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.

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.

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.

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.

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].

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.

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.

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.

Interstitial Lung Abnormalities in Patients With Locally Advanced Esophageal Cancer: Prevalence, Risk Factors, and Clinical Implications.

PURPOSE: Interstitial lung abnormalities (ILAs) represent nondependent abnormalities on chest computed tomography (CT) indicating lung parenchymal damages due to inflammation and fibrosis. Interstitial lung abnormalities have been studied as a predictor of clinical outcome in lung cancer, but not in other thoracic malignancies. The present study investigated the prevalence of ILA in patients with esophageal cancer and identified risk factors and clinical implications of ILA in these patients. METHODS: The study included 208 patients with locally advanced esophageal cancer (median age, 65.6 years; 166 males, 42 females). Interstitial lung abnormality was scored on baseline CT scans before treatment using a 3-point scale (0 = no evidence of ILA, 1 = equivocal for ILA, 2 = ILA). Clinical characteristics and overall survival were compared in patients with ILA (score 2) and others. RESULTS: An ILA was present in 14 of 208 patients (7%) with esophageal cancer on pretreatment chest CT. Patients with ILA were significantly older (median age, 69 vs 65, respectively; P = 0.011), had a higher number of pack-years of smoking ( P = 0.02), and more commonly had T4 stage disease ( P = 0.026) than patients with ILA score of 1 or 0. Interstitial lung abnormality on baseline scan was associated with a lack of surgical resection after chemoradiotherapy (7/14, 50% vs 39/194, 20% respectively; P = 0.016). Interstitial lung abnormality was not associated with overall survival (log-rank P = 0.75, Cox P = 0.613). CONCLUSIONS: An ILA was present in 7% of esophageal cancer patients, which is similar to the prevalence in general population and in smokers. Interstitial lung abnormality was strongly associated with a lack of surgical resection after chemoradiotherapy, indicating an implication of ILA in treatment selection in these patients, which can be further studied in larger cohorts.

Developing a Method to Estimate the Downstream Metabolite Signals from Hyperpolarized [1-13C]Pyruvate.

Hyperpolarized carbon-13 MRI has the advantage of allowing the study of glycolytic flow in vivo or in vitro dynamically in real-time. The apparent exchange rate constant of a metabolite dynamic signal reflects the metabolite changes of a disease. Downstream metabolites can have a low signal-to-noise ratio (SNR), causing apparent exchange rate constant inconsistencies. Thus, we developed a method that estimates a more accurate metabolite signal. This method utilizes a kinetic model and background noise to estimate metabolite signals. Simulations and in vitro studies with photon-irradiated and control groups were used to evaluate the procedure. Simulated and in vitro exchange rate constants estimated using our method were compared with the raw signal values. In vitro data were also compared to the Area-Under-Curve (AUC) of the cell medium in 13C Nuclear Magnetic Resonance (NMR). In the simulations and in vitro experiments, our technique minimized metabolite signal fluctuations and maintained reliable apparent exchange rate constants. In addition, the apparent exchange rate constants of the metabolites showed differences between the irradiation and control groups after using our method. Comparing the in vitro results obtained using our method and NMR, both solutions showed consistency when uncertainty was considered, demonstrating that our method can accurately measure metabolite signals and show how glycolytic flow changes. The method enhanced the signals of the metabolites and clarified the metabolic phenotyping of tumor cells, which could benefit personalized health care and patient stratification in the future.

Genetic Disruption of KLF1 K74 SUMOylation in Hematopoietic System Promotes Healthy Longevity in Mice.

The quest for rejuvenation and prolonged lifespan through transfusion of young blood has been studied for decades with the hope of unlocking the mystery of the key substance(s) that exists in the circulating blood of juvenile organisms. However, a pivotal mediator has yet been identified. Here, atypical findings are presented that are observed in a knockin mouse model carrying a lysine to arginine substitution at residue 74 of Krüppel-like factor 1 (KLF1/EKLF), the SUMOylation-deficient Klf1K74R/K74R mouse, that displayed significant improvement in geriatric disorders and lifespan extension. Klf1K74R/K74R mice exhibit a marked delay in age-related physical performance decline and disease progression as evidenced by physiological and pathological examinations. Furthermore, the KLF1(K74R) knockin affects a subset of lymphoid lineage cells; the abundance of tumor infiltrating effector CD8+ T cells and NKT cells is increased resulting in antitumor immune enhancement in response to tumor cell administration. Significantly, infusion of hematopoietic stem cells (HSCs) from Klf1K74R/K74R mice extends the lifespan of the wild-type mice. The Klf1K74R/K74R mice appear to be an ideal animal model system for further understanding of the molecular/cellular basis of aging and development of new strategies for antiaging and prevention/treatment of age-related diseases thus extending the healthspan as well as lifespan.

Uterine fibroid-like tumors: spectrum of MR imaging findings and their differential diagnosis.

Uterine leiomyoma, also known as uterine fibroid, is the most common gynecological tumor, affecting almost 80% of women at some point during their lives. In the same time, other fibroid-like tumors have similar clinical presentations and about 0.5% of resected tumors of which were presumed benign fibroids in the preoperative diagnosis revealed as malignant sarcomas in the final histopathological examination. Amid the emergence of nonsurgical or minimally invasive procedures for symptomatic benign uterine fibroids, such as uterine artery embolization, high-intensity-focused ultrasound, or laparoscopic myomectomy, the preoperative diagnosis of uterine tumors through imaging becomes all the more relevant. Preoperative tissue sampling is challenging because of the variable location of the myometrial mass; thus, the preoperative evaluation of size and location is increasingly performed through magnetic resonance imaging. Features in images might also be useful for examining the full spectrum of such growths, from benign fibroids to neoplasms of uncertain behavior and malignant sarcomas. Benign fibroids include usual-type leiomyomas, myomas with degeneration, and mitotically active leiomyomas. Neoplasms of uncertain behavior include smooth muscle tumors of uncertain malignant potential, leiomyomas with bizarre nuclei, and cellular leiomyomas. Malignant sarcomas comprise leiomyosarcomas, endometrial stromal sarcomas, adenosarcomas, and carcinosarcomas. The purpose of this article is to review the spectrum of MRI findings of uterine fibroid-like tumors, from benign variants, uncertain behavior to malignant sarcomas, and update the advanced imaging modalities, including diffusion-weighted imaging, positron emission tomography/computed tomography, combining texture analysis and radiomics, to tackle this important issue.

Magnetic Resonance-Based Synthetic Computed Tomography Using Generative Adversarial Networks for Intracranial Tumor Radiotherapy Treatment Planning.

The purpose of this work is to develop a reliable deep-learning-based method that is capable of synthesizing needed CT from MRI for radiotherapy treatment planning. Simultaneously, we try to enhance the resolution of synthetic CT. We adopted pix2pix with a 3D framework, which is a conditional generative adversarial network, to map the MRI data domain into the CT data domain of our dataset. The original dataset contains paired MRI and CT images of 31 subjects; 26 pairs were used for model training and 5 were used for model validation. To identify the correctness of the synthetic CT of models, all of the synthetic CTs were calculated by the quantized image similarity formulas: cosine angle distance, Euclidean distance, mean square error, peak signal-to-noise ratio, and mean structural similarity. Two radiologists independently evaluated the satisfaction score, including spatial, detail, contrast, noise, and artifacts, for each imaging attribute. The mean (±standard deviation) of the structural similarity indices (CAD, L2 norm, MSE, PSNR, and MSSIM) between five real CT scans and the synthetic CT scans were 0.96 ± 0.015, 76.83 ± 12.06, 0.00118 ± 0.00037, 29.47 ± 1.35, and 0.84 ± 0.036, respectively. For synthetic CT, radiologists rated the results as evincing excellent satisfaction in spatial geometry and noise level, good satisfaction in contrast and artifacts, and fair imaging details. The similarity index and clinical evaluation results between synthetic CT and original CT guarantee the usability of the proposed method.

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI.

OBJECTIVE: Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRefF), femoral head/muscle (AutoRefFH) and pelvic bone/muscle (AutoRefPB). MATERIALS AND METHODS: T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases. RESULTS: AutoRefFH pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 (AutoRefFH), 0.739 (AutoRefF) and 0.726 (AutoRefPB). DISCUSSION: All AutoRef versions reduced variation in the multicenter data. AutoRefFH pseudo-T2s were closest to experimentally measured T2s.

Diagnostic Accuracy of Whole-Body Computed Tomography for Incidental Ovarian Tumors in Patients with Prior Breast Cancer.

Whole-body computed tomography (WBCT) serves as the first-line imaging modality for breast cancer follow-up. To investigate the imaging characteristics and diagnostic accuracy of WBCT for incidental ovarian tumors in patients with prior breast cancer, we retrospectively reviewed a consecutive cohort of 13,845 patients with breast cancer, of whom 149 had pathologically-proven ovarian lesions. We excluded patients with ovarian diagnosis before breast cancer, CT scan not including ovary, CT-pathology interval >30 days, and severe CT artifact. Among our 60 breast cancer patients (median age, 46 years) with pathologically proven ovarian lesions, 49 patients had benign diseases, seven had primary ovarian cancer and four had ovarian metastasis from breast cancer. The histologic types of breast cancer with ovarian metastases included invasive ductal carcinoma, lobular carcinoma and angiosarcoma. Cystic ovarian lesions identified on WBCT during the breast cancer follow-up are more likely to be benign, while solid-cystic lesions are likely to be primary ovarian cancers, and solid lesions may indicate ovarian metastasis. The diagnostic accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve of WBCT were 98.3%, 100.0%, 98.0%, and 0.99 (malignant vs. benign); 90.0%, 100.0%, 85.7%, and 0.93 (metastasis vs. primary ovarian cancer), respectively. The only false positive solid lesion was a Sertoli-Leydig tumor. In conclusion, WBCT may help diagnose incidental ovarian tumors in patients with prior breast cancers and guide disease management.