Predicting treatment response in multicenter non-small cell lung cancer patients based on federated learning

BACKGROUND

Multicenter non-small cell lung cancer (NSCLC) patient data is information-rich. However, its direct integration becomes exceptionally challenging due to constraints involving different healthcare organizations and regulations. Traditional centralized machine learning methods require centralizing these sensitive medical data for training, posing risks of patient privacy leakage and data security issues. In this context, federated learning (FL) has attracted much attention as a distributed machine learning framework. It effectively addresses this contradiction by preserving data locally, conducting local model training, and aggregating model parameters. This approach enables the utilization of multicenter data with maximum benefit while ensuring privacy safeguards. Based on pre-radiotherapy planning target volume images of NSCLC patients, a multicenter treatment response prediction model is designed by FL for predicting the probability of remission of NSCLC patients. This approach ensures medical data privacy, high prediction accuracy and computing efficiency, offering valuable insights for clinical decision-making.

METHODS

We retrospectively collected CT images from 245 NSCLC patients undergoing chemotherapy and radiotherapy (CRT) in four Chinese hospitals. In a simulation environment, we compared the performance of the centralized deep learning (DL) model with that of the FL model using data from two sites. Additionally, due to the unavailability of data from one hospital, we established a real-world FL model using data from three sites. Assessments were conducted using measures such as accuracy, receiver operating characteristic curve, and confusion matrices.

RESULTS

The model's prediction performance obtained using FL methods outperforms that of traditional centralized learning methods. In the comparative experiment, the DL model achieves an AUC of 0.718/0.695, while the FL model demonstrates an AUC of 0.725/0.689, with real-world FL model achieving an AUC of 0.698/0.672.

CONCLUSIONS

We demonstrate that the performance of a FL predictive model, developed by combining convolutional neural networks (CNNs) with data from multiple medical centers, is comparable to that of a traditional DL model obtained through centralized training. It can efficiently predict CRT treatment response in NSCLC patients while preserving privacy.

Localization and Risk Stratification of Thyroid Nodules in Ultrasound Images Through Deep Learning

OBJECTIVE

Deep learning algorithms have commonly been used for the differential diagnosis between benign and malignant thyroid nodules. The aim of the study described here was to develop an integrated system that combines a deep learning model and a clinical standard Thyroid Imaging Reporting and Data System (TI-RADS) for the simultaneous segmentation and risk stratification of thyroid nodules.

METHODS

Three hundred four ultrasound images from two independent sites with TI-RADS 4 thyroid nodules were collected. The edge connection and Criminisi algorithm were used to remove manually induced markers in ultrasound images. An integrated system based on TI-RADS and a mask region-based convolution neural network (Mask R-CNN) was proposed to stratify subclasses of TI-RADS 4 thyroid nodules and to segment thyroid nodules in the ultrasound images. Accuracy and the precision-recall curve were used to evaluate stratification performance, and the Dice similarity coefficient (DSC) between the segmentation of Mask R-CNN and the radiologist's contour was used to evaluate the segmentation performance of the model.

RESULTS

The combined approach could significantly enhance the performance of the proposed integrated system. Overall stratification accuracy of TI-RADS 4 thyroid nodules, mean average precision and mean DSC of the proposed model in the independent test set was 90.79%, 0.8579 and 0.83, respectively. Specifically, stratification accuracy values for TI-RADS 4a, 4b and 4c thyroid nodules were 95.83%, 84.21% and 77.78%, respectively.

CONCLUSION

An integrated system combining TI-RADS and a deep learning model was developed. The system can provide clinicians with not only diagnostic assistance from TI-RADS but also accurate segmentation of thyroid nodules, which improves the applicability of the system in clinical practice.

Characterization of posterior circulation blood perfusion in patients with different degrees of basilar artery tortuosity

OBJECTIVE

The morphology of basilar artery (BA) may affect posterior circulation blood perfusion. We aimed to investigate whether different degrees of BA tortuosity could lead to the alterations of posterior circulation perfusion.

METHODS

We collected 138 subjects with different BA tortuosity scores, including 32 cases of score 0, 45 cases of score 1, 43 cases of score 2, and 18 cases of score 3. A higher score represented a higher degree of BA tortuosity. Ordered logistic regression analysis was performed to investigate the risk factors for BA tortuosity. We quantitatively measured the cerebral blood flow (CBF) in eight posterior circulation brain regions using arterial spin labeling. SPSS 25.0 was used for statistical analysis. The correlation between the CBF and BA tortuosity was corrected by the Bonferroni method. The significance level was set at 0.006 (0.05/8).

RESULTS

Hypertension (HR: 2.39; 95%CI: 1.23-4.71; P = 0.01) and vertebral artery dominance (HR: 2.38; 95%CI: 1.10-4.67; P = 0.03) were risk factors for BA tortuosity. CBF in occipital gray matter (R = -0.383, P < 0.001), occipital white matter (R = -0.377, P < 0.001), temporal gray matter (R = -0.292, P = 0.001), temporal white matter (R = -0.297, P < 0.001), and cerebellum (R = -0.328, P < 0.001) were negatively correlated with BA tortuosity degree. No significant correlation was found between the BA tortuosity degree and CBF in hippocampus (R = -0.208, P = 0.014), thalamus (R = -0.001, P = 0.988) and brainstem (R = -0.204, P = 0.016).

CONCLUSIONS

BA tortuosity could affect posterior circulation blood perfusion. CBF was negatively correlated with BA tortuosity degree. The morphology of BA may serve as a biomarker for posterior circulation and the severity of posterior circulation ischemia.

Performance and application of the total-body PET/CT scanner: a literature review

BACKGROUND

The total-body positron emission tomography/computed tomography (PET/CT) system, with a long axial field of view, represents the state-of-the-art PET imaging technique. Recently, the total-body PET/CT system has been commercially available. The total-body PET/CT system enables high-resolution whole-body imaging, even under extreme conditions such as ultra-low dose, extremely fast imaging speed, delayed imaging more than 10 h after tracer injection, and total-body dynamic scan. The total-body PET/CT system provides a real-time picture of the tracers of all organs across the body, which not only helps to explain normal human physiological process, but also facilitates the comprehensive assessment of systemic diseases. In addition, the total-body PET/CT system may play critical roles in other medical fields, including cancer imaging, drug development and immunology.

MAIN BODY

Therefore, it is of significance to summarize the existing studies of the total-body PET/CT systems and point out its future direction. This review collected research literatures from the PubMed database since the advent of commercially available total-body PET/CT systems to the present, and was divided into the following sections: Firstly, a brief introduction to the total-body PET/CT system was presented, followed by a summary of the literature on the performance evaluation of the total-body PET/CT. Then, the research and clinical applications of the total-body PET/CT were discussed. Fourthly, deep learning studies based on total-body PET imaging was reviewed. At last, the shortcomings of existing research and future directions for the total-body PET/CT were discussed.

CONCLUSION

Due to its technical advantages, the total-body PET/CT system is bound to play a greater role in clinical practice in the future.

Pseudo-time Series Structural MRI Revealing Progressive Gray Matter Changes with Elevated Intraocular Pressure in Primary Open-Angle Glaucoma: A Preliminary Study

RATIONALE AND OBJECTIVES

Primary open-angle glaucoma (POAG) is accompanied with gray matter (GM) changes across the brain. However, causal relationships of the GM changes have not been fully understood. Our aim was to investigate the causality of GM progressive changes in POAG using Granger causality (GC) analysis and structural MRI.

MATERIALS AND METHODS

Structural MRI from 20 healthy controls and 30 POAG patients with elevated intraocular pressure (IOP) were collected. We performed voxel-wise GM volume comparisons between control and POAG groups, and between control and four POAG subgroups (categorized by IOP). Then, we sequenced the structural MRI data of all POAG patients and conducted both voxel-wise and region of interest (ROI)-wise GC analysis to investigate the causality of GM volume changes in POAG brain.

RESULTS

Compared to healthy controls, reduced GM volumes across the brain were found, GM volume enlargements in the thalamus, caudate nucleus and cuneus were also observed in POAG brain (false discovery rate (FDR) corrected at q< 0.05). As IOP elevated, the reductions of GM volume were more severe in the cerebellum and frontal lobe. GC analysis revealed that the bilateral cerebellum, visual cortices, and the frontal regions served independently as primary hubs of the directional causal network, and projected causal effects to the parietal and temporal regions of the brain (FDR corrected at q<0.05).

CONCLUSION

POAG exhibits progressive GM alterations across the brain, with oculomotor regions and visual cortices as independent primary hubs. The current results may deepen our understanding of neuropathology of POAG.

Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients

BACKGROUND

Bone metastasis is a common event in lung cancer progression. Early diagnosis of lung malignant tumor with bone metastasis is crucial for selecting effective treatment strategies. However, 14.3% of patients are still difficult to diagnose after SPECT/CT examination.

PURPOSE

Machine learning analysis of [99mTc]-methylene diphosphate (99mTc-MDP) SPECT/CT scans to distinguish bone metastases from benign bone lesions in patients with lung cancer.

METHODS

One hundred forty-one patients (69 with bone metastases and 72 with benign bone lesions) were randomly assigned to the training group or testing group in a 7:3 ratio. Lesions were manually delineated using ITK-SNAP, and 944 radiomics features were extracted from SPECT and CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomics features in the training set, and the single/bimodal radiomics models were established based on support vector machine (SVM). To further optimize the model, the best bimodal radiomics features were combined with clinical features to establish an integrated Radiomics-clinical model. The diagnostic performance of models was evaluated using receiver operating characteristic (ROC) curve and confusion matrix, and performance differences between models were evaluated using the Delong test.

RESULTS

The optimal radiomics model comprised of structural modality (CT) and metabolic modality (SPECT), with an area under curve (AUC) of 0.919 and 0.907 for the training and testing set, respectively. The integrated model, which combined SPECT, CT, and two clinical features, exhibited satisfactory differentiation in the training and testing set, with AUC of 0.939 and 0.925, respectively.

CONCLUSIONS

The machine learning can effectively differentiate between bone metastases and benign bone lesions. The Radiomics-clinical integrated model demonstrated the best performance.

[Mid-term efficacy evaluation of ABO incompatible living relative kidney transplantation based on protocol biopsy]

Objective: To evaluate the mid-term efficacy of ABO incompatible living donor kidney transplantation (ABOi-KT) based on the results of routine renal biopsy for transplantation. Methods: Retrospective collection of clinical data from 23 pairs of ABOi-KT donors and recipients at the First Affiliated Hospital of Sun Yat-sen University from July 2015 to November 2021. ABOi-KT was performed on recipients after desensitization treatment, and the results of routine kidney transplant biopsy at 1 week, 1 month, 3 months, 6 months, and 12 months after surgery were analyzed. Combined with blood type antibody levels and renal function recovery, the mid-term efficacy of ABOi-KT was evaluated. Results: Among the 23 recipients, there were 19 males and 4 females; age range from 19 to 47 years old [(29.6±6.7) years old], all underwent ABOi-KT successfully after receiving desensitization treatment. The follow-up time was (44.6±22.4) months, of which 22 cases were followed up for more than 1 year. The incidence rates of rejection reactions at 1 week, 1 month, 3 months, 6 months, and 12 months after surgery were 15.0% (3/20), 11.1% (1/9), 7.7% (1/13), 25.0% (3/12), and 12.5% (1/8), respectively. For receptors with rejection reactions, targeted anti-rejection therapy was performed based on clinical symptoms and various indicators. Borderline T cell mediated rejection (TCMR) can be converted to mild tubular inflammation after anti-rejection treatment. The positive rate of complement C4d in peritubular capillaries was 95.0% (19/20) one week after surgery, and the positive rate of complement C4d was 100% at 3 and 12 months after surgery. The cumulative survival rates at 1, 3, 5, and 7 years after surgery were all 100%. The cumulative survival rates at 1, 3, 5, and 7 years after kidney transplantation were 100%, 93.3%, 84.0%, and 84.0%, respectively. Except for 2 recipients who underwent transplantation in 2017 and experienced kidney failure at 30 and 49 months after surgery, all other transplanted kidneys survived. Conclusions: The results of routine renal transplant biopsy show that ABOi-KT has a good mid-term therapeutic effect. The pathological changes of ABOi-KT can be dynamically observed through routine renal transplant biopsy and targeted treatment for rejection reactions can be provided accordingly.

Copper(II)-based metal-organic framework delivery of calcium ascorbate for enhanced chemodynamic therapy via H2O2 self-supply and glutathione depletion

Chemodynamic therapy (CDT) is a promising cancer treatment strategy. However, mild acidic pH, insufficient H2O2 content, and overexpressed glutathione (GSH) in the tumor microenvironment (TME) severely impair CDT efficiency. In this study, a novel therapeutic nanosystem (Cu/ZIF-8/Vc-Ca/HA) was constructed for H2O2 self-supply and GSH depletion co-enhanced CDT. Typically, calcium ascorbate (Vc-Ca) loaded on the surface of Cu2+-doped ZIF-8 (Cu/ZIF-8) was designed as an original source for H2O2 generation, and a hyaluronic acid (HA) shell was subsequently coated to act as a tumor-targeted "guide" and protective layer. Along with the HA shell disintegrated in the TME, exposed Cu/ZIF-8/Vc-Ca dissociated in the tumor acidic microenvironment, thus triggering the release of Vc-Ca and Cu2+. Vc-Ca selectively produced H2O2 in tumor cells, which provided abundant H2O2 for boosting Fenton-like reactions. Meanwhile, the released Cu2+ could get converted into Cu+ by consuming excess intracellular GSH, which could reduce the tumor antioxidant capability of the nanosystem. Moreover, byproduct Cu+ reacted with abundant H2O2 by a highly efficient Fenton-like reaction to generate toxic ˙OH. Biological assays indicated that the Cu/ZIF-8/HA@Vc-Ca nanosystem showed significant anticancer activity by enhancing the CDT process. This study may provide a new strategy for improving the effectiveness of CDT.

Elevated Intraocular Pressure Moderated Brain Morphometry in High-tension Glaucoma: a Structural MRI Study

High-tension glaucoma (HTG) is one of the most common forms of primary open angle glaucoma. The purpose of this study was to assess in HTG brain, whether the elevated intraocular pressure (IOP) had an effect on the brain morphological alterations via structural MRI. We acquired T1WI structural MRI images from 56 subjects including 36 HTG patients and 20 healthy controls. We tested whether the brain morphometry was associated with the mean IOP in HTG patients. Moreover, we conducted moderation analysis to assess the interactions between subject type (HTG - healthy controls) and IOP. In HTG group, cortical thickness was negatively correlated with the mean IOP in the left rostral middle frontal gyrus, left pars triangularis, right precentral gyrus, left postcentral gyrus, left superior temporal gyrus (p < 0.05, FDR corrected). Four of the five regions negatively correlated with mean IOP showed reduced cortical thickness in HTG group compared with healthy controls, which were the left rostral middle frontal gyrus, left pars triangularis, left postcentral gyrus and left superior temporal gyrus (p < 0.05, FDR corrected). IOP moderated the interaction between subject type and cortical thickness of the left rostral middle frontal gyrus (p = 0.0017), left pars triangularis (p = 0.0011), left postcentral gyrus (p = 0.0040) and left superior temporal gyrus (p = 0.0066). Elevated IOP may result brain morphometry alterations such as cortical thinning. The relationship between IOP and brain morphometry underlines the importance of the IOP regulation for HTG patients.

Breast cancer diagnosis from contrast-enhanced mammography using multi-feature fusion neural network

OBJECTIVES

To develop an end-to-end deep neural network for the classification of contrast-enhanced mammography (CEM) images to facilitate breast cancer diagnosis in the clinic.

METHODS

In this retrospective mono-centric study, patients who underwent CEM examinations from January 2019 to August 2021 were enrolled. A multi-feature fusion network combining low-energy (LE) and dual-energy subtracted (DES) images and dual view, as well as bilateral information, was trained and tested using a large CEM dataset with a diversity of breast tumors for breast lesion classification. Its generalization performance was further evaluated on two external datasets. Results were reported using AUC, accuracy, sensitivity, and specificity.

RESULTS

A total of 2496 patients (mean age, 53 years ± 12 (standard deviation)) were included and divided into a training set (1718), a validation set (255), and a testing set (523). The proposed CEM-based multi-feature fusion network achieved the best diagnosis performance with an AUC of 0.96 (95% confidence interval (CI): 0.95, 0.97), compared with the no-fusion model, the left-right fusion model, and the multi-feature fusion network with only LE image inputs. Our models reached an AUC of 0.90 (95% CI: 0.85, 0.94) on a full-field digital mammograph (FFDM) external dataset (86 patients), and an AUC of 0.92 (95% CI: 0.89, 0.95) on a CEM external dataset (193 patients).

CONCLUSION

The developed multi-feature fusion neural network achieved high performance in CEM image classification and was able to facilitate CEM-based breast cancer diagnosis.

CLINICAL RELEVANCE STATEMENT

Compared with low-energy images, CEM images have greater sensitivity and similar specificity in malignant breast lesion detection. The multi-feature fusion neural network is a promising computer-aided diagnostic tool for the clinical diagnosis of breast cancer.

KEY POINTS

• Deep convolutional neural networks have the potential to facilitate contrast-enhanced mammography-based breast cancer diagnosis. • The multi-feature fusion neural network reaches high accuracies in the classification of contrast-enhanced mammography images. • The developed model is a promising diagnostic tool to facilitate clinical breast cancer diagnosis.

Effect of Menopause Status on Brain Perfusion Hemodynamics

BACKGROUND

The menopause transition is associated with an increasing risk of cerebrovascular disorders. However, the direct effect of menopause status on brain perfusion hemodynamics remains unclear. This study aimed to explore the influence of menopause status on cerebral blood flow (CBF) using arterial spin labeling magnetic resonance imaging.

METHODS

In this cross-sectional study, 185 subjects underwent arterial spin labeling magnetic resonance imaging at a hospital in China between September 2020 and December 2022, including 38 premenopausal women (mean age, 47.74±2.02 years), 42 perimenopausal women (mean age, 50.62±3.15 years), 42 postmenopausal women (mean age, 54.02±4.09 years), and 63 men (mean age, 52.70±4.33 years) of a similar age range. Mean CBF values in the whole brain, gray matter, white matter, cortical gray matter, subcortical gray matter, juxtacortical white matter, deep white matter, and periventricular white matter were extracted. ANCOVA was used to compare mean CBF among the 4 groups, controlling for confounding factors. Student t test was applied to compare mean CBF between the 3 female groups and age-matched males, respectively. Multivariable regression analysis was used to analysis the effect of age, sex, and menopause status on the CBF of the whole brain, gray matter, white matter, and subregions.

RESULTS

Perimenopausal and postmenopausal women showed a higher proportion of white matter hyperintensities compared with the other 2 groups (P<0.001). Premenopausal women exhibited higher CBF in the whole brain, gray matter, white matter, and subregions, compared with perimenopausal, postmenopausal women and men (P≤0.001). Multivariable regression analysis demonstrated significant effect of age and insignificant effect of sex on CBF for all participants. In addition, menopause status and the interaction between age and menopause status on CBF of whole brain, gray matter, white matter, and the subregions were observed in female participants, except for the deep and periventricular white matter regions, with premenopausal women exhibited a slight increase in CBF with age, while perimenopausal and postmenopausal women exhibited declines in CBF with age.

CONCLUSIONS

The current findings suggest that alterations of brain perfusion hemodynamics begin during the perimenopause period, which may be due to the increased burden of white matter hyperintensities.

Age-associated cortical similarity networks correlate with cell type-specific transcriptional signatures

Human brain structure shows heterogeneous patterns of change across adults aging and is associated with cognition. However, the relationship between cortical structural changes during aging and gene transcription signatures remains unclear. Here, using structural magnetic resonance imaging data of two separate cohorts of healthy participants from the Cambridge Centre for Aging and Neuroscience (n = 454, 18-87 years) and Dallas Lifespan Brain Study (n = 304, 20-89 years) and a transcriptome dataset, we investigated the link between cortical morphometric similarity network and brain-wide gene transcription. In two cohorts, we found reproducible morphometric similarity network change patterns of decreased morphological similarity with age in cognitive related areas (mainly located in superior frontal and temporal cortices), and increased morphological similarity in sensorimotor related areas (postcentral and lateral occipital cortices). Changes in morphometric similarity network showed significant spatial correlation with the expression of age-related genes that enriched to synaptic-related biological processes, synaptic abnormalities likely accounting for cognitive decline. Transcription changes in astrocytes, microglia, and neuronal cells interpreted most of the age-related morphometric similarity network changes, which suggest potential intervention and therapeutic targets for cognitive decline. Taken together, by linking gene transcription signatures to cortical morphometric similarity network, our findings might provide molecular and cellular substrates for cortical structural changes related to cognitive decline across adults aging.

Deep learning-based grading of white matter hyperintensities enables identification of potential markers in multi-sequence MRI data

BACKGROUND

White matter hyperintensities (WMHs) are widely-seen in the aging population, which are associated with cerebrovascular risk factors and age-related cognitive decline. At present, structural atrophy and functional alterations coexisted with WMHs lacks comprehensive investigation. This study developed a WMHs risk prediction model to evaluate WHMs according to Fazekas scales, and to locate potential regions with high risks across the entire brain.

METHODS

We developed a WMHs risk prediction model, which consisted of the following steps: T2 fluid attenuated inversion recovery (T2-FLAIR) image of each participant was firstly segmented into 1000 tiles with the size of 32 × 32 × 1, features from the tiles were extracted using the ResNet18-based feature extractor, and then a 1D convolutional neural network (CNN) was used to score all tiles based on the extracted features. Finally, a multi-layer perceptron (MLP) was constructed to predict the Fazekas scales based on the tile scores. The proposed model was trained using T2-FLAIR images, we selected tiles with abnormal scores in the test set after prediction, and evaluated their corresponding gray matter (GM) volume, white matter (WM) volume, fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF) via longitudinal and multi-sequence Magnetic Resonance Imaging (MRI) data analysis.

RESULTS

The proposed WMHs risk prediction model could accurately predict the Fazekas ratings based on the tile scores from T2-FLAIR MRI images with accuracy of 0.656, 0.621 in training data set and test set, respectively. The longitudinal MRI validation revealed that most of the high-risk tiles predicted by the WMHs risk prediction model in the baseline images had WMHs in the corresponding positions in the longitudinal images. The validation on multi-sequence MRI demonstrated that WMHs were associated with GM and WM atrophies, WM micro-structural and perfusion alterations in high-risk tiles, and multi-modal MRI measures of most high-risk tiles showed significant associations with Mini Mental State Examination (MMSE) score.

CONCLUSION

Our proposed WMHs risk prediction model can not only accurately evaluate WMH severities according to Fazekas scales, but can also uncover potential markers of WMHs across modalities. The WMHs risk prediction model has the potential to be used for the early detection of WMH-related alterations in the entire brain and WMH-induced cognitive decline.

Metabolic interactions between organs in overweight and obesity using total-body positron emission tomography

BACKGROUND AND OBJECTIVES

Overweight and obesity is a complex condition resulting from unbalanced energy homeostasis among various organs. However, systemic abnormalities in overweight and obese people are seldom explored in vivo by metabolic imaging techniques. The aim of this study was to determine metabolic abnormities throughout the body in overweight and obese adults using total-body positron emission tomography (PET) glucose uptake imaging.

METHODS

Thirty normal weight subjects [body mass index (BMI) < 25 kg/m2, 55.47 ± 13.94 years, 16 men and 14 women], and 26 overweight and obese subjects [BMI ≥ 25 kg/m2, 52.38 ± 9.52 years, 21 men and 5 women] received whole-body 18F-fluorodeoxyglucose PET imaging using the uEXPLORER. Whole-body standardized uptake value normalized by lean body mass (SUL) images were calculated. Metabolic networks were constructed based on the whole-body SUL images using covariance network approach. Both group-level and individual-level network differences between normal weight and overweight/obese subjects were evaluated. Correlation analysis was conducted between network properties and BMI for the overweight/obese subjects.

RESULTS

Compared with normal weight subjects, overweight/obese subjects exhibited altered network connectivity strength in four network nodes, namely the pancreas (p = 0.033), spleen (p = 0.021), visceral adipose tissue (VAT) (p = 1.12 × 10-5) and bone (p = 0.021). Network deviations of overweight/obese subjects from the normal weight were positively correlated with BMI (r = 0.718, p = 3.64 × 10-5). In addition, overweight/obese subjects experienced altered connections between organs, and some of the altered connections, including pancreas-right lung and VAT-bilateral lung connections were significantly correlated with BMI.

CONCLUSION

Overweight/obese individuals exhibit metabolic alterations in organ level, and altered metabolic interactions at the systemic level. The proposed approach using total-body PET imaging can reveal individual metabolic variability and metabolic deviations between organs, which would open up a new path for understanding metabolic alterations in overweight and obesity.

[Analysis of the current status and related factors of human papillomavirus infection among community-dwelling women aged 18-24 years without a history of vaccination in Shanghai City]

Objective: To understand the status of human papillomavirus (HPV) infection among young women without a history of vaccination in Shanghai, and analyze the related factors of HPV infection in this population. Methods: A total of 2 660 women aged 18-24 years old who had made an appointment for HPV vaccine at 36 community health service centers in Shanghai from July 2022 to February 2023 were selected as the study subjects. Basic information (including demographic characteristics, previous disease history, female menstrual and reproductive history, sexual life history, etc.) was collected by a self-filling electronic questionnaire. Cervical secretions were detected by HPV nucleic acid typing. The multivariate logistic regression model was used to analyze the factors related to high-risk HPV (HR-HPV) infection in the target population. Results: The age of the subjects was (23±1) years old, and the infection rate of HPV was 14.51% (386 cases), among which the infection rates of HR-HPV and low-risk HPV were 13.53% (360 cases) and 1.84% (49 cases), respectively. The main subtypes of HR-HPV infection were HPV52, 16, 58, 39 and 66. The multivariate logistic regression model analysis showed that compared with the control group, the OR (95%CI) values for HR-HPV infection in the group of married, earned less than 2 000 yuan/month, drank alcohol occasionally, gynecological disease history, had two or more sexual partners in the past year, and did not know whether the partners had other sexual partners were 0.41 (0.25-0.66), 0.39 (0.21-0.70), 1.45 (1.13-1.86), 1.29 (1.00-1.66), 2.18-5.18 (1.02-16.05), and 1.82 (1.31-2.54), respectively. Conclusion: The infection rate of HPV among women aged 18-24 years old in Shanghai remains at a high level. The main subtypes of HR-HPV infection are HPV52, 16, 58, 39 and 66. The marital status, economic income level, drinking status, gynecological disease history and sexual life history are related to HR-HPV infection.

Rabbit systemic glucose metabolism map by total-body dynamic PET/CT technology

BACKGROUND

This study evaluated total-body glucose metabolism in a preclinical lab animal, the rabbit, by employing a dynamic glucose metabolic image obtained with total-body fluorine-18 fluorodeoxyglucose ( 18 F-FDG) PET/computed tomography (PET/CT).

METHODS

The dynamic total-body PET/CT system was used to obtain glucose metabolic imaging from 10 sedated body-matched rabbits. The standard uptake value (SUV) of 18 F-FDG was used to evaluate glucose metabolism. In addition, the correlation between glucose metabolism and sexes was assessed, as well as metabolic differences between left- and right sides.

RESULTS

We found significant distribution heterogeneity of glucose in several organs across the entire body. There were no significant metabolic differences between sexes and between bilateral sides in the 10 rabbits. Thereafter, we assayed the major organ SUV changes by dynamic PET/CT of the major organs. The heart, liver, and urinary system showed more 18 F-FDG, whereas the skeletal muscle, brain, spinal cord, and lungs incorporated less 18 F-FDG. The phenotype of 18 F-FDG uptake was highly correlated with the physiological functions. The 18 F-FDG accumulation in urinary system were observed which could reflect the renal parenchyma glucose metabolism indirectly. However, the low 18 F-FDG uptake in the brain and spinal cord was due to sedation.

CONCLUSION

The total-body glucose metabolic atlas depicted with 18 F-FDG dynamic PET/CT may be used as a reference for assessing pathological 18 F-FDG uptake. Furthermore, this study could be a reference for preclinical research involving abnormality of glucose metabolism.

Biomedical application of terahertz imaging technology: a narrative review

Background and Objective

Terahertz (THz) imaging has wide applications in biomedical research due to its properties, such as non-ionizing, non-invasive and distinctive spectral fingerprints. Over the past 6 years, the application of THz imaging in tumor tissue has made encouraging progress. However, due to the strong absorption of THz by water, the large size, high cost, and low sensitivity of THz devices, it is still difficult to be widely used in clinical practice. This paper provides ideas for researchers and promotes the development of THz imaging in clinical research.

Methods

The literature search was conducted in the Web of Science and PubMed databases using the keywords "Terahertz imaging", "Breast", "Brain", "Skin" and "Cancer". A total of 94 English language articles from 1 January, 2017 to 30 December, 2022 were reviewed.

Key Content and Findings

In this review, we briefly introduced the recent advances in THz near-field imaging, single-pixel imaging and real-time imaging, the applications of THz imaging for detecting breast, brain and skin tissues in the last 6 years were reviewed, and the advantages and existing challenges were identified. It is necessary to combine machine learning and metamaterials to develop real-time THz devices with small size, low cost and high sensitivity that can be widely used in clinical practice. More powerful THz detectors can be developed by combining graphene, designing structures and other methods to improve the sensitivity of the devices and obtain more accurate information. Establishing a THz database is one of the important methods to improve the repeatability and accuracy of imaging results.

Conclusions

THz technology is an effective method for tumor imaging. We believe that with the joint efforts of researchers and clinicians, accurate, real-time, and safe THz imaging will be widely applied in clinical practice in the future.

[Relationship between the neutrophil-to-lymphocyte ratio and estimated glomerular filtration rate in patients with primary aldosteronism: a cross-sectional study]

Objective: To investigate the associations between neutrophil-to-lymphocyte ratio (NLR) and estimated glomerular filtration rate (eGFR) in patients with primary aldosteronism (PA). Methods: This study was a cross-sectional study. Consecutive patients diagnosed with PA and admitted to the Second Affiliated Hospital of Nanchang University from October 2017 to April 2022 were enrolled. General information, blood routine, renal function, and other clinical data of the patients were collected. Based on the median NLR of the enrolled patients, NLR Collapse

Key Words Collapse

MESH Headings

• Humans
• Female
• Male
• Neutrophils
• Glomerular Filtration Rate
• Cross-Sectional Studies
• Lymphocytes
• Hyperaldosteronism/diagnosis
• Hyperlipidemias

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Grants

• 3226090026, 81960088 National Natural Science Foundation of China

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Radiomics‑Clinical model based on 99mTc-MDP SPECT/CT for distinguishing between bone metastasis and benign bone disease in tumor patients

BACKGROUND

To establish a radiomics-clinical model based on 99mTc-MDP SPECT/CT for distinguishing between bone metastasis and benign bone disease in tumor patients.

METHODS

We retrospectively analyzed 256 patients (122 with bone metastasis and 134 with benign bone disease) and randomized them in the ratio of 6:2:2 into training, test and validation sets. All patients underwent 99mTc-labeled methylene diphosphonate (99mTc-MDP) SPECT/CT. We manually outlined the volumes of interest (VOIs) of lesions using ITK-SNAP from SPECT and CT images. In the training set, radiomics features were extracted using PyRadiomics and selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Then, we established three radiomics models (CT, SPECT and SPECT-CT models) using support vector machine (SVM). In addition, a radiomics-clinical model was constructed using multivariable logistic regression analysis. The four models' performance was assessed using the area under the receiver operating characteristic curve (AUC). Using DeLong test to make comparisons between the ROC (receiver operating characteristic) curves of different models. The clinical utility of the models was evaluated using decision curve analysis (DCA).

RESULTS

The radiomics-clinical displayed excellent performance, and its AUC was 0.941 and 0.879 in the training and test sets. The DCA of radiomics-clinical model showed the highest clinical utility.

CONCLUSIONS

The radiomics-clinical nomogram for identifying bone metastasis and benign bone disease in tumor patients was suitable to assist in clinical decision.

Application of Intravoxel Incoherent Motion in Clinical Liver Imaging: A Literature Review

Intravoxel incoherent motion (IVIM) modeling is a widely used double-exponential model for describing diffusion-weighted imaging (DWI) signal, with a slow component related to pure molecular diffusion and a fast component associated with microcirculatory perfusion, which compensates for the limitations of traditional DWI. IVIM is a noninvasive technique for obtaining liver pathological information and characterizing liver lesions, and has potential applications in the initial diagnosis and treatment monitoring of liver diseases. Recent studies have demonstrated that IVIM-derived parameters are useful for evaluating liver lesions, including nonalcoholic fatty liver disease (NAFLD), liver fibrosis and liver tumors. However, the results are not stable. Therefore, it is necessary to summarize the current applications of IVIM in liver disease research, identify existing shortcomings, and point out the future development direction. In this review, we searched for studies related to hepatic IVIM-DWI applications over the past two decades in the PubMed database. We first introduce the fundamental principles and influential factors of IVIM, and then discuss its application in NAFLD, liver fibrosis, and focal hepatic lesions. It has been found that IVIM is still unstable in ensuring the robustness and reproducibility of measurements in the assessment of liver fibrosis grade and liver tumors differentiation, due to inconsistent and substantial overlap in the range of IVIM-derived parameters for different fibrotic stages. In the end, the future direction of IVIM-DWI in the assessment of liver diseases is discussed, emphasizing the need for further research on the stability of IVIM-derived parameters, particularly perfusion-related parameters, in order to promote the clinical practice of IVIM-DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

MRI-based radiomics analysis of bladder cancer: prediction of pathological grade and histological variant

AIM

To develop magnetic resonance imaging (MRI)-based radiomics models for the prediction of the pathological grade and histological variant of bladder cancer.

MATERIALS AND METHODS

A total of 227 patients who underwent bladder MRI and had histopathologically confirmed grades and variants were included retrospectively from January 2017 to March 2022. They were assigned to a training set (n=131) and a testing set (n=96) based on the MRI system. MRI-based radiomics features were extracted from manually segmented volumes of interest from high-b-value DWI images and ADC maps. The radiomics models were trained with all possible pipelines in the training set. One optimal model was selected using the fivefold cross-validation method and verified by the testing set according to the pathological results. Univariate and multivariate analyses were performed to identify the significant clinical and imaging factors for developing clinical-radiomics models.

RESULTS

The radiomics model for grade prediction had area under the curve (AUC) values of 0.784, 0.786, and 0.733 in the training, cross-validation, and testing sets, respectively. The radiomics model for variant prediction had AUC values of 0.748, 0.757, and 0.789 in the training, cross-validation, and testing sets, respectively. The performance of the clinical-radiomics model was significantly improved compared with the radiomics models alone for the total dataset (AUC for grade: 0.846 versus 0.756; AUC for variant: 0.810 versus 0.757, p<0.05).

CONCLUSION

MRI-based radiomics models could be used to predict the pathological grade and histological variants of bladder cancer with relatively good performance.

Antifungal bio-coating of endotracheal tube built by overexpressing the MCP1 gene of Saccharomyces boulardii and employing hydrogel as a "house" to antagonize Candida albicans

BACKGROUND

For some ICU patients, an artificial airway must be established with an endotracheal tube, but Candida albicans can easily adhere to the tube surface and form a biofilm, leading to potentially life threatening fungal infections. Therefore, it is urgent to prevent and reduce C. albicans infections introduced by the endotracheal tube. However, there are few antifungal drugs effective against C. albicans, and each of these drugs may have adverse effects on human cells. Saccharomyces boulardii is regarded as an alternative strategy to inhibit the adhesion of C. albicans, but it is affected by environmental stress. We hypothesized that it is feasible to strengthen the antagonistic ability of S. boulardii via encapsulating and genetically modification.

METHODS

In this study, a bioactive material carrying the overexpressed MCP1 gene of Saccharomyces boulardii was constructed based on one-step photo-crosslinking. This material achieved spatial growth control of S. boulardii by encapsulating each S. boulardii cell within a hydrogel pore. The bioactive material was coated on an endotracheal tube and tested for its ability to inhibit the adhesion of C. albicans. Additionally, the material's antagonistic activity towards C. albicans was evaluated by detecting intracellular Adenosine-triphosphate content, reactive oxygen species level and the activity of antioxidative enzymes. Tissue invasion experiment was executed to further evaluate the anti-adhesion ability of S. boulardii bio-coating.

RESULTS

Encapsulating the overexpression of MCP1 by S. boulardii in hydrogel pores enhanced the viability of probiotics in the presence of high salt and oxidation stress. When used as the coating of an endotracheal tube, the S. boulardii bioactive material efficiently inhibited the adhesion of C. albicans by impairing the activities of superoxide dismutase and catalase and disturbing mitochondrial functions. In vivo, the S. boulardii bioactive material coating displayed good biocompatibility and reduced the host tissue invasion and virulence of C. albicans.

CONCLUSIONS

The integration of genetic modification and immobilization model breaks the bottleneck of previous application of microorganisms, and provides a new way to prevent fungal infections introduced by endotracheal tubes.

Application of the Ion Chamber Array in Magnetic Resonance Accelerator QA

PURPOSE/OBJECTIVE(S)

The magnetic resonance accelerator (MR-Linac) is gradually widely used due to high-quality soft tissue contrast and real-time tracking. However, the special dosimetry characteristics and wide field sizes of MR-Linac increase the QA difficulty with conventional measurement method. The purpose of this study was to confirm an ion chamber array could be used for measuring the beam quality, the profiles, as well as the positioning accuracy of all MLC leaves efficiently, by comparing results with the conventional method. To propose a new QA approach for solving the common problem in data acquisition caused by the wide fields of MR-Linac.

MATERIALS/METHODS

The research was based on a MR-Linac fixed with 1.5T MR and 7MeV energy photon beam. The conventional QA method adopted the MR water tank with a gantry angle of 0°and an SSD of 133.5 cm, both microdiamond and ionization chamber detector were used to acquire the dose profiles (PDD, inline, crossline and diagonal). Field sizes 1 × 1 cm2, 2 × 2 cm2, 3 × 3 cm2, 5 × 5 cm2, 10 × 10 cm2, 15 × 15 cm2, 22 × 22 cm2, 40 × 22 cm2,57 × 22 cm2 were measured with depth 13mm, 50mm, 100mm for vertical beam. As for the wide fields (larger than 15 × 15 cm2), two profiles of x axis (one from left to right, the other from right to left) needed to be gathered and then stitched into one final profile. A boot phantom with an ionization chamber detector was used for measuring beam quality. We defined the profiles measured by conventional method as the baseline. An ion chamber array was adopted to acquire TPR, PDD, profiles and MLC positioning, comparing to the conventional method. The center of ion chamber array was placed to the isocenter of MR-Linac, the array could move to the right and left offset positions through engaging the pin into correct hole of QA platform, such 'once positioning and twice movements' operation could finish within 3 minutes. The central detector of the ion chamber array was used for measuring beam quality. TPRs for different depths were acquired by stacking solid water on the ion chamber array. As for the profiles, we could get the final profile by 'once positioning and twice movements' efficiently. As for the positioning accuracy of MLC leaves, firstly the central leaf pair was put on y = 0 to measure 'open profile' under the open field. Then we moved the MLC leaves to different positions to get the n profile (n for different leaf positions). The ratio of n profile to open profile could show the positioning accuracy of MLC.

RESULTS

We adopted 2D gamma (1mm / 2%) to compare the profiles between the ion chamber array and the conventional method, the results were within 98%. The beam quality consistency of ion chamber array comparing to the wedge tank was within 1% according to daily measurement. The ion chamber array could reflect the MLC positioning differences, the sensitivity was 0.5 mm.

CONCLUSION

The ion chamber array showed a convenient QA method both for the dosimetry and for the MLC positioning accuracy which did reduce the overall measurement time, it was recommended for daily and monthly QA for MR-Linac.

Performance Evaluation in Automatic Plan Generation for Ethos Intelligent Optimization Engine

PURPOSE/OBJECTIVE(S)

To evaluate the automatic optimization performance and clinical feasibility of the Intelligent Optimization Engine (IOE) of Ethos online adaptive radiotherapy platform.

MATERIALS/METHODS

Eleven patients with cervical cancer treated with Halcyon accelerator were retrospectively selected. All the patients manually planned with four full arc volume rotating intensity modulated radiotherapy (VMAT) (Manual-4Arc), and the prescription dose was 45 Gy/25F. All patient images and structures were imported into Ethos simulator, and clinical goals were added appropriately based on clinical requirements. The target coverage was normalized to 95%. 7F, 9F, 12F IMRT plans and 2Arc, 3Arc VMAT plans were automatically generated by IOE. Dosimetric index comparisons were made among the Manual-4Arc plans and five group IOE generated plan to evaluate the automatic optimization performance of IOE.

RESULTS

In terms of hot dose area, for PTV, D1% of IMRT-12F plans was the lowest, and there were significant differences between IMRT-12F plans and Manual-4Arc plans (46.936 ± 0.241 vs 48.639 ± 2.395, p = 0.004); In terms of target coverage, the CTVs of all groups meet clinical requirements. Although the Ethos online adaptive plans have been normalized during planning, the PTV coverage is slightly insufficient (12F: 94.913 ± 0.154; 9F: 94.585 ± 1.148). For OARs close to target, such as bladder, V30Gy, V40Gy and Dmean have significant differences among the six group plans. The order of bladder dose is basically followed by IMRT-12F

CONCLUSION

The plans automatically generated by Ethos IOE can achieve similar performance as the manual plan, and the automatically generated IMRT-12F and 9F plans are preferred for clinical use.

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