Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population.

As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.

Diffusely Charged Polymeric Zwitterions as Loosely Hydrated Marine Antifouling Coatings.

Polymeric zwitterions exhibit exceptional fouling resistance through the formation of a strongly hydrated surface of immobilized water molecules. While being extensively tested for their performance in biomedical, membrane, and, to a lesser extent, marine environments, few studies have investigated how the molecular design of the zwitterion may enhance its performance. Furthermore, while theories of zwitterion antifouling mechanisms exist for molecular-scale foulant species (e.g., proteins and small molecules), it remains unclear how molecular-scale mechanisms influence the micro- and macroscopic interactions of relevance for marine applications. The present study addresses these gaps through the use of a modular zwitterion chemistry platform, which is characterized by a combination of surface-sensitive sum frequency generation (SFG) vibrational spectroscopy and marine assays. Zwitterions with increasingly delocalized cations demonstrate improved fouling resistance against the green alga Ulva linza. SFG spectra correlate well with the assay results, suggesting that the more diffuse charges exhibit greater surface hydration with more bound water molecules. Hence, the number of bound interfacial water molecules appears to be more influential in determining the marine antifouling activities of zwitterionic polymers than the binding strength of individual water molecules at the interface.

Comparative genomics reveals evolutionary drivers of the dietary shift in Hemiptera.

Hemiptera insects exhibit a close relationship to plants and demonstrate a diverse range of dietary preferences, encompassing phytophagy as the predominant feeding habit while a minority engages in carnivorous or haematophagous behaviour. To counteract the challenges posed by phytophagous insects, plants have developed an array of toxic compounds, causing significant evolutionary selection pressure on these insects. In this study, we employed a comparative genomics approach to analyse the expansion and contraction of gene families specific to phytophagous insect lineages, along with their adaptive evolutionary traits, utilising representative species from the Hemiptera order. Our investigation revealed substantial expansions of gene families within the phytophagous lineages, especially in the Pentatomomorpha branch represented by Oncopeltus fasciatus and Riptortus pedestris. Notably, these expansions of gene families encoding enzymes are potentially involved in hemipteran-plant interactions. Moreover, the adaptive evolutionary analysis of these lineages revealed a higher prevalence of adaptively evolved genes in the Pentatomomorpha branch. The observed branch-specific gene expansions and adaptive evolution likely contribute significantly to the diversification of species within Hemiptera. These results help enhance our understanding of the genomic characteristics of the evolution of different feeding habits in hemipteran insects.

Six first-line tyrosine kinase inhibitors reveal novel inhibition potential for the EGFR S768I mutation.

Lung cancer, the leading cause of cancer-related mortality, is the most commonly diagnosed cancer. Tyrosine kinase inhibitors (TKIs) are considered a drug-targeted therapy for non-small cell lung cancers (NSCLCs) with epidermal growth factor receptor (EGFR) mutations. However, limited data are available involving the activity of EGFR TKIs against rare EGFR mutations. Here, based on an endogenous EGFR-depleted cell Line H3255 by CRISPR, H3255 cells with rare mutant EGFRS768I and compound mutations EGFRS768I+L858R were tested using cell proliferation assay, cytotoxicity, membrane potential, flow cytometry and Western blot analysis. We conducted cytotoxicity screening of EGFR mutations on six front-line TKIs based on first-, second-, and third-generation TKIs (afatinib, dacomitinib, osimertinib, erlotinib, gefitinib, and icotinib). The results showed that the sensitivity of these mutants containing rare variants EGFRS768I to six front-line TKIs was enriched in the irreversible TKI cytotoxicity assays by determining their change in cytotoxicity, apoptosis, cell proliferation and signal pathway factors. Importantly, the variants harboring EGFRL858R (H3255), EGFRS768I (H3255S768I) and EGFRS768I+L858R (H3255S768I+L858R) were sensitive to six TKIs and induced cytotoxicity through different pathways. Moreover, the compound mutations EGFRS768I+L858R showed more TKI resistance than EGFRS768I mutation and EGFRL858R mutation. We present a comprehensive reference for the sensitivity of EGFRS768I variants to six front-line TKIs. For patients with the EGFR S768I mutation and compound mutations EGFRS768I+L858R, six first-line TKIs appear to be reasonable therapeutic options.

Self-Amplifying Assembly of Peptides in Macrophages for Enhanced Inflammatory Treatment.

Enzyme-regulated in situ self-assembly of peptides represents one versatile strategy in the creation of theranostic agents, which, however, is limited by the strong dependence on enzyme overexpression. Herein, we reported the self-amplifying assembly of peptides precisely in macrophages associated with enzyme expression for improving the anti-inflammatory efficacy of conventional drugs. The self-amplifying assembling system was created via coassembling an enzyme-responsive peptide with its derivative functionalized with a protein ligand. Reduction of the peptides by the enzyme NAD(P)H quinone dehydrogenase 1 (NQO1) led to the formation of nanofibers with high affinity to the protein, thereby facilitating NQO1 expression. The improved NQO1 level conversely promoted the assembly of the peptides into nanofibers, thus establishing an amplifying relationship between the peptide assembly and the NQO1 expression in macrophages. Utilization of the amplifying assembling system as vehicles for drug dexamethasone allowed for its passive targeting delivery to acute injured lungs. Both in vitro and in vivo studies confirmed the capability of the self-amplifying assembling system to enhance the anti-inflammatory efficacy of dexamethasone via simultaneous alleviation of the reactive oxygen species side effect and downregulation of proinflammatory cytokines. Our findings demonstrate the manipulation of the assembly of peptides in living cells with a regular enzyme level via a self-amplification process, thus providing a unique strategy for the creation of supramolecular theranostic agents in living cells.

Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction.

Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50-85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50-85 years) downloaded from multiple open-source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia-associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aß40, Aß42, T-tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70-85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters.

A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis.

OBJECTIVES: An accurate and rapid diagnosis is crucial for the appropriate treatment of pulmonary tuberculosis (TB). This study aims to develop an artificial intelligence (AI)-based fully automated CT image analysis system for detection, diagnosis, and burden quantification of pulmonary TB. METHODS: From December 2007 to September 2020, 892 chest CT scans from pathogen-confirmed TB patients were retrospectively included. A deep learning-based cascading framework was connected to create a processing pipeline. For training and validation of the model, 1921 lesions were manually labeled, classified according to six categories of critical imaging features, and visually scored regarding lesion involvement as the ground truth. A "TB score" was calculated based on a network-activation map to quantitively assess the disease burden. Independent testing datasets from two additional hospitals (dataset 2, n = 99; dataset 3, n = 86) and the NIH TB Portals (n = 171) were used to externally validate the performance of the AI model. RESULTS: CT scans of 526 participants (mean age, 48.5 ± 16.5 years; 206 women) were analyzed. The lung lesion detection subsystem yielded a mean average precision of the validation cohort of 0.68. The overall classification accuracy of six pulmonary critical imaging findings indicative of TB of the independent datasets was 81.08-91.05%. A moderate to strong correlation was demonstrated between the AI model-quantified TB score and the radiologist-estimated CT score. CONCLUSIONS: The proposed end-to-end AI system based on chest CT can achieve human-level diagnostic performance for early detection and optimal clinical management of patients with pulmonary TB. KEY POINTS: • Deep learning allows automatic detection, diagnosis, and evaluation of pulmonary tuberculosis. • Artificial intelligence helps clinicians to assess patients with tuberculosis. • Pulmonary tuberculosis disease activity and treatment management can be improved.

Supramolecular Antagonists Promote Mitochondrial Dysfunction.

Mitochondrion-targeting therapy exhibits great potential in cancer therapy but significantly suffers from limited therapeutic efficiency. Here we report on mitochondrion-targeting supramolecular antagonist-inducing tumor cell death via simultaneously promoting cellular apoptosis and preventing survival. The supramolecular antagonist was created via coassembly of a mitochondrion-targeting pentapeptide with its two derivatives functionalized with a BH3 domain or the drug camptothecin (CPT). While drug CPT released from the antagonist induced cellular apoptosis via decreasing the mitochondrial membrane potential, the BH3 domain prevented cellular survival through facilitating the association between the supramolecular antagonists and antiapoptotic proteins, thereby initiating mitochondrial permeabilization. Both in vitro and in vivo studies confirmed the combinatorial therapeutic effect arising from the BH3 domain and CPT drug within the supramolecular antagonist on cell death and thereby inhibiting tumor growth. Our findings demonstrate an efficient combinatorial mechanism for mitochondrial dysfunction, thus potentially serving as novel organelle-targeting medicines.

Optimization of cationic polymer-mediated transfection for RNA interference.

Transfection efficiency was estimated to optimize the conditions for RNA interference (RNAi), including transfection time, validity, and nucleic acid concentration and type, using the EZ Trans Cell Reagent, a cationic polymer. An shRNA against GFP was designed and transfected into cells using the EZ transfection reagent. The shRNA significantly decreased the expression of GFP. In addition, pre-diluted transfection reagent at room temperature and small nucleic acids increased the transfection efficiency, which peaked at 24 h. Compared with circular nucleic acids, linear nucleic acids showed higher transfection efficiency and a higher genome integration rate. We optimized cationic polymer-mediated RNAi conditions, and our data will be useful for future RNAi studies.

Structural and functional radiomics for lung cancer.

INTRODUCTION: Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. METHODS: Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. CONCLUSION: The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form "Medomics."

Bimetallic oxygen electrocatalyst derived from metallocenes doped MOFs.

Herein, metallocens (ferrocene, ruthenocene and cobaltocene) werein situhybridized with two dimensional metal-organic frameworks (MOFs) via an improved inter-diffusion approach. The metallocens@MOFs hybrids were direct carbonized and transformed to Fe(Ru,Co)-Co@C composites. These bimetallic electrocatalysts show remarkable oxygen evolution reaction (OER) activity by taking advantage of the synergetic effect between the binary metals. Among of them, the obtained Fe-Co@C exhibits lower overpotential (320 mV at 10 mA cm-2) and satisfactory stability toward OER. The study presents a novel and facile strategy to synthesize low-cost bimetallic catalyst, showing great promise in electrocatalysis of oxygen.

Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study.

Background Solid components of part-solid nodules (PSNs) at CT are reflective of invasive adenocarcinoma, but studies describing radiomic features of PSNs and the perinodular region are lacking. Purpose To develop and to validate radiomic signatures diagnosing invasive lung adenocarcinoma in PSNs compared with the Brock, clinical-semantic features, and volumetric models. Materials and Methods This retrospective multicenter study (https://ClinicalTrials.gov, NCT03872362) included 291 patients (median age, 60 years; interquartile range, 55-65 years; 191 women) from January 2013 to October 2017 with 297 PSN lung adenocarcinomas split into training (n = 229) and test (n = 68) data sets. Radiomic features were extracted from the different regions (gross tumor volume [GTV], solid, ground-glass, and perinodular). Random-forest models were trained using clinical-semantic, volumetric, and radiomic features, and an online nodule calculator was used to compute the Brock model. Performances of models were evaluated using standard metrics such as area under the curve (AUC), accuracy, and calibration. The integrated discrimination improvement was applied to assess model performance changes after the addition of perinodular features. Results The radiomics model based on ground-glass and solid features yielded an AUC of 0.98 (95% confidence interval [CI]: 0.96, 1.00) on the test data set, which was significantly higher than the Brock (AUC, 0.83 [95% CI: 0.72, 0.94]; P = .007), clinical-semantic (AUC, 0.90 [95% CI: 0.83, 0.98]; P = .03), volumetric GTV (AUC, 0.87 [95% CI: 0.78, 0.96]; P = .008), and radiomics GTV (AUC, 0.88 [95% CI: 0.80, 0.96]; P = .01) models. It also achieved the best accuracy (93% [95% CI: 84%, 98%]). Both this model and the model with added perinodular features showed good calibration, whereas adding perinodular features did not improve the performance (integrated discrimination improvement, -0.02; P = .56). Conclusion Separating ground-glass and solid CT radiomic features of part-solid nodules was useful in diagnosing the invasiveness of lung adenocarcinoma, yielding a better predictive performance than the Brock, clinical-semantic, volumetric, and radiomics gross tumor volume models. Online supplemental material is available for this article. See also the editorial by Nishino in this issue. Published under a CC BY 4.0 license.

Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. RESULTS: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.

Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study.

OBJECTIVES: Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM). METHODS: This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested. RESULTS: The radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume. CONCLUSIONS: Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients. KEY POINTS: • A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.

Enhanced proton conduction of imidazole localized in one-dimensional Ni-metal-organic framework nanofibers.

Metal-organic frameworks (MOFs) show possibilities to be potential candidates for proton exchange membranes (PEMs). However, the poor flexibility and processability of MOFs due to their crystalline nature limit their applications significantly. An efficient approach to overcome this limitation is to combine MOFs with polymers. In this work, novel lightweight and flexible Ni-MOFs/polyacrylonitrile nanofibers were fabricated by electrospinning. The nanofibers consisted of one-dimensional proton conduction channels for imidazole and show enhanced proton conductivity. A proton conductivity of 6.04 × 10-5 Scm-1 was achieved at 363 K and 90% RH. Furthermore, the proton transport dynamics of the fibers were investigated using the AC impedance technique.

Nickel nanoparticles embedded in porous carbon nanofibers and its electrochemical properties.

Flexible porous carbon nanofibers containing nickel nanoparticles were synthesized by direct carbonization of electrospun Ni-MOFs/polyacrylonitrile fibers. The as-synthesized composite nanofibers were employed as binder-free electrodes, and exhibit high specific capacitance (up 672 F g-1 at current density of 2 A g-1) and superior rate capability (57% capacitance retention from current density of 2-10 A g-1), which may be attributed to their binder-free nature, unique one-dimensional (1D) structure and highly dispersed electrochemically active nickel nanoparticles. Furthermore, a symmetric supercapacitor was assembled using the fiber electrodes in 6 M KOH, and the energy density of 17.8 Wh kg-1 was achieved in a potential window of 1.5 V. This self-standing fiber with abundant mesopores and macropores is expected to become a promising electrode material for high-performance supercapacitors.

The Emerging Role of Radiomics in COPD and Lung Cancer.

Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineable data, by extracting and correlating quantitative imaging features with patients' outcomes and tumor phenotype - a process termed radiomics. While this process has already been widely researched in lung oncology, the evaluation of COPD in this fashion remains in its infancy. Here we outline the main applications of radiomics in lung cancer and briefly review the workflow from image acquisition to the evaluation of model performance. Finally, we discuss the current assessments of COPD and the potential application of radiomics in COPD.

Deep learning in fracture detection: a narrative review.

Artificial intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI, particularly deep learning, has recently made substantial strides in perception tasks allowing machines to better represent and interpret complex data. Deep learning is a subset of AI represented by the combination of artificial neuron layers. In the last years, deep learning has gained great momentum. In the field of orthopaedics and traumatology, some studies have been done using deep learning to detect fractures in radiographs. Deep learning studies to detect and classify fractures on computed tomography (CT) scans are even more limited. In this narrative review, we provide a brief overview of deep learning technology: we (1) describe the ways in which deep learning until now has been applied to fracture detection on radiographs and CT examinations; (2) discuss what value deep learning offers to this field; and finally (3) comment on future directions of this technology.

MRI analysis of hydrogel-loaded apatinib for local therapy of hepatocellular carcinoma model in nude mice.

AIM: To investigate the effect of local treatment of gadolinium-polyethylene glycol (Gd-PEG) hydrogel containing apatinib injected into hepatocellular carcinoma model of HepG2 in nude mice, and to evaluate the MRI findings in vivo. METHODS: HepG2 cells were treated in vitro and OD 450 value were measured. The four groups (n = 6) were Apatinib-Gd-PEG hydrogel, Gd-PEG hydrogel, Apatinib, and Saline. T1WI and DWI scans were performed before and 1d, 3d, and 14d postoperatively. The samples were examined by histomorphology and immunohistochemistry for CD34 and VEGFR2. Microvessel density (MVD) was evaluated and the average optical density (AOD) of VEGFR2 was obtained by IPP6.0 image software. RESULTS: The OD450-time curves of Gd-PEG hydrogel and phosphate buffer saline (PBS) were similar and that of apatinib at all concentrations are located below; the higher the concentration, the lower the curve. On T1WI and DWI, the newly injected Gd-PEG hydrogel showed significant high signal and was immobilized in the tumor. Subsequently, the size and signal of Gd-PEG hydrogel gradually decreased with time. In Apatinib-Gd-PEG hydrogel group, compared with other three groups, MRI and histomorphology showed that the necrotic area of hepatocellular carcinoma model was larger, immunohistochemistry displayed minimal expression of CD34 and VEGFR2, the AOD of VEGFR2 and MVD differed markedly. CONCLUSION: Gd-PEG hydrogel can significantly enhance and prolong the inhibitory effect of apatinib. It can be visualized by MRI, which can be used to evaluate the local therapeutic effect.

CT Characteristics and Pathologic Basis of Solitary Cystic Lung Cancer.

There are increasing reports of a type of lung cancer that manifests as solitary cystic airspaces. The purpose of this case series was to identify the CT features and possible mechanisms of solitary cystic lung cancer, on the basis of CT observations and pathologic characteristics. The clinical, imaging, and pathologic data of 106 patients with solitary cystic lung cancer were collected and analyzed between January 2011 and December 2017. CT images were reviewed independently by three radiologists who were blinded to pathologic findings. Demographic data and clinical and smoking status were extracted from the medical records. The mean age was 58.8 years 6 10.6 (standard deviation) (range, 30­82 years). CT features in the 106 patients included nonuniform cystic walls in 96 (90.6%) patients, cyst septations in 62 (58.5%) patients, nodular walls in 58 (54.7%) patients, ground-glass opacity around the cyst in 53 patients (50.0%), and irregular margins in 42 (39.6%) patients. At histologic examination, the majority of cases (81 [87.1%] of 93) were adenocarcinoma.