[Progress in the diagnose and treatment of pulmonary arterial thrombosis in situ]

In situ pulmonary arterial thrombosis (ISPAT) refers to the formation of new blood clots in the pulmonary arterial system in the absence of pre-existing clots in the peripheral venous system. With the emergence and prevalence of COVID-19, ISPAT has become an increasingly important cause of pulmonary arterial thrombosis (PAT) alongside thromboembolism. Several factors such as hypoxia, inflammation, endothelial dysfunction, and hypercoagulable state can lead to ISPAT, which is associated with a number of conditions such as thoracic trauma, partial lung resection, pulmonary infectious disease, pulmonary vasculitis, connective tissue diseases, severe pulmonary hypertension, radiation pneumonitis, and acute chest syndrome in sickle cell disease. It is important to differentiate between pulmonary thromboembolism (PTE) and ISPAT for proper disease management and prognosis. In this review, we summarized the characteristics of ISPAT under different disease conditions, the methods to distinguish ISPAT from PTE, and the best treatment strategies. We hoped that this review could improve clinicians' understanding of this independent disease and provide guidance for the refined treatment of patients with PAT.

[Interpretation of specification for service of cancer screening for workers]

As the backbone force of China's social and economic construction, the health status of workers is closely related to the nation's productivity and social development. Currently, cancers have become one of the major diseases threatening the health of workers. However, there are still many shortcomings in the cancer screening services for the workers. To standardize cancer screening services for workers, ensure the quality of screening services, and improve the overall screening effectiveness, 19 institutions, including Peking Union Medical College Hospital of the Chinese Academy of Medical Sciences, have jointly formulated the Group Standard "Specification for service of cancer screening for workers (T/CHAA 023-2023)". This standard follows the principles of "legality, scientific rigor, advancement, and feasibility" and combines the frontier scientific advances in cancer screening. It clarifies the relevant requirements for service principles, service design, service delivery, service management, service evaluation, and improving worker cancer screening. Implementing this group standard will help connect the common screening needs of workers, employers, and cancer screening service providers, standardize the screening process, improve screening quality, and ultimately increase the early diagnosis rate and survival rate of cancer patients. Consequently, this group standard will help safeguard workers' health rights and interests, ensure the labor force resources, promote the comprehensive coordinated and sustainable development of society, and contribute to realizing the "Healthy China 2030" strategic policy.

Terahertz in vivo imaging of human skin: Toward detection of abnormal skin pathologies

Terahertz (THz) imaging has long held promise for skin cancer detection but has been hampered by the lack of practical technological implementation. In this article, we introduce a technique for discriminating several skin pathologies using a coherent THz confocal system based on a THz quantum cascade laser. High resolution in vivo THz images (with diffraction limited to the order of 100 μm) of several different lesion types were acquired and compared against one another using the amplitude and phase values. Our system successfully separated pathologies using a combination of phase and amplitude information and their respective surface textures. The large scan field (50 × 40 mm) of the system allows macroscopic visualization of several skin lesions in a single frame. Utilizing THz imaging for dermatological assessment of skin lesions offers substantial additional diagnostic value for clinicians. THz images contain information complementary to the information contained in the conventional digital images.

Neurosonographic evaluation of corpus callosum-fastigium and tectal length in late-onset small fetuses

Linked article: This Correspondence comments on Lip‐Sosa et al. Click here to view the article.

Hepatitis E virus infection increases the risk of obstetric complications and perinatal adverse outcomes in pregnant women with chronic hepatitis B virus infection

OBJECTIVE

Hepatitis E virus (HEV) infection may occur in pregnant women who had chronic hepatitis B virus (HBV) infection. This study aimed to evaluate whether HEV-HBV co-infection increases the risk of obstetric complications and perinatal adverse outcomes in pregnant women.

PATIENTS AND METHODS

We investigated the clinical data of 3,251 pregnant women with chronic HBV infection. The obstetric complications and perinatal adverse outcomes were compared between patients with HEV-HBV co-infection and patients who had pure chronic HBV infection.

RESULTS

Of the 3,251 pregnant women with chronic HBV infection, 98 patients (3%) had HEV-HBV co-infection. Compared with healthy controls, there is an increased risk of obstetric complications in pregnant women with pure HEV infection [odds ratio (OR)= 3.99, p < 0.001], pure chronic HBV infection (OR = 2.76, p < 0.001), and HEV-HBV co-infection (OR = 5.41,p < 0.001). The rate of obstetric complications and perinatal adverse outcomes is significantly higher in pregnant women with HEV-HBV co-infection compared with those with pure chronic HBV infection or those with pure HEV infection (all p< 0.05). The HEV-HBV co-infection is the most significant risk factor for perinatal adverse outcomes (OR = 15.47, p < 0.001), followed by pure HEV infection (OR = 10.22, p < 0.001), and pure HBV infection (OR = 5.82, p < 0.001).

CONCLUSIONS

HEV infection increases the risk of obstetric complications and perinatal adverse outcomes in pregnant women with chronic HBV infection.

AI's Role in Improving Social Connection and Oral Health for Older Adults: A Synergistic Approach

KNOWLEDGE TRANSFER STATEMENT

This study explored how artificial intelligence (AI) can revolutionize geriatric care by improving oral health and alleviating social disconnection among isolated older adults. The findings can guide clinicians in integrating AI tools into practices, assist policymakers in developing AI-inclusive health policies, and inform patients about the potential benefits of AI in enhancing their health outcomes and social connection.

Association of Cognitive Reserve Indicator with Cognitive Decline and Structural Brain Differences in Middle and Older Age: Findings from the UK Biobank

BACKGROUND

Cognitive reserve (CR) contributes to preserving cognition when facing brain aging and damage. CR has been linked to dementia risk in late life. However, the association between CR and cognitive changes and brain imaging measures, especially in midlife, is unclear.

OBJECTIVE

We aimed to explore the association of CR with cognitive decline and structural brain differences in middle and older age.

DESIGN

This longitudinal study was from the UK Biobank project where participants completed baseline surveys between 2006 to 2010 and were followed (mean follow-up: 9 years).

SETTING

A population-based study.

PARTICIPANTS

A total of 42,301 dementia-free participants aged 40-70 were followed-up to detect cognitive changes. A subsample (n=34,041) underwent brain magnetic resonance imaging scans.

MEASUREMENTS

We used latent class analysis to generate a CR indicator (categorized as high, moderate, and low) based on education, occupation, and multiple cognitively stimulating activities. Cognitive tests for global and domain-specific cognition were administrated at baseline and follow-up. Total brain, white matter, grey matter, hippocampal, and white matter hyperintensity volumes (TBV, WMV, GMV, HV, and WMHV) were assessed at the follow-up examination. Data were analyzed using mixed-effects models and analysis of covariance.

RESULTS

At baseline, 16,032 (37.9%), 10,709 (25.3%), and 15,560 (36.8%) participants had low, moderate, and high levels of CR, respectively. Compared with low CR, high CR was associated with slower declines in global cognition (β [95% confidence interval]: 0.10 [0.08, 0.11]), prospective memory (0.10 [0.06, 0.15]), fluid intelligence (0.07 [0.04, 0.10]), and reaction time (0.04 [0.02, 0.06]). Participants with high CR had lower TBV, WMV, GMV, and WMHV, but higher HV when controlling for global cognition (corrected P <0.01 for all). The significant relationships between CR and cognition and TBV were present among both middle-aged (<60 years) and older (≥60 years) participants. The CR-cognition association remained significant despite reductions in brain structural properties.

CONCLUSIONS

Higher CR is associated with slower cognitive decline, higher HV, and lower microvascular burden, especially in middle age. Individuals with high CR could tolerate smaller brain volumes while maintaining cognition. The benefit of CR for cognition is independent of structural brain differences. Our findings highlight the contribution of enhancing CR to helping compensate for neuroimaging alterations and ultimately prevent cognitive decline.

Responses of the maize rhizosphere soil environment to drought-flood abrupt alternation stress

Changes in the soil environment in the root zone will affect the growth, development and resistance of plants. The mechanism underlying the effect of drought and flood stress on rhizosphere bacterial diversity, soil metabolites and soil enzyme activity is not clear and needs further study. To analyze the dynamic changes in bacteria, metabolites and enzyme activities in the rhizosphere soil of maize under different drought-flood abrupt alternation (DFAA) stresses, the barrel test method was used to set up the 'sporadic light rain' to flooding (referring to trace rainfall to heavy rain) (DFAA1) group, 'continuous drought' to flooding (DFAA2) group and normal irrigation (CK) group from the jointing to the tassel flowering stage of maize. The results showed that Actinobacteria was the most dominant phylum in the two DFAA groups during the drought period and the rewatering period, and Proteobacteria was the most dominant phylum during the flooding period and the harvest period. The alpha diversity index of rhizosphere bacteria in the DFAA2 group during the flooding period was significantly lower than that in other stages, and the relative abundance of Chloroflexi was higher. The correlation analysis between the differential genera and soil metabolites of the two DFAA groups showed that the relative abundance of Paenibacillus in the DFAA1 group was higher during the drought period, and it was significantly positively correlated with the bioactive lipid metabolites. The differential SJA-15 bacterium was enriched in the DFAA2 group during the flooding period and were strongly correlated with biogenic amine metabolites. The relative abundances of Arthrobacter, Alphaproteobacteria and Brevibacillus in the DFAA2 group were higher compared with DFAA1 group from rewatering to harvest and were significantly positively correlated with hydrocarbon compounds and steroid hormone metabolites. The acid phosphatase activity of the DFAA1 group was significantly higher than that of the DFAA2 group during the flooding period. The study suggests that there is a yield compensation phenomenon in the conversion of 'continuous drought' to flooding compared with 'sporadic light rain', which is related to the improvement in the flooding tolerance of maize by the dominant bacteria Chloroflexi, bacterium SJA-15 and biogenic amine metabolites. These rhizosphere bacteria and soil metabolites may have the potential function of helping plants adapt to the DFAA environment. The study revealed the response of the maize rhizosphere soil environment to DFAA stress and provided new ideas for exploring the potential mechanism of crop yield compensation under DFAA.

[Analysis on the current situation and influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative]

Objective: To understand the current situation and the main influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative. Methods: From the list of China's hygienic cities (excluding county-level cities), 61 cities were randomly selected in equal proportion and the eligible respondents were randomly selected by using the "Questionnaire Star" network platform to carry out the online questionnaire survey. A self-made satisfaction evaluation scale was used to investigate the satisfaction of the included respondents with the urban built environment and search for relevant data on the city level. The two-level multi-factor mixed effect model was constructed to analyze the influencing factors of residents' satisfaction with the built environment of China's Hygienic City Initiative. Results: The age range of 2 465 respondents was mainly between 18 and 40 years old (79.9%), with males being the main group (45.8%). The total score of residents' satisfaction with the built environment of China's hygienic cities was (69.14±13.24) points. Based on four standardized dimensions of sense of gain, the result showed that the satisfaction of urban governance had the highest score (65.08 points), followed by urban environmental sanitation (63.68 points), urban lifestyle (59.97 points) and urban basic function (59.02 points). The analysis results of the two-level multi-factor mixed effect model showed that compared with residents with an annual average concentration of inhalable fine particles in the environment>48 micrograms/cubic meter, residents with an average concentration between 38 and 48 micrograms/cubic meter [β (95%CI): 1.65 (0.08, 3.21)] and≤37 micrograms/cubic meter or less [β (95%CI): 1.98 (0.53, 3.43)] had higher satisfaction. Compared with residents whose proportion of the secondary industry to GDP was≤40.9%, residents in cities with a larger proportion had a lower satisfaction level [residents with a proportion of 40.9%-48.03%, β (95%CI):-2.21 (-3.93, -0.49); residents with a proportion greater than 48.03%, β (95%CI):-2.58 (-4.58, -0.59)]. Compared with residents with a junior high school or lower education level, residents with a higher education level had a lower satisfaction level [β (95%CI):-2.37 (-4.57, -0.17)]. Residents of universities and above [β (95%CI):-3.82 (-6.05, -1.60)], regularly participate in physical exercise [β (95%CI): 5.78 (4.71, 6.84)] and self-rated good health status [β (95%CI): 6.39 (5.33, 7.45)] had a higher satisfaction level. Conclusion: The satisfaction of residents with the built environment of China's hygienic cities is still acceptable. Satisfaction is related to individual characteristics such as residents' cultural level, type of residence, frequent participation in physical exercise, and self-rated good health status, as well as urban-level factors such as green coverage rate in built-up areas, annual average concentration of inhalable fine particles, and the proportion of GDP in the secondary industry.

Different responses of abundant and rare bacterial composition to groundwater depth and reduced nitrogen application in summer maize field

Introduction

It is well known that reduced nitrogen application and groundwater depth can change soil microbial communities, but the associated difference in the response of abundant and rare bacterial composition to these local environmental changes remains unclear.

Methods

In this study a lysimeter experiment was carried out to examine the impact of reduced nitrogen and groundwater depth on the composition of abundant and rare bacteria.

Results and discussion

Our results demonstrated that the summer maize field soil species composition of rare bacterial sub-communities was significantly regulated by reduced nitrogen application, groundwater depth change and their interactions. However, only reduced nitrogen application had a significant influence on the species composition of abundant bacterial sub-communities. The structural equation model (SEM) indicated that reduced nitrogen application and groundwater depth change also could indirectly regulate the species composition of abundant and rare bacteria by altering soil attributes. The changes in soil pH and TSN had the most significant effects on the community composition of abundant and rare bacteria, respectively. More importantly, rare bacterial sub-communities were more sensitive to the changes in nitrogen input, groundwater depth and soil factors. Collectively, our study first demonstrated that abundant and rare microbial sub-communities responded differently to reduced nitrogen application and groundwater depth change. This study highlights that summer maize farmland production management should take nitrogen input and groundwater depth into consideration to maintain the compositional stability of soil rare microbial sub-communities.

Polygenic adaptation leads to a higher reproductive fitness of native Tibetans at high altitude

The adaptation of Tibetans to high-altitude environments has been studied extensively. However, the direct assessment of evolutionary adaptation, i.e., the reproductive fitness of Tibetans and its genetic basis, remains elusive. Here, we conduct systematic phenotyping and genome-wide association analysis of 2,252 mother-newborn pairs of indigenous Tibetans, covering 12 reproductive traits and 76 maternal physiological traits. Compared with the lowland immigrants living at high altitudes, indigenous Tibetans show better reproductive outcomes, reflected by their lower abortion rate, higher birth weight, and better fetal development. The results of genome-wide association analyses indicate a polygenic adaptation of reproduction in Tibetans, attributed to the genomic backgrounds of both the mothers and the newborns. Furthermore, the EPAS1-edited mice display higher reproductive fitness under chronic hypoxia, mirroring the situation in Tibetans. Collectively, these results shed new light on the phenotypic pattern and the genetic mechanism of human reproductive fitness in extreme environments.

Differences in Cadmium Uptake and Accumulation in Seedlings of Wheat Varieties with Low- and High-Grain Cadmium Accumulation under Different Drought Stresses

Cadmium (Cd) and drought, as abiotic stresses, have long been significant challenges for crop growth and agricultural production. However, there have been relatively few studies conducted on the effects of drought stress on Cd uptake, especially regarding the differences in Cd uptake characterization in varieties with varying Cd accumulation under different drought stress. To investigate the effects of drought conditions on Cd uptake by wheat in different genotypes under specific background levels of Cd pollution, we validated the differences in root absorption characteristics of low- (YM) and high-grain Cd accumulating wheat genotypes (XM) using non-invasive micro-test technology, and we conducted a hydroponic experiment on the Cd addition and different drought levels in a climate-controlled chamber. The biomass, root morphology, Cd uptake, and accumulation were determined under Cd (100 µmol L-1) and different drought levels of 0% (0 MPa), 5% (-0.100 Mpa), 10% (-0.200 Mpa), and 15% (-0.388 Mpa) simulated by polyethylene glycol (PEG-6000). We found that the simultaneous exposure to Cd and drought had a suppressive effect on the total root lengths, root surface areas, and root volumes of XM and YM, albeit with distinct patterns of variation. As the concentration of PEG-6000 increased, the Cd concentrations and the amount of Cd accumulated in the roots and shoots of XM and YM decreased. Specifically, the Cd concentration in the roots exhibited a reduction ranging from 12.51% to 66.90%, while the Cd concentration in the shoots experienced an even greater decrease of 50.46% to 80.57%. The PEG-6000 concentration was significantly negatively correlated (p < 0.001) with Cd concentration of roots and shoots and Cd accumulation in roots, shoots, and the whole plants and significantly negatively correlated (p < 0.05) with the total length, surface area, and volume of roots. This study confirms that drought stress (5% PEG-6000) can decrease the uptake and accumulation of Cd in wheat seedlings without significant inhibition of biomass, and the change of root morphology (root length) and the decrease of Cd concentration in roots may be the main direct pathways for achieving these effects under drought stress. This research provides a new perspective and idea for water management in Cd-contaminated farmland.

Objective Boundary Generation for Gross Target Volume and Organs at Risk Using 3D Multi-Modal Medical Images

PURPOSE/OBJECTIVE(S)

Accurate delineation of Gross Target Volume (GTV) and Organs at Risk (OARs) in medical images is an essential but challenging step in radiotherapy. Deep-learning based automated delineation methods, which learn from manual annotations, are currently prevalent in academic research. However, the limited resolution of medical images and the fuzzy boundaries of lesions and organs present a challenge to the precision of manual annotations. By leveraging the complementary information from multi-modal medical images, this study proposed a novel method to generate objective boundaries of GTV and OARs.

MATERIALS/METHODS

We present a novel method of objective boundary generation, inspired by image matting primarily used for 2D RGB natural images, to process 3D grayscale medical images. The proposed method has the following advantages. 1) It allows for flexible input modalities and assigns weights to each modality according to their relative significance when computing information flows in the matting algorithm. 2) It computes 3D spatial information flow among voxels, which has more advantages over its 2D counterpart. 3) It has a closed-form solution that generates deterministic results. To evaluate the characteristics of the generated boundaries, patients with stage I nasopharyngeal carcinoma (NPC) were studied, with CT images and multi-modal MR images (T1, T1C, T2) aligned using deformable registration. Region of Interests (ROIs), i.e., GTV and parotid gland, were used, with a rough trimap marking extremely few foreground voxels, many background voxels, and a large unknown region. The proposed algorithm leverages the connection between each voxel and its nearest neighbors in the feature space, to propagate the opacity information.

RESULTS

We evaluated the results by employing both qualitative and quantitative methods. Using qualitative evaluation, experienced clinicians confirmed that the results were in agreement with the input data, especially for areas where borders were visible in most modalities (e.g., between air and tumor). For more challenging regions, where boundaries were unclear in the images, the results displayed fine-grained opacity transitions indicating the confidence of each voxel belonging to the ROI. When compared to the delineations made by clinicians, we found our results are usually more compact. We define a precision metric that evaluates the ratio of the matted foreground inside clinicians' delineations versus the entire matted foreground. Using a threshold of 0.4, our binarized result scored 0.95 for GTV and 0.92 for parotid gland.

CONCLUSION

The proposed method demonstrated satisfactory results on challenging ROIs. The objective boundaries generated by this method have advantages in many aspects, including improvement of delineation protocols, enhancement of manual annotation consistency, and increase of deep-learning based automated delineation accuracy.

Potential Dosimetric Predictors of Patient-Reported Quality of Life for Head and Neck Cancer Following Chemoradiation IMRT

PURPOSE/OBJECTIVE(S)

This study aims to identify both acute and late patient patient-reported salivary quality of life outcomes in patients with head and neck cancer treated with chemoradiation therapy on a prospective trial.

MATERIALS/METHODS

A cohort of 40 patients with head and neck cancers were included in the study. All patients underwent concurrent chemoradiation therapy using IMRT delivery (1 patient on one Linac, 24 patients on a different Linac, and 15 on a helical delivery machine). All patients were asked to complete the University of Washington Quality of Life (UOW-QOL) questionnaire at baseline, immediately after treatment, as well as at 1 month, 3 months, 6 months, 12 month and 18 months post-treatment. For the salivary quality of life (QOL) outcome scores, the possible responses were scored on a discreet scale of 100, 70, 30, and 0, with 100 as normal and 0 as dysfunctional. Dosimetric endpoints achieved based on the treatment plan, such as maximum/mean/minimum doses, V30 (percent volume receiving 30 Gy dose), and Dy (dose received to y percent volume) were collected for the bilateral salivary glands, bilateral temporomandibular joint and bilateral submandibular glands. The associations between these dosimetric parameters and the corresponding salivary QOL scores at each time point were analyzed. A Wilcoxon test was performed to identify any differences in the dosimetry and salivary QOL scores among the four different responses.

RESULTS

At short-term follow-up including 1- and 6-month, the distribution of the mean dose received by the right parotid was significantly different between the patients that reported a salivary QOL score of 30 and those that reported 100, with p-values of 0.007 for the 1-month comparison and 0.006 for the 6-month comparison. This was also seen for the V30, with p-values of 0.027 for the 1-month comparison and 0.013 for the 6-month comparison. At 3 months, the maximum dose received by the left temporomandibular joint was significantly different between the patients that reported 30 and those that reported 70, with a p-value of 0.038. At 6 months, the average dose distribution of the right submandibular gland received between the patients that reported a score of 30 and 100 was also significantly different, with a p-value of 0.006. At the long-term follow-up time points of 12 and 18 months, no significant differences were found.

CONCLUSION

The significant differences seen in the data suggest that the dosimetry may have effects on patient reported salivary QOL at short-term follow-up but not long-term. This provides a new perspective into how a patient's QOL over a period of time could be affected by the amount of dose to critical organs. These results also serve as the basis for further investigation into the actual delivered dose, which could differ from the planned dose due to daily anatomic changes over the course of head and neck radiotherapy delivery. These daily volumetric and dosimetric changes may guide early adaptive treatment to improve patient-reported QOL outcomes.

A Generalized Deep Learning Method for Synthetic CT Generation

PURPOSE/OBJECTIVE(S)

The application of deep learning to generate synthetic CT (sCT) has been widely studied in radiotherapy. Existing methods generally involve data from two different image modalities, such as CBCT-CT or MRI-CT, the quality of sCT is adversely affected by source image quality. We propose a unique method of synthesizing MRI and CBCT into sCT based on single-modal CT for training, and call it SmGAN.

MATERIALS/METHODS

We used planning CT of a group of 35 head and neck cases to as training data. We then applied two different spatial transformations to the planning CT image to produce the transformed CT1 and CT2. And We used a random style enhancement technique (Shuffle Remap) to modify the image distribution of CT1 which we termed CT1+E. CT1+E was used to simulate the patient's "image of the day" while CT2 to simulate the "planning image". After feeding both CT1+E and CT2 into the generator, we obtained the sCT predicted by the generator. The generator was trained using the Mean Absolute Error (MAE) loss between sCT and CT1. In the actual clinical process, we use the patient's CBCT or MRI instead of CT1+E and the patient's planning CT instead of CT2 as the input of the generator. After processing, we get an sCT that can maintain the spatial position of the image taken on the day, while presenting features similar to the planning CT. The evaluation data we have includes 10 pairs of MRI-Def_CT and 10 pairs of CBCT-Def_CT Head and Neck patients. Def_CT is obtained from the planning CT based on the spatial position deformation of MRI and CBCT. To evaluate the accuracy of sCT based on MRI and CBCT with Def CT, we use a range of metrics, including Hounsfield Unit (HU) difference, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and gamma pass rate. All results will be benchmarks against the advanced method RegGAN for comparison.

RESULTS

Compared to RegGAN, the results of SmGAN were significantly better. The mean absolute errors within the body were (44.7±216.2 HU vs. 36.7±131.4 HU) and (64.9±123.7 HU vs. 58.2±152.8 HU) for the CBCT-SCT and MRI-SCT, respectively (Table 1). In addition, experimental results show that SmGAN also outperforms RegGAN in dose calculation accuracy. For example, under the 10% threshold, SmGAN's gamma pass rate of 1mm and 1% is 0.926±0.02, compared with gamma rate of 0.896±0.02 for RegGAN.

CONCLUSION

We proposed a generalized deep learning model for synthetic CT generation, based on CBCT or MRI images. The proposed algorithm achieved high accuracy of dosimetric metrics, as well as excellent IMRT QA verification results. Compared to other existing synthetic CT generation methods, the proposed SmGAN required a single-modal image for training, which is considered as a major breakthrough in the industry, and is expected to have wide spread of clinical applications.

An Adaptive Multi-Feature Fusion Network for Predicting Overall Survival of Patients with Head and Neck Cancer

PURPOSE/OBJECTIVE(S)

Accurate prognostic prediction could allow personalized treatment to achieve optimal clinical outcome. We aimed to develop a highly predictive overall survival model, considering the complementary relationships between clinical information, traditional radiomics and deep image information, to further improve the overall prediction accuracy by constructing a richer feature set and adaptive weighting.

MATERIALS/METHODS

A total of 427 patients with Oropharyngeal Cancer (OPC) patients from the TCIA database were included. 341 cases were used for training, 86 cases were used as an independent cohort. Patient characteristics, including TMN, age, gender, HPV status, smoking or drinking status, etc. were considered as potential predictors. Traditional radiomics features of gross tumor volume (GTV) was extracted from planning CT using open-source software. In addition, a two-dimensional convolutional network (2D_CNN) was designed to extract deep image features. An adaptive multi-feature fusion network was developed to predict overall survival of patients based on three types of features. The fusion network integrates an attention mechanism to the channel dimension to obtain proper weighting of each channel in the feature graph through the fully connected network by focusing on effective feature channels and automatic learning according to the loss, thus improving the utilization rate of effective features. The model performance was evaluated using the area-under-ROC-curve (AUC), accuracy, precision, recall, f1-score.

RESULTS

The AUCs of predictive models based on clinical features, traditional radiomics features and deep image features were 0.7, 0.61 and 0.72, respectively. Combining patient characteristics, radiomic features and deep imaging features, the AUCs of the prediction models was significantly improved to 0.85 and 0.86 (with attention mechanisms) for the independent test cohort (Table 1).

CONCLUSION

The proposed adaptive multi-channel network assigned effective weights to the potential predictors, selectively enhanced useful features while suppressed irrelevant features, enabling more accurate feature map weights. We demonstrated the improved predictive value, with a multi-channel fusion network integrated with an attention mechanism, for overall survival of OPC patients.

Quality and Safety Considerations in Image Guided Radiation Therapy: An ASTRO Safety White Paper Update

PURPOSE/OBJECTIVE(S)

This updated report on image guided radiation therapy (IGRT) is based on a consensus-based white paper previously published by the American Society for Radiation Oncology (ASTRO) addressing patient safety. In the past decade, IGRT technology and procedures have progressed significantly and are now more commonly used. The use of IGRT has now extended beyond high-precision treatments, such as stereotactic radiosurgery and stereotactic body radiation therapy, and into routine clinical practice for many treatment techniques and anatomic sites. Therefore, quality and treatment planning and delivery considerations for these techniques are paramount for patient safety.

MATERIALS/METHODS

In 2021, ASTRO convened an interdisciplinary task force to assess the original IGRT white paper and update content where appropriate. Recommendations were created using a consensus-building methodology, and task force members indicated their level of agreement based on a 5-point Likert scale from "strongly agree" to "strongly disagree." A prespecified threshold of ≥75% of raters who selected "strongly agree" or "agree" indicated consensus.

RESULTS

The IGRT white paper was published (Pract Radiat Oncol. 2022 Dec) and endorsed by the American Association of Physicists in Medicine (AAPM), American Association of Medical Dosimetrists, and American Society of Radiologic Technologists. Since the first IGRT paper was published by ASTRO in 2013, significant technological advancement has taken place. New and updated considerations in personnel requirements, staffing, education and training, equipment and technological requirements, quality management and assurance, IGRT program management, and safety considerations were reported. A 17-point consensus was reached and recommended in 5 areas surrounding program development, quality assurance, quality management, treatment delivery, and vendor engagement (Table 5, Summary of key recommendations).

CONCLUSION

This IGRT white paper builds on the previous version and uses other guidance documents to primarily focus on processes related to quality and safety. IGRT requires an interdisciplinary team-based approach, staffed by appropriately trained specialists, as well as significant personnel resources, specialized technology, and implementation time. A thorough feasibility analysis of resources is required and should be discussed with all personnel before undertaking new imaging techniques. A comprehensive quality-assurance program must be developed to ensure IGRT is performed safely and effectively. As IGRT technologies continue to improve or emerge, existing practice guidelines should be updated regularly according to the latest AAPM Task Group reports. Patient safety in the application of IGRT is everyone's responsibility, and professional organizations, regulators, vendors, and end-users must demonstrate strong commitments to ensure that the highest levels of safety are achieved.

Early Prediction of Radiation Treatment Response via Longitudinal Analysis of CBCT Radiomic Features for Locally Advanced Rectal Cancer

PURPOSE/OBJECTIVE(S)

Patients respond to the same radiation treatment course differently due to inter- and intra- patient variability in radiosensitivity. Despite widespread use of AI/ML in radiation oncology, there is a lack of monitoring strategies used during treatment courses to evaluate early predictors of treatment response in a systematic fashion. This work advances a straightforward, yet effective, method for the early detection of treatment response through systematically analyzing daily CBCT radiomic features. The goal is to aid clinicians in assessing the treatment efficacy routinely with a view towards optimizing personalized treatment.

MATERIALS/METHODS

We included a cohort of 30 patients diagnosed with locally advanced rectal cancer who underwent neo-adjuvant fractionated radiation treatment (RT) with a prescription dose of 50.4 Gy (28 fractions), followed by total mesorectal excision surgery after completion of ChemoRT. Daily IGRT imaging was acquired prior to each fraction resulting in a total of 840 CBCTs. Patients were divided into responder (14 patients) and non-responder (16 patients) groups based on post-RT pathological response. Mutual information algorithms were utilized to rigorously register daily CBCT images to the planning CT, and longitudinal radiomic features of the target were extracted from the daily CBCTs during the entire treatment course. All longitudinal features for a given patient were standardized with Z-Score normalization, followed by linear fitting using the least square method, resulting in radiomic feature trends (RFT) represented by slope values. Statistical significance was established via a two-sample U test and P-value with a threshold of 0.05. Logistic regression was performed to eliminate RFT with accuracy rates lower than 0.5. The final trending model was developed using random forest. For each patient at fraction N, our investigation involved independent 27 group experiments, where each experiment considered image group from fraction #1 to N, to confirm the effectiveness and stability of the model.

RESULTS

The proposed RFT demonstrated a high level of precision and consistency for post-RT response based on longitudinal CBCT images for LARC patients. The trending model yielded an accuracy of 0.9556, 95% CI (0.94, 0.972) when each daily image was considered, the prediction consistency was 0.964. Given the first 14 experiments (considering group images of fraction #1-15), the prediction accuracy was 0.9357, 95% CI (0.915, 0.956) and the prediction consistency was 0.952.

CONCLUSION

A strategy for monitoring and early prediction of LARC patients' radioresponse was evaluated via longitudinal CBCT assessment. Our trending models demonstrate a significant difference between the responder vs non-responder groups as early as the 15th fraction. Our strategy achieved superior accuracy and consistency to predict post-RT response of LARC patients.

Ceruloplasmin regulating fibrosis in orbital fibroblasts provides a novel therapeutic target for Graves' orbitopathy

PURPOSE

In diagnosing the pathogenesis of Graves' orbitopathy (GO), there is a growing interest in fibrosis generated by orbital fibroblasts (OFs); nevertheless, the involvement of ceruloplasmin (CP) in OFs remains unknown.

METHODS

Differentially expressed genes (DEGs) were identified through bioinformatic analysis. OFs were isolated from orbital tissue and identified with immunofluorescent staining. The levels of DEGs were validated in GO tissue samples and TGF-β-challenged OFs, and CP was selected for the following laboratory investigations. CP overexpression or knockdown was achieved, and cell viability and fibrosis-associated proteins were investigated to assess the cell phenotype and function. Signaling pathways were subsequently investigated to explore the mechanism of CP function in OFs.

RESULTS

CP and cathepsin C (CTSC) are two overlapped DEGs in GSE58331 and GSE105149. OFs were isolated and identified through fibrotic biomarkers. CP and CTSC were downregulated in GO tissue samples and TGF-β-challenged OFs. CP overexpression or knockdown was achieved in OFs by transducing a CP overexpression vector or small interfering RNA against CP (si1-CP or si2-CP) and verified using a qRT-PCR. CP overexpression inhibited cell viability and reduced the levels of α-SMA, vimentin, fibronectin, and collagen I, whereas CP knockdown exerted opposite effects on OFs. CP overexpression inhibited the phosphorylation of Smad3, Erk1/2, p38, JNK, and AKT; conversely, CP knockdown exerted opposite effects on the phosphorylation of factors mentioned above.

CONCLUSION

CP was downregulated in GO and suppressed the expression of fibrosis-associated proteins in both GO and normal OFs. CP might serve as a promising therapeutic agent in the treatment regimens for GO.

A Novel Multi-Objective Based Feature Selection Method for Response Prediction

PURPOSE/OBJECTIVE(S)

Accurate response prediction is essential towards personalized treatment in radiation therapy. Excessive imaging features, extracted from medical images, pose a great challenge in radiomic analyses. Feature selection is an essential step to remove redundant and irrelevant features for model construction.

MATERIALS/METHODS

We proposed a novel multi-objective based radiomic feature selection method (MRMOPSO), where the number of features, sensitivity, and specificity are jointly considered as optimization objectives for feature selection. The MRMOPSO innovated by three aspects: 1) Fisher score initialize the feature population to speed up the convergence; 2) Min-redundancy particle generation operations to reduce the redundancy between radiomic features, a truncation strategy was also introduced; 3) Particle selection operation guided by elitism strategies to improve local search ability of the algorithm. We evaluated the effectiveness of the proposed MRMOPSO method using a cohort of oropharyngeal cancer patients from The Cancer Imaging Archive (TCIA). 357 patients were used for model training and additional 64 patients were used for independent evaluation. The proposed methods were compared with (a) classical feature selection methods, i.e., Lasso, minimal-redundancy-maximal-relevance criterion (mRMR), F-score, and mutual information (MI), (b) single-objective feature selection methods, i.e., genetic algorithm (GA), particle swarm optimization algorithm (PSO) and (c) multi-objective feature selection methods, i.e., multiple objective particle swarm optimization (MOPSO), nondominated sorting genetic algorithm II (NSGA II).

RESULTS

The other feature selection methods yielded AUCs, sensitivity, specificity of (0.48-0.71), (0.49-0.86), (0.33-0.67), respectively. The MRMOPSO achieved significantly highly AUC of 0.84 with smaller number of selected features on the independent dataset (Table 1). Additionally, the MRMOPSO remarkably improved the sensitivity (0.81), specificity (0.81) and achieved an excellent balance between sensitively and specificity.

CONCLUSION

We demonstrated a novel multi-objective based radiomic feature selection method. The proposed algorithm effectively reduced feature dimension, and achieved superior AUC with simultaneous improved sensitivity and specificity, for radiomic response prediction.

Spatiotemporal distribution, sources apportionment and ecological risks of PAHs: a study in the Wuhan section of the Yangtze River

This study investigated the sources, contamination and ecological risks of polycyclic aromatic hydrocarbons (PAHs) based on their spatiotemporal distribution in aquatic environment in the Wuhan section of the Yangtze River (WYR). The fugacity ratio evaluation indicated that sediment was secondary release sources of two- and three-ring PAHs and sinks of four- and five-ring PAHs. The total concentrations of PAHs (Σ16PAHs) ranged from 2.51 to 102.5 ng/L in water with the dominant contribution of 47.8% by two-ring PAHs. Σ16PAHs in sediments varied from 5.90 to 2926 ng/g with the contribution of 35.4% by four-ring PAHs. The higher levels of PAHs occurred around developed industrial areas during the wet season, which was related to local industrial emissions and influenced by rainfall/runoff. Annual flux of Σ16PAHs was estimated of 28.77 t. The PMF model analysis revealed that petroleum and industrial emissions were the dominant sources in water accounting for 58.5% of the total pollution, although traffic emission was the main source for sediment accounting for 44.6%. Risk assessments showed that PAHs in water were at low risks, whereas about 44% of the sediments were identified as medium risks. Therefore, energy structure adjustment and further implement of regulation and monitoring are necessary to reduce PAH emissions.

Impact of Prophylactic Pelvic Lymph Node Irradiation in De-Novo Oligometastatic Prostate Cancer

PURPOSE/OBJECTIVE(S)

To evaluate the impact of prophylactic pelvic nodal irradiation in de-novo oligometastatic prostate cancer treated with radiotherapy (RT) for both primary tumor and all metastatic lesions.

MATERIALS/METHODS

This was a single-center prospective cohort study. De novo oligometastatic prostate cancer patients with RT for both primary tumor and all metastatic lesions were included. Kaplan-Meier method, log rank test and cox regression were used to calculate OS and PFS. PFS included PSA failure, local or distant failure assessed by imaging.

RESULTS

This study analyzed 202 patients from 10/2011 to 1/2022 with median follow-up of 48 months. A total of 126 (62.4%) patients were treated with pelvic lymph node RT. The dose was 47.5 Gy with 1.9 Gy per fraction. Among them, 66 (32.7%) patients were treated with whole pelvic RT (WPRT), which the upper limit was at the aortic bifurcation. 60 (29.7%) patients were treated with mini-WPRT, which the upper limit was at the lower margin of obturator foramen. The incidence of diarrhea (P = 0.038) and leukocyte reduction (P = 0.040) in the WPRT subgroup during radiotherapy was significantly higher than that in the mini-WPRT and non-pelvic RT subgroup. For the whole cohort, the median OS and PFS were not reached. The subgroup analysis showed that the elective pelvic nodal irradiation could improve PFS (P = 0.042). However, there was no difference of PFS between standard WPRT and mini-WPRT.

CONCLUSION

The study suggests that for de-novo oligometastatic prostate cancer, elective pelvic nodal irradiation may improve PFS. For patients who cannot tolerate WPRT, mini-WPRT may be an alternative option. However, it needs to be verified in the prospective RCT study.

Unsupervised Domain Adaptation of Auto-Segmentation on Multi-Source MRIs

PURPOSE/OBJECTIVE(S)

Deep learning has achieved great success in medical image segmentation. Most existing deep learning (DL) approaches make no adjustments to the model prior to inference. These models can perform well on the data of the same distribution, but their performance usually degrades when applied to the images from different source, i.e., different scanners. To tackle the problem caused by domain shift, we proposed an unsupervised domain adaptation (UDA) method based on entropy minimization and physical consistency constraints.

MATERIALS/METHODS

The proposed method combines feature-level and instance-level domain adaptation techniques to transfer knowledge from the source to the target domain. Specifically, the feature-level adaptation technique uses a graph-based entropy minimization to reduce the discrepancy between the source and target domains. The instance-level adaptation technique employs a novel consistency loss to regularize the physical consistency of the same object, such as volume, length, and centroid, thus improving the segmentation accuracy of the target domain. A collection of 93 abdominal MR images, comprising 45 cases from a 0.35T MRI scanner (TRUFI) and 48 cases from a 1.5T MRI scanner (T2), was utilized to evaluate the effectiveness of the proposed method. The contours of 6 organs-at-risk were delineated by a senior radiation oncologist, serving as the ground truth. Three models, the source model (SRC) trained on the source domain, the target model (TGT) trained on the target domain, and the UDA model adapted from the source domain to the target domain, were compared on the target domain using the Dice Similarity Coefficient (DSC).

RESULTS

In the experiment of 0.35T-to-1.5T, the proposed UDA method outperformed the source model, achieving an average DSC score of 0.82 ± 0.11, compared to 0.58 ± 0.23 (SRC) and 0.85 ± 0.09 (TGT), respectively. In the inverse experiment 1.5T-to-0.35T, the UDA model achieved an average DSC score of 0.79±0.13, compared to DSCs of 0.52 ± 0.25 and 0.81 ± 0.11 for the SRC and TGT respectively. The UDA method yielded a significant improvement of 46%, compared to the SRC. Particularly, OARs (organ at risk) with higher deformability such as the stomach and duodenum achieved a 58% and 63% improvement in performance, respectively.

CONCLUSION

This work presents a compelling approach of UDA for auto-segmentation on multi-source MRIs. Experimental results demonstrate that the UDA effectively improve the segmentation performance of the source model in the target domain, resulting in a more robust segmentation model.

Feature Selection Based on Unsupervised Clustering Mechanism on Multiple-Sequence MRIs for Predicting Neoadjuvant Chemoradiation Response in Locally Advanced Rectal Cancer

PURPOSE/OBJECTIVE(S)

Accurate response prediction allows for personalized cancer management. We developed an unsupervised clustering mechanism to improve effectiveness and efficiency in feature selection operation for accurate patient stratification.

MATERIALS/METHODS

Forty-three locally advanced rectal cancer (LARC) patients underwent neoadjuvant chemoradiation were included, pre-treatment T2 and ADC MRIs were acquired for each patient. An initial feature space consisting of 200 radiomic features extracted from manually delineated GTVs from two sequences of MR images. Additional 960 high-order radiomic features extracted from a 3D convolutional neural network (CNN). To remove redundant and irrelevant features, we developed an unsupervised clustering-based feature selection operation to determine the combination of features with potential best performance. The normal process of feature selection involves searching new feature combinations and training new classifiers for evaluating their performance via an iterative process based on selected feature set, the overall time cost is tremendous. To balance the computational cost and search efficiency, firstly, we proposed an unsupervised clustering analysis metric- Comprehensive Cluster Analysis Index (CCAI) through the K-means algorithm, where the average distances between the sample points and the cluster centroids and so on, to construct a multiple linear regression model. Secondly, we extracted sample points by varying the number of features and feature ratios between radiomic features and 3D-CNN features in the output of feature selection. Thirdly, we optimized the model using the sampling points to calculate the CCAI. Two typical feature combination search algorithms, the random forest recursive feature elimination (RF-RFE) and the differential evolution (DE), were used to perform feature selection with CCAI.

RESULTS

The accuracy, area-under-curve (AUC) and specificity, based on combined 3D-CNN and radiomic features extracted from combined T2 and ADC images, were 0.852, 0.871, and 0.735, respectively. Our experiments illustrated higher predictive power (AUC = 0.846) based on high-order abstract features extracted from the CNN on ADC and T2 images, compared to the traditional radiomic model (AUC = 0.714). Additionally, the predictive models constructed based on radiomics and CNN features extracted from ADC images were more predictable in terms of treatment responses than the radiomic and CNN imaging features extracted from T2 images. The average computational time of DE and RF-RFE were 50.5s and 128.6s in one single computation, the average computational time were 24.2s and 91.3s with CCAI, respectively.

CONCLUSION

We proposed an unsupervised clustering analysis mechanism to improve the effectiveness of feature selection while decreasing its time cost markedly, which highlight the correlation and complementarity between low- and high-level imaging features, achieving better predictive accuracy.

Deep phenotyping of 11,880 highlanders reveals novel adaptive traits in native Tibetans

Tibetans are the ideal population to study genetic adaptation in extreme environments. Here, we performed systematic phenotyping of 11,880 highlanders, covering 133 quantitative traits of 13 organ systems. We provided a comprehensive phenotypic atlas by comparing altitude adaptation and altitude acclimatization. We found the differences between adaptation and acclimatization are quantitative rather than qualitative, with a whole-system "blunted effect" seen in the adapted Tibetans. We characterized twelve different functional changes between adaptation and acclimatization. More importantly, we established a landscape of adaptive phenotypes of indigenous Tibetans, including 45 newly identified Tibetan adaptation-nominated traits, involving specific changes of Tibetans in internal organ state, metabolism, eye morphology, and skin pigmentation. In addition, we observed a sex-biased pattern between altitude acclimatization and adaptation. The generated atlas of phenotypic landscape provides new insights into understanding of human adaptation to high-altitude environments, and it serves as a valuable blueprint for future medical and physiological studies.