Supplementary Table S1. Multivariable Cox regression analysis of various clinicopathologic characteristics and the twelve-feature-based signature with progression-free survival; Supplementary Table S2. Detail of treatment of all the patients. Including the administration of TKIs, mean time of TKI therapy and chemotherapy of each department; Supplementary Table S3. Univariate association of twelve features and progression-free survival in the training dataset; Supplementary Table S4. Comparison of clinical variables between the rapid-progression subgroup and slow-progression subgroup in EGFR-TKI cases; Supplementary Figure S1. X-tile plots of the twelve selected key features; Supplementary Figure S2. Turning parameter (λ) selection in the LASSO model; Supplementary Figure S3. Stratified analysis of the signature; Supplementary Figure S4. Time-dependent ROC curves of the risk characteristics; Supplementary Figure S5. Progression probability of three different patient cohorts.

Distant metastasis (DM) is the leading cause of death in advanced lung cancer, which is diagnosed by positron emission tomography (PET) scanning. Compared with the expensive price and nocuous contrast medium in PET, using computed tomography (CT) for DM diagnosis is more economical and convenient in clinical practice. However, most existing methods only analyze tumor regions to extract local features for DM prediction, which neglects the rich whole-lung information. To alleviate this problem, we propose a novel deep learning framework based mask-guided attention mechanism called Mask-Guided Two-stream Attention network (MGTA) for DM prediction, including a 3D pseudo-siamese feature pyramid network (PSFPN) to learn both global features in the whole lung and local features in tumor; and a deep cascaded attention module (DCAM) for further feature fusion. The proposed MGTA enjoys several merits. First, to the best of our knowledge, this is the first work to simultaneously mine tumor and whole-lung information through a mask-guided mechanism for DM prediction. Second, the proposed deep cascade attention module can effectively leverage the complementary multi-level features in PSFPN to enhance the feature recognition capacity of small tumors. Extensive experiments on a large-scale DM dataset including 2814 lung cancer patients show that the MGTA achieves good performance with area under the curve (AUC) of 0.822; sensitivity of 0.753; and specificity of 0.743, which outperforms state-of-the-art lung cancer diagnosis methods and the commonly used tumor-based methods. Furthermore, the MGTA shows large improvement especially when the training data size is small.

Background Epidermal growth factor receptor (EGFR) genotype is crucial for treatment decision making in lung cancer, but it can be affected by tumour heterogeneity and invasive biopsy during gene sequencing. Importantly, not all patients with an EGFR mutation have good prognosis with EGFR-tyrosine kinase inhibitors (TKIs), indicating the necessity of stratifying for EGFR-mutant genotype. In this study, we proposed a fully automated artificial intelligence system (FAIS) that mines whole-lung information from CT images to predict EGFR genotype and prognosis with EGFR-TKI treatment. Methods We included 18 232 patients with lung cancer with CT imaging and EGFR gene sequencing from nine cohorts in China and the USA, including a prospective cohort in an Asian population (n=891) and The Cancer Imaging Archive cohort in a White population. These cohorts were divided into thick CT group and thin CT group. The FAIS was built for predicting EGFR genotype and progression-free survival of patients receiving EGFR-TKIs, and it was evaluated by area under the curve (AUC) and Kaplan-Meier analysis. We further built two tumour-based deep learning models as comparison with the FAIS, and we explored the value of combining FAIS and clinical factors (the FAIS-C model). Additionally, we included 891 patients with 56-panel next-generation sequencing and 87 patients with RNA sequencing data to explore the biological mechanisms of FAIS. Findings FAIS achieved AUCs ranging from 0.748 to 0.813 in the six retrospective and prospective testing cohorts, outperforming the commonly used tumour-based deep learning model. Genotype predicted by the FAIS-C model was significantly associated with prognosis to EGFR-TKIs treatment (log-rank p<0.05), an important complement to gene sequencing. Moreover, we found 29 prognostic deep learning features in FAIS that were able to identify patients with an EGFR mutation at high risk of TKI resistance. These features showed strong associations with multiple genotypes (p<0.05, t test or Wilcoxon test) and gene pathways linked to drug resistance and cancer progression mechanisms. Interpretation FAIS provides a non-invasive method to detect EGFR genotype and identify patients with an EGFR mutation at high risk of TKI resistance. The superior performance of FAIS over tumour-based deep learning methods suggests that genotype and prognostic information could be obtained from the whole lung instead of only tumour tissues. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.

BACKGROUND AND OBJECTIVES:There is a noticeable gap in diagnostic evidence strength between the thick and thin scans of Low-Dose CT (LDCT) for pulmonary nodule detection. When the thin scans are needed is unknown, especially when aided with an artificial intelligence nodule detection system. METHODS:A case study is conducted with a set of 1,000 pulmonary nodule screening LDCT scans with both thick (5.0mm), and thin (1.0mm) section scans available. Pulmonary nodule detection is performed by human and artificial intelligence models for nodule detection developed using 3D convolutional neural networks (CNNs). The intra-sample consistency is evaluated with thick and thin scans, for both clinical doctor and NN (neural network) models. Free receiver operating characteristic (FROC) is used to measure the accuracy of humans and NNs. RESULTS:Trained NNs outperform humans with small nodules < 6.0mm, which is a good complement to human ability. For nodules > 6.0mm, human and NNs perform similarly while human takes a fractional advantage. By allowing a few more FPs, a significant sensitivity improvement can be achieved with NNs. CONCLUSIONS:There is a performance gap between the thick and thin scans for pulmonary nodule detection regarding both false negatives and false positives. NNs can help reduce false negatives when the nodules are small and trade off the false negatives for sensitivity. A combination of human and trained NNs is a promising way to achieve a fast and accurate diagnosis.

Background:Resistance inevitably develops in epidermal growth factor receptor (EGFR)-mutated advanced non-small-cell lung cancer (NSCLC) patients after treatment of EGFR tyrosine kinase inhibitors (EGFR-TKIs). The albumin-to-alkaline phosphatase ratio (AAPR), a novel index, has been reported to be associated with survival in various cancers. In this study, we explored the prognostic value of AAPR in EGFR-mutated advanced NSCLC patients treated with first-line EGFR-TKIs.Methods:The clinical and pretreatment laboratory data were retrospectively extracted from hospital medical system. The Log-rank and Kaplan-Meier analyses were adopted to detect differences in survival between groups. Univariate and multivariate Cox's proportional hazard regression models were applied to assess the prognostic value of AAPR for progression-free survival (PFS) and overall survival (OS).Results:Totally, 598 EGFR-mutated NSCLC patients with stage IIIB-IV were enrolled into this study. The median age of all patients was 60 years, and 56.9% were women. About 97% patients had common EGFR gene mutations of deletions in exon 19 (19 del) or a point mutation in exon 21 (L858R). Using receiver operating characteristic (ROC) curve analysis and the Youden index, the optimal cut-off value of pretreatment AAPR was 0.47. Patients with high AAPR achieved longer median PFS and OS than patients with low AAPR (14.0 months vs 10.4 months, P<0.01; 58.2 months vs 36.7 months, P<0.001, respectively). The multivariate analysis by Cox's proportional hazards regression model demonstrated that AAPR was an independent prognostic factor for both PFS (HR: 0.813, 95% CI: 0.673-0.984, P=0.033) and OS (HR: 0.629, 95% CI: 0.476-0.830, P=0.001).Conclusion:Pretreatment AAPR, measured as part of routine blood biochemical test, may be a reliable prognostic indicator in EGFR-mutated advanced NSCLC patients treated with first-line first-generation EGFR-TKIs.

BackgroundLow-dose computed tomographic (LDCT) screening has been proven to be powerful in detecting lung cancers in early stage. However, it’s hard to carry out in less-developed regions in lacking of facilities and professionals. The feasibility and efficacy of mobile LDCT scanning combined with remote reading by experienced radiologists from superior hospital for lung cancer screening in deprived areas was explored in this study.MethodsA prospective cohort was conducted in rural areas of western China. Residents over 40 years old were invited for lung cancer screening by mobile LDCT scanning combined with remote image reading or local hospital-based LDCT screening. Rates of positive pulmonary nodules and detected lung cancers in the baseline were compared between the two groups.ResultsAmong 8073 candidates with preliminary response, 7251 eligibilities were assigned to the mobile LDCT with remote reading (n = 4527) and local hospital-based LDCT screening (n = 2724) for lung cancer. Basic characteristics of the subjects were almost similar in the two cohorts except that the mean age of participants in mobile group was relatively older than control (61.18 vs. 59.84 years old, P < 0.001). 1778 participants with mobile LDCT scans with remote reading (39.3%) revealed 2570 pulmonary nodules or mass, and 352 subjects in the control group (13.0%) were detected 472 ones (P < 0.001). Proportions of nodules less than 8 mm or subsolid were both more frequent in the mobile LDCT group (83.3% vs. 76.1%, 32.9% vs. 29.8%, respectively; both P < 0.05). In the baseline screening, 26 cases of lung cancer were identified in the mobile LDCT scanning with remote reading cohort, with a lung cancer detection rate of 0.57% (26/4527), which was significantly higher than control (4/2724 = 0.15%, P = 0.006). Moreover, 80.8% (21/26) of lung cancer patients detected by mobile CT with remote reading were in stage I, remarkedly higher than that of 25.0% in control (1/4, P = 0.020).ConclusionMobile LDCT combined with remote reading is probably a potential mode for lung cancer screening in rural areas.Trial registrationNo. of registration trial was ChiCTR-DDD-15007586 (http://www.chictr.org).

BACKGROUND:Lung cancer causes more deaths worldwide than any other cancer. For early-stage patients, low-dose computed tomography (LDCT) of the chest is considered to be an effective screening measure for reducing the risk of mortality. The accuracy and efficiency of cancer screening would be enhanced by an intelligent and automated system that meets or surpasses the diagnostic capabilities of human experts. METHODS:Based on the artificial intelligence (AI) technique, i.e., deep neural network (DNN), we designed a framework for lung cancer screening. First, a semi-automated annotation strategy was used to label the images for training. Then, the DNN-based models for the detection of lung nodules (LNs) and benign or malignancy classification were proposed to identify lung cancer from LDCT images. Finally, the constructed DNN-based LN detection and identification system was named as DeepLN and confirmed using a large-scale dataset. RESULTS:A dataset of multi-resolution LDCT images was constructed and annotated by a multidisciplinary group and used to train and evaluate the proposed models. The sensitivity of LN detection was 96.5% and 89.6% in a thin section subset [the free-response receiver operating characteristic (FROC) is 0.716] and a thick section subset (the FROC is 0.699), respectively. With an accuracy of 92.46%±0.20%, a specificity of 95.93%±0.47%, and a precision of 90.46%±0.93%, an ensemble result of benign or malignancy identification demonstrated a very good performance. Three retrospective clinical comparisons of the DeepLN system with human experts showed a high detection accuracy of 99.02%. CONCLUSIONS:In this study, we presented an AI-based system with the potential to improve the performance and work efficiency of radiologists in lung cancer screening. The effectiveness of the proposed system was verified through retrospective clinical evaluation. Thus, the future application of this system is expected to help patients and society.

The accurate identification of malignant lung nodules using computed tomography (CT) screening images is vital for the early detection of lung cancer. It also offers patients the best chance of cure, because non-invasive CT imaging has the ability to capture intra-tumoral heterogeneity. Deep learning methods have obtained promising results for the malignancy identification problem; however, two substantial challenges still remain. First, small datasets cannot insufficiently train the model and tend to overfit it. Second, category imbalance in the data is a problem. In this paper, we propose a method called MSCS-DeepLN that evaluates lung nodule malignancy and simultaneously solves these two problems. Three light models are trained and combined to evaluate the malignancy of a lung nodule. Three-dimensional convolutional neural networks (CNNs) are employed as the backbone of each light model to extract the lung nodule features from CT images and preserve lung nodule spatial heterogeneity. Multi-scale input cropped from CT images enables the sub-networks to learn the multi-level contextual features and preserve diverse. To tackle the imbalance problem, our proposed method employs an AUC approximation as the penalty term. During training, the error in this penalty term is generated from each major and minor class pair, so that negatives and positives can contribute equally to updating this model. Based on these methods, we obtain state-of-the-art results on the LIDC-IDRI dataset. Furthermore, we constructed a new dataset collected from a grade-A tertiary hospital and annotated using biopsy-based cytological analysis to verify the performance of our method in clinical practice.

BackgroundEarly and accurate prognosis prediction of the patients was urgently warranted due to the widespread popularity of COVID-19. We performed a meta-analysis aimed at comprehensively summarizing the clinical characteristics and laboratory abnormalities correlated with increased risk of mortality in COVID-19 patients.MethodsPubMed, Scopus, Web of Science, and Embase were systematically searched for studies considering the relationship between COVID-19 and mortality up to 4 June 2020. Data were extracted including clinical characteristics and laboratory examination.ResultsThirty-one studies involving 9407 COVID-19 patients were included. Dyspnea (OR = 4.52, 95%CI [3.15, 6.48], P < 0.001), chest tightness (OR = 2.50, 95%CI [1.78, 3.52], P<0.001), hemoptysis (OR = 2.00, 95%CI [1.02, 3.93], P = 0.045), expectoration (OR = 1.52, 95%CI [1.17, 1.97], P = 0.002) and fatigue (OR = 1.27, 95%CI [1.09, 1.48], P = 0.003) were significantly related to increased risk of mortality in COVID-19 patients. Furthermore, increased pretreatment absolute leukocyte count (OR = 11.11, 95%CI [6.85,18.03], P<0.001) and decreased pretreatment absolute lymphocyte count (OR = 9.83, 95%CI [6.72, 14.38], P<0.001) were also associated with increased mortality of COVID-19. We also compared the mean value of them between survivors and non-survivors, and found that non-survivors showed significantly raise in pretreatment absolute leukocyte count (WMD: 3.27×109/L, 95%CI [2.34, 4.21], P<0.001) and reduction in pretreatment absolute lymphocyte count (WMD = -0.39×109/L, 95%CI [-0.46, -0.33], P<0.001) compared with survivors. The results of pretreatment lactate dehydrogenase (LDH), procalcitonin (PCT), D-Dimer and ferritin showed the similar trend with pretreatment absolute leukocyte count.ConclusionsAmong the common symptoms of COVID-19 infections, fatigue, expectoration, hemoptysis, dyspnea and chest tightness were independent predictors of death. As for laboratory examinations, significantly increased pretreatment absolute leukocytosis count, LDH, PCT, D-Dimer and ferritin, and decreased pretreatment absolute lymphocyte count were found in non-survivors, which also have an unbeneficial impact on mortality among COVID-19 patients. Motoring these indicators during the hospitalization plays a very important role in predicting the prognosis of patients.

Abstract Background Accumulating evidence indicates inherited risk in the aetiology of lung cancer, although smoking exposure is the major attributing factor. Family history is a simple substitute for inherited susceptibility. Previous studies have shown some possible yet conflicting links between family history of cancer and EGFR mutation in lung cancer. As EGFR-mutated lung cancer favours female, never-smoker, adenocarcinoma and Asians, it may be argued that there may be some underlying genetic modifiers responsible for the pathogenesis of EGFR mutation. Methods We searched four databases for all original articles on family history of malignancy and EGFR mutation status in lung cancer published up to July 2018. We performed a meta-analysis by using a random-effects model and odds ratio estimates. Heterogeneity and sensitivity were also investigated. Then we conducted a second literature research to curate case reports of familial lung cancers who studied both germline cancer predisposing genes and their somatic EGFR mutation status; and explored the possible links between cancer predisposing genes and EGFR mutation. Results Eleven studies have been included in the meta-analysis. There is a significantly higher likelihood of EGFR mutation in lung cancer patients with family history of cancer than their counterparts without family history, preferentially in Asians (OR = 1.35[1.06–1.71], P = 0.01), those diagnosed with adenocarcinomas ((OR = 1.47[1.14–1.89], P = 0.003) and those with lung cancer-affected relatives (first and second-degree: OR = 1.53[1.18–1.99], P = 0.001; first-degree: OR = 1.76[1.36–2.28, P < 0.0001]). Familial lung cancers more likely have concurrent EGFR mutations along with mutations in their germline cancer predisposition genes including EGFR T790 M, BRCA2 and TP53. Certain mechanisms may contribute to the combination preferences between inherited mutations and somatic ones. Conclusions Potential genetic modifiers may contribute to somatic EGFR mutation in lung cancer, although current data is limited. Further studies on this topic are needed, which may help to unveil lung carcinogenesis pathways. However, caution is warranted in data interpretation due to limited cases available for the current study.

OBJECTIVES:Current evidence suggests that microorganisms are associated with neoplastic diseases; however, the role of the airway microbiome in lung cancer remains unknown. To investigate the taxonomic profiles of the lower respiratory tract (LRT) microbiome in patients with lung cancer.MATERIALS AND METHODS:BALF samples were collected in a discovery set comprising 150 individuals, including 91 patients with lung cancer, 29 patients with nonmalignant pulmonary diseases and 30 healthy subjects, and an independent validation set including 85 participants. The samples were assessed by metagenomics analysis. Random forest regression analysis was performed to select a diagnostic panel.RESULTS:In the discovery set, richness was reduced in lung cancer patients compared with that in healthy subjects, and the microbiome of patients with nonmalignant diseases resembled that of patients with lung cancer. Interestingly, Bradyrhizobium japonicum was only found in patients with lung cancer, whereas Acidovorax was found in patients with cancer and nonmalignant pulmonary diseases. A microbiota-related diagnostic model consisting of age, pack year of smoking and eleven types of bacteria was built, and the area under the curve (AUC) for discriminating the patients with cancer was 0.882 (95%CI: 0.807-0.957) in the training set and 0.796 (95%CI: 0.673-0.920) in the independent validation set.CONCLUSION:Our study demonstrates that the LRT microbiome richness is diminished in lung cancer patients compared with that in healthy subjects and that microbiota-specific biomarkers may be useful for diagnosing patients for whom lung biopsy is not feasible.

Abstract Purpose: We established a CT-derived approach to achieve accurate progression-free survival (PFS) prediction to EGFR tyrosine kinase inhibitors (TKI) therapy in multicenter, stage IV EGFR-mutated non–small cell lung cancer (NSCLC) patients. Experimental Design: A total of 1,032 CT-based phenotypic characteristics were extracted according to the intensity, shape, and texture of NSCLC pretherapy images. On the basis of these CT features extracted from 117 stage IV EGFR-mutant NSCLC patients, a CT-based phenotypic signature was proposed using a Cox regression model with LASSO penalty for the survival risk stratification of EGFR-TKI therapy. The signature was validated using two independent cohorts (101 and 96 patients, respectively). The benefit of EGFR-TKIs in stratified patients was then compared with another stage-IV EGFR-mutant NSCLC cohort only treated with standard chemotherapy (56 patients). Furthermore, an individualized prediction model incorporating the phenotypic signature and clinicopathologic risk characteristics was proposed for PFS prediction, and also validated by multicenter cohorts. Results: The signature consisted of 12 CT features demonstrated good accuracy for discriminating patients with rapid and slow progression to EGFR-TKI therapy in three cohorts (HR: 3.61, 3.77, and 3.67, respectively). Rapid progression patients received EGFR TKIs did not show significant difference with patients underwent chemotherapy for progression-free survival benefit (P = 0.682). Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinicopathologic-based characteristics model (P < 0.0001). Conclusions: The proposed CT-based predictive strategy can achieve individualized prediction of PFS probability to EGFR-TKI therapy in NSCLCs, which holds promise of improving the pretherapy personalized management of TKIs. Clin Cancer Res; 24(15); 3583–92. ©2018 AACR.

With the rapid development of the next generation sequencing,it is believed that the lower respiratory tract has been confirmed to have a variety of different microbial communities.At the same time,more and more researches have proved that the microbiome plays an important role in the development of immune system.The disorder of microbiome in respiratory tract may be closely related to various respiratory diseases,including tuberculosis,chronic obstructive pulmonary disease and asthma.This article mainly reviewed the research of respiratory tract microbial of asthma patients and healthy people in recent years,analyzed the change trend of bacterial colonization in the respiratory tract of patients with asthma and the relationship between the changes of respiratory tract microbiome and the pathogenesis of asthma,and discussed the research progress of respiratory tract microbiome in the treatment of asthma.

RPS6KB1 is the kinase of ribosomal protein S6 which is 70 kDa and is required for protein translation. Although the abnormal activation of RPS6KB1 has been found in types of diseases, its role and clinical significance in non-small cell lung cancer (NSCLC) has not been fully investigated. In this study, we identified that RPS6KB1 was over-phosphorylated (p-RPS6KB1) in NSCLC and it was an independent unfavorable prognostic marker for NSCLC patients. In spite of the frequent expression of total RPS6KB1 and p-RPS6KB1 in NSCLC specimens by immunohistochemical staining (IHC), only p-RPS6KB1 was associated with the clinicopathologic characteristics of NSCLC subjects. Kaplan-Meier survival analysis revealed that the increased expression of p-RPS6KB1 indicated a poorer 5-year overall survival (OS) for NSCLC patients, while the difference between the positive or negative RPS6KB1 group was not significant. Univariate and multivariate Cox regression analysis was then used to confirm the independent prognostic value of p-RPS6KB1. To illustrate the underlying mechanism of RPS6KB1 phosphorylation in NSCLC, LY2584702 was employed to inhibit the RPS6KB1 phosphorylation specifically both in lung adenocarcinoma cell line A549 and squamous cell carcinoma cell line SK-MES-1. As expected, RPS6KB1 dephosphorylation remarkably suppressed cells proliferation in CCK-8 test, and promoted more cells arresting in G0-G1 phase by cell cycle analysis. Moreover, apoptotic A549 cells with RPS6KB1 dephosphorylation increased dramatically, with an elevating trend in SK-MES-1, indicating a potential involvement of RPS6KB1 phosphorylation in inducing apoptosis. In conclusion, our data suggest that RPS6KB1 is over-activated as p-RPS6KB1 in NSCLC, rather than just the total protein overexpressing. The phosphorylation level of RPS6KB1 might be used as a novel prognostic marker for NSCLC patients.

Since the discovery of X-rays at the end of the 19th century, medical imageology has progressed for 100 years, and medical imaging has become an important auxiliary tool for clinical diagnosis. With the launch of the human genome project (HGP) and the development of various high-throughput detection techniques, disease exploration in the post-genome era has extended beyond investigations of structural changes to in-depth analyses of molecular abnormalities in tissues, organs and cells, on the basis of gene expression and epigenetics. These techniques have given rise to genomics, proteomics, metabolomics and other systems biology subspecialties, including radiogenomics. Radiogenomics is an important revolution in the traditional visually identifiable imaging technology and constitutes a new branch, radiomics. Radiomics is aimed at extracting quantitative imaging features automatically and developing models to predict lesion phenotypes in a non-invasive manner. Here, we summarize the advent and development of radiomics, the basic process and challenges in clinical practice, with a focus on applications in pulmonary nodule evaluations, including diagnostics, pathological and molecular classifications, treatment response assessments and prognostic predictions, especially in radiotherapy.

Background: To perform a systematic review and meta-analysis of case-control and cohort studies assessing the association of early-life respiratory infections with the risk of asthma. Methods: Relevant studies were identified by a search of PubMed, Cochrane Library, ScienceDirect, Springer, China National Knowledge Infrastructure (CNKI), WANFANG and VIP before April 2016 with no restrictions. We included studies that reported odds ratio (OR) estimates with 95% confidence intervals (CIs) for the association between early-life respiratory infection and the risk of asthma. Results: Thirteen studies involving 9172 participants from several countries were included in the meta-analysis. In a pooled analysis of all studies, respiratory infection was associated with an increased risk of asthma (OR: 1.62; 95% CI: 1.52-1.74). Childhood asthma was significantly correlated with respiratory tract infections among children who acquired infections within 6 months (OR: 1.47; 95% CI: 1.20-1.80), 8 months (OR: 2.00; 95% CI: 1.34-2.99), 12 month (OR: 2.72; 95% CI: 2.18-3.40) and 24 months (OR: 2.41; 95% CI: 1.74-3.34) of life. Asthma was significantly positively correlated with both human rhinovirus infection (OR: 2.88; 95% CI: 1.79-4.64) and respiratory syncytial virus infection (OR: 1.61; 95% CI: 1.44-1.80) and significantly negatively correlated with herpes simplex virus infection (OR: 0.48; 95% CI: 0.26-0.89), but was not significantly correlated with coronavirus infection (OR: 1.97; 95% CI: 0.75-5.19) or measles virus infection (OR: 0.63; 95% CI: 0.08-4.90). Conclusions: Early-life respiratory infections are positively associated with the risk of asthma.

Background: The proportion of nonsmoking female with pulmonary adenocarcinoma in lung cancer is increasing. Although many biomarkers have existed in non-small cell lung cancer, the predictive value of serum tumor markers in distant metastasis is not fully defined. Objectives: In this study, we analysed the relationship between serum biomarkers and distant metastasis in nonsmoking female with lung adenocarcinoma. Methods: We evaluated 464 nonsmoking female with lung adenocarcinoma between 2008-2014. Serum tumor markers in patients who had been newly diagnosed lung adenocarcinoma, including carcinoembryonic antigen, cytokeratin 19 fragments and neuron specific enolase were analysed. Results: Serum carcinoembryonic antigen level was significantly different between patients with distant metastasis and those without distant metastasis (p < 0.001). Serum levels of cytokeratin 19 fragments and neuron specific enolase showed no significance between the two groups. The area under the receiver operating curve of serum carcinoembryonic antigen to predict distant metastasis was 0.595 (p < 0.001). Conclusions: The pretreatment serum level of carcinoembryonic antigen maybe significantly correlated with distant metastasis in nonsmoking female with lung adenocarcinoma. More attention should be given to patients with high serum carcinoembryonic antigen level and distant metastasis.

Wernicke encephalopathy (WE) is an acute or subacute neurological syndrome caused by thiamine (Vitamin B1) deficiency and is usually underestimated in clinical practice. WE is suspected in merely about 6% of non-alcoholic patients and one-third of alcoholic patients.[1] Non-alcoholic causes include gastrointestinal surgery and disease, malnutrition, cancer and chemotherapeutic treatments, and long-term parenteral nutrition.[2] However, few cases were reported about WE in patients with tuberculosis. Early diagnosis and medication are vital for the prognosis of this disease. Written informed consent was obtained from the legally authorized representative of the patient for the publication of this case report. A 46-year-old man was admitted to hospital for cough, abdominal pain, and blurred vision. Five months before this visit, he had the onset of cough and fever. One month before the admission, he presented abdominal pain, nausea, vomit, fever, and melena. Mesenteric biopsy confirmed the diagnosis of abdominal tuberculosis. Four days before this visit, he presented blurred vision. The reason for blurred vision remained unclear. Medical history showed gastrointestinal bleeding, and others were unremarkable. The patient had a history of chronic alcoholism consumption for 20 years but stopped drinking 8 years ago. At admission, physical examination showed that he had slight mental confusion. However, other neurological examinations were unremarkable. Blood tests showed erythrocyte sedimentation rate (30.0 mm/h), C-reactive protein (23.3 mg/L). The results of routine hematological tests and arterial blood gas analysis were unremarkable. Tuberculosis infected T cells gamma interferon release test result was positive. The chest and abdominal computed tomography scan results were suggestive of pulmonary and abdominal tuberculosis. Vision acuity test showed hand movement in the right eye and CF/15 mm in the left eye. The test of anterior segment of eyeball was unremarkable. Ophthalmic fundus examination showed the disc was hyperemia and edema, and the margin was blurring with flame-shaped hemorrhages. Optic neuritis or encephalitis caused by intracranial lesions was suspected. After admission, he was given anti-tuberculosis medication and parenteral nutrition. At the 4th day after admission, the patient suddenly developed headache and hematemesis. Brain computed tomography scan result was unremarkable. Brain magnetic resonance imaging (MRI) scan was not performed due to his noncooperation. The pressure of cerebrospinal fluid was 58 mmH2O (1 mmH2O = 0.0098 kPa). The routine test and biochemical indicators of cerebrospinal fluid were unremarkable. The result of occult blood test of vomit was positive. Fecal occult blood test result was negative. Gastrointestinal bleeding was considered. The reason for blurred vision and headache still remained unclear. Symptomatic and supportive treatments were administered to him, all of which were given through parenteral route. At the 7th day after admission, his symptoms significantly got worse. At the same time, he developed diplopia and dysphoria. Physical examination showed nystagmus, unsteady gait, weakness of extremities, and negative pathological reflex. Arterial blood gas analysis result was suggestive of Type I respiratory failure. Other laboratory data were as follows: sodium ions (130.5 mmol/L), chloride ion (95.4 mmol/L), blood ammonia (27.0 μmol/L, reference range, 9.0–33.0 μmol/L), 25-OH-VD (20.5 nmol/L, reference range, 47.7–144.0 nmol/L), creatine kinase (52 IU/L), lactate dehydrogenase (118 IU/L), and hydroxybutyrate dehydrogenase (97 IU/L). The results of brain MRI scan showed symmetric increased T1 and T2 signals in the medial dorsal thalamus, hypothalamus, and periaqueductal region [Figure 1a]. WE or encephalitis was suspected. WE was finally diagnosed on the basis of his clinical manifestations and brain MRI scan results after a consultation to a neurologist. Therefore, he was immediately given intramuscular Vitamin B1 with 100 mg twice a day for 12 days. At the following day, his mental confusion, blurred vision, and nystagmus significantly improved. However, he developed anterograde amnesia. At the 7th day after administering Vitamin B1, symptoms including diplopia, unsteady gait, and memory deterioration were resolved. A follow-up brain MRI scan results showed significantly improved [Figure 1b]. Finally, he was given oral Vitamin B1 for a year.Figure 1: High signal lesions in brain magnetic resonance imaging results of the patient before receiving thiamine therapy, as indicated by the red arrows (a). Magnetic resonance imaging results of the patient after receiving thiamine therapy for 3 months (b).WE is a clinical emergency resulting from hypovitaminosis. It is usually considered as a metabolic disease caused by chronic alcoholism. In recent years, more and more cases about WE were diagnosed in non-drinkers. In our case, tuberculosis and subsequent incomplete ileus decreased the absorption of nutrients. Finally, the patient appeared blurred vision due to long-term lack of absorption of Vitamin B1. We reviewed many English literatures related to this disease and found that there were a few cases about WE associated with tuberculosis. The typical triad of symptoms in WE includes mental disorder, cerebellar ataxia, and ophthalmoplegia. However, which were reported in only about 8% of patients in clinical practice.[1] It is difficult for clinical practitioners to distinguish it from other neuropsychiatric disorders, especially in pediatric patients for their atypical clinical symptoms[3] and in patients with liver failure due to the difficulty of distinguishing WE from hepatic encephalopathy.[4] The patient in our case gradually developed blurred vision, mental confusion and unsteady gait, which made it difficult for a non-neurologist to diagnose early. At the beginning, tuberculous meningitis (TBM) was considered. However, due to lack of the typical cerebrospinal fluid profile (a predominance of lymphocytes, low glucose, and elevated protein) and MRI signs (tuberculomas, hydrocephalus, infarction, pachymeningitis, etc.), TBM was excluded subsequently. Considering WE is a neuropsychiatric disorder, clinical practitioners may routinely examine brain spinal fluid and prescribe brain computed tomography scan. However, these results may be unremarkable in the early stage. Brain MRI is more powerful to support the diagnosis of acute WE. Typically, MRI scan results are symmetrical lesions in the mammillary bodies, thalami, tectal plate, and periaqueductal area which showed increased T2 signal in these sites.[5] In our case, brain computed tomography scan result was unremarkable. Brain MRI scan was finally performed and showed typical imaging results. Delayed diagnosis and treatment can lead to progression of this disease. Ultimately, the patient may develop infantile beriberi, shock, Wernicke–Korsakoff syndrome, or even death. The EFNS guidelines recommend that thiamine should be given 200 mg three times a day and the best route is intravenous instead of intramuscular.[1] The patient in our case received intramuscular Vitamin B1 with 100 mg twice daily for 12 days. His mental confusion, blurred vision, and nystagmus significantly improved although we just prescribed a small dose of Vitamin B1. WE was diagnosed in a tuberculous patient who had no history of alcohol consumption in recent years. It is difficult for us to early diagnose WE. Carefully collection of medical history and cooperation with the department of neurology is necessary. Although our patient got rapid relief and had a good prognosis after a small dose of thiamine, we still need more researches to get specific and better recommendation for dosage, route, and duration of Vitamin B1. Financial support and sponsorship This study was supported by a grant from International Cooperation Project of Sichuan Science and Technology Department, China (No. 2014HH0003). Conflicts of interest There are no conflicts of interest.