Robertsallen3944

With the advent of artificial intelligence techniques such as radiomics and deep learning, these hybrid medical images can be mined for quantitative data, providing incremental value over current standard-of-care clinical and biological data. This approach has the potential to produce a major paradigm shift toward data-driven precision medicine with the ultimate goal of personalized medicine. In this review, we highlight current literature reporting the role of 18F-FDG PET in supporting personalized management decisions for patients with MIBC. Specific topics reviewed include the incremental value of 18F-FDG PET in prognostication, pre-operative planning, response assessment, prediction of recurrence, and diagnosing drug toxicity.Background and Purpose Automatic segmentation model is proven to be efficient in delineation of organs at risk (OARs) in radiotherapy; its performance is usually evaluated with geometric differences between automatic and manual delineations. However, dosimetric differences attract more interests than geometric differences in the clinic. Therefore, this study aimed to evaluate the performance of automatic segmentation with dosimetric metrics for volumetric modulated arc therapy of esophageal cancer patients. Methods Nineteen esophageal cancer cases were included in this study. Clinicians manually delineated the target volumes and the OARs for each case. Another set of OARs was automatically generated using convolutional neural network models. The radiotherapy plans were optimized with the manually delineated targets and the automatically delineated OARs separately. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC) and mean distance to agreement (MDA). Dosimetric metrics of manually and adelineation for esophageal cancer radiotherapy planning based on the dosimetric evaluation in this study.Osteosarcoma is a malignancy with high aggressiveness and poor prognosis, which occurs mainly in children. The therapeutic strategy against osteosarcoma includes surgery combined with chemotherapy and radiotherapy. Although the treatment of osteosarcoma has been improved in recent years, there is a large proportion of patients with incurable osteosarcoma. Investigation of the mechanism of osteosarcoma progression would be of great help in discovering therapeutic targets for this disease. Long non-coding RNAs play critical roles in the pathogenesis of different types of cancer. The current study showed that long non-coding RNA NR_027471 was downregulated in osteosarcoma cells. In vitro and in vivo studies indicated that upregulation of NR_027471 impeded the viability, proliferation, and invasion of osteosarcoma, as well as induced cell cycle arrest at G1. In addition, binding of miR-8055 to NR_027471 was demonstrated, thereby influencing the expression of tumor protein p53 inducible nuclear protein 1 (TP53INP1). https://www.selleckchem.com/products/conteltinib-ct-707.html Knockdown of NR_027471 promoted epithelial-mesenchymal transition by inhibiting E-cadherin and increasing the expression of zinc finger E-box-binding homeobox 1 (ZEB1), Snail, and fibronectin. These results suggested that overexpression of NR_027471 upregulated TP53INP1 by sponging to miR-8055, leading to suppression of osteosarcoma cell proliferation and progression.Since type and duration of an appropriate adjuvant chemotherapy in early-stage ovarian cancer (OC) are still being debated, novel markers for a better stratification of these patients are of utmost importance for the design of an improved chemotherapeutical strategy. In contrast to numerous cancer studies on cellular proliferation based on the immunohistochemistry-driven evaluation of protein expression, we compared mRNA and protein expression of two independent markers of cellular proliferation, Ki-67 and Plk1, in a large cohort of 243 early-stage OC and their relationship with clinicopathological features and survival. Based on marker expression we demonstrate that early-stage OC patients (stages I/II, low-grade, serous) with high expression (Ki-67, Plk1) had a significantly shorter progression-free survival (PFS) and overall survival (OS) compared to patients with low expression (Ki-67, Plk1). Remarkably, based on mRNA expression this significant difference got lost in advanced stages (III/IV) At least for PFS, high levels of Ki-67 and Plk1 correlate with moderately better survival compared to patients with low expressing tumors. Our data suggest that in addition to Ki-67, Plk1 is a novel marker for the stratification of early-stage OC patients to maximize therapeutic efforts. Both, Ki-67 and Plk1, seem to be better suited in early-stages (I/II) as therapeutical targets compared to advanced-stages (III/IV) OC.Objective This retrospective study aimed to analyze the ultrasound (US) imaging features of solitary papillary thyroid carcinoma (PTC) located in the isthmus and to assess the risk factors for lymph node metastasis (LNM) and tumor capsular invasion. Methods We included a total of 135 patients with solitary PTC located in the isthmus. All the cases underwent US, total thyroidectomy, and prophylactic central lymph node dissection. Patients' demographic and thyroid isthmus nodules' US characteristics, as well as risk factors associated with LNM and tumor capsular invasion, were analyzed. Results It was revealed that the occurrence of LNM was higher in male patients than in female patients (P less then 0.001). As risk factors, the size of PTC in the isthmus was found to be associated with LNM and tumor capsular invasion (P = 0.005 and 0.000, respectively). The area under the receiver operating characteristic curve (AUC) of the size of the isthmus PTC was 0.64 [95% confidence interval (CI) = 0.55-0.72], indicaticcur. When a US image shows a thyroid isthmus nodule with an ETE, tumor capsular invasion was likely to occur. ETE and wider-than-tall may be indicators of FNA under US guidance, even though the size of thyroid isthmus nodule may be less then 1 cm.The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.Collagen is significantly upregulated in colorectal liver metastasis (CRLM) compared to liver tissue. Expression levels of specific collagen types in CRLM resemble those in colorectal cancer (CRC) and colon tissue. We investigated whether the collagen hydroxylation pattern from the primary tumor also migrates with the metastatic tumor. The degree of collagen alpha-1(I) hydroxylation in colon, CRC, liver, and CRLM tissue of the same individuals (n = 14) was studied with mass spectrometry. The degree of hydroxylation was investigated in 36 collagen alpha-1(I) peptides, covering 54% of the triple helical region. The degree of hydroxylation in liver tissue was similar to that in colon tissue. The overall degree of hydroxylation was significantly lower (9 ± 14%) in CRC tissue and also significantly lower (12 ± 22%) in CRLM tissue compared to colon. Furthermore, eleven peptides with a specific number of hydroxylations are significantly different between CRLM and liver tissue; these peptides could be candidates for the detection of CRLM. For one of these eleven peptides, a matching naturally occurring peptide in urine has been identified as being significantly different between patients suffering from CRLM and healthy controls. The hydroxylation pattern in CRLM resembles partly the pattern in liver, primary colorectal cancer and colon.Recent studies have revealed that long non-coding RNAs (lncRNAs) involve in the progression of oral squamous cell carcinoma (OSCC). These lncRNAs have emerged as biomarkers or therapeutic targets for OSCC. We here aimed to investigate the role of lncRNA LINC01315 in OSCC and the related mechanisms. LINC01315 and DLG3 were determined to be poorly expressed while microRNA-211 (miR-211) was highly expressed in OSCC tissues and cells using RT-qPCR and western blot analysis. Based on the results obtained from dual-luciferase reporter gene, RIP, and FISH assays, LINC01315 was found to upregulate DLG3 expression by competitively binding to miR-211. Upon altering the expression of LINC01315, and/or miR-211 in OSCC cells with shRNA, mimic, or an inhibitor, we assessed their effects on OSCC cell proliferation, migration, invasion, and apoptosis. LINC01315 knockdown enhanced OSCC cell proliferation, migration and invasion, but dampened their apoptosis, all of which could be reversed by miR-211 inhibition. Elevation of DLG3, a target gene of miR-211, activated the Hippo signaling pathway, whereby suppressing OSCC progression in vitro. Finally, their roles in tumor growth were validated in vivo. These findings suggest that LINC01315 elevates DLG3 expression by competitively binding to miR-211, thereby suppressing OSCC progression.Receptor for advanced glycation end-products (RAGE) is a multiligand binding and single-pass transmembrane protein taken in diverse chronic inflammatory conditions. RAGE behaves as a pattern recognition receptor, which binds and is engaged in the cellular response to a variety of damage-associated molecular pattern molecules, as well as HMGB1, S100 proteins, and AGEs (advanced glycation end-products). The RAGE activation turns out to a formation of numerous intracellular signaling mechanisms, resulting in the progression and prolongation of colorectal carcinoma (CRC). The RAGE expression correlates well with the survival of colon cancer cells. RAGE is involved in the tumorigenesis, which increases and develops well in the stressed tumor microenvironment. In this review, we summarized downstream signaling cascade activated by the multiligand activation of RAGE, as well as RAGE ligands and their sources, clinical studies, and tumor markers related to RAGE particularly in the inflammatory tumor microenvironment in CRC. Furthermore, the role of RAGE signaling pathway in CRC patients with diabetic mellitus is investigated. RAGE has been reported to drive assorted signaling pathways, including activator protein 1, nuclear factor-κB, signal transducer and activator of transcription 3, SMAD family member 4 (Smad4), mitogen-activated protein kinases, mammalian target of rapamycin, phosphoinositide 3-kinases, reticular activating system, Wnt/β-catenin pathway, and Glycogen synthase kinase 3β, and even microRNAs.