone or in combination with other potential markers may effectively combat aggressive PTC.

The characteristics of lymphedema on radionuclide lymphoscintigraphy were studied, and the diagnostic value of radionuclide lymphoscintigraphy in lymphedema was evaluated. In this report radionuclide lymphoscintigraphy was performed in 110 cases of clinically suspected lymphedema. A retrospective study method was used to analyze the imaging results. The typical pattern of lymphedema on radionuclide lymphoscintigraphy was summarized. It was found that the characteristics of lymphedema on radionuclide lymphoscintigraphy were diverse. The most common pattern was increased radiotracer accumulation in the soft tissue and lymphatic webs. Surgery and infection dominated as the causes of lymphedema in this study. It was concluded that radionuclide lymphoscintigraphy is a useful noninvasive method for diagnosing lymphedema. It is easy to operate and provides reliable results.

Melanoma represents the most aggressive form of skin cancer, and its incidence continues to rise worldwide. 18 F–FDG PET imaging has transformed diagnostic nuclear medicine and has become an essential component in the management of melanoma, but still has its drawbacks. With the rapid growth in the field of nuclear medicine and molecular imaging, a variety of promising probes that enable early diagnosis and detection of melanoma have been developed. The substantial preclinical success of melanin- and peptide-based probes has recently resulted in the translation of several radiotracers to clinical settings for noninvasive imaging and treatment of melanoma in humans. In this review, we focus on the latest developments in radiolabeled molecular imaging probes for melanoma in preclinical and clinical settings, and discuss the challenges and opportunities for future development.

Multiple myeloma (MM) is a heterogeneous malignancy with prognosis significantly affected by high-risk cytogenetic abnormalities (HRCAs). Traditional detection using fluorescence in situ hybridisation is invasive and limited in capturing disease heterogeneity. We aimed to develop and validate radiomics model based on pretreatment [18F] fluoro-deoxyglucose (FDG) positron emission tomography/computed tomographic (18F-FDG PET/CT) imaging to non-invasively predict HRCAs in newly diagnosed MM patients.BACKGROUNDMultiple myeloma (MM) is a heterogeneous malignancy with prognosis significantly affected by high-risk cytogenetic abnormalities (HRCAs). Traditional detection using fluorescence in situ hybridisation is invasive and limited in capturing disease heterogeneity. We aimed to develop and validate radiomics model based on pretreatment [18F] fluoro-deoxyglucose (FDG) positron emission tomography/computed tomographic (18F-FDG PET/CT) imaging to non-invasively predict HRCAs in newly diagnosed MM patients.Among the 42 candidate models, the Decision Tree classifier utilizing PET active lesions features demonstrated optimal performance in the validation cohort, exhibiting excellent predictive ability (Area Under the Curve (AUC) = 0.89), significantly outperforming the PET metrics model (AUC = 0.84) and clinical model (AUC = 0.74). SHapley Additive exPlanations analysis identified the PET-derived feature as the most important contributor to the model's predictive capacity. The model stratified patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly worse PFS and OS (median PFS: high-risk 24.5 months vs. low-risk 29 months; p = 0.0360; median OS: high-risk 33.5 months vs. low-risk 50 months; p = 0.0023).RESULTSAmong the 42 candidate models, the Decision Tree classifier utilizing PET active lesions features demonstrated optimal performance in the validation cohort, exhibiting excellent predictive ability (Area Under the Curve (AUC) = 0.89), significantly outperforming the PET metrics model (AUC = 0.84) and clinical model (AUC = 0.74). SHapley Additive exPlanations analysis identified the PET-derived feature as the most important contributor to the model's predictive capacity. The model stratified patients into high-risk and low-risk groups, with the high-risk group exhibiting significantly worse PFS and OS (median PFS: high-risk 24.5 months vs. low-risk 29 months; p = 0.0360; median OS: high-risk 33.5 months vs. low-risk 50 months; p = 0.0023).As a non-invasive imaging biomarker, PET/CT radiomics holds potential for predicting high-risk cytogenetic status and facilitating patient prognosis stratification Further large-scale, multi-center prospective validations are essential to confirm its utility for personalized therapeutic decision-making in MM.CONCLUSIONAs a non-invasive imaging biomarker, PET/CT radiomics holds potential for predicting high-risk cytogenetic status and facilitating patient prognosis stratification Further large-scale, multi-center prospective validations are essential to confirm its utility for personalized therapeutic decision-making in MM.