Metabolomic profiling of maternal plasma identifies inverse associations of acetate and urea with anti-SARS-CoV-2 antibody titers following COVID-19 vaccination during pregnancy.

We conducted a comprehensive metabolomic analysis of plasma samples obtained from pregnant women who displayed varying post-vaccination antibody titers after receiving mRNA-1273-SARS-CoV-2 vaccines. The study involved 62 pregnant women, all of whom had been vaccinated after reaching 24 weeks of gestation. To quantify post-vaccination plasma antibody titers, we employed binding antibody units (BAU) in accordance with the World Health Organization International Standard. Subsequently, we classified the study participants into three distinct BAU/mL categories: those with high titers (above 2000), medium titers (ranging from 1000 to 2000), and low titers (below 1000). Plasma metabolomic profiling was conducted using 1H nuclear magnetic resonance spectroscopy, and the obtained data were correlated with the categorized antibody titers. Notably, in pregnant women exhibiting elevated anti-SARS-CoV-2 antibody titers, reduced plasma concentrations of acetate and urea were observed. A significant negative correlation between these compounds and antibody titers was also evident. An analysis of metabolomics pathways revealed significant inverse associations between antibody titers and four distinct amino acid metabolic pathways: (1) biosynthesis of phenylalanine, tyrosine, and tryptophan; (2) biosynthesis of valine, leucine, and isoleucine; (3) phenylalanine metabolism; and (4) degradation of valine, leucine, and isoleucine. Additionally, an association between the synthesis and degradation pathways of ketone bodies was evident. In conclusion, we identified different metabolic pathways that underlie the diverse humoral responses triggered by COVID-19 mRNA vaccines during pregnancy. Our data hold significant implications for refining COVID-19 vaccination approaches in expectant mothers. KEY MESSAGES : Anti-SARS-CoV-2 antibody titers decline as the number of days since COVID-19 vaccination increases. Anti-SARS-CoV-2 antibody titers are inversely associated with acetate, a microbial-derived metabolite, and urea. Amino acid metabolism is significantly associated with SARS-CoV-2 antibody titers.

Ultrasonography of acute retroperitoneum.

The retroperitoneum is an important space in the human body that is often implicated in a range Epub ahead of print of acute medical conditions, some of which can be life-threatening. Ultrasonography may serve as a pivotal first-line imaging technique when assessing patients with suspected retroperitoneal abnormalities. Effective ultrasonography of the retroperitoneum requires a comprehensive grasp of its anatomy, adjacent structures, and potential pathologies. Being well-acquainted with the imaging characteristics of acute conditions can meaningfully assist in an accurate diagnosis and guide subsequent management. This review article summarizes and illustrates the acute conditions involving the retroperitoneum through the lens of ultrasound imaging.

Hyperpolarized [1-13C]-pyruvate MRS evaluates immune potential and predicts response to radiotherapy in cervical cancer.

BACKGROUND: Monitoring pyruvate metabolism in the spleen is important for assessing immune activity and achieving successful radiotherapy for cervical cancer due to the significance of the abscopal effect. We aimed to explore the feasibility of utilizing hyperpolarized (HP) [1-13C]-pyruvate magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) to evaluate pyruvate metabolism in the human spleen, with the aim of identifying potential candidates for radiotherapy in cervical cancer. METHODS: This prospective study recruited six female patients with cervical cancer (median age 55 years; range 39-60) evaluated using HP [1-13C]-pyruvate MRI/MRS at baseline and 2 weeks after radiotherapy. Proton (1H) diffusion-weighted MRI was performed in parallel to estimate splenic cellularity. The primary outcome was defined as tumor response to radiotherapy. The Student t-test was used for comparing 13C data between the groups. RESULTS: The splenic HP [1-13C]-lactate-to-total carbon (tC) ratio was 5.6-fold lower in the responders than in the non-responders at baseline (p = 0.009). The splenic [1-13C]-lactate-to-tC ratio revealed a 1.7-fold increase (p = 0.415) and the splenic [1-13C]-alanine-to-tC ratio revealed a 1.8-fold increase after radiotherapy (p = 0.482). The blood leukocyte differential count revealed an increased proportion of neutrophils two weeks following treatment, indicating enhanced immune activity (p = 0.013). The splenic apparent diffusion coefficient values between the groups were not significantly different. CONCLUSIONS: This exploratory study revealed the feasibility of HP [1-13C]-pyruvate MRS of the spleen for evaluating baseline immune potential, which was associated with clinical outcomes of cervical cancer after radiotherapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT04951921 , registered 7 July 2021. RELEVANCE STATEMENT: This prospective study revealed the feasibility of using HP 13C MRI/MRS for assessing pyruvate metabolism of the spleen to evaluate the patients' immune potential that is associated with radiotherapeutic clinical outcomes in cervical cancer. KEY POINTS: • Effective radiotherapy induces abscopal effect via altering immune metabolism. • Hyperpolarized 13C MRS evaluates patients' immune potential non-invasively. • Pyruvate-to-lactate conversion in the spleen is elevated following radiotherapy.

Combined Plasma DHA-Containing Phosphatidylcholine PCaa C38:6 and Tetradecanoyl-Carnitine as an Early Biomarker for Assessing the Mortality Risk among Sarcopenic Patients.

The coming of the hyper-aged society in Taiwan prompts us to investigate the relationship between the metabolic status of sarcopenic patients and their most adverse outcome-death. We studied the association between any plasma metabolites and the risk for mortality among older Taiwanese sarcopenic patients. We applied a targeted metabolomic approach to study the plasma metabolites of adults aged ≥65 years, and identified the metabolic signature predictive of the mortality of sarcopenic patients who died within a 5.5-year follow-up period. Thirty-five sarcopenic patients who died within the follow-up period (Dead cohort) had shown a specific plasma metabolic signature, as compared with 54 patients who were alive (Alive cohort). Only 10 of 116 non-sarcopenic individuals died during the same period. After multivariable adjustment, we found that sex, hypertension, tetradecanoyl-carnitine (C14-carnitine), and docosahexaenoic acid (DHA)-containing phosphatidylcholine diacyl (PCaa) C38:6 and C40:6 were important risk factors for the mortality of sarcopenic patients. Low PCaa C38:6 levels and high C14-carnitine levels correlated with an increased mortality risk; this was even the same for those patients with hypertension (HTN). Our findings suggest that plasma PCaa C38:6 and acylcarnitine C14-carnitine, when combined, can be a better early biomarker for evaluating the mortality risk of sarcopenia patients.

Distinct changes to pancreatic volume rather than pancreatic autoantibody positivity: insights into immune checkpoint inhibitors induced diabetes mellitus.

BACKGROUND: Immune checkpoint inhibitors (ICI) are promising treatment options for various cancers. However, their use is associated with immune-related adverse events (irAEs), including ICI-induced diabetes mellitus (ICI-DM). This study aimed to investigate the clinical features of ICI-DM, with a particular focus on alterations to pancreatic volume. METHODS: We conducted a retrospective review of 2829 patients who received ICI treatment at the Chang Gung Memorial Hospital, Linkou, between January 2014 and December 2021. New-onset diabetes or diabetic ketoacidosis (DKA) was identified in ten patients receiving ICI therapy. Pancreatic volumes were assessed by manual segmentation of computed tomography (CT) images before and after ICI-DM diagnosis. RESULTS: Among these ten patients, nivolumab was the most commonly used ICI (50.0%), followed by pembrolizumab (30.0%) and atezolizumab (20.0%). One patient received combination therapy with nivolumab and ipilimumab. The median age was 63.01 years (range: 40.1 - 87.8). ICI-DM developed after a median of 13.5 cycles (range: 2 - 42) of ICI treatment or 9.85 months (range:1.5 - 21.3) since ICI initiation. The initial presentation was DKA in 60.0% of patients. All patients had low or undetectable C-peptide levels (range: <0.033 - 0.133 nmol/L) and were negative for most type 1 diabetes mellitus (T1DM)-related autoantibodies; only one patient tested positive for glutamic acid decarboxylase antibodies. CT imaging revealed significant pancreatic atrophy, with a median pancreatic volume decrease of 19.92% (P = 0.038) from baseline and sustained significant decline at last follow-up (median - 37.14%, P = 0.012). CONCLUSIONS: ICI-DM is often accompanied by pancreatic atrophy and approximately two-thirds of patients initially present with DKA. Although the majority of ICI-DM patients lack T1DM-related autoantibodies, identifying diminished pancreatic volumes through CT imaging provides valuable clues into the subclinical aspects of ICI-DM development, aiding in the prevention of diabetic emergencies. TRIAL REGISTRATION: Not applicable.

The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels.

PURPOSE: To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels. PATIENTS AND METHODS: Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CS CCT , by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CS AICCT , by AI from the standard CCT; CS ChCTsoft , by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CS ChCTsharp , by AI from CCT image with same parameters for CS ChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (ρ), intraclass correlation (ICC), Bland-Altman plots, and weighted kappa analysis (κ). RESULTS: The CS AICCT was consistent with CS CCT (ρ = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (κ = 1.000). The correlation between CS ChCTsoft and CS ChCTsharp was good (ρ = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland-Altman mean difference and 95% upper and lower limits of agreements were 329.1 [-798.9 to 1457] and 335.3 [-651.9 to 1322], respectively). The CV risk category agreement between CS ChCTsoft and CS ChCTsharp was moderate (κ = 0.556 and 0.537, respectively). CONCLUSIONS: There was an excellent correlation between CS CCT and CS AICCT , with excellent agreement between CV risk categories. There was also a good correlation between CS CCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.

Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT.

BACKGROUND: We aimed to develop and validate a preoperative CT-based radiomics signature for differentiating lymphoma versus benign splenomegaly. METHODS: We retrospectively analyzed CT studies from 139 patients (age range 26-93 years, 43% female) between 2011 and 2019 with histopathological diagnosis of the spleen (19 lymphoma, 120 benign) and divided them into developing (n = 79) and testing (n = 60) datasets. The volumetric radiomic features were extracted from manual segmentation of the whole spleen on venous-phase CT imaging using PyRadiomics package. LASSO regression was applied for feature selection and development of the radiomic signature, which was interrogated with the complete blood cell count and differential count. All p values < 0.05 were considered to be significant. RESULTS: Seven features were selected for constructing the radiomic signature after feature selection, including first-order statistics (10th percentile and Robust Mean Absolute Deviation), shape-based (Surface Area), and texture features (Correlation, MCC, Small Area Low Gray-level Emphasis and Low Gray-level Zone Emphasis). The radiomic signature achieved an excellent diagnostic accuracy of 97%, sensitivity of 89%, and specificity of 98%, distinguishing lymphoma versus benign splenomegaly in the testing dataset. The radiomic signature significantly correlated with the platelet and segmented neutrophil percentage. CONCLUSIONS: CT-based radiomics signature can be useful in distinguishing lymphoma versus benign splenomegaly and can reflect the changes in underlying blood profiles.

Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging.

T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T2W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568-0.776), although the difference was not statistically significant (p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T2W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model's ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications.

Metabolomics Assessment of Volume Overload-Induced Heart Failure and Oxidative Stress in the Kidney.

The incidence of heart failure (HF) is increasing and is associated with a poor prognosis. Moreover, HF often coexists with renal dysfunction and is associated with a worsened outcome. In many experimental studies on cardiac dysfunction, the function of other organs was either not addressed or did not show any decline. Until now, the exact mechanisms for initiating and sustaining this interaction are still unknown. The objective of this study is to use volume overload to induce cardiac hypertrophy and HF in aortocaval fistula (ACF) rat models, and to elucidate how volume overload affects metabolic changes in the kidney, even with normal renal function, in HF. The results showed the metabolic changes between control and ACF rats, including taurine metabolism; purine metabolism; glycine, serine, and threonine metabolism; glycerophospholipid metabolism; and histidine metabolism. Increasing the downstream purine metabolism from inosine to uric acid in the kidneys of ACF rats induced oxidative stress through xanthine oxidase. This result was consistent with HK-2 cells treated with xanthine and xanthine oxidase. Under oxidative stress, taurine accumulation was observed in ACF rats, indicating increased activity of the hypotaurine-taurine pathway as a defense mechanism against oxidative stress in the kidney. Another antioxidant, ascorbic acid 2-sulfate, showed lower levels in ACF rats, indicating that the kidneys experience elevated oxidative stress due to volume overload and HF. In summary, metabolic profiles are more sensitive than clinical parameters in reacting to damage to the kidney in HF.

Comparing Magnetic Resonance Fingerprinting (MRF) and the MAGiC Sequence for Simultaneous T1 and T2 Quantitative Measurements in the Female Pelvis: A Prospective Study.

The aim of this study was to explore the potential of magnetic resonance fingerprinting (MRF), an emerging quantitative MRI technique, in measuring relaxation values of female pelvic tissues compared to the conventional magnetic resonance image compilation (MAGiC) sequence. The study included 32 female patients who underwent routine pelvic MRI exams using anterior and posterior array coils on a 3T clinical scanner. Our findings demonstrated significant correlations between MRF and MAGiC measured T1 and T2 values (p < 0.0001) for various pelvic tissues, including ilium, femoral head, gluteus, obturator, iliopsoas, erector spinae, uterus, cervix, and cutaneous fat. The tissue contrasts generated from conventional MRI and synthetic MRF also showed agreement in bone, muscle, and uterus for both T1-weighted and T2-weighted images. This study highlights the strengths of MRF in providing simultaneous T1 and T2 mapping. MRF offers distinct tissue contrast and has the potential for accurate diagnosis of female pelvic diseases, including tumors, fibroids, endometriosis, and pelvic inflammatory disease. Additionally, MRF shows promise in monitoring disease progression or treatment response. Overall, the study demonstrates the potential of MRF in the field of female pelvic organ imaging and suggests that it could be a valuable addition to the clinical practice of pelvic MRI exams. Further research is needed to establish the clinical utility of MRF and to develop standardized protocols for its implementation in clinical practice.

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.

OBJECTIVES: To use convolutional neural network for fully automated segmentation and radiomics features extraction of hypopharyngeal cancer (HPC) tumor in MRI. METHODS: MR images were collected from 222 HPC patients, among them 178 patients were used for training, and another 44 patients were recruited for testing. U-Net and DeepLab V3 + architectures were used for training the models. The model performance was evaluated using the dice similarity coefficient (DSC), Jaccard index, and average surface distance. The reliability of radiomics parameters of the tumor extracted by the models was assessed using intraclass correlation coefficient (ICC). RESULTS: The predicted tumor volumes by DeepLab V3 + model and U-Net model were highly correlated with those delineated manually (p < 0.001). The DSC of DeepLab V3 + model was significantly higher than that of U-Net model (0.77 vs 0.75, p < 0.05), particularly in those small tumor volumes of < 10 cm3 (0.74 vs 0.70, p < 0.001). For radiomics extraction of the first-order features, both models exhibited high agreement (ICC: 0.71-0.91) with manual delineation. The radiomics extracted by DeepLab V3 + model had significantly higher ICCs than those extracted by U-Net model for 7 of 19 first-order features and for 8 of 17 shape-based features (p < 0.05). CONCLUSION: Both DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images, whereas DeepLab V3 + had a better performance than U-Net. CLINICAL RELEVANCE STATEMENT: The deep learning model, DeepLab V3 + , exhibited promising performance in automated tumor segmentation and radiomics extraction for hypopharyngeal cancer on MRI. This approach holds great potential for enhancing the radiotherapy workflow and facilitating prediction of treatment outcomes. KEY POINTS: • DeepLab V3 + and U-Net models produced reasonable results in automated segmentation and radiomic features extraction of HPC on MR images. • DeepLab V3 + model was more accurate than U-Net in automated segmentation, especially on small tumors. • DeepLab V3 + exhibited higher agreement for about half of the first-order and shape-based radiomics features than U-Net.

Exploring the aging process of cognitively healthy adults by analyzing cerebrospinal fluid metabolomics using liquid chromatography-tandem mass spectrometry.

BACKGROUND: During biological aging, significant metabolic dysregulation in the central nervous system may lead to cognitive decline and neurodegeneration. However, the metabolomics of the aging process in cerebrospinal fluid (CSF) has not been thoroughly explored. METHODS: In this cohort study of CSF metabolomics using liquid chromatography-mass spectrometry (LC-MS), fasting CSF samples collected from 92 cognitively unimpaired adults aged 20-87 years without obesity or diabetes were analyzed. RESULTS: We identified 37 metabolites in these CSF samples with significant positive correlations with aging, including cysteine, pantothenic acid, 5-hydroxyindoleacetic acid (5-HIAA), aspartic acid, and glutamate; and two metabolites with negative correlations, asparagine and glycerophosphocholine. The combined alterations of asparagine, cysteine, glycerophosphocholine, pantothenic acid, sucrose, and 5-HIAA showed a superior correlation with aging (AUC = 0.982). These age-correlated changes in CSF metabolites might reflect blood-brain barrier breakdown, neuroinflammation, and mitochondrial dysfunction in the aging brain. We also found sex differences in CSF metabolites with higher levels of taurine and 5-HIAA in women using propensity-matched comparison. CONCLUSIONS: Our LC-MS metabolomics of the aging process in a Taiwanese population revealed several significantly altered CSF metabolites during aging and between the sexes. These metabolic alterations in CSF might provide clues for healthy brain aging and deserve further exploration.

Lung Cancer in Lung Transplant Recipients: Clinical, Radiologic, and Pathologic Characteristics and Treatment Outcome.

OBJECTIVE: This study aimed to investigate clinical and radiologic characteristics of lung cancer in lung transplant recipients and evaluate the treatment course and prognosis. METHODS: The study included 448 patients who underwent lung transplant between 2005 and 2021. All patients had pretransplant chest computed tomography (CT), 429 patients had posttransplant CT, whereas 19 had no posttransplant CT (median number of posttransplant CT, 6; range, 0-24). Medical records of these patients were reviewed to identify patients who developed lung cancer after lung transplant. Computed tomography and positron emission tomography/CT at the time of lung cancer diagnoses were reviewed to obtain imaging features. Demographics, tumor histology, stages, and survival were compared using Fisher exact test and Wilcoxon rank sum test. RESULTS: Among 448 lung transplant recipients with a median follow-up of 71.3 months after lung transplant, 15 patients (3.3%) developed posttransplant lung cancer (13 unilateral, 2 bilateral; 10 men, 5 women; median age, 63.1 years; median time from transplantation to cancer diagnosis, 3.1 years). Twelve cancers were in native lung, and 3 were in transplanted lung. The incidence of lung cancer was higher in single lung transplant recipients than in bilateral lung transplant recipients (10.3% vs 0.6%, respectively; P < 0.0001). Imaging manifestations varied according to tumor stages. Among 12 patients treated for lung cancer, 2 patients developed posttreatment acute respiratory distress syndrome. The median survival from cancer diagnosis of cancer was 6.2 months. CONCLUSIONS: Posttransplant lung cancer was noted in 3% of lung transplant recipients and was more common in unilateral transplant recipients. The prognosis upon diagnosis was poor with rapid clinical deterioration and serious posttreatment complications.

Malonyl-CoA Accumulation as a Compensatory Cytoprotective Mechanism in Cardiac Cells in Response to 7-Ketocholesterol-Induced Growth Retardation.

The major oxidized product of cholesterol, 7-Ketocholesterol (7KCh), causes cellular oxidative damage. In the present study, we investigated the physiological responses of cardiomyocytes to 7KCh. A 7KCh treatment inhibited the growth of cardiac cells and their mitochondrial oxygen consumption. It was accompanied by a compensatory increase in mitochondrial mass and adaptive metabolic remodeling. The application of [U-13C] glucose labeling revealed an increased production of malonyl-CoA but a decreased formation of hydroxymethylglutaryl-coenzyme A (HMG-CoA) in the 7KCh-treated cells. The flux of the tricarboxylic acid (TCA) cycle decreased, while that of anaplerotic reaction increased, suggesting a net conversion of pyruvate to malonyl-CoA. The accumulation of malonyl-CoA inhibited the carnitine palmitoyltransferase-1 (CPT-1) activity, probably accounting for the 7-KCh-induced suppression of ß-oxidation. We further examined the physiological roles of malonyl-CoA accumulation. Treatment with the inhibitor of malonyl-CoA decarboxylase, which increased the intracellular malonyl-CoA level, mitigated the growth inhibitory effect of 7KCh, whereas the treatment with the inhibitor of acetyl-CoA carboxylase, which reduced malonyl-CoA content, aggravated such a growth inhibitory effect. Knockout of malonyl-CoA decarboxylase gene (Mlycd-/-) alleviated the growth inhibitory effect of 7KCh. It was accompanied by improvement of the mitochondrial functions. These findings suggest that the formation of malonyl-CoA may represent a compensatory cytoprotective mechanism to sustain the growth of 7KCh-treated cells.

Glycolytic Plasticity of Metastatic Lung Cancer Captured by Noninvasive 18F-FDG PET/CT and Serum 1H-NMR Analysis: An Orthotopic Murine Model Study.

We aim to establish a noninvasive diagnostic platform to capture early phenotypic transformation for metastasis using 18F-FDG PET and 1H-NMR-based serum metabolomics. Mice with implantation of NCI-H460 cells grew only primary lung tumors in the localized group and had both primary and metastatic lung tumors in the metastatic group. The serum metabolites were analyzed using 1H-NMR at the time of PET/CT scan. The glycolysis status and cell proliferation were validated by Western blotting and staining. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of SUVmean and serum metabolites in metastasis. In the metastatic mice, the SUVmean of metastatic tumors was significantly higher than that of primary lung tumors in PET images, which was supported by elevated glycolytic protein expression of HK2 and PKM2. The serum pyruvate level in the metastatic group was significantly lower than that in the localized group, corresponding to increased pyruvate-catalyzed enzyme and proliferation rates in metastatic tumors. In diagnosing localized or metastatic tumors, the areas under the ROC curves of SUVmean and pyruvate were 0.92 and 0.91, respectively, with p < 0.05. In conclusion, the combination of 18F-FDG PET and 1H-NMR-based serum metabolomics demonstrated the feasibility of a glycolytic platform for diagnosing metastatic lung cancers.

Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI.

PURPOSE: To investigate the generalizability of transfer learning (TL) of automated tumor segmentation from cervical cancers toward a universal model for cervical and uterine malignancies in diffusion-weighted magnetic resonance imaging (DWI). METHODS: In this retrospective multicenter study, we analyzed pelvic DWI data from 169 and 320 patients with cervical and uterine malignancies and divided them into the training (144 and 256) and testing (25 and 64) datasets, respectively. A pretrained model was established using DeepLab V3 + from the cervical cancer dataset, followed by TL experiments adjusting the training data sizes and fine-tuning layers. The model performance was evaluated using the dice similarity coefficient (DSC). RESULTS: In predicting tumor segmentation for all cervical and uterine malignancies, TL models improved the DSCs from the pretrained cervical model (DSC 0.43) when adding 5, 13, 26, and 51 uterine cases for training (DSC improved from 0.57, 0.62, 0.68, 0.70, p < 0.001). Following the crossover at adding 128 cases (DSC 0.71), the model trained by combining data from adding all the 256 patients exhibited the highest DSCs for the combined cervical and uterine datasets (DSC 0.81) and cervical only dataset (DSC 0.91). CONCLUSIONS: TL may improve the generalizability of automated tumor segmentation of DWI from a specific cancer type toward multiple types of uterine malignancies especially in limited case numbers.

Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography.

PURPOSE: Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data. METHODS: We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers). RESULTS: Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively. CONCLUSION: Sybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available.[Media: see text].

Using T1 mapping indices to evaluate muscle function and predict conservative treatment outcomes in diabetic patients with peripheral arterial disease.

OBJECTIVES: To investigate interstitial muscle fibrosis via T1 mapping indices and its relationships with muscle function and conservative treatment outcomes. METHODS: A total of 49 DM patients with PAD were prospectively recruited from 2016 to 2017. All PAD patients underwent pre-treatment MRI with conservative treatment via a rehabilitation program and antiplatelet therapy. The need to require percutaneous transluminal angioplasty intervention was recorded as intolerance to conservative treatment outcomes. We quantified calf interstitial muscle fibrosis using T1 mapping indices (native T1, post-contrast T1, and the extracellular volume fraction [ECV]). Muscle function was evaluated using a 6-min walking test (6MWT) and a 3-min stepping test (3MST). PAD patients were divided into two groups according to their tolerance or intolerance of the conservative treatment. Pearson's correlation, reproducibility, and multivariable Cox hazard analyses were performed with p < 0.05 indicating statistical significance. RESULTS: Among the T1 mapping indices in the posterior compartment of the calf in PAD patients, the native T1 value was significantly correlated with 6MWT (r = -0.422, p = 0.010) and 3MST (r = -0.427, p = 0.009). All T1 mapping indices showed excellent intra-observer and inter-observer correlations. ECV was an independent predictor of conservative treatment intolerance (average ECV, hazard ratio: 1.045, 95% confidence interval: 1.011-1.079, p = 0.009). CONCLUSIONS: T1 mapping measurements are reproducible with excellent intra-observer and inter-observer correlations. T1 mapping indices may be predictive of treatment and functional outcomes and carry promise in patient evaluation. TRIAL REGISTRATION: Clinical Trials Identifier: NCT02850432 . KEY POINTS: • T1 mapping measurements of the calf muscles are reproducible with excellent intra-observer and inter-observer correlations (0.98 and 0.95 for anterior and posterior compartment muscle extracellular volume matrix [ECV] measurements, respectively). • ECV is shown to independently predict conservative treatment intolerance. • T1 mapping indices may be predictive of treatment and functional outcomes and carry promise in patient evaluation.

MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy.

A reliable prognostic stratification of patients with locally advanced hypopharyngeal cancer who had been treated with concurrent chemoradiotherapy (CCRT) is crucial for informing tailored management strategies. The purpose of this retrospective study was to develop robust and objective magnetic resonance imaging (MRI) radiomics-based models for predicting overall survival (OS) and progression-free survival (PFS) in this patient population. The study participants included 198 patients (median age: 52.25 years (interquartile range = 46.88-59.53 years); 95.96% men) who were randomly divided into a training cohort (n = 132) and a testing cohort (n = 66). Radiomic parameters were extracted from post-contrast T1-weighted MR images. Radiomic features for model construction were selected from the training cohort using least absolute shrinkage and selection operator-Cox regression models. Prognostic performances were assessed by calculating the integrated area under the receiver operating characteristic curve (iAUC). The ability of radiomic models to predict OS (iAUC = 0.580, 95% confidence interval (CI): 0.558-0.591) and PFS (iAUC = 0.625, 95% CI = 0.600-0.633) was validated in the testing cohort. The combination of radiomic signatures with traditional clinical parameters outperformed clinical variables alone in the prediction of survival outcomes (observed iAUC increments = 0.279 [95% CI = 0.225-0.334] and 0.293 [95% CI = 0.232-0.351] for OS and PFS, respectively). In summary, MRI radiomics has value for predicting survival outcomes in patients with hypopharyngeal cancer treated with CCRT, especially when combined with clinical prognostic variables.

Metabolomic Signature of Diabetic Kidney Disease in Cerebrospinal Fluid and Plasma of Patients with Type 2 Diabetes Using Liquid Chromatography-Mass Spectrometry.

Diabetic kidney disease (DKD) is the major cause of end stage renal disease in patients with type 2 diabetes mellitus (T2DM). The subtle metabolic changes in plasma and cerebrospinal fluid (CSF) might precede the development of DKD by years. In this longitudinal study, CSF and plasma samples were collected from 28 patients with T2DM and 25 controls, during spinal anesthesia for elective surgery in 2017. These samples were analyzed using liquid chromatography-mass spectrometry (LC-MS) in 2017, and the results were correlated with current DKD in 2017, and the development of new-onset DKD, in 2021. Comparing patients with T2DM having new-onset DKD with those without DKD, revealed significantly increased CSF tryptophan and plasma uric acid levels, whereas phosphatidylcholine 36:4 was lower. The altered metabolites in the current DKD cases were uric acid and paraxanthine in the CSF and uric acid, L-acetylcarnitine, bilirubin, and phosphatidylethanolamine 38:4 in the plasma. These metabolic alterations suggest the defective mitochondrial fatty acid oxidation and purine and phospholipid metabolism in patients with DKD. A correlation analysis found CSF uric acid had an independent positive association with the urine albumin-to-creatinine ratio. In conclusion, these identified CSF and plasma biomarkers of DKD in diabetic patients, might be valuable for monitoring the DKD progression.