Distinct biological property of tau in tau-first cognitive proteinopathy: Evidence by longitudinal clinical neuroimaging profiles and compared with late-onset Alzheimer disease.

BACKGROUND: Tau-first cognitive proteinopathy (TCP) denotes a clinical phenotype of Alzheimer disease (AD) showing Florzolotau(18F) positron emission tomography (PET) positivity but a negative amyloid status. AIM: We explored the biological property of tau using longitudinal cognitive and neuroimaging data in TCP and compared with late-onset AD (LOAD). METHOD: We enrolled 56 patients with LOAD, 34 patients with TCP, and 26 cognitive unimpaired controls. All of the participants had historical data of 2 to 4 three-dimensional T1 images and 2 to 6 annual cognitive evaluations over a follow-up period of 7 years. Tau topography was measured using Florzolotau(18F) PET. In the LOAD and TCP groups, we constructed tau or gray matter clusters covarying with the cognitive measurements. We used mediator analysis to explore the regional tau load as predictor, gray matter partitions as mediators, and significant cognitive test scores as outcomes. Longitudinal cognitive decline and cortical thickness degeneration pattern were analyzed using a linear mixed-effects model. RESULTS: The TCP group had longitudinal declines in nonexecutive domains. The deterministic factor predicting the short-term memory score in TCP was the hippocampal volume and not directly via the medial and lateral temporal tau load. These features formed the conceptual differences with LOAD. DISCUSSION: The biological properties of tau and the longitudinal cognitive-imaging trajectory support the conceptual distinction between TCP and LOAD. TCP represents one specific entity featuring salient short-term memory impairment, declines in nonexecutive domains, a slower gray matter degenerative pattern, and a restricted impact of tau.

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.

Gray matter reserve determines glymphatic system function in young-onset Alzheimer's disease: Evidenced by DTI-ALPS and compared with age-matched controls.

BACKGROUND: The diffusion tensor imaging analysis along the perivascular space (ALPS)-index can be used to model the glymphatic system in vivo. AIM: This study explores putative mechanisms between prediction of ALPS-index and cognitive outcomes in young-onset Alzheimer's disease (YOAD) and age-matched controls (CTLs) and analyzes whether the link was mediated by the integrity of ALPS-index-anchored cerebral gray matter (GM). METHODS: We enrolled 130 patients with YOAD and 137 CTLs. All participants underwent three-dimensional T1 -weighted MRI, diffusion tensor imaging and cognitive tests. We constructed GM regions correlated with the ALPS-index in the YOAD and CTL groups. For the GM regions significantly correlated with the ALPS-index and cognitive measures, we extracted a 4-mm radius sphere. In the YOAD and CTL groups, we used mediator analysis to explore the ALPS-index as predictor, GM partitions as mediators, and significant cognitive test scores as outcomes. RESULTS: Patient group had significantly lower ALPS-index. The ALPS-index was associated with GM volume in the cerebellar gray, dorsolateral prefrontal, thalamus, superior frontal, amygdala and hippocampus, and these coherent regions coincided with those showing GM atrophy in the YOAD group. Mediation analysis of the YOAD group suggested that the relationships between the ALPS-index and cognitive performance were fully mediated by the integrity of ALPS-index coherent GM areas. DISCUSSION: Reserved GM mediates the link between the glymphatic system and cognition. Our findings suggest that GM integrity rather than the glymphatic system could serve as a direct cognitive test scores predictor in patients with YOAD.

Classifying Neck Lymph Nodes of Head and Neck Squamous Cell Carcinoma in MRI Images with Radiomic Features.

The aim of this study is to develop a computer-aided diagnosis method to help classify medical images of neck lymph nodes in head and neck cancer patients. According to the current practice guidelines, the classification of lymph node status is critical for patient stratification before treatment. Take extra-nodal extension (ENE) of metastatic neck lymph nodes, the status of ENE has been considered a single factor affecting the decision of whether systemic treatment with toxicity should be given to patients with otherwise non-advanced cancer status. Medical imaging prior to surgery serves as tools for clinical staging and determining the extent of neck lymph node dissection during the tumor resection surgery. The information contained in these images may also help determine the status of ENE and thus stratify patients for more precise treatment. In the current practice, there has been not a reliable computer-aided tool for this task. In this study, we used open-source software to investigate radiomic features that help distinguish malignant from benign and ENE from non-ENE lymph nodes. We have identified 89 features that can differentiate malignant from benign and 4 features that can differentiate ENE from non-ENE lymph nodes. Furthermore, we fed the significant features to a multilayer perceptron neural network to predict malignancy and ENE of lymph nodes and achieved 84% and 77% of accuracy in each task, respectively.

Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs.

OBJECTIVE: To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs). Hip fracture is a leading worldwide health problem for the elderly. A missed diagnosis of hip fracture on radiography leads to a dismal prognosis. The application of a DCNN to PXRs can potentially improve the accuracy and efficiency of hip fracture diagnosis. METHODS: A DCNN was pretrained using 25,505 limb radiographs between January 2012 and December 2017. It was retrained using 3605 PXRs between August 2008 and December 2016. The accuracy, sensitivity, false-negative rate, and area under the receiver operating characteristic curve (AUC) were evaluated on 100 independent PXRs acquired during 2017. The authors also used the visualization algorithm gradient-weighted class activation mapping (Grad-CAM) to confirm the validity of the model. RESULTS: The algorithm achieved an accuracy of 91%, a sensitivity of 98%, a false-negative rate of 2%, and an AUC of 0.98 for identifying hip fractures. The visualization algorithm showed an accuracy of 95.9% for lesion identification. CONCLUSIONS: A DCNN not only detected hip fractures on PXRs with a low false-negative rate but also had high accuracy for localizing fracture lesions. The DCNN might be an efficient and economical model to help clinicians make a diagnosis without interrupting the current clinical pathway. KEY POINTS: • Automated detection of hip fractures on frontal pelvic radiographs may facilitate emergent screening and evaluation efforts for primary physicians. • Good visualization of the fracture site by Grad-CAM enables the rapid integration of this tool into the current medical system. • The feasibility and efficiency of utilizing a deep neural network have been confirmed for the screening of hip fractures.

Risk factors of distant metastasis in the follicular variant of papillary thyroid carcinoma.

BACKGROUND/PURPOSE: The follicular variant of papillary thyroid carcinoma (FVPTC) is the most common variant of papillary thyroid carcinoma (PTC). A previous population-based study revealed its clinical behavior as a mix of classic papillary thyroid carcinoma (C-PTC) and follicular thyroid carcinoma. Whereas locoregional extension was lower in FVPTC than in C-PTC, the distant metastasis rate was higher in FVPTC than in C-PTC. The aim of this study was to evaluate the risk factors of distant metastasis in FVPTC postoperatively. METHODS: A retrospective review of 359 patients with final pathological diagnosis of FVPTC treated at Chang Gung Memorial Hospital between January 2000 and January 2014 was performed. After excluding patients who had inadequate pathological data for analysis or did not attend regular follow up for >1 year, 346 patients were included in this study. Univariate and multivariate statistical analyses were performed to determine the significance of various factors. RESULTS: Of the 346 patients with FVPTC, 19 (5.5%) had lymph node metastases and 32 (9.2%) had distant metastases. Two positive and one negative risk factors were predictive for distant metastasis using multivariate analysis: angiolymphatic invasion [odds ratio (OR), 3.085; 95% confidence interval (CI), 1.008-9.442], extrathyroidal extension (OR, 3.929; 95% CI, 1.330-11.602), and encapsulation (OR, 0.361; 95% CI, 0.154-0.850). CONCLUSION: The presence of angiolymphatic invasion, extrathyroidal extension, or nonencapsulation was associated with distant metastasis in FVPTC in this study. In FVPTC patients, postoperative investigation for distant metastasis may be warranted by the presence of these two positive risk factors or the absence of the one negative risk factor.

Differences in Topography of Individual Amyloid Brain Networks by Amyloid PET Images in Healthy Control, Mild Cognitive Impairment, and Alzheimer's Disease.

Amyloid plaques, implicated in Alzheimer's disease, exhibit a spatial propagation pattern through interconnected brain regions, suggesting network-driven dissemination. This study utilizes PET imaging to investigate these brain connections and introduces an innovative method for analyzing the amyloid network. A modified version of a previously established method is applied to explore distinctive patterns of connectivity alterations across cognitive performance domains. PET images illustrate differences in amyloid accumulation, complemented by quantitative network indices. The normal control group shows minimal amyloid accumulation and preserved network connectivity. The MCI group displays intermediate amyloid deposits and partial similarity to normal controls and AD patients, reflecting the evolving nature of cognitive decline. Alzheimer's disease patients exhibit high amyloid levels and pronounced disruptions in network connectivity, which are reflected in low levels of global efficiency (Eg) and local efficiency (Eloc). It is mostly in the temporal lobe where connectivity alterations are found, particularly in regions related to memory and cognition. Network connectivity alterations, combined with amyloid PET imaging, show potential as discriminative markers for different cognitive states. Dataset-specific variations must be considered when interpreting connectivity patterns. The variability in MCI and AD overlap emphasizes the heterogeneity in cognitive decline progression, suggesting personalized approaches for neurodegenerative disorders. This study contributes to understanding the evolving network characteristics associated with normal cognition, MCI, and AD, offering valuable insights for developing diagnostic and prognostic markers.

Visual reading for [18F]Florzolotau ([18F]APN-1607) tau PET imaging in clinical assessment of Alzheimer's disease.

Introduction: Tau-targeted positron emission tomography (tau-PET) is a potential tool for the differential diagnosis of Alzheimer's disease (AD) and to clarify the distribution of tau deposition. In addition to the quantitative analysis of tau-PET scans, visual reading supports the assessment of tau loading for clinical diagnosis. This study aimed to propose a method for visually interpreting tau-PET using the [18F] Florzolotau tracer and investigate the performance and utility of the visual reading. Materials and methods: A total number of 46 individuals with 12 cognitively unimpaired subjects (CU), 20 AD patients with mild cognitive impairment (AD-MCI), and 14 AD with dementia (AD-D) patients with both [18F]Florbetapir amyloid PET and [18F]Florzolotau tau PET scans were included. Clinical information, cognitive assessment, and amyloid PET scan results were recorded. For visual interpretation, a modified rainbow colormap was created and a regional tau uptake scoring system was proposed to evaluate the degree of tracer uptake and its spatial distribution within five cortical regions. Each region was scored on a scale of [0, 2] as compared to the background, and that resulted in a global scale range of [0, 10]. Four readers interpreted [18F]Florzolotau PET using the visual scale. The global and regional standardized uptake value ratios (SUVr) were also calculated for analysis. Results: The result indicates the average global visual scores were 0 ± 0 in the CU group, 3.43 ± 3.35 in the AD-MCI group, and 6.31 ± 2.97 in the AD-D group (p < 0.001). The consensus among the four observers on image scores was high with an intraclass correlation coefficient of 0.880 (95% CI: 0.767-0.936). The average global visual score was significantly associated with global SUVr (r = 0.884, p < 0.0001) and with the CDR-sum of box (r = 0.677, p < 0.0001). Conclusion: The visual reading method generated a visual score of [18F]Florzolotau tau-PET with good sensitivity and specificity to identify AD-D or CU individuals from the other patients. The preliminary result also showed that the global visual scores are significantly and reliably correlated with global cortical SUVr, and associated well with the clinical diagnosis and cognitive performance.

Therapeutic outcome and prognosis in young patients with papillary and follicular thyroid cancer.

BACKGROUND: Papillary and follicular thyroid cancer is a common malignancy in young patients, and the incidence of this cancer has been increasing. The aims of this study are to assess the clinical characteristics of papillary and follicular thyroid cancer in young patients and evaluate the long-term therapeutic outcomes and prognostic factors for cancer mortality and recurrence. METHODS: We performed a retrospective analysis of 116 patients aged ≤20 years who underwent thyroidectomy and a mean follow-up of 11.1 ± 0.6 years. RESULTS: There were 28 (24.1%) patients classified into the residual cancer or relapse groups. The progression-free survival rate for the young patients was lower than that of the patients between 20 and 45 years of age; however, the difference between the thyroid cancer survival rates was not statistically different. Two of the 28 patients died of thyroid cancer. Thirteen patients who showed relapsed underwent (131)I whole-body scan; 6 of the 13 patients were diagnosed with distant metastases to the lung and 1 was diagnosed with distant metastases to the bones. Among the young patients, the 5- and 10-year progression-free survival rates were 79.1 and 73.4%, respectively, and the corresponding cancer survival rates were 99.1, and 96.5%, respectively. CONCLUSION: The progression-free survival in young patients with papillary and follicular thyroid cancer was lower than the patients of age 20-45 years; otherwise, cancer survival was higher than age group over or equal to 45 years.

Individual cerebrocerebellar functional network analysis decoding symptomatologic dynamics of postoperative cerebellar mutism syndrome.

Introduction: Postoperative cerebellar mutism syndrome (pCMS) consists of three types of symptoms (motoric, linguistic, and neurobehavioral) in patients with posterior fossa pathologies. The evolutional mechanism of this high cognitive syndromic complex from cerebellar origin remains unconfirmed. Previous studies analyzing CMS patients mostly focused on the association between structural abnormalities that occur during CMS, of which proximal efferent cerebellar pathway (pECP) injury appears to be the most common pathogenesis. However, structural imaging may not be sensitive enough to determine the dynamic course of CMS, since the symptomatology is primarily an output of cerebral operation. Method: We took a network approach in a child during her course of development and recovery of the pCMS. On the other hand, a network neuroscience approach using a mathematical model to extract information from functional imaging to generate interregional connectivity provides abundant evidence that the cerebellum is influential in modulating cerebral functions. Result: This study applied a network approach to children with pCMS. An individual cerebrocerebellar functional network analysis using graph theory was then performed to determine the network dynamics during CMS. Cross-validation of clinical neurophysiology and functional neuroscience suggested the critical role of the pECP within CMS from the network analysis. Conclusion: The employed approach was therefore useful in determining the complex clinical symptoms using individual functional network analysis, which bridges the gap between structural neuroimaging and clinical neurophysiology.

Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study.

Accurate organ-at-risk (OAR) segmentation is critical to reduce radiotherapy complications. Consensus guidelines recommend delineating over 40 OARs in the head-and-neck (H&N). However, prohibitive labor costs cause most institutions to delineate a substantially smaller subset of OARs, neglecting the dose distributions of other OARs. Here, we present an automated and highly effective stratified OAR segmentation (SOARS) system using deep learning that precisely delineates a comprehensive set of 42 H&N OARs. We train SOARS using 176 patients from an internal institution and independently evaluate it on 1327 external patients across six different institutions. It consistently outperforms other state-of-the-art methods by at least 3-5% in Dice score for each institutional evaluation (up to 36% relative distance error reduction). Crucially, multi-user studies demonstrate that 98% of SOARS predictions need only minor or no revisions to achieve clinical acceptance (reducing workloads by 90%). Moreover, segmentation and dosimetric accuracy are within or smaller than the inter-user variation.

Primary lung cancer with radioiodine avidity: A thyroid cancer cohort study.

BACKGROUND: A proportion of lung cancers show sodium/iodide symporter (NIS) expression. Lung cancers with NIS expression may uptake radioiodine (RAI) and show RAI-avid lesions on RAI scan for differentiated thyroid cancer (DTC) surveillance. AIM: To investigate the possibility of RAI uptake by lung cancer in a cohort with thyroid cancer. METHODS: RAI-avid lung cancers were analyzed using a prospectively maintained database of patients with thyroid cancer who were registered at a medical center between December 1, 1976 and May 28, 2018. NIS expression in lung cancer was assessed using immunohistochemical staining. RESULTS: Of the 5000 patients with thyroid cancer from the studied dataset, 4602 had DTC. During follow-up, 33 patients developed primary lung cancer. Of these patients, nine received an iodine-131 (131I) scan within 1 year before the diagnosis of lung cancer. One of these nine lung cancers was RAI-avid. NIS expression was evaluated, and three of the eight available lung cancers revealed NIS expression. The proportions of lung cancer cells with NIS expression were 60%, 15%, and 10%. The RAI-avid lung cancer had the highest level of expression (60%). The RAI-avid lung cancer had a spiculated border upon single-photon emission computed tomography/computed tomography, which led to an accurate diagnosis. CONCLUSION: A proportion of lung cancer demonstrates NIS expression and is RAI-avid. Clinicians should be aware of this possibility in the interpretation of RAI scintigraphy.

DeepTarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy.

Gross tumor volume (GTV) and clinical target volume (CTV) delineation are two critical steps in the cancer radiotherapy planning. GTV defines the primary treatment area of the gross tumor, while CTV outlines the sub-clinical malignant disease. Automatic GTV and CTV segmentation are both challenging for distinct reasons: GTV segmentation relies on the radiotherapy computed tomography (RTCT) image appearance, which suffers from poor contrast with the surrounding tissues, while CTV delineation relies on a mixture of predefined and judgement-based margins. High intra- and inter-user variability makes this a particularly difficult task. We develop tailored methods solving each task in the esophageal cancer radiotherapy, together leading to a comprehensive solution for the target contouring task. Specifically, we integrate the RTCT and positron emission tomography (PET) modalities together into a two-stream chained deep fusion framework taking advantage of both modalities to facilitate more accurate GTV segmentation. For CTV segmentation, since it is highly context-dependent-it must encompass the GTV and involved lymph nodes while also avoiding excessive exposure to the organs at risk-we formulate it as a deep contextual appearance-based problem using encoded spatial distances of these anatomical structures. This better emulates the margin- and appearance-based CTV delineation performed by oncologists. Adding to our contributions, for the GTV segmentation we propose a simple yet effective progressive semantically-nested network (PSNN) backbone that outperforms more complicated models. Our work is the first to provide a comprehensive solution for the esophageal GTV and CTV segmentation in radiotherapy planning. Extensive 4-fold cross-validation on 148 esophageal cancer patients, the largest analysis to date, was carried out for both tasks. The results demonstrate that our GTV and CTV segmentation approaches significantly improve the performance over previous state-of-the-art works, e.g., by 8.7% increases in Dice score (DSC) and 32.9mm reduction in Hausdorff distance (HD) for GTV segmentation, and by 3.4% increases in DSC and 29.4mm reduction in HD for CTV segmentation.

Comparative prognostic value of different preoperative complete blood count cell ratios in patients with oral cavity cancer treated with surgery and postoperative radiotherapy.

BACKGROUND: We sought to compare the prognostic significance of different preoperative complete blood count cell ratios in patients with oral cavity squamous cell carcinoma (OSCC) treated with surgery and postoperative radiotherapy (PORT). METHODS: We retrospectively reviewed the clinical records of 890 patients with OSCC who were treated with surgery and PORT. The following preoperative complete blood count cell ratios were collected: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR). Overall survival (OS), local control, regional control, and distant control (DC) served as the main outcomes of interest. RESULTS: The results of multivariate analysis in the entire study cohort revealed that a low NLR was the only independently favorable marker of both OS (adjusted hazard ratio [HR]: 0.794, 95% confidence interval (CI): 0.656-0.961, bootstrap p = 0.028) and DC (adjusted HR: 0.659, 95% CI: 0.478-0.909, bootstrap p = 0.015). Both LMR and PLR were not retained in the model as independent predictors. Subgroup analyses in high-risk patients (i.e., those bearing T4 disease, N3 disease, or poor differentiation) revealed that a high NLR was a significant adverse risk factor for both OS and DC (all p < 0.03)-with a borderline significance being evident for DC in patients with T4 disease (p = 0.058). CONCLUSIONS: A high pretreatment NLR was an independent unfavorable risk factor for both OS and DC in patients with OSCC who underwent surgery and PORT. No other preoperative complete blood count parameters and cell ratios were found to have prognostic significance.

Multi-Institutional Validation of Two-Streamed Deep Learning Method for Automated Delineation of Esophageal Gross Tumor Volume Using Planning CT and FDG-PET/CT.

BACKGROUND: The current clinical workflow for esophageal gross tumor volume (GTV) contouring relies on manual delineation with high labor costs and inter-user variability. PURPOSE: To validate the clinical applicability of a deep learning multimodality esophageal GTV contouring model, developed at one institution whereas tested at multiple institutions. MATERIALS AND METHODS: We collected 606 patients with esophageal cancer retrospectively from four institutions. Among them, 252 patients from institution 1 contained both a treatment planning CT (pCT) and a pair of diagnostic FDG-PET/CT; 354 patients from three other institutions had only pCT scans under different staging protocols or lacking PET scanners. A two-streamed deep learning model for GTV segmentation was developed using pCT and PET/CT scans of a subset (148 patients) from institution 1. This built model had the flexibility of segmenting GTVs via only pCT or pCT+PET/CT combined when available. For independent evaluation, the remaining 104 patients from institution 1 behaved as an unseen internal testing, and 354 patients from the other three institutions were used for external testing. Degrees of manual revision were further evaluated by human experts to assess the contour-editing effort. Furthermore, the deep model's performance was compared against four radiation oncologists in a multi-user study using 20 randomly chosen external patients. Contouring accuracy and time were recorded for the pre- and post-deep learning-assisted delineation process.

Prognostic significance of dynamic changes in lymphocyte-to-monocyte ratio in patients with head and neck cancer treated with radiotherapy: results from a large cohort study.

BACKGROUND AND PURPOSE: We sought to investigate whether dynamic changes in lymphocyte-to-monocyte ratio (LMR) occurring during the course of radiotherapy (RT) may have prognostic value in patients with head and neck cancer (HNC). MATERIALS AND METHODS: We retrospectively reviewed the clinical records of patients with HNC who underwent RT at our center between 2005 and 2013. Generalized estimating equations were used to longitudinally assess changes in LMR through the course of RT. Delta-LMR was calculated as the difference between LMR measured during treatment and baseline LMR values. Freedom from metastasis (FFM) and overall survival (OS) served as the main outcome measures. RESULTS: A total of 1431 patients with HNC were enrolled. After a median follow-up of 9 years, 636 (44.4%) patients died and 240 (16.8%) had distant metastases. Compared with patients with low delta-LMR at two weeks, those with high delta-LMR experienced less favorable outcomes (five-year OS: 73% versus 59%, respectively, p < 0.001; five-year FFM: 87% versus 80%, respectively, p = 0.015). Similar findings were observed for delta-LMR measured at four weeks (five-year OS: 72% versus 60%, p < 0.001; five-year FFM: 86% versus 79%, respectively, p = 0.002) and six weeks (five-year OS: 72% versus 57%, p < 0.001; five-year FFM: 87% versus 79%, respectively, p = 0.002). Multivariate analysis identified delta-LMR as an independent prognostic factor for both FFM and OS. CONCLUSION: Delta-LMR is a simple and inexpensive biomarker that may be clinically useful for predicting FFM and OS in patients with HNC treated with RT.

Prognostic significance of the preoperative systemic immune-inflammation index in patients with oral cavity squamous cell carcinoma treated with curative surgery and adjuvant therapy.

OBJECTIVES: To investigate the prognostic value of the preoperative systemic immune-inflammation index (SII) in patients with oral cavity squamous cell carcinoma (OC-SCC) treated with curative surgery followed by adjuvant radiotherapy (RT) or chemoradiotherapy (CCRT). MATERIALS AND METHODS: We retrospectively reviewed the clinical records of patients with OC-SCC who received surgery and postoperative adjuvant RT/CCRT between January 2005 and December 2012. Blood samples were drawn in the 2 weeks preceding surgery. SII was calculated by multiplying the absolute neutrophil and platelet counts, and then, divided by the absolute lymphocyte count, and its optimal cutoff value was identified using the Youden's index. The study endpoints included overall survival (OS), local control (LC), regional control (RC), and distant control (DC). RESULTS: The study sample consisted of 993 patients (58.8% of them treated with CCRT). The optimal cutoff value for SII was 810.6. A total of 347 (34.9%) study participants had high preoperative SII values. After allowance for potential confounders in multivariable analysis, high SII values were independently associated with less favorable DC (adjusted hazard ratio [HR] = 1.683, p = 0.001) and OS (adjusted HR = 1.466, p < 0.001). No independent association between SII and LC/RC was observed. CONCLUSION: Increased SII values predict poor DC and OS in patients with OC-SCC treated with curative resection and adjuvant RT/CCRT. Owing to the higher risk of systemic failure in this patient group, a thorough follow-up surveillance schedule may be advisable pending independent confirmation of our data.

Expressional Profiling of Carpet Glia in the Developing Drosophila Eye Reveals Its Molecular Signature of Morphology Regulators.

Homeostasis in the nervous system requires intricate regulation and is largely accomplished by the blood-brain barrier (BBB). The major gate keeper of the vertebrate BBB is vascular endothelial cells, which form tight junctions (TJs). To gain insight into the development of the BBB, we studied the carpet glia, a subperineurial glial cell type with vertebrate TJ-equivalent septate junctions, in the developing Drosophila eye. The large and flat, sheet-like carpet glia, which extends along the developing eye following neuronal differentiation, serves as an easily accessible experimental system to understand the cell types that exhibit barrier function. We profiled transcribed genes in the carpet glia using targeted DNA adenine methyl-transferase identification, followed by next-generation sequencing (targeted DamID-seq) and found that the majority of genes expressed in the carpet glia function in cellular activities were related to its dynamic morphological changes in the developing eye. To unravel the morphology regulators, we silenced genes selected from the carpet glia transcriptome using RNA interference. The Rho1 gene encoding a GTPase was previously reported as a key regulator of the actin cytoskeleton. The expression of the pathetic (path) gene, encoding a solute carrier transporter in the developing eye, is specific to the carpet glia. The reduced expression of Rho1 severely disrupted the formation of intact carpet glia, and the silencing path impaired the connection between the two carpet glial cells, indicating the pan-cellular and local effects of Rho1 and Path on carpet glial cell morphology, respectively. Our study molecularly characterized a particular subperineurial cell type providing a resource for a further understanding of the cell types comprising the BBB.

Merkel cell carcinoma in Taiwan: A series of 24 cases and literature review.

Merkel cell carcinoma (MCC) is a rare but aggressive neuroendocrine carcinoma of the skin. The available reports of MCC in Asia are limited; in this study, we report the largest series of MCC in Taiwan to date.The series is composed by 24 pathologically proven MCC cases, which were retrospectively reviewed in Chang Gung Memorial Hospital in Taiwan between 2000 and 2018.The tumor occurred predominantly in men (80%) and in the elderly (median 74.8 years). Twenty-one patients had locoregional MCC and 3 had metastatic MCC at the time of diagnosis. Patients with pathologically proven negative nodes by sentinel lymph node biopsy (SLNB) showed better survival time than those without SLNB in 16 clinically node-negative MCC cases undergoing primary surgery. Salvage surgery for loco-regional recurrence lengthened the survival time and possibly cured recurrent MCC. Palliative chemotherapy with cisplatin and etoposide showed a response rate of 25%, progression-free survival of 3.6 months, and overall survival of 14.8 months in 4 metastatic/recurrent MCC. Avelumab treatment was effective in 1 patient, who achieved a durable disease control.This observational cohort of MCC patients in Taiwan suggests aggressive surgical intervention including wide excision and lymph node management, salvage operation is critical for early MCC patients, and palliative chemotherapy and immunotherapy showed their efficacy for advanced MCC patients.

Sentinel Lymph Node Biopsy Was Associated With Favorable Survival Outcomes For Patients With Clinically Node-Negative Asian Melanoma.

PURPOSE: Sentinel lymph node biopsy (SLNB) is the standard management for clinically node-negative cutaneous melanoma patients. This study aimed to evaluate the role of SLNB in Taiwanese melanoma patients and in particular, patients with acral lentiginous melanoma (ALM). PATIENTS AND METHODS: We retrospectively analyzed the clinicopathological characteristics and survival outcomes of the patients who underwent primary surgery followed by either SLNB or nodal observation at the Linkou Chang Gung Memorial Hospital from January 2000 to December 2011. RESULTS: Among the total of 209 patients, 127 underwent SLNB and 51 underwent nodal observation only after primary surgery. There were no significant differences in clinicopathological features between the two groups except that patients who underwent SLNB were older and had a higher rate of ALM than those under nodal observation. The median follow-up time was 43.5 months until July 2013. The patients who underwent SLNB had significantly better disease-free survival (DFS) (57.1 vs 18.7 months, p < 0.01) and melanoma-specific survival (MSS) (112.4 vs 45.2 months, p < 0.01) than those under observation. Improvement in DFS (HR: 0.51, p < 0.01) and MSS (HR: 0.60, p = 0.03) was observed even after adjusting for age and disease pathology by multivariate analysis. This benefit of clinical outcomes persisted in patients with ALM, Breslow thickness ≤2 mm, or no ulceration, but not in patients with non-ALM, Breslow thickness >2 mm, or ulceration. CONCLUSION: SLNB was associated with favorable outcomes in patients with clinically node-negative cutaneous melanoma, particularly in Taiwanese patients with ALM, Breslow thickness ≤2 mm, and nonulcerated melanoma.