Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

Incidence and characteristic of deep venous thrombosis in hospitalized chronic heart failure patients.

BACKGROUND: This study was conducted to investigate the incidence of deep venous thrombosis (DVT), outcomes and its characteristics in patients with chronic heart failure (CHF) in a retrospective setting. OUTCOMES: Patients died of cardiac shock or acute exacerbation of heart failure (HF), admitted to intensive care unit (ICU) due to acute exacerbation of HF, patients decided to withdraw treatment and return home due to acute exacerbation of HF. METHODS: From January 2015 to June 2022, we admitted 359 patients diagnosed with CHF, and lower limb ultrasonography was performed for the examination of DVT after admission. The incidence of DVT was recorded and patients with known risk factors of VTE were identified and excluded after incidence of DVT was calculated. Patients' clinical data were then collected. RESULTS: The occurrence of DVT was 10.0% (36/359), as calf intramuscular vein thrombosis was the main constitution (n = 28, 75%). DVT patients with other factors (carcinoma, surgery, stroke, previous history of DVT) constituted a considerable part (33.3%, 12/36). Age, history of Diabetes Mellitus (DM), levels of DDi (D-Dimer), levels of alanine transferase (ALT) and left ventricular end-diastolic diameter (LVEDd) were independent predictors or risk factors of DVT in CHF patients, while chronic kidney disease (CKD) stage 1-4, white blood cell (WBC) and direct oral anticoagulant (DOAC) were protective factors. Incidence of DVT was correlated with a poor outcome of CHF patients (Pearson Chi-Square test, Value 19.612, P < 0.001). CONCLUSIONS: In this retrospective study, incidence of DVT was found to be relatively high among hospitalized CHF patients, while patients with DVT was associated with a poor prognosis.

Clinical application of convolutional neural network for mass analysis on mammograms.

Background: The detection of masses on mammogram represents one of the earliest signs of a malignant breast cancer. However, masses may be hard to detect due to dense breast tissue, leading to false negative results. In this study, we aimed to explore the clinical application of the convolutional neural network (CNN)-based deep learning (DL) system constructed in our previous work as an objective and accurate tool for breast cancer screening and diagnosis in Asian women. Methods: This retrospective analysis included 324 patients with masses detected on mammograms at Shenzhen People's Hospital between April and December 2019. (I) Detection: images were independently analyzed by two junior radiologists who were blinded to relative results. Then, a senior radiologist analyzed the images after reviewing all the relevant information as the reference. (II) Classification: masses were classified by the same two junior radiologists and in consensus by two other seniors. Images were also input into the DL system. The sensitivity of detection by junior radiologists and the DL system, effects of different factors [breast density; patient age; morphology, margin, size, breast imaging reporting and data system (BI-RADS) category of the mass] on detection, the accuracy, sensitivity, and specificity of classification, and the area under the receiver operating characteristic (ROC) curve (AUC), were evaluated. Results: A total of 618 masses were detected. The detection sensitivity of the two junior radiologists [78.0% (482/618) and 84.0% (519/618), respectively] was lower than that of the DL system [86.2% (533/618)]. Breast density significantly affected the detection by two junior radiologists (both P=0.030), but not by the DL system (P=0.385). The AUC for classifying masses as negative (BI-RADS 1, 2, 3) or positive (BI-RADS 4A, 4B, 4C, 5) for the DL system was significantly higher compared to those of the two junior radiologists, but not significantly different compared to seniors [DL system, 0.697; junior, 0.612 and 0.620 (P=0.021, 0.019); senior in consensus, 0.748 (P=0.071)]. Conclusions: The CNN-based DL system could assist junior radiologists in improving mass detection and is not affected by breast density. This DL system may have clinical utility in women with dense breasts, including reducing the impact caused by inexperienced radiologists and the potential for missed diagnoses.

Influences of lysine-specific demethylase 1 inhibitors on NO synthase-Kruppel-like factor pathways in human endothelial cells in vitro and zebrafish (Danio rerio) larvae in vivo.

Lysine-specific demethylase 1 (LSD1) inhibitors are being developed for cancer therapy, but their bioeffects on vasculatures are not clear. In this study, we compared the influences of ORY-1001 (an LSD1 inhibitor being advanced into clinical trials) and 199 (a novel LSD1 inhibitor recently developed by us) to human umbilical vein endothelial cells (HUVECs) in vitro and further verified the bioeffects of ORY-1001 to zebrafish (Danio rerio) larvae in vivo. The results showed that up to 10 µM ORY-1001 or 199 did not significantly affect the cellular viability of HUVECs but substantially reduced the release of inflammatory interleukin-8 (IL-8) and IL-6. The signaling molecule in vasculatures, NO, was also increased in HUVECs. As the mechanism, the protein levels of endothelial NO synthase (eNOS) or p-eNOS, and their regulators Kruppel-like factor 2 (KLF2) or KLF4, were also increased after drug treatment. In vivo, 24 h treatment with up to 100 nM ORY-1001 reduced blood speed without changing morphologies or locomotor activities in zebrafish larvae. ORY-1001 treatment reduced the expression of il8 but promoted the expression of klf2a and nos in the zebrafish model. These data show that LSD1 inhibitors were not toxic but capable to inhibit inflammatory responses and affect the function of blood vessels through the up-regulation of the NOS-KLF pathway.

Genomic analysis of matrix metalloproteinases affecting the prognosis and immunogenic profile of gastric cancer.

This study systematically and comprehensively analyzed the characteristics of matrix metalloproteinases (MMPs) in gastric cancer (GC) and revealed the relationship between MMPs and prognoses, clinicopathological features, tumor microenvironment, gene mutations, and drug therapy response in patients with GC. Based on the mRNA expression profiles of 45 MMP-related genes in GC, we established a model that classified GC patients into three groups based on cluster analysis of the mRNA expression profiles. The 3 groups of GC patients showed significantly different prognoses as well as tumor microenvironmental characteristics. Next, we used Boruta's algorithm and PCA method to establish an MMP scoring system and found that lower MMP scores were associated with better prognoses, lower clinical stages, better immune cell infiltration, lower degrees of immune dysfunction and rejection, and more genetic mutations. Whereas a high MMP score was the opposite. These observations were further validated with data from other datasets, showing the robustness of our MMP scoring system. Overall, MMP could be involved in the tumor microenvironment (TME), clinical features, and prognosis of GC. An in-depth study of MMP patterns can better understand the indispensable role of MMP in the development of GC and reasonably assess the survival prognosis, clinicopathological features, and drug efficacy of different patients, thus providing clinicians with a broader vision of GC progression and treatment.

Case report: Corticosteroids-induced acute diabetic peripheral neuropathy.

A 62-year-old man was diagnosed as IgA nephropathy. He had a pancreatic tumor operation 19 years ago and had a normal plasma glucose test every year. One month after the medication of prednisolone acetate was administered his fasting plasma glucose elevated to 7.1mmol/L while he manifested symptoms of thirst, frequent urination, and weight loss. Approximately 3 months after the steroids, he started complaining of numbness, weakness, and muscle cramp in his lower extremities, blood tests showed elevated plasma glucose and electromyography (EMG) revealed impairment of the peripheral nerves in the lower extremity, diabetic peripheral neuropathy was diagnosed. Mecobalamin and Acupuncture were employed and steroids were discontinued, 8 months later he recovered part of his strength and sensation. This case presents a specific adverse drug reaction of corticosteroids that causes diabetes mellitus and subsequently leads to peripheral neuropathy in an acute onset.

miR-4324 inhibits ovarian cancer progression by targeting FEN1.

BACKGROUND: Ovarian cancer is one of the most lethal malignancies, with a 1.9% mortality rate worldwide. The dysregulation of the FEN1 gene and miR-4324 has been associated with cancer progression. However, the relationship between miR-4324 and-FEN1 requires further investigation. METHODS: miR-4324 and FEN1 expressions in ovarian cancer tissues and cell lines were measured via RT-qPCR. The interaction between miR-4324 and FEN1 was assessed using luciferase and RNA pull-down assays. The effects of miR-4324 and FEN1 on cell proliferation, adhesion and apoptosis were determined by CCK-8, BrdU, colony formation, cell adhesion, Caspase-3 and western blot assays in ovarian cancer cell lines CaOV3 and OVCAR3, respectively. RESULTS: The results showed that miR-4324 expression was significantly decreased and FEN1 expression was enhanced in ovarian cancer tissues and cell lines. miR-4324 inhibitor promoted cell proliferation, adhesion and migration, and prevented apoptosis. Furthermore, the downregulation of FEN1 inhibited ovarian cancer cell growth and increased apoptosis. miR-4324 inhibited FEN1 expression and repressed ovarian cancer progression. CONCLUSION: Our study found that miR-4324 inhibited FEN1 expression, suppressed cell growth, and increased apoptosis in ovarian cancer cells. Therefore, we identified miR-4324 and FEN1 as potential therapeutic targets for ovarian cancer treatment.

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography (CT)­based radiomics model for preoperative prediction of STAS in lung adenocarcinoma. METHODS AND MATERIALS: This retrospective study was approved by an institutional review board and included 462 (mean age, 58.06 years) patients with pathologically confirmed lung adenocarcinoma. STAS was identified in 90 patients (19.5%). Two experienced radiologists segmented and extracted radiomics features on preoperative thin-slice CT images with radiomics extension independently. Intraclass correlation coefficients (ICC) and Pearson's correlation were used to rule out those low reliable (ICC < 0.75) and redundant (r > 0.9) features. Univariate logistic regression was applied to select radiomics features which were associated with STAS. A radiomics-based machine learning predictive model using a random forest (RF) was developed and calibrated with fivefold cross-validation. The diagnostic performance of the model was measured by the area under the curve (AUC) of receiver operating characteristic (ROC). RESULTS: With univariate analysis, 12 radiomics features and age were found to be associated with STAS significantly. The RF model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS. CONCLUSION: CT-based radiomics model can preoperatively predict STAS in lung adenocarcinoma with good diagnosis performance. KEY POINTS: • CT-based radiomics and machine learning model can predict spread through air space (STAS) in lung adenocarcinoma with high accuracy. • The random forest (RF) model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS.

Prediction of molecular subtypes of breast cancer using BI-RADS features based on a "white box" machine learning approach in a multi-modal imaging setting.

PURPOSE: To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images. METHODS: We retrospectively assessed 363 breast cancer cases (Luminal A 151, Luminal B 96, HER2 76, and BLBC 40). Eighty-two features defined in the BI-RADS lexicon were visually described. A decision tree model with the Chi-squared automatic interaction detector (CHAID) algorithm was applied for feature selection and classification. A 10-fold cross-validation was performed to investigate the performance (i.e., accuracy, positive predictive value, sensitivity, and F1-score) of the decision tree model. RESULTS: Seven of the 82 variables were derived from the decision tree-based feature selection and used as features for the classification of molecular subtypes including mass margin calcification on mammography, mass margin types of kinetic curves in the delayed phase, mass internal enhancement characteristics, non-mass enhancement distribution on MRI, and breastfeeding history. The decision tree model accuracy was 74.1%. For each molecular subtype group, Luminal A achieved a sensitivity, positive predictive value, and F1-score of 79.47%, 75.47%, and 77.42%, respectively; Luminal B showed a sensitivity, positive predictive value, and F1-score of 64.58%, 55.86%, and 59.90%, respectively; HER2 had a sensitivity, positive predictive value, and F1-scores of 81.58%, 95.38%, and 87.94%, respectively; BLBC showed sensitivity, positive predictive value, and F1-scores of 62.50%, 89.29%, and 73.53%, respectively. CONCLUSIONS: We applied a complete "white box" machine learning method to predict the molecular subtype of breast cancer based on the BI-RADS feature description in a multi-modal setting. By combining BI-RADS features in both mammography and MRI, the prediction accuracy is boosted and robust. The proposed method can be easily applied widely regardless of variability of imaging vendors and settings because of the applicability and acceptance of the BI-RADS.

CT features of metanephric adenoma: a case report and review of the literature.

Metanephric adenoma (MA) is an extremely rare, benign neoplasm of the kidney. Ultrasound and computed tomography (CT) features of a case of MA in a 49-year-old woman were presented in this paper to enrich imaging manifestations of this rare entity. At plane CT, a solid mass with calcification spots was noted. After iv contrast agency, the mass showed slight irregular peripheral enhancement in the corticomedullary phase, which was obviously lower than surrounding renal cortex. Slowly filling-in was noticed in the nephrographic phase with irregular necrosis.

Structurally related cytotoxic effects of flavonoids on human cancer cells in vitro.

Flavonoids exist extensively in the human diet, and a variety of health effects have been ascribed to them. The cytotoxic effects of 23 flavonoids on breast cancer cells (MDA-MB-231 and MCF-7), colorectal carcinoma cells (LoVo and DLD-1) and prostatic cancer cells (PC3) were investigated. By comparing the cytotoxicity (EC(50)) of selected molecules that differ in only one structure element, we identified several structural properties associated with enhanced cytotoxicity, including the presence of the 2,3-double bond, appropriate hydroxyl numbers, 3-OH, 6-OH and ortho-hydroxylation in ring B. Flavonoids with a 5-OH exhibited lower cytotoxicity than their non-hydroxylated counterparts. Results indicated that 3,6-dihydroxylflavone showed the most potent cytotoxic effect on these cancer cells. The appearance of apoptotic cells with DAPI staining was observed in cancer cells under 3,6-dihydroxylflavone treatment, and the apoptosis analysis by flow cytometry also showed that 3,6-dihydroxylflavone induced apoptotic cell death in these cancer cells. These results revealed the structurally related toxicity of flavonoids on human cancer cells, and indicates that 3,6-dihydroxylflavone is an active compound worthy of development for cancer chemotherapy.

[Effects of flavonoids with different structures on proliferation of leukemia cell line HL-60].

BACKGROUND & OBJECTIVE: Flavonoids, with some beneficial biological activities, exist extensively in foods and herbal products. This study was to evaluate the effects of 23 flavonoids on the proliferation of leukemia cell line HL-60, and elucidate the structure-activity relationship (SAR). METHODS: HL-60 cells were treated with 23 flavonoids with high purity and definite structure. Cell proliferation was detected by MTT assay. The 50% inhibition concentrations (IC50) of the 23 flavonoids were calculated. The effects of particular structures on IC50 were evaluated. RESULTS: Most of the 23 flavonoids inhibited the proliferation of HL-60 cells distinctly, and the effects were enhanced along with increasing concentrations. However, the intensity of their effects were different, which were arranged from strong to weak as follows:3,6-dihydroxyflavone > luteolin > geraldol > 2'-hydroxyflavanone > apigenin > 3,7-dihydroxyflavone > myricetin > fisetin > baicalein > quercetin > flavanone > chrysin > galangin > 4'-hydroxyflavanone > 6-hydroxyflavone > genistein > flavone >7-hydroxyflavone > daidzein > hesperetin > naringenin. The 2,3-double bond in ring C, appropriate hydroxyls, ring B attached at position 2, hydroxyls in position 3, ortho-substituting hydroxyls in ring B were related to enhanced inhibitory effects of flavonoids on the proliferation of HL-60 cells, while the lack of 2,3-double bond, deficiency or redundancy of hydroxyl groups, hydroxyl group in position 5, 7 or meta-substituting hydroxyls in ring B, isoflavone structure were related to reduced inhibitory effects of flavonoids. CONCLUSION: The 2,3-double bond in ring C, appropriate hydroxyls, ring B attached at position 2, hydroxyls in position 3, ortho-substituting hydroxyls in ring B may be key structural requirements of flavonoids for potent cytotoxicity to HL-60 cells.

[Effects of different dietary fatty acid on expression of nuclear receptor genes in breast cancer of rats].

OBJECTIVE: To study the effects of different dietary fatty acid on the expression of nuclear receptor genes in the breast cancer of rats. METHODS: Fifty-day-old female Sprague-Dawley rats were fed on eight different diets containing following fatty acids: saturated fatty acid (SFA); monounsaturated fatty acid (MUFA); n-6 polyunsaturated fatty acid (PUFA); n-3 PUFA; 1:1 n-6/n-3; 5:1 n-6/n-3; 10:1 n-6/n-3; 1:2:1 S/M/P (n-6/n-3 at 1:1). The rats were given a single intraperitoneal injection of methyl-nitrosourea (MNU) at 50 mg/kg body weight to establish the rat model of mammary carcinogenesis, the ultrastructure changes of mammary gland cells in rats were observed by transmission electron microscope, the cell proliferation activity was detected by BrdU-labeled immunocytochemistry, and the expression of PPARbeta and PPARgamma mRNA were assayed by RT-PCR. RESULTS: There was no breast cancer occurring in control groups and the MNU-treated n-3 PUFA group, and the ultrastructure and proliferation activity of mammary gland cells in these groups were normal. In contrast, there appeared obvious marker of adenocarcinomas in mammary gland cells of MNU-induced breast cancer, and a high cell proliferation activity was found in tumor growth-enhancing groups (SFA, MUFA, n-6 PUFA, 5:1 n-6/n-3, 10:1 n-6/n-3 and S/M/P, 21% - 22% of BrdU-labeled cells), while a low cell proliferation activity was detected in rats fed with 1:1 n-6/n-3 diet (13% of BrdU-labeled cells, P < 0.05). Moreover, peroxisome proliferator-activated receptors (PPARs), as important nuclear receptor genes of relating lipid metabolism, the expressions of PPARbeta and PPARgamma mRNA were significantly up-regulated in mammary adipose tissues of MNU-induced breast cancer as compared with the control groups, but the expression levels of peroxisome proliferator-activated receptors (PPARs) in rats fed with 1:1 n-6/n-3 group were lowest (P < 0.05). CONCLUSION: The different dietary fatty acid compositions should diversely adjust the expression of PPARs gene in rats, which maybe have an important role in affecting incidence of breast cancer.