Novel alternative tools for metastatic pheochromocytomas/paragangliomas prediction

OBJECTIVE

The existing prediction models for metastasis in pheochromocytomas/paragangliomas (PPGLs) showed high heterogeneity in different centers. Therefore, this study aimed to establish new prediction models integrating multiple variables based on different algorithms.

DESIGN AND METHODS

Data of patients with PPGLs undergoing surgical resection at the Peking Union Medical College Hospital from 2007 to 2022 were collected retrospectively. Patients were randomly divided into the training and testing sets in a ratio of 7:3. Subsequently, decision trees, random forest, and logistic models were constructed for metastasis prediction with the training set and Cox models for metastasis-free survival (MFS) prediction with the total population. Additionally, Ki-67 index and tumor size were transformed into categorical variables for adjusting models. The testing set was used to assess the discrimination and calibration of models and the optimal models were visualized as nomograms. Clinical characteristics and MFS were compared between patients with and without risk factors.

RESULTS

A total of 198 patients with 59 cases of metastasis were included and classified into the training set (n = 138) and testing set (n = 60). Among all models, the logistic regression model showed the best discrimination for metastasis prediction with an AUC of 0.891 (95% CI, 0.793-0.990), integrating SDHB germline mutations [OR: 96.72 (95% CI, 16.61-940.79)], S-100 (-) [OR: 11.22 (95% CI, 3.04-58.51)], ATRX (-) [OR: 8.42 (95% CI, 2.73-29.24)] and Ki-67 ≥ 3% [OR: 7.98 (95% CI, 2.27-32.24)] evaluated through immunohistochemistry (IHC), and tumor size ≥ 5 cm [OR: 4.59 (95% CI, 1.34-19.13)]. The multivariate Cox model including the above risk factors also showed a high C-index of 0.860 (95% CI, 0.810-0.911) in predicting MFS after surgery. Furthermore, patients with the above risk factors showed a significantly poorer MFS (P ≤ 0.001).

CONCLUSIONS

Models established in this study provided alternative and reliable tools for clinicians to predict PPGLs patients' metastasis and MFS. More importantly, this study revealed for the first time that IHC of ATRX could act as an independent predictor of metastasis in PPGLs.

A Colorectal Coordinate-Driven Method for Colorectum and Colorectal Cancer Segmentation in Conventional CT Scans

Automated colorectal cancer (CRC) segmentation in medical imaging is the key to achieving automation of CRC detection, staging, and treatment response monitoring. Compared with magnetic resonance imaging (MRI) and computed tomography colonography (CTC), conventional computed tomography (CT) has enormous potential because of its broad implementation, superiority for the hollow viscera (colon), and convenience without needing bowel preparation. However, the segmentation of CRC in conventional CT is more challenging due to the difficulties presenting with the unprepared bowel, such as distinguishing the colorectum from other structures with similar appearance and distinguishing the CRC from the contents of the colorectum. To tackle these challenges, we introduce DeepCRC-SL, the first automated segmentation algorithm for CRC and colorectum in conventional contrast-enhanced CT scans. We propose a topology-aware deep learning-based approach, which builds a novel 1-D colorectal coordinate system and encodes each voxel of the colorectum with a relative position along the coordinate system. We then induce an auxiliary regression task to predict the colorectal coordinate value of each voxel, aiming to integrate global topology into the segmentation network and thus improve the colorectum's continuity. Self-attention layers are utilized to capture global contexts for the coordinate regression task and enhance the ability to differentiate CRC and colorectum tissues. Moreover, a coordinate-driven self-learning (SL) strategy is introduced to leverage a large amount of unlabeled data to improve segmentation performance. We validate the proposed approach on a dataset including 227 labeled and 585 unlabeled CRC cases by fivefold cross-validation. Experimental results demonstrate that our method outperforms some recent related segmentation methods and achieves the segmentation accuracy in DSC for CRC of 0.669 and colorectum of 0.892, reaching to the performance (at 0.639 and 0.890, respectively) of a medical resident with two years of specialized CRC imaging fellowship.

CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis

Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.

Targeted long-read sequencing for comprehensive detection of CYP21A2 mutations in patients with 21-hydroxylase deficiency

BACKGROUND

21-Hydroxylase deficiency (21-OHD) is caused by pathogenic CYP21A2 variations. CYP21A2 is arranged in tandem with its highly homologous pseudogene CYP21A1P; therefore, it is prone to mismatch and rearrangement, producing different types of complex variations. There were few reports on using only one method to detect different CYP21A2 variants simultaneously.

AIMS

Targeted long-read sequencing method was used to detect all types of CYP21A2 variants in a series of patients with 21-OHD.

METHODS

A total of 59 patients with 21-OHD were enrolled from Peking Union Medical College Hospital. Long-range locus-specific PCR and long-read sequencing (LRS) were performed to detect the pathogenic variants in CYP21A2.

RESULTS

Copy-number variants of CYP21A2 were found in 25.4% of patients, including 5.1% with 3 copies of CYP21A2, 16.9% with 1 copy of CYP21A2, and 3.4% with 0 copy of CYP21A2. The remaining 74.6% of patients had 2 copies of CYP21A2. Pathogenic variants were identified in all 121 alleles of 59 patients. Specifically, single-nucleotide variants and small insertions/deletions (< 50 bp) were detected in 79 alleles, of which conversed from CYP21A1P were detected in 63 alleles, and rare variants were found in the other 16 alleles. Large gene conversions (> 50 bp) from pseudogene were detected in 10 alleles, and different chimeric genes (CYP21A1P/CYP21A2 or TNXA/TNXB) formed by large deletions were detected in 32 alleles. Of all variants, p.I173N was the most common variant (19.0%).

CONCLUSIONS

Our study demonstrated that targeted long-read sequencing is a comprehensive method for detecting CYP21A2 variations, which is helpful for genetic diagnosis in 21-OHD patients.

Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy

AIMS

Accurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and then explore the feasibility of the DL algorithm to endometrial cancer VMAT with different prescriptions.

MATERIALS AND METHODS

We proposed the AtTranNet algorithm for three-dimensional dose prediction. A total of 367 cervical patients were enrolled in this study. Three hundred twenty-two cervical patients from 3 centers were randomly divided into 70%, 10%, and 20% as training, validation, and testing sets, respectively. Forty-five cervical patients from another center were selected for external testing. Moreover, 70 patients of endometrial cancer with different prescriptions were further selected to test the model. Prediction precision was evaluated by dosimetric difference, dose map, and dose-volume histogram metrics.

RESULTS

The prediction results were all clinically acceptable. The mean absolute error within the body in internal testing was 0.66 ± 0.63%. The maximum |δD| for planning target volume was observed in D98, which is 1.24 ± 2.73 Gy. The maximum |δD| for organs at risk was observed in Dmean of bladder, which is 4.79 ± 3.14 Gy. The maximum |δV| were observed in V40 of pelvic bones, which is 4.77 ± 4.48%.

CONCLUSION

AtTranNet showed the feasibility and reasonable accuracy in the dose prediction for cervical cancer in multiple centers. The model can also be generalized for endometrial cancer with different prescriptions without any transfer learning.

[Clinicopathological and molecular characteristics of esophageal carcinoma with ductal differentiation: analysis of 17 cases]

Objective: To investigate the clinicopathological features and molecular genetic characteristics of esophageal carcinoma with ductal differentiation, and to summarize the experiences in its diagnosis and treatment. Methods: A total of 17 cases of esophageal carcinoma with ductal differentiation diagnosed in Ningbo Clinical Pathological Diagnosis Center, Ningbo, China from June 2011 to December 2022 were collected. The clinical information and pathological diagnosis was reviewed. The tumor histological features and immunohistochemical results were analyzed. The next-generation sequencing was performed to detect and analyze the gene mutations in tumor samples. Results: The 17 patients included in this study were 54-77 years old, with a median age of 66 years. There were 16 males and 1 female. Among them, 9 cases were mainly carcinoma with ductal differentiation. The squamous epithelium on the tumor's surface was accompanied by high-grade intraepithelial neoplasia. The tumor and atypical squamous epithelium were transitional, and the focus was accompanied by various proportions of squamous cell carcinoma component (less than 10%). The other 8 cases were mostly squamous cell carcinoma, basaloid squamous cell carcinoma or sarcomatoid carcinoma with various degrees of tumor specific differentiation and focal area of carcinoma with ductal differentiation (less than 10%). The tumor cells in the area with ductal differentiation were mainly arranged in small tubes, while the tubes showed a double-layer structure, including the inner cells and outer cells of the lumen. Immunohistochemical results showed that the outer cells of the tumorous tubules expressed p63, p40, CK5/6 and CK34βE12, while the inner cells expressed CK7. Compared with esophageal squamous cell carcinoma reported in the literature, the frequency of gene mutations such as MYC (P=0.002), TP63 (P=0.002), CDKN1C (P=0.002) and NFE2L2 (P=0.045) was significantly lower in this group of cases. At the signaling pathway level, the mutation frequency of NOTCH signaling pathway (P=0.041) was significantly higher, while the mutation frequencies of NRF2 pathway (P=0.013) and PI3K pathway (P=0.009) were significantly lower than that of esophageal squamous cell carcinoma. Conclusion: Esophageal carcinoma with ductal differentiation is a type of esophageal carcinoma with unique morphology, and its molecular changes are also significantly different from those of conventional esophageal squamous cell carcinoma.

Exploring the Application of Large-Scale Pre-Trained Models on Adverse Weather Removal

Image restoration under adverse weather conditions (e.g., rain, snow, and haze) is a fundamental computer vision problem that has important implications for various downstream applications. Distinct from early methods that are specially designed for specific types of weather, recent works tend to simultaneously remove various adverse weather effects based on either spatial feature representation learning or semantic information embedding. Inspired by various successful applications incorporating large-scale pre-trained models (e.g., CLIP), in this paper, we explore their potential benefits for leveraging large-scale pre-trained models in this task based on both spatial feature representation learning and semantic information embedding aspects: 1) spatial feature representation learning, we design a Spatially Adaptive Residual (SAR) encoder to adaptively extract degraded areas. To facilitate training of this model, we propose a Soft Residual Distillation (CLIP-SRD) strategy to transfer spatial knowledge from CLIP between clean and adverse weather images; 2) semantic information embedding, we propose a CLIP Weather Prior (CWP) embedding module to enable the network to adaptively respond to different weather conditions. This module integrates the sample-specific weather priors extracted by the CLIP image encoder with the distribution-specific information (as learned by a set of parameters) and embeds these elements using a cross-attention mechanism. Extensive experiments demonstrate that our proposed method can achieve state-of-the-art performance under various and severe adverse weather conditions. The code will be made available.

Demonstrating Agreement between Radio and Fluorescence Measurements of the Depth of Maximum of Extensive Air Showers at the Pierre Auger Observatory

We show, for the first time, radio measurements of the depth of shower maximum (X_{max}) of air showers induced by cosmic rays that are compared to measurements of the established fluorescence method at the same location. Using measurements at the Pierre Auger Observatory we show full compatibility between our radio and the previously published fluorescence dataset, and between a subset of air showers observed simultaneously with both radio and fluorescence techniques, a measurement setup unique to the Pierre Auger Observatory. Furthermore, we show radio X_{max} resolution as a function of energy and demonstrate the ability to make competitive high-resolution X_{max} measurements with even a sparse radio array. With this, we show that the radio technique is capable of cosmic-ray mass composition studies, both at Auger and at other experiments.

Activating two-dimensional semiconductors for photocatalysis: a cross-dimensional strategy

The emerging two-dimensional (2D) semiconductors substantially extend materials bases for versatile applications such as semiconductor photocatalysis demanding semiconductive matrices and large surface areas. The dimensionality, while endowing 2D semiconductors the unique properties to host photocatalytic functionality of pollutant removal and hydrogen evolution, hurdles the activation paths to form heterogenous photocatalysts where the photochemical processes are normally superior over these on the mono-compositional counterparts. In this perspective, we present a cross-dimensional strategy to employ thenD (n= 0-2) clusters or nanomaterials as activation partners to boost the photocatalytic activities of the 2D semiconductors. The formation principles of heterogenous photocatalysts are illustrated specifically for the 2D matrices, followed by selection criteria of them among the vast 2D database. The computer investigations are illustrated in the density functional theory route and machine learning benefitted from the vast samples in the 2D library. Synthetic realizations and characterizations of the 2D heterogenous systems are introduced with an emphasis on chemical methods and advanced techniques to understand materials and mechanistic studies. The perspective outlooks cross-dimensional activation strategies of the 2D materials for other applications such as CO2removal, and materials matrices in other dimensions which may inspire incoming research within these fields.

LViT: Language Meets Vision Transformer in Medical Image Segmentation

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT.

[Progress in pathophysiology research and update on diagnosis and treatment strategies for fecal incontinence]

The traditional barrier theory believes that fecal incontinence is related to an imbalance of the recto-anal barrier and the characteristics of stool. However, in clinical practice this theory proves unable to explain all types of fecal incontinence. In recent years, research on the mechanisms related to fecal incontinence has shifted to a new integrative concept with the rectum and anus as functional units, and the central-peripheral nervous system and internal and external anal sphincters as a control loop. The diagnosis and treatment strategy of fecal incontinence, which is replaced by sacral neuromodulation, is undergoing a quiet change. With the progressively aging population in China, the need to improve the diagnosis and treatment of fecal incontinence has become increasingly urgent. This article explores the trends in diagnosis and treatment and mechanism research from the perspective of recent advances in pathophysiological research and updated diagnosis and treatment methods for fecal incontinence.

[A follow-up study on the pain changes trend and effects in patients diagnosed with herpes zoster in Beijing City]

Objective: To understand the changes in pain and its effects in patients with the diagnosis of herpes zoster. Methods: A total of 3 487 patients diagnosed with herpes zoster (HZ) for the first time at the outpatient department of Miyun District Hospital from January 1, 2017, to December 31, 2019, were included in the study. The information of patients was registered and issued with a record card. Patients were required to record the time of pain and rash by themselves. Telephone follow-up was conducted at 21, 90, 180 and 365 days after the onset of rashes, including hospitalization, location of rash and pain, and the time of start and end. The impact of pain on life was evaluated by the Zoster Brief Pain Inventory (ZBPI). Results: The age of 2 999 HZ patients included in the analysis were (53±16) years old, including 1 377 (45.91%) males and 1 903 (63.45%) patients aged 50 years and older. After 21 days of rash, mild, moderate and severe pain accounted for 20.87% (626 cases), 37.98% (1 139 cases) and 33.81% (1 014 cases), respectively. Only 5.07% (152 cases) had no pain or discomfort, and 2.27% (68 cases) had no pain but discomfort. Most of the pain sites were consistent with the rash sites. The chest and back and waist and abdomen were the most common, accounting for 35.58% (1 067 cases) and 29.18% (875 cases), respectively, followed by the limbs and face and neck, accounting for 16.74% (502 cases) and 16.40% (492 cases), respectively. The M (Q1, Q3) of pain days in the HZ patients was 14 (8, 20) days, and the incidence of post-herpetic neuralgia (PHN) was 6.63% (171/2 580) (excluding 419 patients who refused to visit or lost to visit on 90 days after the onset of rash). The pain score of HZ patients within 21 days after the rash was (5.19±2.73) points, and the pain score of PHN patients was (7.61±2.13) points, which was significantly higher than that of non-PHN patients [(5.04±2.69) points] (P<0.001). Daily activities, emotions, walking ability, work, social interaction, sleep and recreation were affected for 21 days after the rash in HZ patients, ranging from 60.79% to 83.83%, with sleep being the most affected (83.83%). The impact scores of pain and life dimensions in PHN patients ranged from 4.59 to 7.61 points on the ZBPI scale, which were higher than those in non-PHN patients (2.49-5.04) (t values ranged from 8.86 to 11.67, all P values <0.001). Conclusion: The proportion of pain in HZ patients after the diagnosis is high, and the pain is more obvious in patients with PHN and HZ patients aged 50 and older, which has a greater impact on their daily lives.

Large-scale pancreatic cancer detection via non-contrast CT and deep learning

Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986-0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.

Polyglucosan body density in the aged mouse hippocampus is controlled by a novel modifier locus on chromosome 1

Aging can be associated with the accumulation of hypobranched glycogen molecules (polyglucosan bodies, PGBs), particularly in astrocytes of the hippocampus. While PGBs have a detrimental effect on cognition in diseases such as adult polyglucosan body disease and Lafora disease, the underlying mechanism and clinical relevance of age-related PGB accumulation remains unknown. Here, we have investigated the genetic basis and functional impact of age-related PGB accumulation in 32 fully sequenced BXD-type strains of mice which exhibit a 400-fold variation in PGB burden in 16-18 month old females. We mapped a major locus controlling PGB density in the hippocampus to chromosome 1 at 72-75 Mb (linkage of 4.9 -logP), which we defined as the Pgb1 locus. To identify potentially causal gene variants within Pgb1, we generated extensive hippocampal transcriptome datasets and identified two strong candidate genes for which mRNA correlates with PGB density-Smarcal1 and Usp37. In addition, both Smarcal1 and Usp37 contain non-synonymous allele variations likely to impact protein function. A phenome-wide association analysis highlighted a trans-regulatory effect of the Pgb1 locus on expression of Hp1bp3, a gene known to play a role in age-related changes in learning and memory. To investigate the potential impact of PGBs on cognition, we performed conditioned fear memory testing on strains displaying varying degrees of PGB burden, and a phenome-wide association scan of ~12,000 traits. Importantly, we did not find any evidence suggesting a negative impact of PGB burden on cognitive capacity. Taken together, we have identified a major modifier locus controlling PGB burden in the hippocampus and shed light on the genetic architecture and clinical relevance of this strikingly heterogeneous hippocampal phenotype.

Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes

INTRODUCTION

A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes.

MATERIALS AND METHODS

Patients (N = 101) who experienced weight changes ≥ 5% were selected for the study, using serial ultrasound studies retrospectively collected from 2013 to 2021. After applying our exclusion criteria, 74 patients from 239 studies were included. We classified images into four scanning views and applied the algorithm. Mean values from 3-5 images in each group were used for the results and correlated against weight changes.

RESULTS

Images from the left lobe (G1) in 45 patients, right intercostal view (G2) in 67 patients, and subcostal view (G4) in 46 patients were collected. In a head-to-head comparison, G1 versus G2 or G2 versus G4 views showed identical steatosis scores (R2 > 0.86, p < 0.001). The body weight and steatosis scores were significantly correlated (R2 = 0.62, p < 0.001). Significant differences in steatosis scores between the highest and lowest body weight timepoints were found (p < 0.001). Men showed a higher liver steatosis/BMI ratio than women (p = 0.026).

CONCLUSIONS

The best scanning conditions are 3-5 images from the right intercostal view. The algorithm objectively quantified liver steatosis, which correlated with body weight changes and gender.

[Mediating effects of obesity and metabolic factors in hyperuricemia and prehypertension]

Objective: To assess the mediating effects of obesity and metabolic factors in the relationship between hyperuricemia (HUA) and prehypertension. Methods: A total of 9 399 individuals were selected using a multistage stratified whole-group random sampling method from 90 villages (neighborhood committees) in 30 towns (streets) of 5 districts (counties) in Fuzhou. A total of 4 754 study subjects were included. A linear regression model was used to analyze the association of HUA with obesity and metabolic factors. Single-factor and multi-factor logistic regression models were used to analyze the association of HUA, obesity, and metabolic factors with prehypertension. Mediating effects models were used to analyze the mediating effects of obesity and metabolic factors on the association between HUA and prehypertension. Results: After adjusting for confounders, the association between HUA and cholesterol, triglycerides, HDL-C, LDL-C, BMI, waist circumference, creatinine, and urea nitrogen were significantly correlated (P<0.001). HUA, waist circumference, BMI, and triglycerides were significantly associated with prehypertension (P<0.001). Waist circumference, BMI, and triglycerides mediated the relationship between HUA and prehypertension, with OR (95%CI) of 1.018 (1.007-1.027), 1.010 (1.002-1.018), and 1.010 (1.003-1.017) (P<0.001), with mediating proportions of 7.76%, 4.31%, and 4.31% respectively. No mediating effect of cholesterol, HDL-C, LDL-C, creatinine, and urea nitrogen was found on the relationship (P>0.05). Conclusions: Waist circumference, BMI, and triglycerides all had mediating effects in the association between HUA and prehypertension. For the general population, weight control, waist circumference, and a high-fat diet should be used to reduce the occurrence of prehypertension.

Deep Learning-Based Multi-Modality Segmentation of Primary Gross Tumor Volume in CT and MRI for Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

The delineation of primary gross tumor volume (GTV) of nasopharyngeal carcinoma (NPC) is an essential step for radiotherapy planning. In clinical practice, radiation oncologists manually delineate the GTV in planning CT with the help of diagnostic MRI. This is because NPC tumors are closely adjacent to many important anatomic structures, and CT and MRI provide complementary strength to accurately determine the tumor extension boundary. Manual delineation is time-consuming with the potential registration errors between MRI and CT decreasing the delineation accuracy. In this study, we propose a fully automated GTV segmentation method based on CT and MRI by first aligning MRI to CT, and then, segmenting the GTV using a multi-modality deep learning model.

MATERIALS/METHODS

We collected 104 nasopharyngeal carcinoma patients with both planning CT and diagnostic MRI scans (T1 & T2 phases). An experienced radiation oncologists manually delineated the GTV, which was further examined by another senior radiation oncologist. Then, a coarse to fine cross-modality registration from MRI to CT was conducted as follows: (1) A rigid transformation was performed on MRI to roughly align MRI to CT with similar anatomic position. (2) Then, the region of interest (RoI) on both CT and rigid-transformed MRI were cropped. (3) A leading cross-modality deformable registration algorithm, named DEEDS, was applied on the cropped MRI and CT RoIs to find an accurate local alignment. Next, using CT and registered MRI as the combined input, a multi-modality deep segmentation network based on nnUNet was trained to generate the GTV prediction. 20% patients were randomly selected as the unseen testing set to quantitatively evaluate the performance.

RESULTS

The quantitative NPC GTV segmentation performance is summarized in Table 1. The deep segmentation model using CT alone achieved reasonable high performance with 76.6% Dice score and 1.34mm average surface distance (ASD). When both CT and registered MRI were used, the segmentation model further improved the performance by 0.9% Dice score increase and 11% relative ASD error reduction, demonstrating the complementary strength of CT and MRI in determining NPC GTV. Notably, the achieved 77.5% Dice score and 1.19mm ASD by the multimodality model is among the top performing results reported in recent automatic NPC GTV segmentation using either CT or MRI modality.

CONCLUSION

We developed a fully automated multi-modal deep-learning model for NPC GTV segmentation. The developed model can segment the NPC GTV in high accuracy. With further optimization and validation, this automated model has potential to standardize the NPC GTV segmentation and significantly decrease the workload of radiation oncologists in clinical practice.

A Novel Strategy to Enhance Radiotherapy Efficacy: Results from the Prospective Phase I Clinical Trial of MR-Guided Focused Ultrasound-Stimulated Microbubbles (MRgFUS+MB) Treatment for Breast Cancer

PURPOSE/OBJECTIVE(S)

Preclinical in vitro and in vivo studies have demonstrated that tumor cell death can be enhanced 10- to 40-fold when radiotherapy (RT) is combined with focused-ultrasound (FUS)-stimulated microbubbles (MB) treatment. MBs are gas microspheres used as intravascular contrast agents. The acoustic exposure of MBs within the target volume causes bubbles cavitation that induces perturbation of tumor vasculature. This activates apoptotic pathways responsible for the ablative effect of stereotactic body radiotherapy, which would otherwise require high-dose radiotherapy (>8-10 Gy/fraction) to be activated. Subsequent irradiation of an MB-sensitized tumor causes increased anoxic tumor killing, which occurs in addition to canonical RT-induced DNA damage. Given the compelling results of preclinical data, we conducted a phase I clinical trial of magnetic resonance (MR)-guided FUS-stimulated MBs (MRgFUS+MB) treatment for breast cancer patients (pts). We report the safety and efficacy results of this new radio enhancement treatment.

MATERIALS/METHODS

This is a single-center, single-arm, investigator-initiated phase 1 clinical trial (NCT04431674). We included pts with stage I-IV breast cancer with tumor in situ for whom breast or chest wall RT was deemed adequate by a multidisciplinary team. Pts were excluded if they had contraindications for contrast-enhanced MR or MB administration. Pts underwent 2-3 MRgFUS+MB treatments throughout the RT course. We used an MR-coupled FUS-device operating at 500 KHz and 540 kPa peak negative pressure to deliver the treatment. The FUS sonicated intravenously administrated MB within the MR-guided target volume. Pts were monitored for 30-min post-procedure and subsequently treated with RT. The primary outcome was acute toxicity per Common Terminology for Adverse Events V5.0. Secondary outcomes were radiological response at 3 months and local control (LC) at 1 year. Kaplan-Meier method was used to estimate local control. All pts signed a written consent form before study participation.

RESULTS

We enrolled 18 females with 20 primary breast cancer treated with MRgFUS+MB therapy. The median age was 60 years (range, 44-90). The molecular subtypes consisted of basal-like (n = 3/20), luminal (n = 11/20), and HER2-enriched (n = 6/20). The prescribed dose was 20 Gy/5 fractions (n = 8/20), 30-35 Gy/5 fractions (n = 7/20), 30-40 Gy/10 fractions (n = 3/20), and 66 Gy/33 fractions (n = 2/20). The median follow-up was 9 months (range, 0.3-29). All pts completed the planned MRgFUS+MB treatments. The only MRgFUS+MB treatment-related toxicity consisted of Grade 1 allergic reaction (mild cough) 30 minutes after the last MB injection. All worst acute toxicities were radiation dermatitis (Grade 3 = 2/20, Grade 2 = 2/20, Grade 1 = 14/20). At 3 months, 75% had partial (n = 6/20) or complete (n = 9/20) response, with a single progression. The LC rate at 1 year was 86%.

CONCLUSION

MRgFUS+MB was a safe and efficient treatment that provided durable responses.

Deep Learning for Automatic Prediction of Lymph Node Station Metastasis in Esophageal Cancer Patients from Contrast-Enhanced CT

PURPOSE/OBJECTIVE(S)

The diagnosis of lymph node (LN) metastasis in computed tomography (CT) is an essential yet challenging task in esophageal cancer staging and treatment planning. Although criteria (e.g., RECIST, morphological/texture features) are proposed to predict LN metastasis, the diagnostic accuracy remains low with sensitivity <50% and specificity <75%, as reported in previous studies. Deep learning (DL) has the potential to address this issue by learning from large-scale labeled data. However, due to the practical surgery procedure in lymph node dissection, it is difficult to pair the metastasis of individual LN reported in the pathology report to the LN instance found in the CT image. Hence, in this study, we first use pathology reports to determine the LNS metastasis, then develop a multiple instance deep learning (MIDL) model to predict lymph node station (LNS) metastasis.

MATERIALS/METHODS

We collected 1200 esophageal cancer patients with preoperative contrast-enhanced CT before surgery. A recently developed automatic mediastinal LNS segmentation model was first applied to segment LNS of 1 to 8 based on the IASLC protocol. For each LNS, the local CT region of interest (ROI) was cropped to generate a station-wise CT patch, where the LNS was labeled as metastatic if at least one metastatic LN was indicated in the pathology report. Using the station-wise CT patch and LNS label, we train a 3D MIDL model, MobileNetV3, to predict LNS metastasis. To better provide the LN position priors in MIDL, LN instances (with a short axis >4mm) were also segmented using an automatic LN detection algorithm and were added to the MIDL model as an auxiliary input. Five-fold cross-validation was conducted to evaluate the MIDL performance.

RESULTS

The MIDL model's performance is summarized in Table 1. The MIDL model incorporating an additional LN instance mask demonstrated a superior overall AUC of 0.7539, surpassing the model without the LN mask input by 2.93%. The specificity was evaluated at a threshold resulting in a recall of 0.7, and the best model outperformed the CT input model in terms of specificity by 2.11%. This highlights the value of including the LN position prior to the MIDL model. Notably, when a threshold was set to result in a specificity of 75%, the best MIDL model demonstrated a significantly higher recall compared to the previously reported clinical diagnostic recall (39.7% vs. 63.21%).

CONCLUSION

We developed a MIDL classification model to predict LNS metastasis using CT scans of 1200 patients. Our findings suggest that the MIDL model can substantially improve LNS metastasis prediction and has the potential to play an essential role in cancer staging, treatment planning, and prognostic analysis.

Anatomy-Guided Deep Learning Model for Accurate and Robust Gross Tumor Volume Segmentation in Lung Cancer Radiation Therapy

PURPOSE/OBJECTIVE(S)

In lung cancer radiation therapy, clinicians must outline the gross tumor volume (GTV) precisely on the planning computed tomography (pCT) for accurate radiation dose delivery. However, due to the limited contrast between tumor and normal tissues in lung parenchyma, accurate delineation of tumor boundaries is difficult leading to large inter-observer variation. In this study, we develop an anatomy-guided lung GTV deep segmentation model using a training cohort of multi-center datasets. The quantitative segmentation performance is evaluated on an independent dataset, where the inter-observer delineation variation is also assessed.

MATERIALS/METHODS

We collected and curated four publicly available lung datasets with GTV annotations (Lung-PET-CT-Dx, LIDC-IDRI, NSCLC-Radiogenomics and RIDER-CT) for deep learning model development. A total of 871 CT scans of patients, who were diagnosed with T1-T4 NSCLC, were available for training after data curation. The GTV annotations of primary tumor were examined and edited by two experienced radiation oncologists following the RTOG 1106 protocol. An anatomy-guided deep learning model was proposed, which consisted two deep networks. The first deep network used CT scan as input and segmented 4 anatomic organs (airway, heart, pulmonary artery and pulmonary vein), while the second deep network took both CT scan and these pre-segmented 4 organs as input and segmented the lung GTV. With the help of anatomic priors from 4 pre-segmented organs, the second deep network could more easily locate the GTV. We used nnUNet as the deep segmentation network. For evaluation, we used NSCLC-Radiomics as the testing dataset, which contains 20 CT scans each annotated by 5 radiation oncologists. The auto-segmented GTV were compared against each of the manual GTV reference. Inter-observer variation was also assessed using the 5 manual GTV references.

RESULTS

The proposed anatomic-guided lung GTV segmentation model achieved a mean Dice score of 82.4% and 95% Hausdorff distance (HD95) of 6.9mm when averaged cross 20 patients and 5 GTV references (Table 1), which outperformed the basic deep GTV segmentation model by markedly reducing 19.4% HD95 error. The performance of proposed model was also comparable to the inter-observer variation (Dice score: 82.4% vs. 81.9%, HD95 6.9 vs. 6.4mm), indicating that our model had similar reproducibility as human observers.

CONCLUSION

We developed and tested an anatomy-guided deep learning model for segmenting GTV in NSCLC patients. The model achieves high quantitative segmentation performance, which is comparable to the human observer variation. It can be potentially used in radiotherapy practice to improve GTV delineation consistency and reduce workloads of radiation oncologists.

Temperature Resistance Properties of Unidirectional Laminated Cf/SiC-Al Prepared by PIP and Vacuum Pressure Infiltration

Material used for aero-engine fan blade requires excellent mechanical properties at high temperature (300 °C). Continuous carbon-fiber-reinforced silicon carbide ceramic matrix composites (Cf/SiC) are necessary candidates in this field, possessing low density, high strength, high modulus, and excellent high-temperature resistance. However, during the preparation process of Cf/SiC, there were inevitably residual pores and defects inside, resulting in insufficient compressive strength and reliability. The vacuum pressure melting infiltration process was used to infiltrate low melting point and high wettability aluminum alloys into the porous Cf/SiC composite material prepared by the precursor impregnation cracking process, repairing the residual pore defects inside the body. The porosity of porous Cf/SiC decreased from 49.65% to 5.1% after aluminum alloy repair and strengthening. The mechanical properties of Cf/SiC-Al composite materials strengthened by aluminum alloy repair after heat treatment were studied. The tensile strength of the as-prepared Cf/SiC-Al was 166 ± 10 MPa, which were degraded by 13~22% after heat treatment. The nonlinear sections of stress-displacement curve of as-treated samples were shorter than that of as-prepared sample. The hardness of aluminum alloy matrix after 300 °C 1 h heat treatment was 58 Hv, which was not obviously reduced compared with the sample without heat treatment. The vacuum infiltration of aluminum alloy is expected to have guiding significance for repairing and strengthening internal defects in ceramic matrix composites.

Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer: A Retrospective Multicenter Study

OBJECTIVE

To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning.

BACKGROUND

Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer.

METHODS

This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker-DeepCT-PDAC-by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness.

RESULTS

Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50-2.75; HR: 2.47, CI: 1.35-4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89-3.28; HR: 2.15, CI: 1.14-4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19-0.64), but did not affect OS in the subgroup with high risk.

CONCLUSIONS

Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

Observation of high-energy neutrinos from the Galactic plane

The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth's atmosphere, is unknown. Because of deflection by interstellar magnetic fields, cosmic rays produced within the Milky Way arrive at Earth from random directions. However, cosmic rays interact with matter near their sources and during propagation, which produces high-energy neutrinos. We searched for neutrino emission using machine learning techniques applied to 10 years of data from the IceCube Neutrino Observatory. By comparing diffuse emission models to a background-only hypothesis, we identified neutrino emission from the Galactic plane at the 4.5σ level of significance. The signal is consistent with diffuse emission of neutrinos from the Milky Way but could also arise from a population of unresolved point sources.

Evaluating the efficacy of endodontic microsurgery for teeth with an undeveloped root apex and periapical periodontitis after nonsurgical treatment failure

AIM

To determine the efficacy of endodontic microsurgery for teeth with an undeveloped root apex and periapical periodontitis caused by an abnormal central cusp fracture after failed nonsurgical treatment.

METHODOLOGY

Eighty teeth in 78 patients were subjected to endodontic microsurgery. All patients were clinically and radiologically examined 1 year postoperatively. The data were statistically analyzed using SPSS 27.0 software.

RESULTS

Of the 80 teeth in 78 patients, periapical lesions had disappeared in 77 teeth at 1-year postoperative follow-up, with a success rate of approximately 96.3% (77/80). The efficacy of endodontic microsurgery was not affected by sex, age, extent of periapical lesions, and presence of the sinus tract. Between-group differences were not statistically significant (P > 0.05).

CONCLUSIONS

Endodontic microsurgery can be an effective alternative treatment option for teeth with an undeveloped root apex and periapical periodontitis caused by an abnormal central cusp fracture after nonsurgical treatment failure.

[Exploration of clinical pathway-oriented optimal management diagnosis and treatment model for rare diseases]

This study takes Cushing's syndrome, a rare disease, as a model, and adopts the path of "Plan, Do, Check, Action" (PDCA) to explore new methods to optimize the clinical path, can improve the quality and efficiency of diagnosis and treatment of rare diseases. After sorting out the problems existing in the previous diagnosis and treatment mode, our team optimizes the path in various ways and establishes a standard operation procedure (SOP) for the new path. In the evaluation of the optimized mode, 55 patients with Cushing's syndrome were admitted to the Department of Endocrinology, Peking Union Medical College Hospital, including 19 males and 36 females, aged (41.8±14.4) years (6-68 years). The pathway group (28 cases) and the control group (27 cases) were divided according to whether they were included in the new path management at the time of admission, and the effect of path optimization was assessed in terms of time, efficacy, safety and cost. The results showed that compared with the control group, the pathway group had a shorter time of hospitalization in the Department of Endocrinology and critical tests, such as blood cortisol rhythm, low-dose dexamethasone inhibition test, and bilateral inferior petrosal sinus sampling (all P<0.05). There was no significant differences in the decrease of total cortisol after operation, the incidence of postoperative complications, and hospitalization expenses (all P>0.05). The optimized path improves the medical efficiency while ensuring medical quality, safety and no increase in cost. This study proposes PDCA path optimization for complex diseases and establishes SOP process, which provides experience in management optimization for the patient-centered and clinical path-oriented diagnosis and treatment mode of rare diseases.

Synthesis of Cyclopenta[c]quinolines by Palladium-Catalyzed Cyclization of 3-Bromoindoles with Internal Alkynes via Spirocyclic Cyclopentadiene Intermediates

A novel method for the construction of a cyclopenta[c]quinoline ring via cyclization of 3-bromoindoles with internal alkynes in the presence of palladium is described. The formation of the cyclopenta[c]quinoline ring is proposed from a double [1,5] carbon sigmatropic rearrangement of the spirocyclic cyclopentadiene intermediate, which is generated in situ from the cyclization of 3-bromoindoles with internal alkynes involving a sequential double alkyne insertion into the carbon-palladium bond and dearomatization of indole. The present studies have developed a novel ring-expansion reaction of the pyrrole ring to pyridine via one carbon insertion into the C2-C3 bond of indoles and provided a simple and distinct route for the construction of tricyclic fused-quinoline derivatives that are not easy to access with conventional methods.

[The past, present and future of sleep medicine in China]

Sleep is a highly conserved phenomenon in endotherms, and has a universal physiological function across all species. In mammals, sleep can be divided into two stages: rapid eye movement (REM) sleep and non-REM (NREM) sleep, which alternate in a cyclic manner. Humans spend about one-third of their lives asleep. Sufficient sleep is necessary for humans to sustain everyday functioning. Sleep plays an important role in regulating energy metabolism, immune defense, endocrine function, and the consolidation of memory process. With the development of social economy and the change of life style, sleep duration of the residents has gradually decreased and the incidence of sleep disturbances has increased. Sleep disturbances can lead to severe mental disorders, such as depression, anxiety disorders, dementia, and other mental diseases, and may increase the risk of physical diseases, such as chronic inflammation, heart disease, diabetes, hypertension, atherosclerosis and others. Maintaining good sleep is of great significance for developing social productive forces, promoting sustainable development of economic society, and is a necessary condition for carrying out the "Healthy China Strategy". The sleep research in China started in 1950s. After decades of development, researchers have made great progress in the molecular mechanisms of sleep and wakefulness, the pathogenesis of sleep disorders and the development of new therapies. With the advancement of science and technology and the public's attention to sleep, the level of clinical diagnosis and therapy of sleep disorders in China is gradually brought in line with international standards. The publication of diagnosis and treatment guidelines in the field of sleep medicine will promote the standardization of the construction. In the future, it is still necessary to promote the development of sleep medicine in the following aspects: Strengthening the professional training and discipline construction, improving the cooperation of sleep research, promoting the intelligent diagnosis and treatment of sleep disorders, and developing the new intervention strategies. Therefore, this review will comprehensively summarize the origin, current situation, and future expectations of sleep medicine in China, including discipline construction of sleep medicine, the number of sleep project grants, research findings, the status and progress of diagnosis and treatment of sleep disorders, and the development direction of sleep medicine.

Targeted co-delivery of PD-L1 monoclonal antibody and sorafenib to circulating tumor cells via platelet-functionalized nanocarriers

BACKGROUND

Circulating tumor cells (CTCs) can adsorb and activate platelets to form a microthrombus protective barrier around them, so that therapeutic drugs and immune cells cannot effectively kill CTCs. The platelet membrane (PM) bionic carrying drug system has the powerful ability of immune escape, and can circulate in the blood for a long time.

MATERIALS AND METHODS

we developed platelet membrane coated nanoparticles (PM HMSNs) to improve the precise delivery of drugs to tumor sites and to achieve more effective immunotherapy combined with chemotherapy strategy.

RESULTS

Successfully prepared aPD-L1-PM-SO@HMSNs particles, whose diameter is 95-130 nm and presenting the same surface protein as PM. Laser confocal microscopy and flow cytometry experimental results showed that the fluorescence intensity of aPD-L1-PM-SO@HMSNs was greater than SO@HMSNs that are not coated by PM. Biodistribution studies in H22 tumor-bearing mice showed that due to the combined action of the active targeting effect and the EPR effect, the high accumulation of aPD-L1-PM-SO@HMSNs in the local tumor was more effective in inhibiting tumor growth than other groups of therapeutic agents.

CONCLUSION

Platelet membrane biomimetic nanoparticles have a good targeted therapeutic effect, which can effectively avoid immune clearance and have little side effects. It provides a new direction and theoretical basis for further research on targeted therapy of CTCs in liver cancer.

[A region-level contrastive learning-based deep model for glomerular ultrastructure segmentation on electron microscope images]

OBJECTIVE

We propose a novel region- level self-supervised contrastive learning method USRegCon (ultrastructural region contrast) based on the semantic similarity of ultrastructures to improve the performance of the model for glomerular ultrastructure segmentation on electron microscope images.

METHODS

USRegCon used a large amount of unlabeled data for pre- training of the model in 3 steps: (1) The model encoded and decoded the ultrastructural information in the image and adaptively divided the image into multiple regions based on the semantic similarity of the ultrastructures; (2) Based on the divided regions, the first-order grayscale region representations and deep semantic region representations of each region were extracted by region pooling operation; (3) For the first-order grayscale region representations, a grayscale loss function was proposed to minimize the grayscale difference within regions and maximize the difference between regions. For deep semantic region representations, a semantic loss function was introduced to maximize the similarity of positive region pairs and the difference of negative region pairs in the representation space. These two loss functions were jointly used for pre-training of the model.

RESULTS

In the segmentation task for 3 ultrastructures of the glomerular filtration barrier based on the private dataset GlomEM, USRegCon achieved promising segmentation results for basement membrane, endothelial cells, and podocytes, with Dice coefficients of (85.69 ± 0.13)%, (74.59 ± 0.13)%, and (78.57 ± 0.16)%, respectively, demonstrating a good performance of the model superior to many existing image-level, pixel-level, and region-level self-supervised contrastive learning methods and close to the fully- supervised pre-training method based on the large- scale labeled dataset ImageNet.

CONCLUSION

USRegCon facilitates the model to learn beneficial region representations from large amounts of unlabeled data to overcome the scarcity of labeled data and improves the deep model performance for glomerular ultrastructure recognition and boundary segmentation.

Acoustic radiation force impulse predicts long-term outcomes in a large-scale cohort: High liver cancer, low comorbidity in hepatitis B virus

BACKGROUND

Acoustic radiation force impulse (ARFI) is used to measure liver fibrosis and predict outcomes. The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus (HBV) than in other etiologies of chronic liver disease.

AIM

To evaluate the performance of ARFI in long-term outcome prediction among different etiologies of chronic liver disease.

METHODS

Consecutive patients who received an ARFI study between 2011 and 2018 were enrolled. After excluding dual infection, alcoholism, autoimmune hepatitis, and others with incomplete data, this retrospective cohort were divided into hepatitis B (HBV, n = 1064), hepatitis C (HCV, n = 507), and non-HBV, non-HCV (NBNC, n = 391) groups. The indexed cases were linked to cancer registration (1987-2020) and national mortality databases. The differences in morbidity and mortality among the groups were analyzed.

RESULTS

At the enrollment, the HBV group showed more males (77.5%), a higher prevalence of pre-diagnosed hepatocellular carcinoma (HCC), and a lower prevalence of comorbidities than the other groups (P < 0.001). The HCV group was older and had a lower platelet count and higher ARFI score than the other groups (P < 0.001). The NBNC group showed a higher body mass index and platelet count, a higher prevalence of pre-diagnosed non-HCC cancers (P < 0.001), especially breast cancer, and a lower prevalence of cirrhosis. Male gender, ARFI score, and HBV were independent predictors of HCC. The 5-year risk of HCC was 5.9% and 9.8% for those ARFI-graded with severe fibrosis and cirrhosis. ARFI alone had an area under the receiver operating characteristic curve (AUROC) of 0.742 for prediction of HCC in 5 years. AUROC increased to 0.828 after adding etiology, gender, age, and platelet score. No difference was found in mortality rate among the groups.

CONCLUSION

The HBV group showed a higher prevalence of HCC but lower comorbidity that made mortality similar among the groups. Those patients with ARFI-graded severe fibrosis or cirrhosis should receive regular surveillance.

[A descriptive analysis of hyperlipidemia in adult twins in China]

Objective: To describe the distribution characteristics of hyperlipidemia in adult twins in the Chinese National Twin Registry (CNTR) and explore the effect of genetic and environmental factors on hyperlipidemia. Methods: Twins recruited from the CNTR in 11 project areas across China were included in the study. A total of 69 130 (34 565 pairs) of adult twins with complete information on hyperlipidemia were selected for analysis. The random effect model was used to characterize the population and regional distribution of hyperlipidemia among twins. The concordance rates of hyperlipidemia were calculated in monozygotic twins (MZ) and dizygotic twins (DZ), respectively, to estimate the heritability. Results: The age of all participants was (34.2±12.4) years. This study's prevalence of hyperlipidemia was 1.3% (895/69 130). Twin pairs who were men, older, living in urban areas, married,had junior college degree or above, overweight, obese, insufficient physical activity, current smokers, ex-smokers, current drinkers, and ex-drinkers had a higher prevalence of hyperlipidemia (P<0.05). In within-pair analysis, the concordance rate of hyperlipidemia was 29.1% (118/405) in MZ and 18.1% (57/315) in DZ, and the difference was statistically significant (P<0.05). Stratified by gender, age, and region, the concordance rate of hyperlipidemia in MZ was still higher than that in DZ. Further, in within-same-sex twin pair analyses, the heritability of hyperlipidemia was 13.04% (95%CI: 2.61%-23.47%) in the northern group and 18.59% (95%CI: 4.43%-32.74%) in the female group, respectively. Conclusions: Adult twins were included in this study and were found to have a lower prevalence of hyperlipidemia than in the general population study, with population and regional differences. Genetic factors influence hyperlipidemia, but the genetic effect may vary with gender and area.

[A descriptive analysis on hypertension in adult twins in China]

Objective: To describe the distribution characteristics of hypertension among adult twins in the Chinese National Twin Registry (CNTR) and to provide clues for exploring the role of genetic and environmental factors on hypertension. Methods: A total of 69 220 (34 610 pairs) of twins aged 18 and above with hypertension information were selected from CNTR registered from 2010 to 2018. Random effect models were used to describe the population and regional distribution of hypertension in twins. To estimate the heritability, the concordance rates of hypertension were calculated and compared between monozygotic twins (MZ) and dizygotic twins (DZ). Results: The age of all participants was (34.1±12.4) years. The overall self-reported prevalence of hypertension was 3.8%(2 610/69 220). Twin pairs who were older, living in urban areas, married, overweight or obese, current smokers or ex-smokers, and current drinkers or abstainers had a higher self-reported prevalence of hypertension (P<0.05). Analysis within the same-sex twin pairs found that the concordance rate of hypertension was 43.2% in MZ and 27.0% in DZ, and the difference was statistically significant (P<0.001). The heritability of hypertension was 22.1% (95%CI: 16.3%- 28.0%). Stratified by gender, age, and region, the concordance rate of hypertension in MZ was still higher than that in DZ. The heritability of hypertension was higher in female participants. Conclusions: There were differences in the distribution of hypertension among twins with different demographic and regional characteristics. It is indicated that genetic factors play a crucial role in hypertension in different genders, ages, and regions, while the magnitude of genetic effects may vary.

Radiomics model and clinical scale for the preoperative diagnosis of silent corticotroph adenomas

OBJECTIVE

Silent corticotroph adenomas (SCAs) are a subtype of nonfunctioning pituitary adenomas that exhibit more aggressive behavior. However, rapid and accurate preoperative diagnostic methods are currently lacking.

DESIGN

The purpose of this study was to examine the differences between SCA and non-SCA features and to establish radiomics models and a clinical scale for rapid and accurate prediction.

METHODS

A total of 260 patients (72 SCAs vs. 188 NSCAs) with nonfunctioning adenomas from Peking Union Medical College Hospital were enrolled in the study as the internal dataset. Thirty-five patients (6 SCAs vs. 29 NSCAs) from Fuzhou General Hospital were enrolled as the external dataset. Radiomics models and an SCA scale to preoperatively diagnose SCAs were established based on MR images and clinical features.

RESULTS

There were more female patients (internal dataset: p < 0.001; external dataset: p = 0.028) and more multiple microcystic changes (internal dataset: p < 0.001; external dataset: p = 0.012) in the SCA group. MRI showed more invasiveness (higher Knosp grades, p ≤ 0.001). The radiomics model achieved AUCs of 0.931 and 0.937 in the internal and external datasets, respectively. The clinical scale achieved an AUC of 0.877 and a sensitivity of 0.952 in the internal dataset and an AUC of 0.899 and a sensitivity of 1.0 in the external dataset.

CONCLUSIONS

Based on clinical information and imaging characteristics, the constructed radiomics model achieved high preoperative diagnostic ability. The SCA scale achieved the purpose of rapidity and practicality while ensuring sensitivity, which is conducive to simplifying clinical work.

[Resting heart rate correlates with major adverse cardiovascular and cerebrovascular events in patients with post-myocardial infarction ventricular aneurysms: a retrospective cohort study]

OBJECTIVE

To analyze the association of resting heart rate (RHR) with the prognosis of patients with post-infarction ventricular aneurysms.

METHODS

We retrospectively analyzed the clinical data of 227 patients with post-infarction ventricular aneurysms admitted to our hospital during 2017-2019. The endpoint event was the occurrence of any major adverse cardiovascular and cerebrovascular events (MACCEs) during the follow-up for 24 months. According to RHR measurements, the patients were divided into 3 groups with baseline RHR < 10%, 10%-90%, and >90%. The Cox proportional risk model and restricted cubic spline (RCS) model were used to analyze the effect of RHR on MACCEs.

RESULTS

During the 24-month followup, 90 patients (39.6%) experienced MACCEs. The fully adjusted RCS curves showed a nonlinear "U" shaped correlation between RHR and the occurrence of MACCEs. In the fully adjusted model, the risk of MACCEs increased by 3.01-fold (Hazard ratio [HR]=4.01, 95% CI: 2.07-7.76, P < 0.001) in patients with RHR>90%, as compared with patients with RHR of 10%-90%. In patients with RHR in 1-9th percentile, 10th-90th percentile and 91st-100th percentile, the incidences of MACCEs were 39.1%, 36.6% and 66.7% (P=0.027), the incidences of ventricular tachycardia/ventricular fibrillation (VT/VF) were 17.4%, 2.7% and 4.8% (P=0.005), and the incidences of readmission for heart failure were 8.7%, 26.8% and 42.9% (P=0.036), respectively.

CONCLUSION

Continuous monitoring and management of heart rate range may provide guidance for prognosis prediction in patients with post-infarction ventricular aneurysms.

A35 FORECASTING THE INCIDENCE AND PREVALENCE OF INFLAMMATORY BOWEL DISEASE: A CANADIAN NATION-WIDE ANALYSIS

Background

Canada is currently in the third epidemiological stage in the evolution of IBD: compounding prevalence. A high incidence of IBD, in conjunction with low mortality, leads to a steadily rising prevalence over time. By understanding historical epidemiological trends, we can forecast incidence and prevalence into the future to inform healthcare systems in Canada of the rising burden of IBD to society.

Purpose

To analyze past epidemiological trends in order to forecast the overall incidence and prevalence of IBD, Crohn’s disease (CD), and ulcerative colitis (UC) and stratified by age (<18, 18-64, 65+).

Method

Canadian population-based administrative data was acquired from: AB, BC, SK, MB, QC, and ON. Data were age and sex standardized to the matching year and provincial data aggregated into a representative sample of the Canadian population for prevalence (2002-2014) and incidence (2007-2014: 5-year washout period). Incidence and prevalence (per 100,000 persons) were calculated, with 95% confidence intervals (CI), using Canadian population estimates from Statistics Canada for IBD, CD, UC (IBD-unclassifiable+UC). Autoregressive Integrated Moving Average models were created, and rates forecasted from 2014 to 2035 with 95% prediction intervals (PI). Poisson (or negative binomial) for incidence and log binomial regression for prevalence estimated the Average Annual Percentage Change (AAPC), with 95% CIs, of the forecasted data.

Result(s)

The 2014 incidence of IBD in Canada was 28.4 per 100,000 (95%CI: 27.8, 29.0) and forecasted to significantly increase (AAPC: 0.58%; 95%CI: 0.04, 1.04) from 30.0 per 100,000 in 2023 to 32.1 (95%PI: 27.9, 36.3) in 2035. Pediatric onset IBD was 13.9 per 100,000 (95%CI: 13.0, 14.9) in 2014 and is forecasted to significantly increase to 18.0 per 100,000 (95%PI: 15.7, 20.2) in 2035 with an AAPC of 1.23% (95%CI: 0.76, 1.63). Adult and elderly onset incidence rates were forecasted to remain stable. Prevalence of IBD increased between 2002 (389 per 100,000) and 2014 (636 per 100,000) and is forecasted to continue to climb by an AAPC of 2.44% (95%CI: 2.34, 2.53). In 2023, the prevalence of IBD is 825 per 100,000. By 2035 prevalence is forecasted to climb to 1075 per 100,000 (95%PI: 1047, 1103) with 470,000 Canadians living with IBD. Prevalence across all age strata were forecasted to significantly increase. The highest AAPC was seen in the elderly (2.76%; 95%CI: 2.73, 2.79) with a prevalence of 841 per 100,000 (95%CI: 834, 849) in 2014 and forecasted to climb to 1534 per 100,000 (95%PI: 1519, 1550) in 2035.

Image

Conclusion(s)

Incidence of IBD continues to rise in Canada, driven by pediatric-onset IBD. In 2023, over 320,000 Canadians (0.83%) will be living with IBD. By 2035 prevalence will exceed 1% of the population with approximately 470,000 individuals in Canada with IBD. Future research should establish the environmental determinates of IBD that may influence temporal trends in the incidence of IBD, while healthcare systems adapt to the compounding prevalence of IBD.

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CIHR, Other

Please indicate your source of funding;

The Leona M. and Harry B. Helmsley Charitable Trust

Disclosure of Interest

S. Coward: None Declared, E. Benchimol Consultant of: Hoffman La-Roche Limited and Peabody & Arnold LLP for matters unrelated to medications used to treat inflammatory bowel disease and McKesson Canada and the Dairy Farmers of Ontario for matters unrelated to medications used to treat inflammatory bowel disease., C. Bernstein Grant / Research support from: Unrestricted educational grants from Abbvie Canada, Janssen Canada, Pfizer Canada, Bristol Myers Squibb Canada, and Takeda Canada. Has received research grants from Abbvie Canada, Amgen Canada, Pfizer Canada, and Sandoz Canada and contract grants from Janssen, Abbvie and Pfizer, Consultant of: Abbvie Canada, Amgen Canada, Bristol Myers Squibb Canada, JAMP Pharmaceuticals, Janssen Canada, Pfizer Canada, Sandoz Canada, and Takeda., Speakers bureau of: Abbvie Canada, Janssen Canada, Pfizer Canada and Takeda Canada, J. A. Avina-Zubieta: None Declared, A. Bitton: None Declared, L. Hracs: None Declared, J. Jones Consultant of: Janssen, Abbvie, Pfizer, Takeda, Speakers bureau of: Janssen, Abbvie, Pfizer, Takeda, E. Kuenzig: None Declared, L. Lu: None Declared, S. Murthy: None Declared, Z. Nugent: None Declared, A. Otley Grant / Research support from: Unrestricted educational grants from AbbVie Canada and Janssen Canada, Consultant of: Advisory boards of AbbVie Canada, Janssen Canada and Nestle, R. Panaccione Consultant of: Abbott, AbbVie, Alimentiv (formerly Robarts), Amgen, Arena Pharmaceuticals, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Celltrion, Cosmos Pharmaceuticals, Eisai, Elan, Eli Lilly, Ferring, Galapagos, Fresenius Kabi, Genentech, Gilead Sciences, Glaxo-Smith Kline, JAMP Bio, Janssen, Merck, Mylan, Novartis, Oppilan Pharma, Organon, Pandion Pharma, Pendopharm, Pfizer, Progenity, Protagonist Therapeutics, Roche, Sandoz, Satisfai Health, Shire, Sublimity Therapeutics, Takeda Pharmaceuticals, Theravance Biopharma, Trellus, Viatris, UCB. Advisory Boards for: AbbVie, Alimentiv (formerly Robarts), Amgen, Arena Pharmaceuticals, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Eli Lilly, Ferring, Fresenius Kabi, Genentech, Gilead Sciences, Glaxo-Smith Kline, JAMP Bio, Janssen, Merck, Mylan, Novartis, Oppilan Pharma, Organon, Pandion Pharma, Pfizer, Progenity, Protagonist Therapeutics, Roche, Sandoz Shire, Sublimity Therapeutics, Takeda Pharmaceuticals, Speakers bureau of: AbbVie, Amgen, Arena Pharmaceuticals, Bristol-Myers Squibb, Celgene, Eli Lilly, Ferring, Fresenius Kabi, Gilead Sciences, Janssen, Merck, Organon, Pfizer, Roche, Sandoz, Shire, Takeda Pharmaceuticals, J.-N. Pena-Sanchez: None Declared, H. Singh Consultant of: Pendopharm, Amgen Canada, Bristol Myers Squibb Canada, Roche Canada, Sandoz Canada, Takeda Canada, and Guardant Health, Inc., L. Targownik Grant / Research support from: Investigator initiated funding from Janssen Canada, Consultant of: [Advisory board] AbbVie Canada, Takeda Canada, Merck Canada, Pfizer Canada, Janssen Canada, Roche Canada, and Sandoz Canada, J. Windsor: None Declared, G. Kaplan Grant / Research support from: Ferring, Janssen, AbbVie, GlaxoSmith Kline, Merck, and Shire, Consultant of: Gilead, Speakers bureau of: AbbVie, Janssen, Pfizer, Amgen, and Takeda

A210 THE BURDEN OF IBD HOSPITALIZATION IN CANADA: AN ASSESSMENT OF THE CURRENT AND FUTURE BURDEN IN A NATION-WIDE ANALYSIS

Background

Hospitalizations pose a significant burden on both the individual and the healthcare system. Those with inflammatory bowel disease (IBD) are at increased risk of hospitalization as compared to the general population due to flaring of disease activity and complications related to IBD. The advent of biologics over the past twenty years may have influenced the rates of hospitalization for IBD.

Purpose

To assess current and forecast the overall hospitalization rates of those with IBD stratified by types of hospitalizations (all cause hospitalizations, IBD-related, and IBD-specific).

Method

Population-based administrative data on hospitalization of IBD (2002-2014) were obtained from: AB, BC, MB, and SK. Data were age and sex standardized to the matching year and aggregated into a representative sample of the Canadian population. Hospitalization rates were assessed as follows: 1. All cause hospitalizations: all admissions regardless of indication; 2. IBD-specific: an admission directly resulting from IBD (e.g., IBD-flare); 3. IBD-related: an admission for IBD, or a symptom or comorbidity associated with IBD (e.g. rheumatoid arthritis). Using prevalence estimates from the provinces, hospitalization rates (per 100 persons with IBD) were calculated, with 95% confidence intervals (CI). Autoregressive Integrated Moving Average models were created to estimate number of hospitalizations and corresponding prevalence to forecast hospitalization rates to 2030 with 95% prediction intervals (PI). Poisson (or negative binomial) regression estimated the Average Annual Percentage Change (AAPC), with 95% CIs, of the forecasted data.

Result(s)

In 2002 there were 35.3 per 100 (95%CI: 34.7, 35.9) all cause hospitalizations for IBD patients and this decreased to 24.9 per 100 (24.5, 25.2) in 2014. Similar trends were seen for IBD-specific hospitalizations [16.8 per 100 (95%CI: 16.4, 17.2) in 2002 to 8.7 per 100 (95%CI: 8.5, 9.0) in 2014] and IBD-related (22.6 per 100 (95%CI: 22.1, 23.1) in 2002 to 13.4 per 100 (95%CI: 13.2, 13.7) in 2014). When forecasted out to 2030 all hospitalization types were significantly decreasing—the AAPC for all cause hospitalizations was -2.12% (95%CI: -2.31, -1.93), -3.77% (95%CI: -4.63, -3.08) for IBD-specific, and -3.09% (95%CI: -3.65, -2.62) for IBD-related. By 2030, the rates of hospitalization are forecasted to be 17.0 per 100 (95%PI: 16.2, 17.9), 4.6 per 100 (95%PI: 3.7, 5.4), and 7.9 per 100 (95%PI: 6.9, 8.9) for all cause, IBD-specific, and IBD-related, respectively.

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Conclusion(s)

In Canada, rates of hospitalizations for those with IBD have decreased from 2002 to 2014. The use of anti-TNF therapy in conjunction with the evolution of clinical monitoring, management and guidelines, likely has contributed to dropping hospitalization rates. Forecast models estimate a continued drop in hospitalization rates out to 2030. Importantly, healthcare resource planning should account for the shift from hospital-based to clinic-centric models of IBD care.

Please acknowledge all funding agencies by checking the applicable boxes below

CIHR

Disclosure of Interest

S. Coward: None Declared, E. Benchimol Consultant of: Hoffman La-Roche Limited and Peabody & Arnold LLP for matters unrelated to medications used to treat inflammatory bowel disease and McKesson Canada and the Dairy Farmers of Ontario for matters unrelated to medications used to treat inflammatory bowel disease., C. Bernstein Grant / Research support from: Unrestricted educational grants from Abbvie Canada, Janssen Canada, Pfizer Canada, Bristol Myers Squibb Canada, and Takeda Canada. Has received research grants from Abbvie Canada, Amgen Canada, Pfizer Canada, and Sandoz Canada and contract grants from Janssen, Abbvie and Pfizer, Consultant of: Abbvie Canada, Amgen Canada, Bristol Myers Squibb Canada, JAMP Pharmaceuticals, Janssen Canada, Pfizer Canada, Sandoz Canada, and Takeda., Speakers bureau of: Abbvie Canada, Janssen Canada, Pfizer Canada and Takeda Canada, J. A. Avina-Zubieta: None Declared, A. Bitton: None Declared, L. Hracs: None Declared, J. Jones Consultant of: Janssen, Abbvie, Pfizer, Takeda, Speakers bureau of: Janssen, Abbvie, Pfizer, Takeda, E. Kuenzig: None Declared, L. Lu: None Declared, S. Murthy: None Declared, Z. Nugent: None Declared, A. Otley Grant / Research support from: Unrestricted educational grants from AbbVie Canada and Janssen Canada, Consultant of: Advisory boards of AbbVie Canada, Janssen Canada and Nestle, R. Panaccione Consultant of: Abbott, AbbVie, Alimentiv (formerly Robarts), Amgen, Arena Pharmaceuticals, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Celltrion, Cosmos Pharmaceuticals, Eisai, Elan, Eli Lilly, Ferring, Galapagos, Fresenius Kabi, Genentech, Gilead Sciences, Glaxo-Smith Kline, JAMP Bio, Janssen, Merck, Mylan, Novartis, Oppilan Pharma, Organon, Pandion Pharma, Pendopharm, Pfizer, Progenity, Protagonist Therapeutics, Roche, Sandoz, Satisfai Health, Shire, Sublimity Therapeutics, Takeda Pharmaceuticals, Theravance Biopharma, Trellus, Viatris, UCB. Advisory Boards for: AbbVie, Alimentiv (formerly Robarts), Amgen, Arena Pharmaceuticals, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Eli Lilly, Ferring, Fresenius Kabi, Genentech, Gilead Sciences, Glaxo-Smith Kline, JAMP Bio, Janssen, Merck, Mylan, Novartis, Oppilan Pharma, Organon, Pandion Pharma, Pfizer, Progenity, Protagonist Therapeutics, Roche, Sandoz Shire, Sublimity Therapeutics, Takeda Pharmaceuticals, Speakers bureau of: AbbVie, Amgen, Arena Pharmaceuticals, Bristol-Myers Squibb, Celgene, Eli Lilly, Ferring, Fresenius Kabi, Gilead Sciences, Janssen, Merck, Organon, Pfizer, Roche, Sandoz, Shire, Takeda Pharmaceuticals, J.-N. Pena-Sanchez: None Declared, H. Singh Consultant of: Pendopharm, Amgen Canada, Bristol Myers Squibb Canada, Roche Canada, Sandoz Canada, Takeda Canada, and Guardant Health, Inc.,, L. Targownik Grant / Research support from: Investigator initiated funding from Janssen Canada, Consultant of: [Advisory board] AbbVie Canada, Takeda Canada, Merck Canada, Pfizer Canada, Janssen Canada, Roche Canada, and Sandoz Canada, J. Windsor: None Declared, G. Kaplan Grant / Research support from: Ferring, Janssen, AbbVie, GlaxoSmith Kline, Merck, and Shire, Consultant of: Gilead, Speakers bureau of: AbbVie, Janssen, Pfizer, Amgen, and Takeda

A169 THE DIRECT COSTS OF INFLAMMATORY BOWEL DISEASE IN CANADA: A POPULATION-BASED ANALYSIS OF HISTORICAL AND CURRENT COSTS

Background

Inflammatory bowel disease (IBD) is a costly disease to manage due to hospitalizations, regular ambulatory monitoring, and expensive pharmaceutical therapies. While hospitalization rates have fallen, the increased use of biologics have escalated the cost of care to the healthcare system.

Purpose

To assess historical direct healthcare costs of the IBD population in Canada.

Method

Population-based administrative costing data were obtained from: Alberta, British Columbia, and Manitoba. Costs were calculated based on administrative data (2009 to 2016) which captured: hospitalizations, physician costs, ambulatory care such as: emergency visits, day surgery, and colonoscopy (AB only), and medication costs of IBD-specific medications, such as: mesalamine, biologics, steroids, and immunomodulators. Costs were converted to 2020 dollars using the consumer price index. Average annual cost per person (ACPP) was calculated for each province. Using province specific IBD prevalence estimates these ACPP were meta-analyzed to obtain the annual weighted costs, with 95% confidence intervals (CI), and these costs underwent meta-regression to ascertain the average annual change in cost per year. An Autoregressive Integrated Moving Average model was created to estimate the ACPP in 2023 with 95% prediction intervals (PI). Canada-wide total direct care costs of IBD patients, in billions (B), were calculated using the ACPP, Canada-specific IBD prevalence estimates (historical and forecasted), and total Canadian population calculations from Statistics Canada (historical and forecasted).

Result(s)

In 2009 the ACPP was $7000 (95%CI: 5389, 8610), representing $1.18B (95%CI: 0.91B, 1.45B) in direct healthcare costs in Canada for all IBD patients. The ACPP in 2016 was increased to $10,336 (95%CI: 6803, 13869), which equates to $2.37B (95%CI: 1.56B, 3.18B) per year in direct healthcare costs. From 2009 to 2016, the ACPP increased an average of $450 (95%CI: 132, 767) per year. If these historical trends continue to 2023 the ACPP is forecasted to be $13,333 (95%PI: 12827, 13839) per person per year. The largest contributor to these costs is medications—accounting for an estimated 50% of the total costs of IBD patients.

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Conclusion(s)

The direct healthcare cost of IBD has risen steadily from 2009 to 2016 when the healthcare system spent over $10,000 per person with IBD and $2.37B nationwide. The primary driver of costs is medical management. Forecast models estimate that the annual cost may be over $13,000 per person in 2023. However, these estimates do not account for advent and increased uptake of novel biologics and small molecules, nor the downward cost pressure of biosimilars. These costs are those paid directly by the healthcare system and do not account for those born by the individual—it is estimated that the true cost of IBD (direct and indirect) is much higher.

Please acknowledge all funding agencies by checking the applicable boxes below

CIHR

Disclosure of Interest

S. Coward: None Declared, E. Benchimol Consultant of: Hoffman La-Roche Limited and Peabody & Arnold LLP for matters unrelated to medications used to treat inflammatory bowel disease and McKesson Canada and the Dairy Farmers of Ontario for matters unrelated to medications used to treat inflammatory bowel disease., C. Bernstein Grant / Research support from: Unrestricted educational grants from Abbvie Canada, Janssen Canada, Pfizer Canada, Bristol Myers Squibb Canada, and Takeda Canada. Has received research grants from Abbvie Canada, Amgen Canada, Pfizer Canada, and Sandoz Canada and contract grants from Janssen, Abbvie and Pfizer, Consultant of: Abbvie Canada, Amgen Canada, Bristol Myers Squibb Canada, JAMP Pharmaceuticals, Janssen Canada, Pfizer Canada, Sandoz Canada, and Takeda., Speakers bureau of: Abbvie Canada, Janssen Canada, Pfizer Canada and Takeda Canada, J. A. Avina-Zubieta: None Declared, A. Bitton: None Declared, L. Hracs: None Declared, J. Jones Consultant of: Janssen, Abbvie, Pfizer, Takeda, Speakers bureau of: Janssen, Abbvie, Pfizer, Takeda, E. Kuenzig: None Declared, L. Lu: None Declared, S. Murthy: None Declared, Z. Nugent: None Declared, A. Otley Grant / Research support from: Unrestricted educational grants from AbbVie Canada and Janssen Canada, Consultant of: Advisory boards of AbbVie Canada, Janssen Canada and Nestle, R. Panaccione Consultant of: Abbott, AbbVie, Alimentiv (formerly Robarts), Amgen, Arena Pharmaceuticals, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Celltrion, Cosmos Pharmaceuticals, Eisai, Elan, Eli Lilly, Ferring, Galapagos, Fresenius Kabi, Genentech, Gilead Sciences, Glaxo-Smith Kline, JAMP Bio, Janssen, Merck, Mylan, Novartis, Oppilan Pharma, Organon, Pandion Pharma, Pendopharm, Pfizer, Progenity, Protagonist Therapeutics, Roche, Sandoz, Satisfai Health, Shire, Sublimity Therapeutics, Takeda Pharmaceuticals, Theravance Biopharma, Trellus, Viatris, UCB. Advisory Boards for: AbbVie, Alimentiv (formerly Robarts), Amgen, Arena Pharmaceuticals, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Eli Lilly, Ferring, Fresenius Kabi, Genentech, Gilead Sciences, Glaxo-Smith Kline, JAMP Bio, Janssen, Merck, Mylan, Novartis, Oppilan Pharma, Organon, Pandion Pharma, Pfizer, Progenity, Protagonist Therapeutics, Roche, Sandoz Shire, Sublimity Therapeutics, Takeda Pharmaceuticals, Speakers bureau of: AbbVie, Amgen, Arena Pharmaceuticals, Bristol-Myers Squibb, Celgene, Eli Lilly, Ferring, Fresenius Kabi, Gilead Sciences, Janssen, Merck, Organon, Pfizer, Roche, Sandoz, Shire, Takeda Pharmaceuticals, J.-N. Pena-Sanchez: None Declared, H. Singh Consultant of: Pendopharm, Amgen Canada, Bristol Myers Squibb Canada, Roche Canada, Sandoz Canada, Takeda Canada, and Guardant Health, Inc.,, L. Targownik Grant / Research support from: Investigator initiated funding from Janssen Canada, Consultant of: [Advisory board] AbbVie Canada, Takeda Canada, Merck Canada, Pfizer Canada, Janssen Canada, Roche Canada, and Sandoz Canada, J. Windsor: None Declared, G. Kaplan Grant / Research support from: Ferring, Janssen, AbbVie, GlaxoSmith Kline, Merck, and Shire, Consultant of: Gilead, Speakers bureau of: AbbVie, Janssen, Pfizer, Amgen, and Takeda

Limits to Gauge Coupling in the Dark Sector Set by the Nonobservation of Instanton-Induced Decay of Super-Heavy Dark Matter in the Pierre Auger Observatory Data

Instantons, which are nonperturbative solutions to Yang-Mills equations, provide a signal for the occurrence of quantum tunneling between distinct classes of vacua. They can give rise to decays of particles otherwise forbidden. Using data collected at the Pierre Auger Observatory, we search for signatures of such instanton-induced processes that would be suggestive of super-heavy particles decaying in the Galactic halo. These particles could have been produced during the post-inflationary epoch and match the relic abundance of dark matter inferred today. The nonobservation of the signatures searched for allows us to derive a bound on the reduced coupling constant of gauge interactions in the dark sector: α_{X}≲0.09, for 10^{9}≲M_{X}/GeV<10^{19}. Conversely, we obtain that, for instance, a reduced coupling constant α_{X}=0.09 excludes masses M_{X}≳3×10^{13}  GeV. In the context of dark matter production from gravitational interactions alone, we illustrate how these bounds are complementary to those obtained on the Hubble rate at the end of inflation from the nonobservation of tensor modes in the cosmological microwave background.

Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma

Background Although deep learning has brought revolutionary changes in health care, reliance on manually selected cross-sectional images and segmentation remain methodological barriers. Purpose To develop and validate an automated preoperative artificial intelligence (AI) algorithm for tumor and lymph node (LN) segmentation with CT imaging for prediction of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, patients with surgically resected, pathologically confirmed PDAC underwent multidetector CT from January 2015 to April 2020. Three models were developed, including an AI model, a clinical model, and a radiomics model. CT-determined LN metastasis was diagnosed by radiologists. Multivariable logistic regression analysis was conducted to develop the clinical and radiomics models. The performance of the models was determined on the basis of their discrimination and clinical utility. Kaplan-Meier curves, the log-rank test, or Cox regression were used for survival analysis. Results Overall, 734 patients (mean age, 62 years ± 9 [SD]; 453 men) were evaluated. All patients were split into training (n = 545) and validation (n = 189) sets. Patients who had LN metastasis (LN-positive group) accounted for 340 of 734 (46%) patients. In the training set, the AI model showed the highest performance (area under the receiver operating characteristic curve [AUC], 0.91) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.58, 0.76, and 0.71, respectively. In the validation set, the AI model showed the highest performance (AUC, 0.92) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.65, 0.77, and 0.68, respectively (P < .001). AI model-predicted positive LN metastasis was associated with worse survival (hazard ratio, 1.46; 95% CI: 1.13, 1.89; P = .004). Conclusion An artificial intelligence model outperformed radiologists and clinical and radiomics models for prediction of lymph node metastasis at CT in patients with pancreatic ductal adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

Lumbar Bone Mineral Density Estimation From Chest X-Ray Images: Anatomy-Aware Attentive Multi-ROI Modeling

Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g., via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. The proposed method first automatically detects Regions of Interest (ROIs) of local CXR bone structures. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. The proposed method is evaluated on 13719 CXR patient cases with ground truth BMD measured by the gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.894 on lumbar 1). When applied in osteoporosis screening, it achieves a high classification performance (average AUC of 0.968). As the first effort of using CXR scans to predict the BMD, the proposed algorithm holds strong potential to promote early osteoporosis screening and public health.

Bibliometric analysis study on cognitive function in developmental coordination disorder from 2010 to 2022

Objective

To identify the research hotspots on cognitive function in developmental coordination disorder (DCD) in recent years, predict the research frontier and development trend, and provide more perspectives for the study of the DCD population.

Methods

Using CiteSpace and VOSviewer software to draw charts, 1,082 pieces of literature about DCD and cognitive function in the Web of Science core collection database from 2010 to 2022 were visually analyzed.

Results and conclusion

Interest in the cognitive function of DCD has been on the rise in the past 10 years. Over 40 countries and regions, 117 institutions and 200 researchers have participated in the corresponding research, mainly in the United States, and their institutions have published more highly influential results. The hot keywords are DCD, children, attention, working memory, performance, and attention-deficit/hyperactivity disorder (ADHD), and the main research hot topics include functional performance, population, cognitive psychology. The research directions include "DCD," "Asperger syndrome," "memory," "infant," "clumsiness," "neurodevelopmental disorder," "occupational therapy," "preschool children," "motor competence," "model," and "online control." Future research should focus on motor imagery and intrinsic models and use more neurophysiological techniques to reveal the cognitive characteristics of children with DCD and develop intervention programs.

Copper(I)-Catalyzed Syntheses of Benzo[b]fluorenes by the Cascade Reactions of 2-Alkynylbenzaldehyde N-Tosylhydrazones and Aromatic Terminal Alkynes

Benzo[b]fluorenes were synthesized via CuI-catalyzed cascade reactions of 2-alkynylbenzaldehyde N-tosylhydrazones and aromatic terminal alkynes in the presence of base including the formation of the benzoenyne-allene intermediate in situ and its Schmittel cyclization. Density functional theory calculation was performed to give the energy difference for the formation of 5-phenyl-11H-benzo[b]fluorene and 1,2-diphenyl-1H-cyclobuta[a]indene from the proposed diradical intermediates.