Citations

Differentiated thyroid carcinomas is associated with an excellent prognosis. The treatment of choice for differentiated thyroid carcinoma is surgery, followed by radioactive iodine ablation (iodine-131) in select patients and thyroxine therapy in most patients. Surgery is also the main treatment for medullary thyroid carcinoma, and kinase inhibitors may be appropriate for select patients with recurrent or persistent disease that is not resectable. Anaplastic thyroid carcinoma is almost uniformly lethal, and iodine-131 imaging and radioactive iodine cannot be used. When systemic therapy is indicated, targeted therapy options are preferred. This article describes NCCN recommendations regarding management of medullary thyroid carcinoma and anaplastic thyroid carcinoma, and surgical management of differentiated thyroid carcinoma (papillary, follicular, Hürthle cell carcinoma).

Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose–volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours.

Abstract SETDB1 is a key regulator of lineage-specific genes and endogenous retroviral elements (ERVs) through its deposition of repressive H3K9me3 mark. Apart from its H3K9me3 regulatory role, SETDB1 has seldom been studied in terms of its other potential regulatory roles. To investigate this, a genomic survey of SETDB1 binding in mouse embryonic stem cells across multiple libraries was conducted, leading to the unexpected discovery of regions bereft of common repressive histone marks (H3K9me3, H3K27me3). These regions were enriched with the CTCF motif that is often associated with the topological regulator Cohesin. Further profiling of these non-H3K9me3 regions led to the discovery of a cluster of non-repeat loci that were co-bound by SETDB1 and Cohesin. These regions, which we named DiSCs (domains involving SETDB1 and Cohesin) were seen to be proximal to the gene promoters involved in embryonic stem cell pluripotency and lineage development. Importantly, it was found that SETDB1-Cohesin co-regulate target gene expression and genome topology at these DiSCs. Depletion of SETDB1 led to localized dysregulation of Cohesin binding thereby locally disrupting topological structures. Dysregulated gene expression trends revealed the importance of this cluster in ES cell maintenance as well as at gene ‘islands’ that drive differentiation to other lineages. The ‘unearthing’ of the DiSCs thus unravels a unique topological and transcriptional axis of control regulated chiefly by SETDB1.

Abstract Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a serious side effect that is highly prevalent among cancer patients undergoing chemotherapy. There is a growing use of cryotherapy (CryTx) and compression therapy (ComTx) to prevent CIPN at cancer centers worldwide. In this study, we examined the awareness and recommendation of these modalities and evaluated factors associated with awareness. In addition, we investigated the type of technology utilized, barriers to implementation, and perceived adverse events of CryTx and ComTx.Methods: Active members of the Multinational Association of Supportive Care of Cancer (MASCC) were invited to complete an electronic survey that was sent via SurveyMonkey between September to October 2021. The survey assessed participants’ awareness, recommendation, usage, barriers to utilization, and perceived adverse events of CryTx and ComTx. Descriptive statistics and multiple logistic regression were utilized to analyze findings.Results: Out of 184 participants, 70.1% were physicians, 73.4% had over 10 years of practice, and 49.5% were practicing in an outpatient setting. While more than half (63.3%) of participants indicated awareness of CryTx for taxane-induced peripheral neuropathy, less than a quarter (22.8%) indicated recommendation in their practice setting. Factors associated with higher awareness of CryTx for patients receiving taxanes include living in Europe (OR=2.69, 95% CI [1.28-5.64], p=0.009), not practicing in an inpatient setting (OR=3.15, 95% CI [1.45-6.85], p=0.004), and self-identifying as non-physician (OR=2.40, 95% CI [1.03-4.37], p=0.041). Commercial cooling (31.5%) and compression (16.8%) gloves and socks were the most used modalities for CryTx and ComTx, respectively. The most identified barriers to CryTx and ComTx utilization include insufficient evidence (53.5%), logistics (34.8%), and patient discomfort (23.4%). Redness/irritation of skin (27.7%) and numbness/tingling (24.5%) accounted for about half of the perceived adverse events associated with use of CryTx and ComTx. Conclusion: Results of our global survey illustrated that there are varying modes in the delivery of CryTx and ComTx among cancer centers around the world. Education of the utilization of CryTx and ComTx, in addition to efficacy and implementation studies, are needed to close the gap between awareness and implementation in clinical practice.

Objective Analyzing pressure injury (PI) risk factors is complex because of multiplicity of associated factors and the multidimensional nature of this injury. The main objective of this study was to identify patients at risk of developing PI. Method Prediction performances of multiple popular supervised learning were tested. Together with the typical steps of a machine learning project, steps to prevent bias were carefully conducted, in which analysis of correlation covariance, outlier removal, confounding analysis, and cross-validation were used. Result The most accurate model reached an area under receiver operating characteristic curve of 99.7%. Ten-fold cross-validation was used to ensure that the results were generalizable. Random forest and decision tree had the highest prediction accuracy rates of 98%. Similar accuracy rate was obtained on the validation cohort. Conclusions We developed a prediction model using advanced analytics to predict PI in at-risk hospitalized patients. This will help address appropriate interventions before the patients develop a PI.

Abstract Diabetes mellitus may cause tau protein hyperphosphorylation and neurodegeneration, but the exact mechanism by which diabetic conditions induce tau pathology remains unclear. Tau protein hyperphosphorylation is considered a major pathological hallmark of neurodegeneration and can be triggered by diabetes. Various tau‐directed kinases, including P38, can be activated upon diabetic stress and induce tau hyperphosphorylation. Despite extensive research efforts, the exact tau specie(s) and kinases driving neurodegeneration in diabetes mellitus have not been clearly elucidated. We herein employed different techniques to determine the exact molecular mechanism of tau pathology triggered by diabetes in in vivo and in vitro models. We showed that diabetes‐related stresses and glucose metabolism deficiency could induce cis P‐tau (an early driver of the tau pathology) accumulation in the midbrain and corpus callosum of the diabetic mice models and cells treated with 2‐deoxy‐D‐glucose, respectively. We found that the active phosphorylated level of P38 was increased in the treated cells and diabetic mice models. We observed that oxidative stress activated P38, which directly and indirectly drove tau pathology in the GABAergic and glutamatergic neurons of the midbrain of the diabetic mice after 96 h, which accumulated in the other neighboring brain areas after 2 months. Notably, P38 inhibition suppressed tau pathogenicity and risk‐taking behaviors in the animal models after 96 h. The data establish P38 as a central mediator of diabetes mellitus‐induced tau pathology. Our findings provide mechanistic insight into the consequences of this metabolic disorder on the nervous system.

AbstractPurposeImage guidance is used to improve the accuracy of radiation therapy delivery but results in increased dose to patients. This is of particular concern in children who need be treated per Pediatric Image Gently Protocols due to long‐term risks from radiation exposure. The purpose of this study is to design a deep neural network architecture and loss function for improving soft‐tissue contrast and preserving small anatomical features in ultra‐low‐dose cone‐beam CTs (CBCT) of head and neck cancer (HNC) imaging.MethodsA 2D compound U‐Net architecture (modified U‐Net++) with different depths was proposed to enhance the network capability of capturing small‐volume structures. A mask weighted loss function (Mask‐Loss) was applied to enhance soft‐tissue contrast. Fifty‐five paired CBCT and CT images of HNC patients were retrospectively collected for network training and testing. The output enhanced CBCT images from the present study were evaluated with quantitative metrics including mean absolute error (MAE), signal‐to‐noise ratio (SNR), and structural similarity (SSIM), and compared with those from the previously proposed network architectures (U‐Net and wide U‐Net) using MAE loss functions. A visual assessment of ten selected structures in the enhanced CBCT images of each patient was performed to evaluate image quality improvement, blindly scored by an experienced radiation oncologist specialized in HN cancer.ResultsAll the enhanced CBCT images showed reduced artifactual distortion and image noise. U‐Net++ outperformed the U‐Net and wide U‐Net in terms of MAE, contrast near structure boundaries, and small structures. The proposed Mask‐Loss improved image contrast and accuracy of the soft‐tissue regions. The enhanced CBCT images predicted by U‐Net++ and Mask‐Loss demonstrated improvement compared to the U‐Net in terms of average MAE (52.41 vs 42.85 HU), SNR (14.14 vs 15.07 dB), and SSIM (0.84 vs 0.87), respectively (, in all paired t‐tests). The visual assessment showed that the proposed U‐Net++ and Mask‐Loss significantly improved original CBCTs (), compared to the U‐Net and MAE loss.ConclusionsThe proposed network architecture and loss function effectively improved image quality in soft‐tissue contrast, organ boundary, and small structure preservation for ultra‐low‐dose CBCT following Image Gently Protocol. This method has potential to provide sufficient anatomical representation on the enhanced CBCT images for accurate treatment delivery and potentially fast online‐adaptive re‐planning for HN cancer patients.

Gastric cancer is the third leading cause of cancer-related deaths worldwide. Over 95% of gastric cancers are adenocarcinomas, which are typically classified based on anatomic location and histologic type. Gastric cancer generally carries a poor prognosis because it is often diagnosed at an advanced stage. Systemic therapy can provide palliation, improved survival, and enhanced quality of life in patients with locally advanced or metastatic disease. The implementation of biomarker testing, especially analysis of HER2 status, microsatellite instability (MSI) status, and the expression of programmed death-ligand 1 (PD-L1), has had a significant impact on clinical practice and patient care. Targeted therapies including trastuzumab, nivolumab, and pembrolizumab have produced encouraging results in clinical trials for the treatment of patients with locally advanced or metastatic disease. Palliative management, which may include systemic therapy, chemoradiation, and/or best supportive care, is recommended for all patients with unresectable or metastatic cancer. Multidisciplinary team management is essential for all patients with localized gastric cancer. This selection from the NCCN Guidelines for Gastric Cancer focuses on the management of unresectable locally advanced, recurrent, or metastatic disease.

Abstract Background Breastfeeding is beneficial to both mother and infant. However, overlap of lactation with pregnancy and short recuperative intervals may impact mothers nutritionally. We aimed to investigate the possible effects of pregnancy during breastfeeding. Methods In October 2018, we searched systematically in nine electronic databases to investigate any association of breastfeeding during pregnancy with fetal and/or maternal outcomes. The study protocol was registered in PROSPERO (CRD41017056490). A meta-analysis was done to detect maternal and fetal outcomes and complications during pregnancy. Quality assessment was performed using the Australian Cancer Council bias tool for included studies. Results With 1992 studies initially identified, eight were eligible for qualitative analysis and 12 for quantitative analysis. Our results showed no significant difference in different abortion subtypes between lactating and non-lactating ones. In delivery, no difference between two groups regarding the time of delivery in full-term healthy, preterm delivery and preterm labor. No significant difference was detected in rates of antepartum, postpartum hemorrhage and prolonged labor between two groups. The women with short reproductive intervals may have higher supplemental intake and greater reduction fat store. The present studies showed that breastfeeding during pregnancy does not lead to adverse outcomes in the mother and her fetus in normal low-risk pregnancy, although it may lead to the nutritional burden on the mother. Conclusion The present studies showed that breastfeeding during pregnancy did not lead to the adverse outcomes in the mother and her fetus.